Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Sangeetha, J. | Priyanka, M. | Jayakumar, C.

Article Type: Research Article

Abstract: Audio Event Detection (AED) and classification of acoustic events has become a notable task for machines to interpret the auditory information around us. Nevertheless, it has been a difficult and cumbersome task to extract the most basic characteristics of acoustic events that encapsulate the fundamental elements of the audio events. Previous works on audio event classification utilized supervised pre-training as well as meta-learning approaches that happened to depend on labeled data therefore facing instability. Deep Learning is progressing in an increasingly mature direction, and the application of deep learning methods to detect acoustic event has become more and more sought …after. The proposed hybrid method called Greedy Regression-based Convolutional Neural Network and Differential Convex Bidirectional Gated Recurrent Unit (GRCNN-DCBGRU) is introduced to learn a vector representation of an audio sequence for Audio Event Classification (AEC). Differential Convex Bidirectional Gated Recurrent Unit is analogous to long short-term memory and involves time-cyclic long-term dependencies with a lesser processing complexity. The model first extracts acoustic features from the sound event dataset through a Differential Convex Bidirectional Gated Recurrent Unit employing Gabor Filter bank features and then extracts the local static acoustic features through the Greedy Regression-based Convolutional Neural Network by utilizing Mel Frequency Cepstral Coefficients (MFCC). Finally, the Differential Convex Meta-Learning classifier is used for the final acoustic event classification. Extensive evaluation on large-size publicly available acoustic event database like Findsounds2016 will be performed in Python programming language to demonstrate the efficiency of the proposed method for the AEC task. To demonstrate the visualizations of individual modules and their influence on overall representation learning for AEC tasks, several parameters like audio detection time, audio detection accuracy, precision, and recall are measured. Show more

Keywords: Audio event detection, audio event classification, deep learning, greedy regression, convolutional neural network, differential convex, bidirectional gated recurrent unit

DOI: 10.3233/JIFS-232561

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Gulistan, Muhammad | Pedrycz, Witold | Yaqoob, Naveed

Article Type: Research Article

Abstract: We explore switching techniques between q-fractional fuzzy sets (qFr sets) and various other classes of fuzzy sets to establish connections and provide a comprehensive framework. In particular, we examine the relationships between qFr sets and interval-valued fuzzy sets (IVFS), type 2 fuzzy sets(T2FS), intuitionistic fuzzy sets(IFS), Pythagorean fuzzy sets(PFS), q-rung orthopair fuzzy sets (q-ROFS), and linear diophantine fuzzy sets(LDFS). By examining these interconnections, we aim to understand better qFr sets and their applications in a wide range of fuzzy systems. It is possible to convert qFr sets into other fuzzy set models using the derived switching techniques, facilitating the utilization …of existing methods and algorithms. The versatility of qFr sets, combined with the bridging techniques presented, holds promise for addressing complex problems in decision-making, pattern recognition, and other applications where uncertainty and imprecision play significant roles. Through case studies and practical applications, we illustrate the effectiveness and usefulness of the proposed switching techniques, showcasing their potential impact on real-world scenarios. Show more

Keywords: q-fractional fuzzy sets, fuzzy set, interval-valued fuzzy sets, type 2 fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, q-rung orthopair fuzzy sets, linear diophantine fuzzy sets, switching techniques, uncertainty, imprecision

DOI: 10.3233/JIFS-233563

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Wang, Xin-Fan | Zhang, Li-Na | Zhou, Huan | Wang, Xue-Bin

Article Type: Research Article

Abstract: The intuitionistic uncertain linguistic information aggregation problems considering different priority levels of criteria are investigated. Firstly, we extended the prioritized averaging (PA) operator to intuitionistic uncertain linguistic environment, defined two new prioritized aggregation operators called the intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator and the intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator, and established various desirable properties of the proposed operators. Secondly, we developed a multi-criteria decision making (MCDM) approach based on the IULPWA operator (or the IULPWG operator) to deal with the MCDM problems in which the criterion values take the form of intuitionistic uncertain linguistic numbers …(IULNs) and the criteria are in different priority levels. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed method, and a comparison analysis is conducted to make clear the differences among the IULPWA operator, the IULPWG operator, the intuitionistic uncertain linguistic number weighted averaging (IULNWA) operator and the intuitionistic uncertain linguistic weighted geometric average (IULWGA) operator. Show more

Keywords: Multi-criteria decision making (MCDM), intuitionistic uncertain linguistic number (IULN), intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator, intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator

DOI: 10.3233/JIFS-223829

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Zhou, Sijiang | Mo, Kanglin | Yang, Xia | Ning, Zong

Article Type: Research Article

Abstract: OBJECTIVE: This research aims to pinpoint key biomarkers and immunological infiltration of idiopathic pulmonary fibrosis (IPF) through bioinformatics analysis. METHODS: From the GEO database, 12 gene expression profiles were obtained. The LIMMA tool in Bioconductor accustomed to identify the genes that are expressed differently (DEGs), and analyses of functional enrichment were performed. A protein-protein interaction network (PPI) was constructed using STRING and Cytoscape, and a modular analysis was performed. Analysis of the immunological infiltration of lung tissue between IPF and healthy groups was done using the CIBERSORTx method. RESULTS: 11,130 genes with differential expression (including 7,492 …up-regulated and 3,638 down-regulated) were found. The selected up-regulated DEGs were mainly involved in the progression of pulmonary fibrosis and the selected down-regulated DEGs maintain the relative stability of intracellular microenvironment, according to functional enrichment analysis. KEGG enrichment analysis revealed that up-regulated DEGs were primarily abundant in the PI3K-Akt signaling mechanism, whereas down-regulated DEGs were associated with cancer pathways. The most significant modules involving 8 hub genes were found after the PPI network was analyzed. IPF lung tissue had a greater percentage of B memory cells, plasma cells, T cells follicular helper, T cells regulatory, T cells gamma delta, macrophages M0 and resting mast cells. while a relatively low proportion of T cells CD4 memory resting, NK cells resting and neutrophils. CONCLUSION: This research demonstrates the differences of hub genes and immunological infiltration in IPF. Show more

Keywords: Idiopathic pulmonary fibrosis, biomarkers, immunological infiltration, lung tissue, bioinformatics analysis

DOI: 10.3233/JIFS-234957

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Srihari, P. | Harikiran, J. | Chandana, B. Sai | Reddy, V. Surendra

Article Type: Research Article

Abstract: Recognizing human activity is the process of using sensors and algorithms to identify and classify human actions based on the data collected. Human activity recognition in visible images can be challenging due to several factors of the lighting conditions can affect the quality of images and, consequently, the accuracy of activity recognition. Low lighting, for example, can make it difficult to distinguish between different activities. Thermal cameras have been utilized in earlier investigations to identify this issue. To solve this issue, we propose a novel deep learning (DL) technique for predicting and classifying human actions. In this paper, initially, to …remove the noise from the given input thermal images using the mean filter method and then normalize the images using with min-max normalization method. After that, utilizing Deep Recurrent Convolutional Neural Network (DRCNN) technique to segment the human from thermal images and then retrieve the features from the segmented image So, here we choose a fully connected layer of DRCNN as the segmentation layer is utilized for segmentation, and then the multi-scale convolutional neural network layer of DRCNN is used to extract the features from segmented images to detect human actions. To recognize human actions in thermal pictures, the DenseNet-169 approach is utilized. Finally, the CapsNet technique is used to classify the human action types with Elephant Herding Optimization (EHO) algorithm for better classification. In this experiment, we select two thermal datasets as LTIR dataset and IITR-IAR dataset for good performance with accuracy, precision, recall, and f1-score parameters. The proposed approach outperforms “state-of-the-art” methods for action detection on thermal images and categorizes the items. Show more

Keywords: Human action recognition, deep recurrent convolutional neural network, thermal images, classification, CapsNet, feature extraction, DenseNet-169

DOI: 10.3233/JIFS-230505

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2023

Authors: Nunsanga, Morrel V.L. | Pakray, Partha | Devi, Toijam Sonalika | Singh, L. Lolit Kr

Article Type: Research Article

Abstract: The process of associating words with their relevant parts of speech is known as part-of-speech (POS) tagging. It takes a substantial amount of well-organized data or corpora and significant target language research to obtain good performance for a tagger. Mizo is a language that needs more research attention in computational linguistics due to its under-resourced nature. The limited availability of corpora and relevant literature adds complexity to the task of assigning POS labels to Mizo text. This paper explores two methods to potentially improve the Hidden Markov Model (HMM)-based POS tagger for the Mizo language. The proposed taggers are compared …with the baseline HMM tagger and the N-gram taggers on the designed Mizo corpus, which consists of 72,077 manually tagged tokens. The experimental results proved that the two proposed taggers enhanced the HMM-based Mizo POS tagger, achieving 81.52% and 84.29% accuracy, respectively. Moreover, a comprehensive analysis of the performance of the suggested hybrid tagger was conducted, yielding a weighted average precision, recall, and F1-score of 83.09%, 77.88%, and 79.64% respectively. Show more

Keywords: Hybrid POS tagger, rule-based POS tagger, N-gram tagger, Mizo POS tagger, Hidden Markkov Model

DOI: 10.3233/JIFS-224220

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Zhu, Yiping | Huang, Jiajia | Zhu, Yi | Guo, Yang

Article Type: Research Article

Abstract: Online teaching platforms have developed into mainstream knowledge learning and exchange platform. The research on the quality evaluation of online teaching platforms and the construction of an applicable and scientific evaluation index system model can help explore the key factors affecting the quality of online teaching platforms and provide some references for evaluating online teaching platforms and improving online teaching quality. This study combines the rough set theory (RS) with the BP (Back Propagation) neural networks to build an RS-BP neural network model to evaluate the quality of online teaching platforms. Firstly, an initial online teaching platform quality evaluation index …system is constructed based on knowledge transfer theory from four aspects: course content, knowledge transmitter, knowledge receiver and teaching platform. Then, 12 core evaluation indicators were generated by attribute reduction using rough set theory, and the weights of each core indication were determined. The normalized data input was then trained, validated, and tested to generate a rough set neural network quality evaluation model for online teaching platforms. After that, three representative online education platforms of content, interaction and compatibility are selected for empirical research. The accuracy of the model is first tested by the error between the simulated and output values, after which the core metric scores and the overall scores are calculated for the three types of platforms. The empirical results demonstrate that the model has certain advantages in terms of index simplification and adaptive training when evaluating online teaching platforms, as well as strong operability and practicality. The evaluation results show that the content online teaching platform has the highest comprehensive score, followed by the compatible and interactive online teaching platforms. According to the index scores, the quality of the course content, stage assessments, and contact between professors and students were identified as major elements influencing the quality of the online teaching platform. Finally, suggestions for optimization for each of the three types of online teaching platforms were made based on the core indicators and their weights, as well as the scores and characteristics of the three types of online teaching platforms, with the goal of improving the quality of online teaching platforms. Show more

Keywords: Knowledge transfer, online teaching platform, rough set theory, BP neural network

DOI: 10.3233/JIFS-231381

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2023

Authors: Shanthi, A.S. | Ignisha Rajathi, G. | Velumani, R. | Srihari, K.

Article Type: Research Article

Abstract: In older people, mild cognitive impairment (MCI) is a precursor to more severe forms of dementia like AD (AD). In diagnosing patients with primary AD and amnestic MCI, modern neuroimaging techniques, especially MRI, play a key role. To efficiently categorize MRI images as normal or abnormal, the research presents a machine learning-based automatic labelling system, with a focus on boosting performance via texture feature analysis. To this end, the research implements a preprocessing phase employing Log Gabor filters, which are particularly well-suited for spatial frequency analysis. In addition, the research uses Gray Wolf Optimization (GWO) to acquire useful information from …the images. For classification tasks using the MRI images, the research also make use of DenseNets, a form of deep neural network. The proposed method leverages Log Gabor filters for preprocessing, Gray Wolf Optimization (GWO) for feature extraction, and DenseNets for classification, resulting in a robust approach for categorizing MRI images as normal or abnormal. When compared to earlier trials performed without optimization, the proposed systematic technique shows a significant increase in classification accuracy of 15% . For neuroimaging applications, our research emphasizes the use of Log Gabor filters for preprocessing, GWO for feature extraction, and DenseNets for classification, which can help with the early detection and diagnosis of MCI and AD. Show more

Keywords: Dementia, mild cognitive impairment, MRI, AD, Gray Wolf Optimization, DenseNets, log gabor filter

DOI: 10.3233/JIFS-235118

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Sermakani, A.M. | Paulraj, D.

Article Type: Research Article

Abstract: The contemporary development of cloud is a next generation federated cloud technology envisioned by virtualization to enable cost-efficient usage of computing resources. The resources are intended on scalability as data grows enormously with on demand services. Federated cloud is an efficient networked computing environment that can adopt infrastructure which aims for virtual unlimited pool during on demand services. The challenging task for federated cloud includes managing workloads of individual cloud, progressing virtual machine volumes, cost utilization, fair load distribution. In order to addresses these challenges, this approach uses “Optimized Bit Matrix based Node Acquisition for Federated cloud (BMNF)”. The framework …process two different approaches: managing bit matrix and fulfilling load distribution in federated cloud based on cost aware workloads. The formation of bit matrix designed by each member in cloud services that validates load availability status. Load distribution factor concentrates on fair allocation with cost aware policy at individual level. BMNF policy segregates the request among various clouds by analyzing bits patterns. In addition to load distribution using bit matrix, it also focuses on improving cost utilization and targets with better quality of load distribution. The proposed working model is highly efficient with computation and communication overhead for federated cloud. Show more

Keywords: Cloud computing, load distribution, virtualization, federated cloud, virtual machine allocation, quality of service

DOI: 10.3233/JIFS-232897

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Huang, Feidan | Deng, Zexi | Cao, Fasheng

Article Type: Research Article

Abstract: Concepts of generalized commutative intuitionistic fuzzy finite state machines and generalized switching intuitionistic fuzzy finite state machines are proposed in this paper, and properties of these two kinds of intuitionistic fuzzy finite state machines are investigated. By using the conditions associated with generalized commutativity, a kind of congruences of intuitionistic fuzzy finite state machines is defined. Homomorphic properties of generalized commutative intuitionistic fuzzy finite state machine are also discussed. Moreover, products of generalized commutative intuitionistic fuzzy finite state machines and products of generalized switching intuitionistic fuzzy finite state machines are studied, and some related properties are proved.

Keywords: Intuitionistic fuzzy finite state machine, generalized commutativity, congruence, homomorphism, product

DOI: 10.3233/JIFS-231549

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2023

Authors: Wenjun, Zhou | Jianmin, Ma

Article Type: Research Article

Abstract: In the speaker verification task based on Gaussian Mixture Model-Universal Background Model (GMM-UBM), by constructing the UBM as a tree structure, the kernel Gaussians suitable for different speakers can be quickly selected, which speeds up the modeling of speaker acoustic space by GMM. The tree-based kernel selection algorithm (TBKS) introduces a beam-width, which increases the candidate range of kernels and improves the kernel selection accuracy. In this paper, we improve the TBKS algorithm by introducing a recall rate to adjust the number of nodes recalled in each layer of the tree structure. This adjustment refines the quantity and resolution of …Gaussian distributions in various subspaces within the acoustic space, compensating for the loss caused by discarding some significant Gaussians erroneously. Speaker verification experiments are carried out based on the Aishell2 dataset. The results reveal that the modified TBKS algorithm reduces EER by 7.5% relatively and increses computational reduction factor to 42.93, enhancing both recognition accuracy and speed. In addition, the test speech is spliced into different lengths and common environmental noise is added to verify the universality of the improved algorithm. Show more

Keywords: Speaker verification, fast scoring, gaussian mixture model, tree-based kernel selection

DOI: 10.3233/JIFS-232304

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Muthulakshmi, V. | Hemapriya, N.

Article Type: Research Article

Abstract: The advent of deep learning techniques has ignited interest in medical image processing. The proposed work in this paper suggests one of the edge technologies in deep learning, which is recommended, based on a Radiomics feature extraction model for the effective detection of Kaposi sarcoma, a vascular skin lesion expression that indicates the most prevalent cancer in AIDS patients. This work investigates the role and impact of medical image fusion on deep feature learning based on ensemble learning in the medical domain. The model is crafted wherein the pre-built ResNet50 (Residual network) and Visual Geometry Group (VGG16) are fine-tuned and …an ensemble learning approach is applied. The pre-defined CNN was incrementally regulated to determine the appropriate standards for classification efficiency improvements. Our findings show that layer-by-layer fine-tuning can improve the performance of middle and deep layers. This work would serve the purpose of masking and classification of skin lesion images, primarily sarcoma using an ensemble approach. Our proposed assisted framework could be deployed in assisting radiologists by classifying Kaposi sarcoma as well as other related skin lesion diseases, based on the positive classification findings. Show more

Keywords: Kaposi sarcoma, vascular skin lesions, ensemble learning, ResNet50, VGG16, radiomics

DOI: 10.3233/JIFS-230426

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: Gong, Kaixin | Ma, Weimin | Ren, Zitong | Wang, Jia

Article Type: Research Article

Abstract: Large-scale group decision-making (LSGDM) issues are increasingly prevalent in modern society across various domains. The preference information has emerged as a widely adopted approach to tackle LSGDM problems. However, a significant challenge lies in facilitating consensus among decision-makers (DMs) with diverse backgrounds while considering their hesitation and psychological behavior. Consequently, there is a pressing need to establish a novel model that enables DMs to evaluate alternatives with heterogeneous preference relations (HPRs). To this end, this research presents a new consensus-building method to address LSGDM problems with HPRs. First, a novel approach for solving collective priority weight is introduced based on …cosine similarity and prospect theory. In particular, a new cosine similarity measure is defined for HPRs. Subsequently, a consensus index is provided to gauge the consensus level among DMs by considering their psychological behavior and risk attitudes. Further, a consensus-reaching model is developed to address LSGDM with HPRs. Finally, an instance of supplier selection is presented to demonstrate the practicality and efficacy of the proposed method. Show more

Keywords: Large-scale group decision-making, prospect theory, heterogeneous preference relations, consensus reaching, risk attitudes

DOI: 10.3233/JIFS-231456

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Ramasamy, Karthikeyan | Sundaramurthy, Arivoli | Vaithiyalingam, Chitra

Article Type: Research Article

Abstract: The primary goal is to enhance the PSN by maintaining stable and consistent MGS operation and reestablishing stable operating conditions after generational interruptions. The artificial neural network is created using a bio-inspired optimization algorithm, such as particle swarm optimization, second generation particle swarm optimization, and new model particle swarm optimization., which directs the evolutionary learning process to determine the most optimal solution. For the best result, the ANN and bio-inspired algorithm (BIANN) are coupled. The suggested BIANN-based controller is made comprised of an internal current and an external power loop. The proper PI gain parameter is tuned using BIANN, allowing …the MGS to be stable. Three PSOs are used to investigate the suggested method, and the Matlab Simulink platform is used to create the fitness functions. The results are examined and contrasted. The new model’s particle swarm optimization provides MGS functioning and stability that is largely accurate and reliable. Show more

Keywords: Engineering optimization, Micro-grid, BIANN, stability assessment, mathematical model

DOI: 10.3233/JIFS-233112

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Wang, Liming | Liu, Yingming | Pang, Xinfu | Wang, Qimin | Wang, Xiaodong

Article Type: Research Article

Abstract: A low-carbon economic scheduling method based on a Q-learning-based multiobjective memetic algorithm (Q-MOMA) is proposed to improve the economy of cogeneration system scheduling and reduce carbon emission. First, the model incorporates a carbon capture device, a heat storage device, and a demand response mechanism to enhance the system’s flexibility and wind power consumption. In addition, the Q-MOMA algorithm combines global and local search and uses a Q-learning algorithm to dynamically adjust the crossover and mutation probabilities to improve the algorithm’s searchability. Finally, the fuzzy membership function method is used to make a multiobjective decision, which balances the economy and low …carbon of the system, and a compromise scheduling scheme is given. The effectiveness of the proposed model and solution method is verified through the simulation calculation of the improved system and compared with the simulation results of various optimization algorithms. The simulation results show that the proposed model can improve the wind power consumption space and the system’s economy and reduce carbon emissions. The Q-MOMA algorithm has a relatively better optimization ability in the low-carbon economic scheduling of the cogeneration system. Show more

Keywords: Bi-objective optimization, carbon capture, demand response, memetic algorithm, Q-learning

DOI: 10.3233/JIFS-231824

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Kalaichelvi, K. | Sundaram, M. | Sanmugavalli, P.

Article Type: Research Article

Abstract: The research tends to suggest a spin-orbit torque magnetic random access memory (SOT-MRAM)-based Binary CNN In-Memory Accelerator (BIMA) to minimize power utilization and suggests an In-Memory Computing (IMC) for AdderNet-based BIMA to further enhance performance by fully utilizing the benefits of IMC as well as a low current consumption configuration employing SOT-MRAM. And recommended an IMC-friendly computation pipeline for AdderNet convolution at the algorithm level. Additionally, the suggested sense amplifier is not only capable of the addition operation but also typical Boolean operations including subtraction etc. The architecture suggested in this research consumes less power than its spin-orbit torque (STT) …MRAM and resistive random access memory (ReRAM)-based counterparts in the Modified National Institute of Standards and Technology (MNIST) data set, according to simulation results. Based to evaluation outcomes, the pre-sented strategy outperforms the in-memory accelerator in terms of speedup and energy efficiency by 17.13× and 18.20×, respectively. Show more

Keywords: Energy efficiency, IMC, SOT-MRAM, speedup

DOI: 10.3233/JIFS-223898

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Wang, Tianhui | Liu, Renjing | Liu, Jiaohui | Qi, Guohua

Article Type: Research Article

Abstract: With the development of artificial intelligence technology, the assessment method based on machine learning, especially the ensemble learning method, has attracted more and more attention in the field of credit assessment. However, most of the ensemble assessment models are complex in structure and costly in time for parameter tuning, few of them break through the limitations of lightweight, universal and efficient. This paper present a new ensemble model for personal credit assessment. First, considering the conflicts and differences among multiple sources of information, a new method is proposed to correct the category prior information by using the difference measure. Then, …the revised prior information is fused with the current sample information with the help of Bayesian data fusion theory. The model can integrate the advantages of multiple benchmark classifiers to reduce the interference of uncertain information. To verify the effectiveness of the proposed model, several typical ensemble classification models are selected and empirically studied using real customer credit data from a commercial bank in China, and the results show that among various assessment criteria: the proposed model not only effectively improves the multi-class classification performance, but also outperforms other advanced multi-class classification credit assessment models in terms of parameter tuning and generalizability. This paper supports commercial banks and other financial institutions examination and approval work. Show more

Keywords: Ensemble model, multi-class credit assessment, information fusion theory

DOI: 10.3233/JIFS-233141

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Li, Yue | Mao, Liang

Article Type: Research Article

Abstract: Automatic detection of defects in mature litchi plays a vital role in the classification of fruit grades. The existing method mainly relies on manual, it is difficult to meet the needs of different varieties of litchi various types of commodity packaging, and there are problems such as low efficiency, high cost and poor quality of goods. To address the above problems, this paper proposes an improved You Only Look Once(YOLO)v7 algorithm for the automatic detection of post-harvest mature litchi epidermal defects. First, a dataset of litchi defects (black spot, fall off, crack) was constructed, in which the train and test …sets had 4133 and 516; Next, A Simple Parameter-Free Attention(SimAM) mechanism is introduced into the original YOLOv7 backbone network, while GSconv is used in the neck instead of convolution, and the shallow network is used instead of the deep network for lateral linking, finally, the Mish function is used as the activation function. Experimental results show the precious and mAP of the original YOLOv7 are 87.66% and 88.98%, and those of the improved YOLOv7 are 91.56% and 93.42%, improvements of 3.9% and 4.44% . A good foundation is laid for the automated classification of ripe litchi after harvesting. Show more

Keywords: YOLOv7, litchi epidermal defects, SimAM, GSconv, shallow networks

DOI: 10.3233/JIFS-233440

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Vallabhaneni, Nagalakshmi | Prabhavathy, Panneer

Article Type: Research Article

Abstract: Numerous people are interested in learning yoga due to the increased tension levels in the modern lifestyle, and there are a variety of techniques or resources available. Yoga is practiced in yoga centers, by personal instructors, and through books, the Internet, recorded videos, etc. As the aforementioned resources may not always be available, a large number of people will opt for self-study in fast-paced lifestyles. Self-learning makes it impossible to recognize an incorrect posture. Incorrect poses will have a negative effect on the patient’s health, causing severe agony and long-term chronic issues. Computer vision (CV)-related techniques derive pose features and …conduct pose analysis using non-invasive CV methods. The application of machine learning (ML) and artificial intelligence (AI) techniques to an inter-disciplinary field like yoga becomes quite difficult. Due to its potent feature learning ability, deep learning (DL) has recently achieved an impressive level of performance in classifying yoga poses. In this paper, an artificial algae optimizer with hybrid deep learning-based yoga pose estimation (AAOHDL-YPE) model is presented. The presented AAOHDL-YPE model analyzes yoga video clips to estimate pose. Utilizing Part Confidence Map and Part Affinity Field with bipartite equivalent and parsing, OpenPose can be employed to determine the joint location. The deep belief network (DBN) model is then used for Yoga recognition. Finally, the AAO algorithm is utilized to enhance the EfficientNet model’s recognition performance. The results of a comprehensive experimentation analysis reveal that the AAOHDL-YPE technique produces superior results in comparison to existing methods. Show more

Keywords: Yoga posture, activity recognition, deep learning, metaheuristics, computer vision

DOI: 10.3233/JIFS-233583

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Ganesh, Aurobind | Ramachandiran, R.

Article Type: Research Article

Abstract: Globally, the two main causes of young people dying are mental health issues and suicide. A mental health issue is a condition of physiological disorder that inhibits with the vital process of the brain. The amount of individuals with psychiatric illnesses has considerably increased during the past several years. The majority of individuals with mental disorders reside in India. The mental illness can have an impact on a person’s health, thoughts, behaviour, or feelings. The capacity of controlling one’s thoughts, emotions, and behaviour might help an individual to deal with challenging circumstances, build relationships with others, and navigate life’s problems. …With a primary focus on the healthcare domain and human-computer interaction, the capacity to recognize human emotions via physiological and facial expressions opens up important research ideas as well as application-oriented potential. Affective computing has recently become one of the areas of study that has received the greatest interest from professionals and academics in a variety of sectors. Nevertheless, despite the rise in articles published, the reviews of a particular aspect of affective computing in mental health still are limited and have certain inadequacies. As a result, a literature survey on the use of affective computing in India to make decisions about mental health issues is discussed. As a result, the paper focuses on how traditional techniques used to monitor and assess physiological data from humans by utilizing deep learning and machine learning approaches for humans’ affect recognition (AR) using Affective computing (AfC) which is a combination of computer science, AI, and cognitive science subjects (such as psychology and psychosocial). Show more

Keywords: Affective computing, mental Health, decision making, machine learning, deep learning

DOI: 10.3233/JIFS-235503

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Ma, Chao | Yager, Ronald R. | Liu, Jing | Yatsalo, Boris | Garg, Harish | Senapati, Tapan | Jin, LeSheng

Article Type: Research Article

Abstract: Uncertainty exists in numerous evaluation and decision making problems and therefore it also provides space for the subjective preferences of decision makers to affect the aggregation and evaluation results. Recently, relative basic uncertain information is proposed to further generalize basic uncertain information, but currently there is no research on how to apply this type of uncertainty in both theory and practices. There is also a paucity of decision methodology about how to build systematic preference involved decision model considering this new type of uncertainty. The relative basic uncertain information can serve as a general frame to enable the possibility for …simultaneously handling heterogeneous uncertain information including interval information, basic uncertain information, and relative basic uncertain information. Different types of bipolar subjective preferences commonly should be taken into consideration in practical decision making. With the individual heterogeneous uncertain information and the involved two types of subjective preferences, namely bipolar preferences for uncertainties and bipolar optimism-pessimism preferences, the evaluation and decision making become more complex. This work proposes a systematic intersubjective decision model which can effectively and reasonably deal with the decision scenario with such complex uncertainty, in which Yager preference induced weights allocation is applied. Some novel preference conversion and transformation functions, specified techniques, and the related decision making procedures and sub-modules are proposed and analyzed. An application is also presented to showthe practicality of the proposed decision models and related conversion and transformation functions. Show more

Keywords: Basic uncertain information, decision making, information fusion, relative basic uncertain information, uncertain decision making, Yager induced weights allocation

DOI: 10.3233/JIFS-231395

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Vidya, S. | Jagannathan, Veeraraghavan | Guhan, T. | Kumar, Jogendra

Article Type: Research Article

Abstract: Rainfall forecasting is essential because heavy and irregular rainfall creates many impacts like destruction of crops and farms. Here, the occurrence of rainfall is highly related to atmospheric parameters. Thus, a better forecasting model is essential for an early warning that can minimize risks and manage the agricultural farms in a better way. In this manuscript, Deep Neural Network (DNN) optimized with Flamingo Search Optimization Algorithm (FSOA) is proposed for Long-term and Short-term Rainfall forecasting. Here, the rainfall data is obtained from the standard dataset as Sudheerachary India Rainfall Analysis (IRA). Moreover, the Morphological filtering and Extended Empirical wavelet transformation …(MFEEWT) approach is utilized for pre-processing process. Also, the deep neural network is utilized for performing rainfall prediction and classification. Additionally, the parameters of the DNN model is optimizing by Flamingo Search Optimization Algorithm. Finally, the proposed MFEEWT-DNN- FSOA approach has effectively predict the rainfall in different locations around India. The proposed model is implemented in Python tool and the performance metrics are calculated. The proposed MFEEWT-DNN- FSOA approach has achieved 25%, 26%, 25.5% high accuracy and 35.8%, 24.7%, 15.9% lower error rate for forecasting rainfall in Cannur at Kerala than the existing Map-Reduce based Exponential Smoothing Technology for rainfall prediction (MR-EST-RP), modular artificial neural networks with support vector regression for rainfall prediction (MANN-SVR-RP), and biogeography-based extreme learning machine (BBO-ELM) (BBO-ELM-RP) methods respectively. Show more

Keywords: Deep neural network, extended empirical wavelet transformation, flamingo search optimization, morphological filtering, long-term and short-term rainfall

DOI: 10.3233/JIFS-235798

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2023

Authors: Cui, Wei | Zhang, Xuerui | Shang, Mingsheng

Article Type: Research Article

Abstract: An increasing number of fake news combining text, images and other forms of multimedia are spreading rapidly across social platforms, leading to misinformation and negative impacts. Therefore, the automatic identification of multimodal fake news has become an important research hotspot in academia and industry. The key to multimedia fake news detection is to accurately extract features of both text and visual information, as well as to mine the correlation between them. However, most of the existing methods merely fuse the features of different modal information without fully extracting intra- and inter-modal connections and complementary information. In this work, we learn …physical tampered cues for images in the frequency domain to supplement information in the image space domain, and propose a novel multimodal frequency-aware cross-attention network (MFCAN) that fuses the representations of text and image by jointly modelling intra- and inter-modal relationships between text and visual information whin a unified deep framework. In addition, we devise a new cross-modal fusion block based on the cross-attention mechanism that can leverage inter-modal relationships as well as intra-modal relationships to complement and enhance the features matching of text and image for fake news detection. We evaluated our approach on two publicly available datasets and the experimental results show that our proposed model outperforms existing baseline methods. Show more

Keywords: Fake news detection, multimoal, cross attention, frequency domain

DOI: 10.3233/JIFS-233193

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2023

Authors: Jasmine, J. Aruna | Genitha, C. Heltin

Article Type: Research Article

Abstract: Predicting the landslide-prone area is critical for various applications, including emergency response, land planning, and disaster mitigation. There needs to be a thorough landslide inventory in current studies and appropriate sampling uncertainty issues. Landslide risk mapping has expanded significantly as machine learning techniques have developed. However, one of the primary issues in Landslide Prediction is data imbalance (DI). This is problematic since it is challenging or expensive to generate an accurate inventory map of landslides based on previous data. This study proposes a novel landslide prediction method using Generative Adversarial Networks (GAN) for generating the synthetic data, Synthetic Minority Oversampling …Technique (SMOTE) for overcoming the data imbalance problem, and Bee Collecting Pollen Algorithm (BCPA) for feature extraction. Combining 184 landslides and ten criteria, including topographic wetness index (TWI), aspect, distance from the road, total curvature, sediment transport index (STI), height, slope, stream, lithology, and slope length, a geographical database was produced. The data was generated using GAN, a Deep Convolutional Neural Network (DCNN) technique to populate the dataset. The proposed DCNN-BCPA approach findings were merged with current machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM), logistic regression (LR). The model’s accuracy, precision, recall, f-score, and RMSE were measured using the following metrics: 92.675%, 96.298%, 90.536%, 96.637%, and 45.623%. This study suggests that harmonizing landslide data may have a substantial impact on the predictive capabilities of machine learning models. Show more

Keywords: Bee collecting pollen algorithm, data balancing, generative adversarial network, landslide susceptibility, synthetic data

DOI: 10.3233/JIFS-234924

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2023

Authors: Li, Weidong | Li, Zhenying | Wang, Chisheng | Zhang, Xuehai | Duan, Jinlong

Article Type: Research Article

Abstract: Accurate identification and monitoring of aircraft on the airport surface can assist managers in rational scheduling and reduce the probability of aircraft conflicts, an important application value for constructing a "smart airport." For the airport surface video monitoring, there are small aircraft targets, aircraft obscuring each other, and affected by different weather, the aircraft target clarity is low, and other complex monitoring problems. In this paper, a lightweight model network for video aircraft recognition in airport field video in complex environments is proposed based on SSD network incorporating coordinate attention mechanism. First, the model designs a lightweight feature extraction network …with five feature extraction layers. Each feature extraction layer consists of two modules, Block_A and Block_I. The Block_A module incorporates the coordinate attention mechanism and the channel attention mechanism to improve the detection of obscured aircraft and to enhance the detection of small targets. The Block_I module uses multi-scale feature fusion to extract feature information with rich semantic meaning to enhance the feature extraction capability of the network in complex environments. Then, the designed feature extraction network is applied to the improved SSD detection algorithm, which enhances the recognition accuracy of airport field aircraft in complex environments. It was tested and subjected to ablation experiments under different complex weather conditions. The results show that compared with the Faster R-CNN, SSD, and YOLOv3 models, the detection accuracy of the improved model has been increased by 3.2% , 14.3% , and 10.9% , respectively, and the model parameters have been reduced by 83.9% , 73.1% , and 78.2% respectively. Compared with the YOLOv5 model, the model parameters are reduced by 38.9% when the detection accuracy is close, and the detection speed is increased by 24.4% , reaching 38.2fps, which can well meet the demand for real-time detection of aircraft on airport surfaces. Show more

Keywords: complex environment, airport surface, aircraft recognition, SSD network, coordinate attention

DOI: 10.3233/JIFS-231423

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Sendhil, R. | Arulmurugan, A. | Jose Moses, G. | Kaviarasan, R. | Ramadoss, P.

Article Type: Research Article

Abstract: Occult peritoneal metastasis often emerges in sick persons having matured gastric cancer (GC) and is inexpertly detected with presently feasible instruments. Due to the existence of peritoneal metastasis that prevents the probability of healing crucial operation, there relies upon a discontented requirement for an initial diagnosis to accurately recognize sick persons having occult peritoneal metastasis. The proffered paradigm of this chapter identifies the initial phases of occult peritoneal metastasis in GC. The initial phase accompanies metabolomics for inspecting biomarkers. If the sick person undergoes the initial signs of occult peritoneal metastasis in GC, early detection is conducted. Yet, the physical …prognosis of this cancer cannot diagnose it, and so, automated detection of the images by dissecting the preoperational Computed Tomography (CT) images by conditional random fields accompanying Pro-DAE (Post-processing Denoising Autoencoders) and the labeling in the images is rid by denoising strainers; later, the ensued images and the segmented images experience the Graph Convolutional Networks (GCN), and the outcome feature graph information experience the enhanced categorizer (Greywold and Cuckoo Search Naïve Bayes categorizer) procedure that is employed for initial diagnosis of cancer. Diagnosis of cancer at the initial phase certainly lessens the matured phases of cancer. Hence, this medical information is gathered and treated for diagnosing the sickness. Show more

Keywords: Gastric Cancer, MIoT, Greywold and Cuckoo Search Naïve Bayes categorizer, Cuckoo-Grey Wolf search Correlative Naïve Bayes categorizer

DOI: 10.3233/JIFS-233510

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Sowmya, S. | Jose, Deepa

Article Type: Research Article

Abstract: In order to assess the fetus health and make timely decisions throughout pregnancy, Fetal Electrocardiography (FECG) monitoring is essential. Huge datasets for electrocardiograms are freely accessible from Physionet ATM Dataset1- Abdominal and Direct Fetal ECG Database (adfecgdb), Dataset2- Fetal ECG Synthetic Database (fecgsyndb), Dataset3- Non-Invasive Fetal ECG Database(nifecgdb). In this study,categorization is done based on normal and abnormal (Atrial fibrillation) FECG from three online dataset which contains FECG recordings as major details. Deep learning models like Transfer Learning (TL) and Convolutional Neural Networks (CNN) are being investigated. The composite abdominal signal and the FECG are separated using a wavelet transform …approach. The best model for categorizing the parameters of the FECG is determined through a comparative analysis and performance is improved using Continuous Wavelet Transform (CWT). The accuracy of the CNN-based technique is found to be 98.59%, whereas the accuracy of the transfer learning model is 99.01% for FECG classification. The computation of metric parameters for all the datasets is done. The classification of normal and abnormal (Atrial fibrillation) is best performed in TL model compared to CNN. Real-time data analysis is done for PQRST plotting and comparative study is done using Net Reclassification Improvement (NRI) and obtained NRI = 13%, z static 0f 3.7641, p -Value of 0.00016721. Acute Myocardial Infraction (AMI) identification is done based on ST segment of Maternal ECG (MECG) images to analyze the heart attack risk. The proposed work can be utilized to track FECG waveforms in real-time for wearable technology because of its end-to-end properties and expandable intrinsic for diagnosing multi-lead heart disorders. Show more

Keywords: Fetal electrocardiogram, convolutional neural networks, transfer learning, physio net ATM, deep learning models

DOI: 10.3233/JIFS-231681

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Sánchez-DelaCruz, Eddy | Abdul-Kareem, Sameem | Pozos-Parra, Pilar

Article Type: Research Article

Abstract: Background: Many neurodegenerative diseases affect human gait. Gait analysis is an example of a non-invasive manner to diagnose these diseases. Nevertheless, gait analysis is difficult to do because patients with different neurodegenerative diseases may have similar human gaits. Machine learning algorithms may improve the correct identification of these pathologies. However, the problem with many classification algorithms is a lack of transparency and interpretability for the final user. Methods: In this study, we implemented the PS -Merge operator for the classification, employing gait biomarkers of a public dataset. Results: The highest classification percentage was 83.77%, which means …an acceptable degree of reliability. Conclusions: Our results show that PS -Merge has the ability to explain how the algorithm chooses an option, i.e., the operator can be seen as a first step to obtaining an eXplainable Artificial Intelligence (XAI). Show more

Keywords: PS-Merge, Classification, Neurodegenerative diseases, XAI

DOI: 10.3233/JIFS-235053

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Ren, Jianji | Yang, Donghao | Yuan, Yongliang | Liu, Haiqing | Hao, Bin | Zhang, Longlie

Article Type: Research Article

Abstract: The utilization of green edge has emerged as a promising paradigm for the development of new energy vehicle (NEV). Nevertheless, the recharging of these vehicles poses a significant challenge in due to limited power resources and enormous transmission demands. A novel architecture based on Wifi-6 communication is proposed, which makes the most of heterogeneous edge nodes to achieve real-time processing and computation of tasks. To address the collaborative power resource optimization problem, the interference between different vehicles is considered, and the task offloading is optimized. In particular, the power contention among recharging clusters is modeled as an exact game and …a task offloading strategy model is proposed jointly with the Deep Q-Network (DQN) algorithm, which is employed by a secondary application. Thereby, the recharging efficiency and task offloading computation are optimized and improved. Results indicate that the total resource consumption is favorably improved with this architecture and algorithm and the Nash equilibrium is also demonstrated. Show more

Keywords: Energy management, vehicle recharging, heterogeneous node gaming, computation offloading, recharging efficiency

DOI: 10.3233/JIFS-233990

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Priya, S. Baghavathi | Rani, P. Sheela | Chokkalingam, S.P. | Prathik, A. | Mohan, M. | Anitha, G. | Thangavel, M. | Suthir, S.

Article Type: Research Article

Abstract: Traditional testimony and electronic endorsements are extremely challenging to uphold and defend, and there is a problem with challenging authentication. The identity of the student is typically not recognized when it comes to requirements for access to a student’s academic credentials that are scattered over numerous sites. This is an issue with cross-domain authentication methods. On the one hand, whenever the volume of cross-domain authentication requests increases dramatically, the response time can become intolerable because of the slow throughput associated with blockchain mechanisms. These systems still do not give enough thought to the cross-domain scenario’s anonymity problem. This research proposes …an effective cross-domain authentication mechanism called XAutn that protects anonymity and integrates seamlessly through the present Certificate Transparency (CT) schemes. XAutn protects privacy and develops a fast response correctness evaluation method that is based on the RSA (Rivest, Shamir, and Adleman) cryptographic accumulator, Zero Knowledge Proof Algorithm, and Proof of Continuous work consensus Algorithm (POCW). We also provide a privacy-aware computation authentication approach to strengthen the integrity of the authentication messages more securely and counteract the discriminatory analysis of malevolent requests. This research is primarily used to validate identities in a blockchain network, which makes it possible to guarantee their authenticity and integrity while also increasing security and privacy. The proposed technique greatly outperformed the current methods in terms of authentication time, period required for storage, space for storage, and overall processing cost. The proposed method exhibits a speed gain of authentication of roughly 9% when compared to traditional blockchain systems. The security investigation and results from experiments demonstrate how the proposed approach is more reliable and trustworthy. Show more

Keywords: Zero Knowledge Proof, RSA accumulator, educational certificates, cross-domain authentication, blockchain

DOI: 10.3233/JIFS-235140

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: Ghavidel, Motahare | Yadollahzadeh-Tabari, Meisam | GolsorkhTabariAmiri, Mehdi

Article Type: Research Article

Abstract: In this paper, we proposed classification and clustering algorithms that are proper for analyzing customer-related datasets, which are mostly high-dimensional with too many instances. For the clustering purpose, This paper presents a Cuckoo-Search-based Variable Weighting (CSVW) Clustering algorithm to obtain optimal variable weights of high-dimensional data for each cluster. This paper also proposes a deep Inferarer Classifier for categorizing customers using Bi-Directional Long Short-Term Memory (Bi-LSTM) neural network, which uses a Fuzzy Inferential Classifier on its last layer. The Insurance Company (TIC) and InstaCart datasets are utilized for the experiments and performance evaluation. Simulation results reveal that the proposed clustering …algorithm generates appropriate Silhouette and Elbow criteria scores in a few cycles of execution in comparison to ordinal clustering algorithms. Also, the proposed classification algorithm with fuzzy soft-max classifier hits the better Classification Criteria in comparison. Show more

Keywords: Customer clustering, Cuckoo optimization, variable-sensitive clustering, deep learning

DOI: 10.3233/JIFS-230675

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Rahim, Muhammad | Amin, Fazli | Tag Eldin, ElSayed M. | Abd El-Wahed Khalifa, Hamiden | Ahmad, Sadique

Article Type: Research Article

Abstract: The selection of an appropriate third-party logistics (3PL) provider has become an inescapable option for shippers in today’s business landscape, as the outsourcing of logistics activities continues to increase. Choosing the 3PL supplier that best meets their requirements is one of the most difficult difficulties that logistics consumers face. Effective decision-making (DM) is critical in dealing with such scenarios, allowing shippers to make well-informed decisions within a restricted timeframe. The importance of DM arises from the possible financial repercussions of poor decisions, which can result in significant financial losses. In this regard, we introduce p, q-spherical fuzzy set (p, q …-SFS), a novel concept that extends the concept of T-spherical fuzzy sets (T-SFSs). p, q- SFS is a comprehensive representation tool for capturing imprecise information. The main contribution of this article is to define the basic operations and a series of averaging and geometric AOs under p, q -spherical fuzzy (p, q -SF) environment. In addition, we establish several fundamental properties of the proposed aggregation operators (AOs). Based on these AOs, we propose a stepwise algorithm for multi-criteria DM (MCDM) problems. Finally, a real-life case study involving the selection of a 3PL provider is shown to validate the applicability of the proposed approach. Show more

Keywords: T-spherical fuzzy set, aggregation operators, decision-making, p, q-spherical fuzzy set, multi-criteria decision-making

DOI: 10.3233/JIFS-235297

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2023

Authors: Peng, Li-Ling | Bi, Xiao-Feng | Fan, Guo-Feng | Wang, Ze-Ping | Hong, Wei-Chiang

Article Type: Research Article

Abstract: This paper proposes a new epidemic prediction model that hybridizes several models, such as the autoregressive integrated moving average model (ARIMA), random forest (RF), and response surface method (RSM). The modeling process based on ensemble empirical mode decomposition (EEMD) is particularly suitable for dealing with non-stationary and nonlinear data. ARIMA’s timeliness and difference have strong deterministic information extraction ability. RF is robust and stable, with fast speed, and strong generalization ability. Under the adjustability and correspondence of the response surface, the comprehensiveness of the model is well demonstrated. Taking the United States as an example, the proposed ARIMA-RF-RSM model is …used to explore the development mechanism of the early epidemic according to the data of the early epidemic of coronavirus disease 2019 (COVID-19). The proposed model has high prediction accuracy (mean absolute percentage error (MAPE) is 1.97% and root mean square error (RSME) is 7.24%). It helps to take effective prevention and control measures in time. In addition, the model has universal applicability to the analysis of disease transmission in relevant areas. Show more

Keywords: COVID-19, random forest (RF), response surface method (RSM), average model

DOI: 10.3233/JIFS-231588

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: An, Xiaogang | Chen, Mingming

Article Type: Research Article

Abstract: This paper explores the relationship between fuzzy logic algebra and non associative groupoid. As a groupoid which can satisfy type-2 cyclic associative (T2CA) law, T2CA-groupoid is characterized by generalized symmetry. Fuzzy logic algebra is a major direction in the study of fuzzy logic. Residuated lattices are a class of fuzzy logic algebras with widespread applications. The inflationary pseudo general residuated lattice (IPGRL), a generalization of the residuated lattice, does not need to satisfy the associative law and commutative law. Moreover, the greatest element of IPGRL is no longer the identity element. In this paper, the notion of T2CA-IPGRL (IPGRL in …T2CA-groupoid) is proposed and its properties are investigated in combination with the study of IPGRL and T2CA-groupoid. In addition, the generalized symmetry and regularity of T2CA-groupoid are investigated based on the characteristics of commutative elements. Meanwhile, the decomposition of T2CA-root of band with T2CA-unipotent radical is studied as well. The result shows that every T2CA-root of band is the disjoint union of T2CA-unipotent radicals. Show more

Keywords: Semigroup, cyclic associative groupoid, generalized regular T2CA-groupoid, fuzzy logic, pseudo general residuated lattice

DOI: 10.3233/JIFS-232966

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Minh, K.D. | Nguyen, X.H. | Nguyen, V.P.

Article Type: Research Article

Abstract: With the rapid expansion of artificial intelligence (AI) and machine learning, the evaluation of AI cloud platforms has become a critical research topic. Given the availability of many platforms, selecting the best AI cloud services that can satisfy the requirements and budget of an organization is crucial. Several solutions, each with its advantages and disadvantages, are available. In this study, a combinative-distance-based assessment approach was proposed in probabilistic linguistic hesitant fuzzy sets (PLHFSs) to accommodate the multiple characteristics of group decision-making. The original data were normalized using a standardized process that integrated numerous methodologies. Furthermore, under PLHFSs, the statistical variance …approach was used to generate the weighted objective of the vector of assessment criteria. Finally, an AI cloud platform evaluation and comparison analysis case study was used to validate the feasibility of this method. Show more

Keywords: Combinative-distance-based assessment (CODAS) method, probabilistic linguistic hesitant fuzzy sets (PLHFSs), AI cloud platform evaluation, multiple attribute decision-making (MADM), fuzzy environments

DOI: 10.3233/JIFS-232546

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Li, Yundong | Yan, Yunlong | Wang, Xiang

Article Type: Research Article

Abstract: Timely detection of building damage after a disaster can provide support and help in saving lives and reducing losses. The emergence of transfer learning can solve the problem of difficulty in obtaining several labeled samples to train deep models. However, some degree of differences exists among different scenarios, which may affect the transfer performance. Furthermore, in reality, data can be collected from multiple historical scenarios but cannot be directly combined using single-source domain adaptation methods. Therefore, this study proposes a multi-source variational domain adaptation (MVDA) method to complete the task of post-disaster building assessment. The MVDA method consists of two …stages: first, the distributions of each pair of source and target domains in specific feature spaces are aligned separately; second, the outputs of the pre-trained classifiers are aligned using domain-specific decision boundaries. This method maximizes the relevant information in the historical scene, solves the problem of inconsistent image classification in the current scene, and improves the migration efficiency from the history to the current disaster scene. The proposed approach is validated by two challenging multi-source transfer tasks using the post-disaster hurricane datasets. The average accuracy rate of 83.3% for the two tasks is achieved, obtaining an improvement of 0.9% compared with the state-of-the-art methods. Show more

Keywords: Building damage detection, domain adaptation, multi-source domain, transfer learning, remote sensing

DOI: 10.3233/JIFS-232613

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Prabu, Saranya | Padmanabhan, Jayashree

Article Type: Research Article

Abstract: Software-Defined Networking (SDN) is a strategy that leads the network via software by separating its control plane from the underlying forwarding plane. In support of a global digital network, multi-domain SDN architecture emerges as a viable solution. However, the complex and ever-evolving nature of network threats in a multi-domain environment presents a significant security challenge for controllers in detecting abnormalities. Moreover, multi-domain anomaly detection poses a daunting problem due to the need to process vast amounts of data from diverse domains. Deep learning models have gained popularity for extracting high-level feature representations from massive datasets. In this work, a novel …deep neural network architecture, supervised learning based LD-BiHGA (Low Dimensional Bi-channel Hybrid GAN Attention) system is designed to learn class-specific features for accurate anomaly detection. Two asymmetric GANs are employed for learning the normal and abnormal network flows separately. Then, to extract more relevant features, a bi-channel attention mechanism is added. This is the first study to introduce an innovative hybrid architecture that merges bi-channel hybrid GANs with attention models for the purpose of anomaly detection in a multi-domain SDN environment that effectively handles real-time unbalanced data. The suggested architecture demonstrates its effectiveness on three benchmark datasets, achieving an average accuracy improvement of 7.225% on balanced datasets and 3.335% on imbalanced datasets compared to previous intrusion detection system (IDS) architectures in the literature. Show more

Keywords: Hybrid GAN, intrusion detection, deep learning, attention model, dimensionality reduction, denoising autoencoder

DOI: 10.3233/JIFS-233668

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2023

Authors: Yuan, Hao | Yang, Hao | Li, Ruiqi | Wang, Jun | Tian, Lin

Article Type: Research Article

Abstract: For the purpose of real-time monitoring the hazard information on the electric power construction site, a personal safety monitoring system based on Artificial intelligence internet of things (AIoT) technology is designed. After the system sensing layer collects the gas information of the construction site through the gas sensor, limit current oxygen sensor and DS1820B temperature sensor, the edge computing device of the edge layer directly stores its calculation in the database of the platform layer through the data gateway. The Artificial Intelligence (AI) analysis module of this layer invokes the monitoring data of the power construction site of the database, …and uses the personal safety identification method of the power construction site based on artificial intelligence technology, to complete the abnormal identification of monitoring data and realize personal safety monitoring. In addition, the system is also equipped with a power-fail detection module, which can collect the working voltage through the voltage transformer and compare it with the mains power standard to judge whether there is a power-fail risk, so as to prevent the problem of threatening personal safety due to the power-fail of the energized equipment. After testing, the system can monitor the operation status of the construction site in real time to protect personal safety. Show more

Keywords: AIoT technology, power construction, operation site, personal safety, monitoring system

DOI: 10.3233/JIFS-235087

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Praveen Kumar, B. | Padmavathy, T. | Muthunagai, S.U. | Paulraj, D.

Article Type: Research Article

Abstract: Data mining is one of the emerging technologies used in many applications such as Market analysis and Machine learning. Temporal data mining is used to get a clear knowledge about current trend and to predict the upcoming future. The rudimentary challenge in introducing a data mining procedure is, processing time and memory consumption are highly increasing while trying to improve the accuracy, precision or recall. As well as, while trying to reduce the processing time or memory consumption, accuracy, precision and recall values are reducing significantly. So, for improving the performance of the system and to preserve the memory and …processing time, Three-Dimensional Fuzzy FP-Tree (TDFFPT) is proposed for Temporal data mining. Three functional modules namely, Three-Dimensional Temporal data FP-Tree (TTDFPT), Fuzzy Logic based Temporal Data Tree Analyzer (FTDTA) and Temporal Data Frequent Itemset Miner (TDFIM) are integrated in the proposed method. This algorithm scans the database and generates frequent patterns as per the business need. Every time a client purchases a new item, it gets stored in the recent database layer instead of rescanning the entire records which are placed in the old layer. The results obtained shows that the performance of the proposed model is more efficient than that of the existing algorithm in terms of overall accuracy, processing time, reduction in the memory utilization, and the number of databases scans. In addition, the proposed model also provides improved decision making and accurate pattern prediction in the time series data. Show more

Keywords: Data mining, FP-Tree, fuzzy logic, market analysis, temporal data mining, prediction accuracy, precision, processing time, recall

DOI: 10.3233/JIFS-223030

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Lv, Zhaoming

Article Type: Research Article

Abstract: Metaheuristics are widely used in science and industry because it as a high-level heuristic technique can provide robust or advanced solutions compared to classical search algorithms. Flow Regime Algorithm is a novel physics-based optimization approach recently proposed, and it is one of the candidate algorithms for solving complex optimization problems because of its few parameter configurations, simple coding, and good performance. However, the population that initialized randomly may have poor diversity issues, resulting in insufficient global search, and premature convergence to local optimum. To solve this problem, in this paper, a novel enhanced Flow Regime Algorithm based on opposition learning …scheme is proposed. The proposed algorithm introduces the opposition-based learning strategy into the generation of some populations to enhance the global search performance while maintaining a fast convergence rate. In order to verify the performance of the proposed algorithm, 23 benchmark numerical optimization functions were studied experimentally in detail and compared with six well-known algorithms. Experimental results show that the proposed algorithm outperforms all other metaheuristic algorithms in all unimodal functions with higher accuracy, and can obtain competitive results on more multimodal cases. A statistical comparison shows that the proposed algorithm has superiority. Finally, an ontology alignment application study demonstrate that the proposed algorithm can achieve higher quality alignment compared to most other metaheuristic-based systems and OAEI ontology alignment systems. Show more

Keywords: Meta-heuristic algorithms, flow regime algorithm, opposition-based learning, benchmark functions

DOI: 10.3233/JIFS-233329

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Zhang, Lei | Gu, Yue | Xia, Pengfei | Wei, Chuyuan | Yang, Chengwei

Article Type: Research Article

Abstract: Knowledge graphs are knowledge bases that represent entities and relations in the objective world through graph structure, and they have a boosting effect on many artificial intelligence tasks. To facilitate the development of downstream artificial intelligence tasks, knowledge graph embedding (KGE) is proposed. It aims to express semantic information for each entity and relation in the knowledge graph within a low-dimensional space. However, when it comes to semantic hierarchy, multiple relation patterns and multi-fold relational structures in knowledge graphs, most of the existing models tend to focus on only one or two aspects, often neglecting the importance of considering all …three simultaneously. Therefore, we propose a new knowledge graph embedding model, Hierarchical relation and Entity Rotation-based Multi-Feature knowledge graph Embedding (HERotMFE). Concerning hierarchical relation rotation and entity rotation, it can represent semantic hierarchy, multiple relation patterns and multi-fold relations simultaneously. Self-attention mechanism is used to learn the weights of the two-part rotation to further enhance the model’s performance. According to the findings of the experiments, HERotMFE outperforms existing models on most metrics and achieves state-of-the-art results. Show more

Keywords: Knowledge graph embedding, multi-feature, multi-fold relation, hierarchical relation, self-attention mechanism

DOI: 10.3233/JIFS-231774

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Ji, Bin | Zhou, Chuhao | Chen, Ze | Zheng, Shuai

Article Type: Research Article

Abstract: The use of similarity measures in interval-valued neutrosophic sets (IVNSs) theory is essential for comparing and assessing the degree of IVNSs difference. However, existing similarity measures for IVNSs suffer from several issues such as lacking precise axiomatic definitions, counterintuitive results, division by zero errors, inability to distinguish between positive and negative differences, and failure to satisfy the ranking definition. To address these limitations, we propose a novel multi-parameter similarity measure for IVNSs based on the tangent function. We demonstrate that our measure satisfies the axiomatic definition and apply it to medical diagnosis, achieving accurate diagnostic results. Additionally, we consider the …interactions between symptoms, adjust the proposed similarity measure using Choquet integrals, and provide analytical comparisons to demonstrate the advantages of our improved similarity measure, highlighting its stability and high confidence in the field of medical diagnosis.This study contributes to the advancement of similarity measures in IVNSs theory and provides valuable insights for the field of medical diagnosis. Show more

Keywords: Interval-valued neutrosophic sets, similarity measures, medical diagnosis, tangent function, choquet integrals

DOI: 10.3233/JIFS-232444

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2023

Authors: Karthikeyan, P. | Brindha, K.

Article Type: Research Article

Abstract: Decentralised fog computing can provide real-time interaction, minimize latency, heterogeneity, and provide networking services between edge devices and cloud data centers. One of the biggest challenges in the fog layer network is finding a trustworthy fog node. Trust management encompasses the process of being trustworthy and the act of assessing the reliability of other nodes. It is essential to carry out a comprehensive review using a systematic approach in this field to advance our understanding, address emerging challenges, and foster secure and efficient trust management practices. This research paper considers a comprehensive analysis of high-quality fog computing trust management literature …from 2018 to 2022. A variety of distinct approaches have been chosen by fog computing-based trust management and these techniques are classified into three categories: algorithms, challenges, and limitations. Further, it reviews the various trust attacks in fog environments, details the solutions proposed in the current literature, and concludes with a discussion of the open challenges and potential future research directions in fog computing. Show more

Keywords: Trust management, fog computing, cloud computing, edge devices, security

DOI: 10.3233/JIFS-232892

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2023

Authors: Ma, Junpeng | Liu, Feiyan | Xiao, Chenggang | Wang, Kairan | Liu, Zirui

Article Type: Research Article

Abstract: The wake effect of wind farm can reduce the incoming wind speed at the wind turbine located in the downstream direction, resulting in the decrease of global output. WRF model adopts a three-layer two-way nested grid division scheme to simulate the upper atmospheric circulation, obtain wind speed, wind direction and other data that can truly reproduce the fluid characteristics of the regional wind farm group. The boundary conditions and solution conditions of CFD model are set, and the computational fluid dynamics model of the region is obtained. WRF is coupled with CFD, and Fitch wake model is introduced into it. …By introducing the drag coefficient of wind turbine into the calculation of wind speed and turbulent kinetic energy in CFD-WRF coupling model, the wind field characteristics and wake effect of wind farm are simulated online. Monte Carlo sampling method is used to obtain random wind resource data in CFD-WRF coupling model, and then the sampled data is used to calculate the group output of wind farms, and evaluate the impact of wake effect on wind farm treatment. The experimental results show that this method can effectively analyze the characteristic data of regional wind field, and the calculation time of RANS method is about 3 s. Due to the wake effect, the overall output and efficiency of wind field will be significantly reduced. Show more

Keywords: CFD-WRF coupling, wind resource map, wind farm group, wake effect evaluation, wind speed and direction data, fitch wake model

DOI: 10.3233/JIFS-233273

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Pi, Feng | Tian, Shengwei | Pei, Xinjun | Chen, Peng | Wang, Xin | Wang, Xiaowei

Article Type: Research Article

Abstract: With the development of the Internet of Things (IoT), mobile devices are playing an increasingly important role in our daily lives. There are various malware threats present in these mobile devices, which can steal users’ personal information. Some malware exploits Inter-Component Communication (ICC) to execute malicious activities for unauthorized data access and system control, enabling communication between different components within an app and between different apps. In this paper, we propose an Adaptive Transformer-based malware framework (named AdaTrans) that combines sensitive Application Programming Interface (API)- and ICC-related features. The framework first extracts sensitive function call subgraphs (SFCS) to reflect the …caller-callee relationships, and then utilizes ICC interactions to reveal hidden communication patterns in malicious activities. Moreover, we propose a novel adaptive Transformer model to detect malicious behaviors. We evaluate our framework on real-world datasets and demonstrate that AdaTrans consistently outperforms other existing state-of-the-art systems. Show more

Keywords: Internet of things, ICC, Malware detection, transformer

DOI: 10.3233/JIFS-233556

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Saraswathi, C. | Pushpa, B.

Article Type: Research Article

Abstract: Alopecia Areata (AA) is one of the most widespread diseases, which is generally classified and diagnosed by the Computer Aided Diagnosis (CAD) models. Though it improves AA diagnosis, it has limited interoperability and needs skilled radiologists in medical image interpretation. This problem can be solved by developing Deep Learning (DL) models with CAD for accurately diagnosing AA patients. Many studies engaged only in specific DL models such as Convolutional Neural Network (CNN) in medical imaging, which provides different independent results and many parameters, which limits their generalizability for different datasets. To combat this limitation, this work proposes an Ensemble Pre-Learned …DL and an Optimized Long Short-Term Memory (EPL-OLSTM) model for AA classification. Initially, many healthy and AA scalp hair images are separately fed to the pre-learned CNN structures, i.e. AlexNet, ResNet, and InceptionNet to extract the deep features. Then, these features are passed to the OLSTM, in which the Battle Royale Optimization (BRO) algorithm is applied to optimize the LSTM’s hyperparameters. Moreover, the output of the LSTM is classified by the fuzzy-softmax into the associated AA classes, including mild, moderate, and severe. Thus, this model can increase the accuracy of differentiating between healthy and multiple AA scalp hair classes. Finally, an extensive experiment using the Figaro1k (for healthy scalp hair images) and DermNet (for different AA scalp hair images) datasets demonstrates that the EPL-OLSTM achieves 93.1% accuracy compared to the state-of-the-art DL models. Show more

Keywords: Alopecia areata, computer-aided diagnosis, deep learning, pre-learned CNN, LSTM, battle royale optimizer, fuzzy-softmax

DOI: 10.3233/JIFS-232172

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Shahi, Samira | Navidi, Hamidreza

Article Type: Research Article

Abstract: This paper proposes an efficient interval solidarity value that operates well for interval cooperative games. In addition to the axioms of symmetry, efficiency, and additivity, this value also satisfies two new axioms, namely, interval-egalitarian A-null player and interval differential marginality. The interval-egalitarian A-null player axiom equally divides the result of the difference between the grand coalition value and the sum of the solidarity value of players in the degenerate interval game among A-null players. The interval differential marginality axiom is an interval version of the Casajus differential marginality axiom. This property states that the difference in the interval solidarity value …of two players is determined by the difference between their average marginal contributions in the degenerate interval game. Eventually, the efficiency results and applicability of the proposed approach are compared with those of the other methods. Show more

Keywords: Interval cooperative games, solidarity value, efficiency, uncertainty

DOI: 10.3233/JIFS-223736

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2023

Authors: Yu, Shanshan | Wang, Yajun

Article Type: Research Article

Abstract: The street design and landscape in China include cultural elements representing the Heritage and history of this generation. Such designs are planned, fabricated, and implemented based on previous elements and novel findings from the past. The novel findings are identified using sophisticated technologies like the Internet of Things (IoT). Therefore, this article introduces a Cultural Design Planning Method (CDPM) for Street Landscape (SL) in maintaining the renowned Heritage of Chinese roads. The proposed method relies on IoT-based data and cultural elements from the previous design and its impact on society. In this case, the impact is computed using attraction and …cultural progression from the tourists and location. The cultural element’s connectivity and resemblance to the current location display the cultural progression. Such progression and impacts are recurrently validated using deep learning; the learning process identifies the elements and their associated impact on society. The previous and current street designs are augmented in the learning process to leverage placement and street design precision. The landscapes are periodically validated based on the current trends and associations. Show more

Keywords: Chinese cultural elements, deep learning, IoT, street landscape

DOI: 10.3233/JIFS-232292

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Mostafa, Ayman Mohamed | Rushdy, Ehab | Medhat, Reham | Hanafy, Asmaa

Article Type: Research Article

Abstract: Cloud computing is a cost-effective way for organizations to access and use IT resources. However, it also exposes data to security threats. Authentication and authorization are crucial components of access control that prevent unauthorized access to cloud services. Organizations are turning to identity management solutions to help IT administrators face and mitigate security concerns. Identity management (IDM) has been recognized as a more robust solution for validating and maintaining digital identities. Identity management (IDM) is a key security mechanism for cloud computing that helps to ensure that only authorized users have access to data and resources. Traditional IDM solutions are …centralized and rely on a single authority to manage user identities, which makes them vulnerable to attack. However, existing identity management solutions need to be more secure and trustworthy. Blockchain technology can create a more secure and trustworthy cloud transaction environment. Purpose: This paper investigates the security and trustworthiness of existing identity management solutions in cloud computing. Comparative results: We compared 14 traditional IDM schemes in cloud systems to explore contributions and limitations. This paper also compared 17 centralized, decentralized, and federated IDM models to explain their functions, roles, performance, contribution, primary metrics, and target attacks. About 17 IDM models have also been compared to explore their efficiency, overhead consumption, effectiveness to malicious users, trustworthiness, throughput, and privacy. Major conclusions: Blockchain technology has the potential to make cloud transactions more secure and reliable. It featured strong authentication and authorization mechanisms based on smart contracts on the Ethereum platform. As a result, it is still regarded as a reliable and immutable solution for protecting data sharing between entities in peer-to-peer networks. However, there is still a large gap between the theoretical method and its practical application. This paper also helps other scholars in the field discover issues and solutions and make suggestions for future research. Show more

Keywords: Cloud computing, identity management, blockchain, security-as-a-service, single-sign-on model

DOI: 10.3233/JIFS-231911

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2023

Authors: Lyu, Yucheng | Mo, Yuanbin | Yue, Songqing | Hong, Lila

Article Type: Research Article

Abstract: Optimization problems in the field of industrial engineering usually involve massive amounts of information and complex scheduling process with the characteristics of high-dimension and non-convexity, which bring many challenges to finding an optimal solution. We proposed an improved beetle swarm optimization (IBSO) algorithm demonstrating the potential to solve different problems of path planning in static environment with good performance. Firstly, the algorithm is an upgrade of the original beetle antennae search (BAS) algorithm and the search strategy is improved by replacing a single beetle by multiple beetles. Secondly, the global search ability gets enhanced, and the diversity of optimization is …improved through introducing nonlinear sinusoidal disturbance with Levy flight mechanism in beetles’ position. Finally, the search performance of beetle swarm is improved by simulating the characteristics of employment bees to search for a better solution near the honey source field in the Artificial Bee Colony (ABC) algorithm. Our experiment results show that IBSO algorithm can achieve higher search efficiency and wider search ranges through well balancing the advantages of local search and fast optimization of the BAS algorithm with the global search of the improved mechanism. The IBSO algorithm has shown the potential to provide a new solution for several optimization problems in path planning in static environment. Show more

Keywords: Path planning, beetle antennae search algorithm, Levy flight, artificial bee colony algorithm

DOI: 10.3233/JIFS-224163

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2023

Authors: Yu, Xulong | Yu, Qiancheng | Zhang, Yue | Wang, Aoqiang | Wang, Jingyun

Article Type: Research Article

Abstract: Traditional methods for detecting surface defects on ceramic tiles result in misdetection and missed detection, which makes it difficult to guarantee product stability and consistency within the same batch. Therefore, this article proposes an improved YOLOv5 algorithm for detecting surface defects on ceramic tiles. Firstly, the Res2Net module is combined with self-attention to fully utilize local and global information and improve the feature extraction effect of defects. Secondly, the GS-BiFPN neck network is designed to enhance the fusion capability of shallow detail and deep semantic information and alleviate ambiguity and redundancy on the feature map. Then, a lightweight attention module …is introduced to improve the detection capability of difficult-to-recognize defects and anti-background interference. Finally, the SIoU loss function improves the model’s convergence speed and accuracy. Experimental results demonstrate that the improved algorithm’s mean average precision (mAP) reaches 73.3%, 6.3% higher than the baseline model. Even when compared with YOLOv7-tiny, the mAP of the improved algorithm has increased by 8.7%. Additionally, the detection speed of the model can reach 92 frames per second, which can meet the requirements of ceramic tile surface defect detection in industrial scenarios. Show more

Keywords: Defect detection, YOLO, Attention mechanism, multi-scale feature fusion, SIoU

DOI: 10.3233/JIFS-231991

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Mannanuddin, Khaja | Vimal, V.R. | Srinivas, Angalkuditi | Uma Mageswari, S.D. | Mahendran, G. | Ramya, J. | Kumar, Ashok | Das, Pranjal | Vidhya, R.G.

Article Type: Research Article

Abstract:  Diseases of the retina continue to be a leading cause of blindness and visual impairment around the world. In the field of medical image analysis, specifically retinal disease identification, deep learning techniques, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have showed remarkable potential. In this paper, we present a unique method for detecting retinal diseases by combining the advantages of the Inception-V3, ResNet-50, and Vision Transformer architectures into a single model called a Cascade CNN-ViT. The suggested Cascade CNN-ViT model extracts local features from retinal pictures by leveraging the spatial hierarchy learning capabilities of Inception-V3 and ResNet-50. …The Vision Transformer takes these regional characteristics and uses self-attention mechanisms to pick up global context information and long-range interdependence. The model successfully combines fine-grained local information with semantically significant global contextual cues by merging the output representations from the CNNs and Vision Transformer. undertaking comprehensive experiments on a large and varied dataset of multimodal retinal pictures to evaluate the performance of the proposed technique. Cascade CNN-ViT model outperforms standalone CNNs and Vision Transformers, as shown by the experimental findings. The model is also resilient across all classes of retinal diseases and is able to successfully deal with the complications introduced by using multiple picture types. Overall, the power of cascading Inception-V3, ResNet-50, and Vision Transformer topologies for improved retinal illness diagnosis has been demonstrated. Potentially improving the management of retinal illnesses and preserving visual health, the proposed approach could have important consequences for early detection and timely intervention. Show more

Keywords: Multimodal retinal images, deep learning, Inception-V3, vision transformer, cascade CNN-ViT

DOI: 10.3233/JIFS-235055

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Mahalakshmi, G. | Uma, E.

Article Type: Research Article

Abstract: Intelligent Transportation Systems have become integral to daily life, with VANETs (vehicular ad-hoc networks) playing the pivotal role. VANETs, the subsets of MANETs, employ vehicles as nodes to establish intelligent transport systems. However, due to critical applications such as military use, these networks are susceptible to attacks. With features like high mobility, dynamic network topology, and coverage issues, security breaches are a concern. This necessitates a secure routing algorithm to mitigate attacks and ensure message delivery. In our study, we utilize the UNSW-NB15 intrusion detection dataset to develop training and testing models. Our proposed novel intrusion detection system employs a …feature selection algorithm that prioritizes significant arriving traffic attributes. This algorithm enhances abnormal activity detection while minimizing associated features. To achieve this, we modify the Conditional Random Field algorithm with fuzzy-based rules, resulting in a more efficient selection of influential and contributing features for detecting attacks such as DoS, Worms, Fuzzers, and Shellcode. Through appropriate feature selection using the modified Conditional Random Field and Support Vector Machine classification system in our experiments, we demonstrate a notable increase in security by reducing the false positive rate. Additionally, our approach excels in detecting accuracy of Fuzzers (98.86%), DoS (98.80%), Worms (34.45%), and Shellcode (89.308%), ultimately enhancing network performance. These findings underscore the effectiveness of our proposed method in enhancing intrusion detection and overall network efficiency. Show more

Keywords: Vehicular ad-hoc networks, intrusion detection, feature selection, classification

DOI: 10.3233/JIFS-234192

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Kalaiarasan, D. | Ahilan, A. | Ramalingam, S.

Article Type: Research Article

Abstract: Image security plays a vital role in communication networks. Despite advancements in encryption, securing protective image data remains a computationally challenging problem, requiring the use of a secure environment to secure data transmitted from the device through the network. Encryption is essential for safeguarding essential data against unauthorized access or malfunction, especially for images. The contribution of the proposed model is to provide an analytical hybrid Harris Hawk Optimization (HHO) with a chaotic map approach which can be used to improve the overall performance of standard encryption techniques in image-based encrypted communication. Previously, the algorithm computes numerous chaotic Logistic map …and an encrypted images, where the pending plain image calculates the session key for the map’s input parameters. Afterwards, encrypted images are presented as hawks and made to work well with HHO. The optimized ciphered image is expressed as a fitness function using correlation coefficients related to nearby pixels. When compared to existing algorithms, the proposed NPCR (99%), UACI (33%), and entropy (7.97) demonstrate superior performance. The proposed hybrid approach outperforms traditional algorithms in terms of security while preserving image quality. Show more

Keywords: Harris Hawk Optimization, image security, multimedia, chaotic map, cryptography

DOI: 10.3233/JIFS-213337

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2023

Authors: Kamran, Muhammad | Ashraf, Shahzaib | Salamat, Nadeem | Naeem, Muhammad | Hameed, Muhammad Shazib

Article Type: Research Article

Abstract: One of the hottest areas for applying the solutions currently available is the internet of things-based smart housing society architecture and its uncertainty analysis. When intelligent parking, waste management, public transportation, public safety, and other automatic methods for housing society’s growth were implemented, it became even more crucial. An intelligent, smart system is necessary to manage these problems and provide smooth services. Additionally, it will be helpful in reducing issues with time waste and societal safety. However, the issue comes up when describing accurate, approximate, or questionable parking, transit, safety, and waste management areas. This paper discusses several mathematical solutions …for the smart housing society that use fuzzy rough sets, probabilistic hesitant fuzzy sets, and their extensions with neutrosophic sets. For further growth, a few studies on the graphic display of the evolution of the smart housing society are also considered. The rough set theory can be useful when dealing with imprecise, incomplete, or indeterminate data sets. The core contribution of this work is the construction of a novel generalized notion of a single-valued neutrosophic probabilistic hesitant fuzzy rough set (SV-NPHFRS), which is a hybrid structure of the single-valued neutrosophic set, the probabilistic hesitant fuzzy set, and the rough set. In contrast to the present literature, the underlying idea of SV-NPHFRS is that it is a powerful mathematical tool for managing uncertainty and imperfect information. This method is particularly beneficial when there are a number of competing criteria to consider. The aggregation technique plays an important role in decision-making concerns, especially when more competing criteria are present. In the study’s comparison phase, the suggested decision support system is compared to relevant existing approaches. The results suggest that, in terms of choice flexibility, the suggested technique has the potential to outperform the drawbacks of the current decision-making tools. The proposed study is expected to be useful for a number of researchers conducting future work on housing societies, waste management, public safety diagnostics, and hybridization. Show more

Keywords: Single-valued neutrosophic probabilistic hesitant fuzzy rough sets, aggregation operators, decision making

DOI: 10.3233/JIFS-224364

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-45, 2023

Authors: Vidyabharathi, D. | Sivanesh, S. | Theetchenya, S. | Vidhya, G.

Article Type: Research Article

Abstract: Detecting of cracks and damages, especially in multi storied buildings is a crucial aspect of infrastructure and building maintenance, as it ensures safety and reliability. An enhanced framework for the crack detection is proposed to identify the fine cracks which are present at greater heights and not captured to the human vision from the ground. The cracks are identified and classified by the deep convolutional neural network model. The Oriented Non-Maximal Suppression module reduces the false positives to improve the classification accuracy and reliability. The proposed method O-CNN(CNN with ONMS)can be used in real-world for the infrastructure inspection and potential …applications in civil engineering construction. The ability to input different types of data, including images and videos, makes the proposed system user-friendly and easy to use. Furthermore the system reduces the risk of human error and prevents the huge damages caused to the building. Also, it prevents the major loss which may be caused to the lives. Overall, the proposed system contributes to the field of deep learning and computer vision by providing an effective and better solution for crack detection in real-world scenarios. Show more

Keywords: Deep learning, convolutional neural networks, oriented non-maximal suppression, O-CNN

DOI: 10.3233/JIFS-232793

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2023

Authors: Deng, Qiao

Article Type: Research Article

Abstract: Offloading strategies in mobile edge computing are hot research, whereas, existing offloading strategies at the edge hard handle the issues of multi-user intensive task scheduling, resulting in the poor utilization of network resource. Therefore, this makes the quality of experience for end users far from satisfactory. To address this, this paper proposes a novel joint offloading strategy consisting of the back propagation neural network and the genetic algorithm. Firstly, using the genetic algorithm optimizes the learning error of the back propagation neural network, and then energy consumption in the system and response delay are jointly optimized by the back propagation …neural network. Under long-term total overhead-cost constraints, the joint strategy can achieve the search of the optimal solutions to generate superior calculated offloading results. Unlike those approaches devoting into reducing response delay only for end users, this work takes account into the total overhead-cost in the system thereby affording more efficient for application service providers. Multiple simulation results indicate that the proposed strategy can not only reduce the average response delay of the mobile edge computing system, but also remain a low average energy consumption. Show more

Keywords: Mobile edge computing, offloading strategy, joint optimization

DOI: 10.3233/JIFS-234396

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Nguyen, Vy Duong Kim | Do, Phuc

Article Type: Research Article

Abstract: People will increasingly get expedited and diverse means of accessing news as societies progress. Furthermore, there is a noticeable increase in the prevalence of incorrect and misleading information. Our research is motivated by the significant concerns regarding the detrimental impacts of disinformation on the general public, political stability, and trust in the media. The scarcity of Vietnamese-language datasets can be attributed to the predominant focus of false news detection studies on datasets only in English. Detection investigations of fake news have predominantly relied on supervised machine learning algorithms, which possess notable limitations when confronted with unclassified news articles that are …either authentic or untrue. The utilization of Knowledge Graphs (KG) and Graph Convolutional Networks (GCN) holds promise in addressing the constraints of supervised machine learning algorithms. To address these problems, we propose an approach that integrates KG)into the procedure for detecting fake news. We utilize the Vietnamese Fake News Detection dataset (VFND-vietnamese-fake-news), comprising authentic and deceptive news articles from reputable Vietnamese newspapers such as vnexpress, tuoitre, and have been collected from 2018 to 2023. News articles are only labeled as real or fake after experiencing independent verification. The Glove embedding (Global Vectors for Word Representation) is employed to establish a knowledge network for the given dataset. This knowledge graph’s construction is accomplished using the Word Mover’s Distance (WMD) algorithm in conjunction with the K-nearest neighbor approach; GCN approach and the input KG train models to discern between real and fake news. With labeling half of the input dataset, the experimental findings indicate a notable level of accuracy, reaching up to 85%. Our research holds significant importance in identifying fake news, particularly within the context of the Vietnamese language. Show more

Keywords: Fake news detection, graph convolutional network, semi-supervised, K-nearest neighbor, word mover’s distance

DOI: 10.3233/JIFS-233260

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Zhang, Shiguang | Guo, Di | Zhou, Ting

Article Type: Research Article

Abstract: Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments …were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model. Show more

Keywords: Extreme learning machine, heteroscedastic Gaussian noise, least squares support vector regression, twin hyperplanes, wind-speed forecasting

DOI: 10.3233/JIFS-232121

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Alphy, Anna | Rajamohamed, | Velusamy, Jayaraj | Vidhya, K. | Ravi, G. | Rajasekaran, Arun Sekar

Article Type: Research Article

Abstract: Age-Related Macular Degeneration is a progressive, irreversible eye condition that causes vision loss and impairs quality of life. The lost potential of the optic nerve cannot be regained, but a patient with Age-Related Macular Degeneration must have early diagnosis and treatment in order to prevent visual loss. The diagnosis of Age-Related Macular Degeneration is based on visual field loss tests, a patient’s medical history, intraocular pressure, and a physical fundus evaluation. Age-Related Macular Degeneration must be diagnosed early in order to avoid irreparable structural damage and vision loss. The objective of the proposed study is to develop a new optimization-driven …strategy-based recurrent neural network using the Internet of Things for the identification of age-related macular degeneration. The Recurrent Neural Network (RNN) classifier is trained using the Particle Swarm Optimization (PSO) technique included into the RNN-IoMT. Initially, the input picture is sent through pre-processing in order to remove noise and artefacts. The generated preprocessed picture is simultaneously sent to optical disc detection and blood vessel detection. In addition, picture level characteristics are extracted from the image that has been preprocessed. Finally, the image-level, optic disc-level, and blood vessel-level features are retrieved and compiled into a feature vector. The acquired feature vector is fed into the RNN classifier, with the suggested PSO used to train the RNN for Age-Related Macular Degeneration detection via the Internet of Medical Things. The suggested PSO+RNN exhibits better performance with enhanced precision of 97.194%, sensitivity of 97.184%, and specificity of 97.2044%, respectively. Show more

Keywords: Wearables, internet of things, teleophthalmology, deep learning, fundus images

DOI: 10.3233/JIFS-233044

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Zhou, Zilong | Yu, Yue | Song, Chaoyang | Liu, Zhen | Shi, Manman | Zhang, Jingxiang

Article Type: Research Article

Abstract: Reducing noise in CT images and extracting key features are crucial for improving the accuracy of medical diagnoses, but it remains a challenging problem due to the complex characteristics of CT images and the limitations of existing methods. It is worth noting that multiple views can provide a richer representation of information compared to a single view, and the unique advantages of the wavelet transform in feature analysis. In this study, a novel Multi-View Weighted Feature Fusion algorithm called MVWF is proposed to address the challenge of enhancing CT image recognition utilizing wavelet transform and convolutional neural networks. In the …proposed approach, the wavelet transform is employed to extract both detailed and primary features of CT images from two views, including high frequency and low frequency. To mitigate information loss, the source domain is also considered as a view within the multi-view structure. Furthermore, AlexNet is deployed to extract deeper features from the multi-view structure. Additionally, the MVWF algorithm introduces a balance factor to account for both specific information and global information in CT images. To accentuate significant multi-view features and reduce feature dimensionality, random forest is used to assess feature importance followed by weighted fusion. Finally, CT image recognition is accomplished using the SVM classifier. The performance of the MVWF algorithm has been compared with classical multi-view algorithms and common single-view methods on COVID-CT and SARS-COV-2 datasets. The experimental results indicate that an average improvement of 6.8% in CT image recognition accuracy can be achieved by utilizing the proposed algorithm. Particularly, the MVF algorithm and MVWF algorithm have attained AUC values of 0.9972 and 0.9982, respectively, under the SARS-COV-2 dataset, demonstrating outstanding recognition performance. The proposed algorithms can capture more robust and comprehensive high-quality feature representation by considering feature correlations across views and feature importance based on Multi-view. Show more

Keywords: Multi-view, CT image recognition, feature fusion, wavelet transform, random forest

DOI: 10.3233/JIFS-233373

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2023

Authors: Yaqoot, Iqra | Riaz, Muhammad | Al-Quran, Ashraf | Tehreem,

Article Type: Research Article

Abstract: This research work proposes a novel approach for multi stage decision analysis (MSDA) using innovative concepts of cubic intuitionistic fuzzy set (CIFS) theory. The paper introduces CIF-technique for order preference by similarity to ideal solution (TOPSIS) as a robust method for MSDA problems, particularly for the diagnosis of epilepsy disorders. To achieve this goal, new similarity measures (SMs) are developed for CIFS, including the Cosine angle between two vectors, a new distance measure, and the Cosine function, presented as three different types of Cosine similarity measures. The proposed CIF-TOPSIS approach is found to be suitable for precise value performance ratings …and is expected to be a viable approach for case studies in the diagnosis of epilepsy disorders. The efficiency and reliability of the proposed MSDA methods is efficiently carried through numerical examples and comparative analysis. Show more

Keywords: CIF information, CIF-TOPSIS, similarity, measures, epilepsy disorders, multi stage decision analysis

DOI: 10.3233/JIFS-232085

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2023

Authors: Bo, Lina

Article Type: Research Article

Abstract: In college education, English is a required course for every college student, and undergraduate colleges have certain requirements for college English proficiency. At the same time, English is directly related to its learning, so improving the quality of college English teaching (CET) is very important. Teaching quality is a key indicator for measuring the effectiveness of English teaching. Learning quality evaluation is a very complex process that involves many factors, such as evaluation indicators, evaluation methods, etc. Therefore, establishing an objective and scientific English quality evaluation system is a challenging issue. The CET quality evaluation is a MAGDM. Then, the …TODIM and VIKOR was used to set up MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are employed as a tool for depicting uncertain information during the CET quality evaluation. In this study, the entropy and score values are employed to establish the objective weights. Then, an integrated interval-valued intuitionistic fuzzy TODIM-VIKOR (IVIF-TODIM-VIKOR) is developed to cope with the MAGDM problem. An illustrative example for CET quality evaluation and some comparative analysis are developed to demonstrate the validity and reliability of IVIF-TODIM-VIKOR method. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2 interval-valued intuitionistic fuzzy sets (IVIFSs), TODIM, VIKOR, CET quality evaluation

DOI: 10.3233/JIFS-234149

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Sreelatha, Tammineni | Maheswari, M. | Ravi, G. | Devarajan, N. Manikanda | Arun, M.

Article Type: Research Article

Abstract: Data compression is the ancestor of image compression, which uses fewer bits to represent the same picture. It is categorised as lossy or lossless depending on the quality required. In a lossless compression situation, no information is lost during the decompression process. Data loss is possible with the lossy technique since it is not reversible. In an effort to boost compression efficiency while maintaining a high xiv reconstruction quality of picture, near lossless approaches have evolved. The medical pictures consist of a large number of items, each of which may be described in detail and utilised for a variety of …purposes. The clinically relevant item in 2D medical pictures is referred to as the Region of Interest (ROI), whereas in 3D images, it is referred to as the Volume of Interest (VOI). Saving energy is crucial since it is one of the most limited resources in these networks. However, DTN has an additional difficulty since communication between nodes is maintained so long as they are in physical proximity to one another. However, because to the nodes’ mobility, this may not be long enough to provide the necessary multimedia data transmission. Wireless networks are susceptible to security assaults, and traditional security solutions are computationally demanding, making them unsuitable for networks that constantly need to recharge their batteries. All of these are reasons for tackling the problems of multimedia data processing and transmission via wireless networks in this dissertation. With this in mind, it has been attempted to investigate low-overhead and safe multimedia data compression as a solution to the issue that energy-constrained nodes in these networks limit complex multimedia processing while keeping at least basic security features. LZW-OMCA compression using the Octagonal Multimedia Compression Algorithm is part of the suggested method. The purpose of this is to improve the compression ratio. The proposed approach uses a little bit of crypt to compress data, which makes the data unreadable to anybody except the intended receiver, hence providing network security. The previous proposed works analysed the performance of several compression algorithms applied to multimedia material. Performance assessment utilising MSE, SSIM, and other metrics are used to show the pros and cons of each segment. Show more

Keywords: Octagonal multimedia compression algorithm, data compression, LZW-OMCA compression, MSE, SSIM

DOI: 10.3233/JIFS-234314

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Wang, Jia-Li | Jiang, Wen-Qi | Tao, Xi-Wen | Yang, Shan-Shan

Article Type: Research Article

Abstract: The processing method of fuzzy information is a critical element in multi-criteria group decision-making (MCGDM). The hesitant Pythagorean fuzzy set (HPFS) has a higher capacity in express the uncertainty of human inherent preference. A composite weighted mathematical programming model with prospect theory and best-worst method (BWM) is proposed to solve the uncertainty of criterion weight acquisition and decision-makers (DMs) psychological behavior under the HPF environment. The decision-making process is as follows: Firstly, a novel spatial distance measurement method is designed which considers the extension space of HPFSs space by five parameters under the HPF environment. Secondly, the optimal criteria weights …model minimizes the total distance between the alternatives and the HPF positive ideal solution (HPFPIS), as well as minimizes the consistency ratio of BWM. Thirdly, we propose the prospect decision matrix by the prospect theory and optimal weights, then use the ordered weighted average operator under the normal distribution to calculate the weight of DMs and rank the decision alternatives. Finally, an example is illustrated here, sensitivity and reliability, and comparative analysis are conducted to verify the effectiveness of the proposed method. Show more

Keywords: Multiple-criteria group decision-making, BWM, prospect theory, mathematical programming model, combination weights, spatial distance measure

DOI: 10.3233/JIFS-233339

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2023

Authors: He, Ping | Chen, Jingfang

Article Type: Research Article

Abstract: In this paper, a question answering method is proposed for educational knowledge bases (KBQA) using a question-aware graph convolutional network (GCN). KBQA provides instant tutoring for learners, improving their learning interest and efficiency. However, most open domain KBQA methods model question sentences and candidate answer entities independently, limiting their effectiveness. The proposed method extracts description information and query entity sets for a specific question, processes them with Transformer and pre-trained embeddings of the knowledge base, and extracts a subgraph of candidate answer sets from the knowledge base. The node information is updated by GCN with two attention mechanisms expressed by …the question description and query entity set, respectively. The query description information, query entity set, and candidate entity representation are fused to calculate the score and predict the answer. Experiments on MOOC Q&A dataset show that the proposed method outperforms benchmark models. Show more

Keywords: Educational knowledge base, data-driven intelligent education, question answering method, Graph convolutional network (GCN), prediction accuracy

DOI: 10.3233/JIFS-233915

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Zhao, Jie | Wang, Shuo | Wu, Haotian

Article Type: Research Article

Abstract: To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2 ) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In …addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOC UC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOC B was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified. Show more

Keywords: DC microgrid, model predictive control, mixed energy storage, objective function, secondary planning

DOI: 10.3233/JIFS-234849

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Poongavanam, N. | Nithiyanandam, N. | Suma, T. | Thatha, Venkata Nagaraju | Shaik, Riaz

Article Type: Research Article

Abstract: In this research, –coverage –connected problem is viewed as multi-objective problem and shuffling frog leaps algorithm is proposed to address multi-objective optimization issues. The shuffled frog leaping set of rules is a metaheuristic algorithm that mimics the behavior of frogs. Shuffled frog leaping algorithms are widely used to seek global optimal solutions by executing the guided heuristic on the given solution space. The basis for the success of this SFL algorithm is the ability to exchange information among a group of individuals which phenomenally explores the search space. SFL improves the overall lifespan of the network, the cost of connection …among the sensors, to enhance the equality of coverage among the sensors and targets, reduced sensor count for increased coverage, etc. When it comes to coverage connectivity issues, each target has to be covered using k sensors to avoid the loss of data and m sensors connected enhance the lifespan of the network. When the targets are covered by k sensors then the loss of data will be reduced to an extended manner. When the sensors are connected with m other sensors then the connectivity among the sensors will not go missing and hence the lifespan of the network will be improved significantly. Therefore, the sensor node number in coverage indicates the total number of sensor nodes utilised to cover a target, and the number of sensor nodes in connected reflects the total number of sensor nodes that provide redundancy for a single failed sensor node. Connectivity between sensor nodes is crucial to the network’s longevity. The entire network backbone acts strategically when all the sensors are connected with one or the other to pertain to the connectivity of the network. Coverage is yet another key issue regarding the loss of data. The proposed algorithm solves the connectivity of sensors and coverage of targets problems without weighted sum approach. The proposed algorithm is evaluated and tested under different scenarios to show the significance of the proposed algorithm. Show more

Keywords: Optimization, wireless sensor networks, throughput, latency, packet delivery, target

DOI: 10.3233/JIFS-233595

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Xing, Yu-Xuan | Wang, Jie-Sheng | Zhang, Shi-Hui | Bao, Yin-Yin | Zheng, Yue | Zhang, Yun-Hao

Article Type: Research Article

Abstract: The p-Hub allocation problem is a classic problem in location assignment, which aims to optimize the network by placing Hub devices and allocating each demand node to the corresponding Hub. A mutation Transit search (TS) algorithm with the introduction of the black hole swallowing strategy was proposed to solve the p-Hub allocation problem. Firstly, the mathematical model for the p-Hub allocation problem is established. Six mutation operators specifically designed for p-Hub allocation problem are introduced to enhance the algorithm’s ability to escape local optima. Additionally, the black hole swallowing strategy was incorporated into TS algorithm so as to accelerate its …convergence rate while ensuring sufficient search in the solution space. The improved TS algorithm was applied to optimize three p-Hub location allocation problems, and the simulation results are compared with those of the basic TS algorithm. Furthermore, the improved TS algorithm is compared with the Honey Badger Algorithm (HBA), Sparrow Search Algorithm (SSA), Harmony Search Algorithm (HS), and Particle Swarm Optimization (PSO) to solve three of p-Hub allocation problems. Finally, the impact of the number of Hubs on the cost of three models was studied, and the simulation results validate the effectiveness of the improved TS algorithm. Show more

Keywords: p-Hub allocation problem, transit search algorithm, black hole strategy, mutation operator

DOI: 10.3233/JIFS-234695

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: Sri Geetha, M. | Grace Selvarani, A.

Article Type: Research Article

Abstract: Breast cancer is responsible for the deaths of hundreds of women every year. The manual identification of breast cancer has more difficulties, and have the possibility of error. Many imaging approaches are being researched for their potential to identify breast cancer (BC). Incorrect identification might sometimes result in unneeded therapy and diagnosis. Because of this, accurate identification of breast cancer may save a great number of patients from needing unneeded surgery and biopsies. Deep learning’s (DL) performance in the processing of medical images has substantially increased as a result of recent breakthroughs in the sector. Because of their improved capacity …to anticipate outcomes, deep learning algorithms are able to reliably detect BC from ultrasound pictures. Transfer learning is a kind of machine learning that reuses knowledge representations from public models that were built with the use of large-scale datasets. Transfer learning has been shown to often result in overfitting. The primary purpose of this research is to develop and provide suggestions for a deep learning model that is effective and reliable in the detection and classification of breast cancer. A tissue biopsy is obtained from the suspicious region in order to ascertain the nature of a breast tumor and whether or not it is cancerous. Tumors may take any of these forms. When the images have been reconstructed with the help of a variational autoencoder (VAE) and a denoising variational autoencoder (DVAE), a convolutional neural network (CNN) model is used. This will be the case because it opens up a new area of the field to be investigated. The histological subtypes of breast cancer are used in conjunction with the degree of differentiation to execute the task of breast cancer categorization. Show more

Keywords: Medical image classification, disease detection, deep learning, breast cancer, convolutional neural network (CNN), variationalautoencoder, histopathology image

DOI: 10.3233/JIFS-231345

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Wei, Qiuyue | Yang, Dong | Zhang, Mingjie

Article Type: Research Article

Abstract: Aspect-based sentiment analysis is a fine-grained task in the field of sentiment analysis. Various GCN approaches have recently emerged to work on this, but many approaches ignored the critical role of aspectual word information and the effect of noise. In view of this situation, we propose an aspect-based word embedding graph convolutional network (AWEGCN) model. In order to make good use of the aspect information and distinguish the contextual information that is more important for a particular aspect, the aspect information is embedded in the output of the hidden layer. To reduce the noise effect when multiple aspect words appear …in a sentence, after going through the bidirectional graph convolutional network, the aspect information is embedded. A specific contextual representation is computed through an attention mechanism, which is used as the final classification feature. Experiments show that our model achieves impressive performance on five public datasets, and we also apply BERT and XLNet pre-trained models to this task and obtain advanced results that validate the effectiveness of our model. Show more

Keywords: Aspect-level sentiment classification, aspect word embeddings, graph convolutional networks, attention mechanisms

DOI: 10.3233/JIFS-230537

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Lakshmi Narayanan, K. | Naresh, R.

Article Type: Research Article

Abstract: Vehicular Ad-Hoc Network (VANET) Technology is advancing due to the convergence of VANET and cloud computing technologies, Vehicular Ad-Hoc Network (VANET) entities can benefit from the cloud service provider’s favourable storage and computing capabilities. Cloud computing, the processing and storage capabilities provided by various cloud service providers, would be available to all VANET enterprises. Digital Twin helps in creating a digital view of the Vehicle. It focuses on the physical behaviour of the Vehicle as well as the software it alerts when it finds issues with the performance. The representation of the Vehicle is created using intelligent sensors, which are …in OBU of VANET that help collect info from the product. The author introduces the Cloud-based three-layer key management for VANET in this study. Because VANET connections can abruptly change, critical negotiation verification must be completed quickly and with minimal bandwidth. When the Vehicles are in movement, we confront the difficulty in timely methods, network stability, and routing concerns like reliability and scalability. We must additionally address issues such as fair network access, inappropriate behaviour identification, cancellation, the authentication process, confidentiality, and vehicle trustworthiness verification. The proposed All-Wheel Control (AWC) method in this study may improve the safety and efficiency of VANETs. This technology would also benefit future intelligent transportation systems. The Rivest–Shamir–Adleman (RSA) algorithm and Chinese Remainder Theorem algorithms generate keys at the group, subgroup, and node levels. The proposed method produces better results than the previous methods. Show more

Keywords: Cloud computing, VANET, RSA, CRT, AWC

DOI: 10.3233/JIFS-233527

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Maguluri, Lakshmana Phaneendra | Vinya, Viyyapu Lokeshwari | Goutham, V. | Uma Maheswari, B. | Kumar, Boddepalli Kiran | Musthafa, Syed | Manikandan, S. | Srivastava, Suraj | Munjal, Neha

Article Type: Research Article

Abstract: Depression is a prevalent mental health disorder that affects people of all ages and origins; therefore, early detection is essential for timely intervention and support. This investigation proposes a novel method for detecting melancholy in young, healthy individuals by analysing their gait and balance patterns. In order to accomplish this, a comprehensive system is designed that incorporates cutting-edge technologies such as a Barometric Pressure Sensor, Beck Depression Inventory (BDI), and t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. The system intends to capitalize on the subtle motor and physiological changes associated with melancholy, which may manifest in a person’s gait and balance. …The Barometric Pressure Sensor is used to estimate variations in altitude and vertical velocity, thereby adding context to the evaluation. The mood states of participants are evaluated using the BDI, a well-established psychological assessment instrument that provides insight into their emotional health. Integrated and pre-processed data from the Barometric Pressure Sensor, BDI responses, and gait and balance measurements. The t-SNE algorithm is then used to map the high-dimensional data into a lower-dimensional space while maintaining the local structure and identifying underlying patterns within the dataset. The t-SNE algorithm improves visualization and pattern recognition by reducing the dimensionality of the data, allowing for a more nuanced analysis of depression-related markers. As the proposed system combines objective physiological measurements Show more

DOI: 10.3233/JIFS-235058

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Tan, Mindong | Qu, Liangdong

Article Type: Research Article

Abstract: Oral English teaching quality evaluation is a complex nonlinear relationship, which is affected by many factors and has low accuracy. Aiming at the problem, a teaching quality evaluation method based on a BP neural network optimized by the improved crow search algorithm (ICSA) is proposed. First, ICSA is put forward and five algorithms are used to compare with the proposed algorithm on 10 benchmarks functions. The results show that ICSA outperforms the other five algorithms on 10 functions. Second, a feature selection method based on the improved binary crow search algorithm (BICSA) is used to select teaching quality evaluation indexes, …and 10 standard datasets from the UCI repository are used for testing experiments. Finally, an oral English teaching evaluation model based on BP neural network is designed, in which BICSA is used for feature selection and ICSA is used to optimize the initial weights of the BP neural network. In the experiment, we designed 5 first-grade indexes and 15 second-grade indexes, and then we collects 23 groups of oral English teaching quality data. BICSA selected 10 features from a set of 15 features. Experimental results show that this method can effectively evaluate the quality of oral English teaching with high accuracy and real-time performance. Show more

Keywords: BP neural network, crow search algorithm, feature selection, oral English teaching, quality evaluation

DOI: 10.3233/JIFS-222455

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: AlAlaween, Wafa’ H. | AlAlawin, Abdallah H. | AbuHamour, Saif O. | Gharaibeh, Belal M.Y. | Mahfouf, Mahdi | Alsoussi, Ahmad | AbuKaraky, Ashraf E.

Article Type: Research Article

Abstract: Right-first-time production enables manufacturing companies to be profitable as well as competitive. Ascertaining such a concept is not as straightforward as it may seem in many industries, including 3D printing. Therefore, in this research paper, a right-first-time framework based on the integration of fuzzy logic and multi-objective swarm optimization is proposed to reverse-engineer the radial based integrated network. Such a framework was elicited to represent the fused deposition modelling (FDM) process. Such a framework aims to identify the optimal FDM parameters that should be used to produce a 3D printed specimen with the desired mechanical characteristics right from the first …time. The proposed right-first-time framework can determine the optimal set of the FDM parameters that should be used to 3D print parts with the required characteristics. It has been proven that the right-first-time model developed in this paper has the ability to identify the optimal set of parameters successfully with an average error percentage of 4.7%. Such a framework is validated in a real medical case by producing three different medical implants with the desired mechanical characteristics for a 21-year-old patient. Show more

Keywords: Fuzzy logic, particle swarm optimization, radial based integrated network, right-first-time production

DOI: 10.3233/JIFS-232135

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Cheng, Tao | Cheng, Hua | Fang, Yiquan | Liu, Yufei | Gao, Caiting

Article Type: Research Article

Abstract: As prototype-based Few-Shot Learning methods, Prototypical Network generates prototypes for each class in a low-resource state and classify by a metric module. Therefore, the quality of prototypes matters but they are inaccurate from the few support instances, and the domain-specific information of training data are harmful to the generalizability of prototypes. We propose a C onceptual P rototype (CP), which contains both rich instance and concept features. The numerous query data can inspire the few support instances. An interactive network is designed to leverage the interrelation between support set and query-detached set to acquire a rich Instance Prototype which is …typical on the whole data. Besides, class labels are introduced to prototype by prompt engineering, which makes it more conceptual. The label-only concept makes prototype immune to domain-specific information in training phase to improve its generalizability. Based on CP, C onceptual P rototypical C ontrastive L earning (CPCL) is proposed where PCL brings instances closer to its corresponding prototype and pushes away from other prototypes. “2-way 5-shot” experiments show that CPCL achieves 92.41% accuracy on ARSC dataset, 2.30% higher than other prototype-based models. Meanwhile, the 0-shot performance of CPCL is comparable to Induction Network in the 5-shot way, indicating that our model is adequate for 0-shot tasks. Show more

Keywords: Prototypical network, text classification, Few-Shot learning, prompt learning

DOI: 10.3233/JIFS-231570

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Uganya, G. | Bommi, R.M. | Muthu Krishnammal, P. | Vijayaraj, N.

Article Type: Research Article

Abstract: Internet of things (IoT) is a recent developing technology in the field of smart healthcare. But it is difficult to transfer the patient’s health record as a centralized network. So, “blockchain technology” has excellent consideration due to its unique qualities such as decentralized network, openness, irreversible data, and cryptography functions. Blockchain technology depends on cryptography hash techniques for safe transmission. For increased security, it transforms the variable size inputs into a constant length hash result. Current cryptographic hash algorithms with digital signatures are only able to access keys up to a size of 256 bytes and have concerns with single …node accessibility. It just uses the bits that serve as the key to access the data. This paper proposes the “Revised Elliptic Curve Cryptography Multi-Signature Scheme” (RECC-MSS) for multinode availability to find the nearest path for secure communications with the medical image as keys. Here, the input image key can be converted into an array of data that can be extended up to 512 bytes of size. The performance of the proposed algorithm is analyzed with other cryptography hash functions like Secure Hashing Algorithms (SHAs) such as “SHA224”, “SHA256”, “SHA384”, “SHA512”, “SHA3-224”, “SHA3-256”, “SHA3-384”, “SHA3-512”, and “Message Digest5” (MD5) by “One-way ANOVA” test in terms of “accuracy”, “throughput” and “time complexity”. The proposed scheme with ECC achieved the throughput of 17.07 kilobytes per 200 nano seconds, 93.25% of accuracy, 1.5 nanoseconds latency of signature generation, 1.48 nanoseconds latency of signature verification, 1.5 nanoseconds of time complexity with 128 bytes of hash signature. The RECC-MSS achieved the significance of 0.001 for accuracy and 0.002 for time complexity which are less than 0.05. From the statistical analysis, the proposed algorithm has significantly high accuracy, high throughput and less time complexity than other cryptography hash algorithms. Show more

Keywords: Internet of Things, blockchain technology, multi-signature, Secure Hash Algorithm, Revised Elliptic Curve Cryptography, medical image

DOI: 10.3233/JIFS-232802

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: Vidhya, R. | Banavath, Dhanalaxmi | Kayalvili, S. | Naidu, Swarna Mahesh | Prabu, V.Charles | Sugumar, D. | Hemalatha, R. | Vimal, S. | Vidhya, R.G.

Article Type: Research Article

Abstract: Early Alzheimer’s disease detection is essential for facilitating prompt intervention and enhancing the quality of care provided to patients. This research presents a novel strategy for the diagnosis of Alzheimer’s disease that makes use of sophisticated sampling methods in conjunction with a hybrid model of deep learning. We use stratified sampling, ADASYN (Adaptive Synthetic Sampling), and Cluster- Centroids approaches to ensure a balanced representation of Alzheimer’s and non-Alzheimer’s cases during model training in order to meet the issues posed by imbalanced data distributions in clinical datasets. This allows us to solve the challenges posed by imbalanced data distributions in clinical …datasets. A strong hybrid architecture is constructed by combining a Residual Neural Network (ResNet) with Residual Neural Network (ResNet) units. This architecture makes the most of both the feature extraction capabilities of ResNet and the capacity of LSTM to capture temporal dependencies. The findings demonstrate that the model is superior to traditional approaches to machine learning and single-model architectures in terms of accuracy, sensitivity, and specificity. The hybrid deep learning model demonstrates exceptional capabilities in identifying early indicators of Alzheimer’s disease with a high degree of accuracy, which paves Show more

DOI: 10.3233/JIFS-235059

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Cheng, Shumin | Zhou, Yan | Bao, Yanling

Article Type: Research Article

Abstract: With the increasing diversification and complexity of information, it is vital to mine effective knowledge from information systems. In order to extract information rapidly, we investigate attribute reduction within the framework of dynamic incomplete decision systems. Firstly, we introduce positive knowledge granularity concept which is a novel measurement on information granularity in information systems, and further give the calculation method of core attributes based on positive knowledge granularity. Then, two incremental attribute reduction algorithms are presented for incomplete decision systems with multiple objects added and deleted on the basis of positive knowledge granularity. Furthermore, we adopt some numerical examples to …illustrate the effectiveness and rationality of the proposed algorithms. In addition, time complexity of the two algorithms are conducted to demonstrate their advantages. Finally, we extract five datasets from UCI database and successfully run the algorithms to obtain corresponding reduction results. Show more

Keywords: Incomplete decision system, positive knowledge granularity, incremental attribute reduction

DOI: 10.3233/JIFS-230349

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Xu, Zan | Lu, TongWei

Article Type: Research Article

Abstract: Some anomaly detection methods are based on CNN to fuse spatial and channel-wise information together within local receptive fields. However, the correlation between feature channels has not been fully utilized. Channel attention has been shown to model the interdependence between convolution feature channels and improve network representation. It is possible to introduce channel attention into anomaly detection. We attempt to directly embed the SE(Squeeze and Excitation) module into the convolutional layer but reduced anomaly detection performance. Therefore, we propose a lightweight channel attention module C-SE(Current Squeeze and Excitation) suitable for anomaly detection. C-SE module not only improves the representation ability …of depth convolutional neural network but also has a significant effect on texture anomaly detection. C-SE module body is constructed by average pooling and maximum pooling branches, which ensure that local salient features of the image are not lost. Then reduce the negative impact of feature calibration through a long connection. In addition, the improvement of classifier plays an important role. Experimental results have shown that the proposed method outperforms the Patch SVDD methods by 3% in image-level AUROC and 0.7% in pixel-level AUROC on the MVTec benchmark. The higher AUROC score and the faster rate of convergence prove the effectiveness of the method. Show more

Keywords: Anomaly detection, channel attention, feature calibration, texture, MVTec

DOI: 10.3233/JIFS-232677

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Bharathi, J. | Nandhini, S.

Article Type: Research Article

Abstract: This paper explores the behaviour of a Bulk Arrival Retrial Queue Model (BARQ) with two phases of service under the Bernoulli Vacation schedule and Breakdown (BVSB). Each batch of customers arriving the system finds if the server is available, instantly utilizes the service. If the server is busy, under breakdown, or taking a vacation, then the customers enter into the orbit. After completing both service stages, the server will either take a vacation with probability p or wait until the next customer arrives with probability 1 - p or q . Our approach considers the nature of the customer as …balking and also takes into account the breakdown of server, which may occur instantaneously during any stage of service. Significant performance measures have been derived and presented. A numerical study of the proposed model is carried out using MATLAB and results were reported. Show more

Keywords: Retrial Queues, two types of service, Bernoulli Vacation, steady-state, Fortuitous Breakdown, impatient customers

DOI: 10.3233/JIFS-231195

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Thilagavathy, A. | Mohanaselvi, S.

Article Type: Research Article

Abstract: Consolidating cubical fuzzy numbers (CFNs) is essential in an uncertain decision-making process. This study focuses on creating innovative cubical fuzzy aggregation operators based on the newly proposed Einstein operational laws, utilizing the Bonferroni mean function to capture the interrelationships among the aggregated CFNs. The first contribution of this paper is introducing a novel cubical fuzzy Einstein Bonferroni mean averaging operator. Building upon this operator, we extend our research to develop cubical fuzzy Einstein Bonferroni mean weighted, ordered weighted, and hybrid averaging operators, taking into account the weights of the aggregated CFNs. To ensure their effectiveness, we thoroughly investigate the desirable …properties of these proposed operators. Furthermore, we leverage the introduced operators to establish a new approach known as the cubical fuzzy linear assignment method, which proves valuable in resolving multiple criteria group decision-making problems. As a practical demonstration of the method’s utility, we apply it to address a real-life challenge: identifying the optimal location for constructing a wind power plant under a cubical fuzzy environment. To validate the effectiveness of our approach, we compare its results with those obtained using existing methods from the literature. Additionally, we conduct a statistical analysis to visualize the correlative conjunction between the ranking outcomes obtained by different operators Show more

Keywords: Cubical fuzzy set, Einstein operational laws, Bonferroni mean, averaging aggregation operators, linear assignment method, wind power plant location selection

DOI: 10.3233/JIFS-232252

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2023

Authors: Feng, Qingyuan

Article Type: Research Article

Abstract: China’s education has entered the era of quality, and in order for higher vocational colleges to develop better, effective measures must be taken to improve the quality of education. Facing the continuous innovation of education in higher vocational colleges, it is necessary to strengthen institutional construction and constrain educational management with institutional constraints, which is the key to ensuring the education quality. The higher vocational education quality evaluation in the new era is regarded as multiple attribute decision-making (MADM). Recently, the EDAS and CRITIC model has been employed to solve MAGDM. The triangular fuzzy neutrosophic sets (TFNSs) are constructed as …an efficient tool for portraying the uncertain information during the higher vocational education quality evaluation in the new era. In this paper, the triangular fuzzy neutrosophic number EDAS (TFNN-EDAS) model based on the Hamming distance and Euclid distance is constructed to solve the MADM under TFNSs. The CRITIC method is utilized to obtain the weight information based on the Hamming distance and Euclid distance under INNSs. Finally, a numerical example of higher vocational education quality evaluation in the new era is constructed and some efficient comparisons are founded to verify the TFNN-EDAS method. Show more

Keywords: Multiple attribute decision making (MADM), triangular fuzzy neutrosophic sets (TFNSs), EDAS method, higher vocational education quality evaluation

DOI: 10.3233/JIFS-234044

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Paul, Ann Rija | Grace Mary Kanaga, E.

Article Type: Research Article

Abstract: In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, …speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc. Show more

Keywords: Vehicle tracking, surveillance, collision detection, trajectory and angle of intersection, deep convolutional neural network

DOI: 10.3233/JIFS-235911

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Koshti, Dipali | Gupta, Ashutosh | Kalla, Mukesh

Article Type: Research Article

Abstract: Visual question Answering (VQA) is a computer vision task that requires a system to infer an answer to a text-based question about an image. Prior approaches did not take into account an image’s positional information or the questions’ grammatical and semantic relationships during image and question processing. Featurization, which leads to the false answering of the question. Hence to overcome this issue CNN –Graph based LSTM with optimized BP Featurization technique is introduced for feature extraction of image as well as question. The position of the subjects in the image has been determined using CNN with a dropout layer and …the optimized momentum backpropagation during the extraction of image features without losing any image data. Then, using a graph-based LSTM with loopy backpropagation, the questions’ syntactic and semantic dependencies are retrieved. However, due to their lack of external knowledge about the input image, the existing approaches are unable to respond to common sense knowledge-based questions (open domain). As a result, the proposed Spatial GCNN knowledge retrieval with PDB Model and Spatial Graph Convolutional Neural Network, which recovers external data from Wikidata, have been used to address the open domain problems. Then the Probabilistic Discriminative Bayesian model, based Attention mechanism predicts the answer by referring to all concepts in question. Thus, the proposed method answers the open domain question with high accuracy of 88.30% . Show more

Keywords: Visual Question Answering, graph-based LSTM, SVO triples sentence, Discriminative Bayesian model, dynamic memory network

DOI: 10.3233/JIFS-230198

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Baskar, A. | Rajaram, A.

Article Type: Research Article

Abstract: Mobile Adhoc Network (MANET) is a dynamic network with mobility nodes. Emerging applications for MANETs in real-time present numerous research challenges. Specifically, the mobile nodes’ dynamic character hinders the routing efficacy in MANET. Previous algorithms for routing like DSDV DSR, AODV, and are inefficient due to an ineffective route discovery method. Route selection becomes more complex and energy-intensive for large-scale applications, such as air pollution monitoring. For air pollution monitoring applications, this research seeks to improve data delivery while reducing energy consumption. In this work, we proposed DeepOptimizer for achieving optimal data transmission. First, the network is segregated into multiple …clusters using the Rough set theory. In the all clusters, Cluster Head is accountable for split a data into normal and emergency. This process is performed by grouping data by K++ means algorithm. For emergency data, Graph-based Route Selection (GRS) algorithm. This is the fast algorithm that selects the optimal route. On the other hand, the normal data transmission route is selected by the Deep-SpikeQNetwok-based Whale Optimization (WO) algorithm. Finally, the network is tested through simulations made in ns-3 based on network lifetime, throughput, energy level, delay and packet delivery ratio. Show more

Keywords: Deep routing, emergency data transmission, spiking networks, MANET

DOI: 10.3233/JIFS-233425

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2023

Authors: Dong, Yanfeng | Wang, Meng

Article Type: Research Article

Abstract: At present, China’s football sports are relatively backward in training theory and practice. If you want to break out of Asia and enter the world, and gain a firm foothold in the international football arena, you must use a scientific and realistic attitude, absorb the successful experience of advanced football countries, reflect on our training concepts and practices, and deeply study the training laws of football, in order to find a way suitable for our development. Athletes’ competitive ability is the core issue of sports training. The failure of our football level is directly related to our systematic understanding of …athletes’ competitive ability. This problem has led to the separation of our training practice from the actual competition, making training unable to meet the needs of the competition. Only by solving this problem, can we improve the level of football in China. The football players’ competitive ability evaluation is affirmed as multiple attribute decision making (MADM). In such paper, motivated by the idea of cotangent similarity measure (CSM), the CSMs are extended to DVNSs and four CSMs are created under DVNSs. Then, two weighted CSMs are built for MADM under DVNSs. Finally, a numerical example for Football players’ competitive ability evaluation is affirmed and some comparative algorithms are produced to affirm the built method. Show more

Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), cotangent similarity measure (CSM), football players’ competitive ability evaluation

DOI: 10.3233/JIFS-231194

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Gui, Zhen

Article Type: Research Article

Abstract: The task of multi-label text classification involves assigning a set of related labels to a given document. However, there are three main problems with this task. Firstly, the joint modeling of label-text and label-label relationships is inadequate. Secondly, the semantic mining of the label itself is insufficient. Lastly, the utilization of the internal structure information of the label is ignored. To address these issues, a new multi-label text classification method has been proposed. This method is based on joint attention and shared semantic space. The joint multi-head attention mechanism models the relationship between labels and documents as well as the …relationship between labels simultaneously. This helps to avoid error transmission and utilizes the interaction information between them. The decouple shared semantic space embedding method improves the method of using labels semantic information and reduces deviation in the phase of modeling correlation. The hierarchical hinting method based on prior knowledge relies on the prior knowledge in the pre-trained model to exploit the labels hierarchy information. Experimental results have shown that this proposed method is superior to existing multi-label text classification methods in public datasets. Show more

Keywords: Multi-label text classification, attention mechanism, label representation, semantic embedding, pre-trained model

DOI: 10.3233/JIFS-234151

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Thakur, Divya | Lalwani, Praveen

Article Type: Research Article

Abstract: The use of mobile devices has contributed greatly to the expansion of Human Activity Recognition (HAR) studies in recent years. Researchers find it attractive because of its versatility, low cost, compact size, ease of usage, and wide range of possible applications. Conventional, biological, and control-based systems are just some of the methods that have been created for humanoid robot movement in recent years. This article specifically targeted improvement in the proposed method, which is different from previous papers. This is being done with the use of the publicly available Human Activity Gait (HAG) data set, which documents a wide range …of different types of activities. IMU sensors were used to collect this data set. Several experiments were conducted using different machine-learning strategies, each with its own set of hyper-parameters, to determine how best to utilize these data. In our proposed model Cuckoo Search Optimization is being used for optimum feature selection. On this data set, we have tested a number of machine learning models, including LR, KNN, DT, and proposed CSOEM (Cuckoo Search-Based Optimized Ensemble Model). The simulation suggests that the proposed model CSOEM achieves an impressive accuracy of 98% . This CSOEM is built by combining the feature selection strategy of Cuckoo Search Optimizations with the ensembling of the LR, KNN, and DT. Show more

Keywords: Bipedal robot locomotion, CSO: cuckoo search optimization, HAG: human activity gait, HAR: human activity recognition

DOI: 10.3233/JIFS-232986

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Xian, Luo | Tian, Lan

Article Type: Research Article

Abstract: In the era of big data, the exponentially increasing data volume and emerging technical tools have put forward new requirements for enterprise information management. Therefore, it is of great significance to enhance the core competitiveness of enterprises to explore how big data can empower the innovation of enterprise information management. Intelligent transportation system combines a variety of technologies and applies them to a large-scale transportation management system, so as to make a reasonable dispatch of traffic conditions. Aiming at the problem of the relatively low accuracy of bus passenger flow forecasting with the existing models, a short-term passenger flow prediction …model combining Stacked Denoising Auto Encoder (SDAE) and improved bidirectional Long-short Term Memory network (Bi-LSTM) is proposed. First, the SDAE model is used to fill in the missing bus passenger flow data, the characteristics of the bus passenger flow data are effectively utilized, and the data with rich information is used to predict the missing values with high accuracy. Second, Bi-LSTM model combined with attention mechanism is used for short-term bus passenger flow prediction. Considering that the data sequence of bus passenger flow is relatively long and there is a two-way information flow, the BiLSTM neural network is used for prediction tasks, and the influence of key factors is highlighted through attention weights to mine the internal laws of passenger flow data. The experimental results show that the proposed method achieves the lowest prediction error among all the comparison methods in the task of short-term bus passenger flow prediction on the public transportation dataset, with MAE, MRE, and RMSE values of 6.014, 0.052, and 9.874, respectively. These findings confirmed the effectiveness of the new model in the passenger flow prediction field. Show more

Keywords: Intelligent transportation system, passenger flow prediction, stacked denoising autoencoder, bidirectional long short-term memory network, attention mechanism introduction

DOI: 10.3233/JIFS-232979

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Ran, Lang | Hong, Chaoqun | Zhang, Xuebai | Tang, Chaohui | Xie, Yuhong

Article Type: Research Article

Abstract: Human pose estimation is a challenging visual task that relies on spatial location information. To improve the performance of human pose estimation, it is important to accurately determine the constraint relationship among keypoints. To address this, we propose MfvPose, a novel hybrid model that leverages rich multi-scale information. The proposed model incorporates the HRFOV module, which uses cascaded atrous convolution to maintain high-resolution representations of the backbone extractor and enrich the multi-scale information. In addition, we introduce learnable scalar weights to the Transformer encoder. In detail, it involves a multiplication by a diagonal matrix with learnable scalar weights on output …of each residual block, which improves the dynamics of model training and enhances the accuracy of human pose estimation. It is experimentally shown that our proposed MfvPose achieves promising results on various benchmarks. Show more

Keywords: Receptive field, multi-head self-attention, atrous convolution, human pose estimation

DOI: 10.3233/JIFS-233375

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Lian, Jing | Chen, Shi | Pi, Jiahao | Li, Linhui | Li, Qingfeng

Article Type: Research Article

Abstract: Localization through intricate traffic scenes poses challenges due to their dynamic, light-variable, and low-textured nature. Existing visual Simultaneous Localization and Mapping (SLAM) methods, which are based on static and texture-rich assumptions, struggle with drift and tracking failures in such complex environments. To address this, we propose a visual SLAM algorithm based on semantic information and geometric consistency in order to solve the above issues and further realize autonomous driving applications in road environments. In dynamic traffic scenes, we employ an object detection network to identify moving objects and further classify them based on geometric consistency as dynamic objects or potential …dynamic objects. This method permits us to preserve more reliable static feature points. In low-texture environments, we propose a method that employs key object categories and geometric parameters of static scene objects for object matching between consecutive frames, effectively resolving the problem of tracking failure in such scenarios. We conducted experiments on the KITTI and ApolloScape datasets for autonomous driving and compared them to current representative algorithms. The results indicate that in the dynamic environment of the KITTI dataset, our algorithm improves the compared metrics by an average of 29.68% . In the static environment of the KITTI dataset, our algorithm’s performance is comparable to that of the other compared algorithms. In the complex traffic scenario R11R003 from the ApolloScape dataset, our algorithm improves the compared metrics by an average of 25.27% . These results establish the algorithm’s exceptional localization accuracy in dynamic environments and its robust localization capabilities in environments with low texture. It provides development and support for the implementation of autonomous driving technology applications. Show more

Keywords: Autonomous vehicles, SLAM, traffic environments, object detection

DOI: 10.3233/JIFS-233068

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Luo, Zhi-Yong | Chen, Ya-Nan | Liu, Xin-Tong

Article Type: Research Article

Abstract: In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, …BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling. Show more

Keywords: Cloud computing, task scheduling, multi-objective optimization, sparrow search algorithm

DOI: 10.3233/JIFS-232527

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Suo, Chunfeng | Yongming, Li | Guo, Li

Article Type: Research Article

Abstract: The polygonal interval-valued fuzzy number is constructed based on the polygonal fuzzy number and the interval-valued fuzzy number. Its main feature is that the linear operation of finite ordered points reduces the complexity of traditional interval-valued fuzzy number operations. This research presents a generalized distance formula between two polygonal interval-valued fuzzy numbers and explores topological properties under the distance of polygonal interval-valued fuzzy numbers. In addition, we adopt the TOPSIS (technique for order preference by similarity to an ideal solution) and prospect theory approach for the multi-attribute decision-making problem. The information of attributes describes with polygonal interval-valued fuzzy numbers, and …we then implement optimized ranking on the alternatives according to the profit and loss ratio. Finally, we verify the effectiveness and practicability of the decision-making method and fuzzy numbers at polygonal interval-valued fuzzy numbers in e-commerce risk assessment. Show more

Keywords: Polygonal interval-valued fuzzy number, human resource recruitment, generalized distance, arithmetic operation

DOI: 10.3233/JIFS-230040

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2023

Authors: Liu, Zhichao | Wang, Yachao | Ma, Zhiyuan | Cao, Mengnan | Liu, Mingda | Yang, Xiaochu

Article Type: Research Article

Abstract: Real-time monitoring of electricity usage details through load monitoring techniques is a crucial aspect of smart power grid management and monitoring, allowing for the acquisition of information on the electricity usage of individual appliances for power users. Accurate detection of electricity load is essential for refined load management and monitoring of power supply quality, facilitating the improvement of power management at the user side and enhancing power operation efficiency. Non-intrusive load monitoring (NILM) techniques require only the analysis of total load data to achieve load monitoring of electricity usage details, and offer advantages such as low cost, easy implementation, high …reliability, and user acceptance. However, with the increasing number of distributed new load devices on the user side and the diversification of device development, simple load recognition algorithms are insufficient to meet the identification needs of multiple devices and achieve high recognition accuracy. To address this issue, a non-intrusive load recognition (NILR) model that combines an adaptive particle swarm optimization algorithm (PSO) and convolutional neural network (CNN) has been proposed. In this model, pixelated images of different electrical V-I trajectories are used as inputs for the CNN, and the optimal network layer and convolutional kernel size are determined by the adaptive PSO optimization algorithm during the CNN training process. The proposed model has been validated on the public dataset PLAID, and experimental results demonstrate that it has achieved a overall recognition accuracy of 97.26% and F-1 score of 96.92%, significantly better than other comparison models. The proposed model effectively reduces the confusion between various devices, exhibiting good recognition and generalization capabilities. Show more

Keywords: Smart grid, non-intrusive load recognition, DL, Convolutional Neural Network, adaptive Particle Swarm Optimization

DOI: 10.3233/JIFS-233813

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Bai, Xuemei | Tan, Jiaqi | Hu, Hanping | Zhang, Chenjie | Gu, Dongbing

Article Type: Research Article

Abstract: The paper proposes a deep learning model based on Chebyshev Network Gated Recurrent Units, which is called Spectral Graph Convolution Recurrent Neural Network, for multichannel electroencephalogram emotion recognition. First, in this paper, an adjacency matrix capturing the local relationships among electroencephalogram channels is established based on the cosine similarity of the spatial locations of electroencephalogram electrodes. The training efficiency is improved by utilizing the computational speed of the cosine distance. This advantage enables our method to have the potential for real-time emotion recognition, allowing for fast and accurate emotion classification in real-time application scenarios. Secondly, the spatial and temporal dependence …of the Spectral Graph Convolution Recurrent Neural Network for capturing electroencephalogram sequences is established based on the characteristics of the Chebyshev network and Gated Recurrent Units to extract the spatial and temporal features of electroencephalogram sequences. The proposed model was tested on the publicly accessible dataset DEAP. Its average recognition accuracy is 88%, 89.5%, and 89.7% for valence, arousal, and dominance, respectively. The experiment results demonstrated that the Spectral Graph Convolution Recurrent Neural Network method performed better than current models for electroencephalogram emotion identification. This model has broad applicability and holds potential for use in real-time emotion recognition scenarios. Show more

Keywords: Electroencephalogram, emotion recognition, chebyshev network gated recurrent units, spectral graph convolution recurrent neural network, adjacency matrix

DOI: 10.3233/JIFS-232465

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2023

Authors: Xu, Yi | Zhou, Meng

Article Type: Research Article

Abstract: As an important extension of classical rough sets, local rough set model can effectively process data with noise. How to effectively calculate three approximation regions, namely positive region, negative region and boundary region, is a crucial issue of local rough sets. Existing calculation methods for approximation regions are based on conditional probability, the time complexity is O  (|X ||U ||C |). In order to improve the computational efficiency of three approximation regions of local rough sets, we propose a double-local conditional probability based fast calculation method. First, to improve the computational efficiency of equivalence class, we define the double-local equivalence …class. Second, based on the double-local equivalence class, we define the double-local conditional probability. Finally, given the probability thresholds and a local equivalence class, the monotonicity of double-local conditional probability is proved, on this basis, a double-local conditional probability based fast calculation method for approximation regions of local rough sets is proposed, and the time complexity is O  (MAX  (|X |2 |C |, |X ||X C ||C |)). Experimental results based on 9 datasets from UCI demonstrate the effectiveness of the proposed method. Show more

Keywords: Local rough sets, approximation regions, double-local equivalence class, double-local conditional probability

DOI: 10.3233/JIFS-232767

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Xia, Wenxin | Che, Jinxing

Article Type: Research Article

Abstract: Wind energy needs to be used efficiently, which depends heavily on the accuracy and reliability of wind speed forecasting. However, the volatility and nonlinearity of wind speed make this difficult. In volatility and nonlinearity reduction, we sequentially apply complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) to secondarily decompose the wind speed data. This framework, however, requires effectively modeling multiple uncertainty components. Eliminating this limitation, we integrate crow search algorithm (CSA) with deep belief network (DBN) to generate a unified optimal deep learning system, which not only eliminates the influence of multiple uncertainties, but …also only adopts DBN as a predictor to realize parsimonious ensemble. Two experiments demonstrate the superiority of this system. Show more

Keywords: Parsimonious ensemble, secondary decomposition, optimal deep learning, crow search algorithm

DOI: 10.3233/JIFS-233782

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2023

Authors: Jingjing, Huang | Xu, Zhang

Article Type: Research Article

Abstract: In view of the individual differences in learners’ abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to provide personalized digital courses for each learner. The concept map and knowledge structure theory are comprehended to create the network structure map of understanding points of tax law offline courses and determine the learning objectives of learners; the project response theory is used to analyze the ability of different learners; According to the learners’ learning objectives and ability level, the intelligent recommendation model of …offline course resources of tax law is established with the minimum concept difference, minimum ability difference, minimum time difference, and minimum learning concept imbalance as the objective functions; Through the cultural framework, the chaotic particle swarm optimization algorithm based on the cultural framework is obtained by combining the particle swarm optimization algorithm and the chaotic mapping algorithm; The algorithm is used to solve the intelligent recommendation model, and the intelligent recommendation results of offline course resources in tax law are obtained. The experiential outcomes indicate that the process has a smaller inverse generation distance, larger super-volume, and smaller distribution performance index when solving the model; that is, the convergence performance and distribution performance of the model is better; This method can effectively recommend offline course resources of tax law for learners intelligently, and the minimum normalized cumulative loss gain is about 0.75, which is significantly higher than other methods, that is, the effect of intelligent recommendation is better. Show more

Keywords: Chaotic mapping, particle swarm, optimization algorithm, offline courses of tax law, resource intelligence recommendation

DOI: 10.3233/JIFS-233095

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Cao, Xianghong | Wu, Kunning | Geng, Xin | Wang, Yongdong

Article Type: Research Article

Abstract: With the acceleration of urbanization, the frequency of building fire incidents has been increasing year by year. Therefore, rapid, efficient, and safe evacuation from buildings has become an urgent and important task. A construction fire escape path planning method based on an improved NavMesh algorithm is proposed in this paper. Firstly, by using the method of local updates in the navigation grid, redundant computation is reduced, and the update time of the improved algorithm is about 6.8% of that of the original algorithm, immediate generation of navigation is achieved. Secondly, the heuristic function of the pathfinding algorithm is improved, and …a multi-exit path planning mechanism is proposed to achieve more efficient, which can quickly plan a safe evacuation path away from the spreading fire and smoke in the event of a fire. Finally, a new evaluation index called Navigation Grid Complexity (NGC) is proposed and demonstrated to measure the quality of navigation grids. The feasibility and effectiveness of the proposed method are validated through simulation experiments on actual building models, which can provide real-time, efficient, intelligent, and safe path planning for rapid evacuation of evacuees in the fire scene. Show more

Keywords: NavMesh, path planning, fire emergency evacuation, dynamic environments

DOI: 10.3233/JIFS-232681

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023