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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Zhou, Yinwei | Hu, Jun
Article Type: Research Article
Abstract: The rough set model has been extended to interval rough number decision systems, but the existing studies do not consider interval rough number decision systems with missing values. To this end, a rough set model of incomplete interval rough number decision systems (IIRNDSs) is proposed, and its uncertainty measures are investigated. Firstly, the similarity of two incomplete interval rough numbers (IIRNs) are defined by calculating their optimistic and pessimistic distances of the lower and upper approximation intervals of IIRNs. Then, the rough sets in IIRNDSs are constructed by the induced similarity relation. Next, four uncertainty measures, including approximation accuracy, approximation …roughness, conditional entropy, and decision rough entropy are given, which exhibit a monotonic variation with changes in the size of attribute sets, α, and θ. Finally, the experimental results demonstrate the proposed rough set model of IIRNDSs is feasible and effective. Show more
Keywords: Incomplete interval rough number decision systems, interval rough number, similarity relation, uncertainty measure, rough sets
DOI: 10.3233/JIFS-237320
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Wan, Huanyu | Qiu, Dong
Article Type: Research Article
Abstract: In order to explore effective management strategies in the context of epidemics, this study introduces a novel concept: Trapezoidal type-2 fuzzy linguistic intuitionistic fuzzy set (TrT2FLIFS) and proposes a trapezoidal type-2 fuzzy linguistic intuitionistic fuzzy matrix game (TrT2FLIFMG). Subsequently, employing sentiment analysis based on the BosonNLP sentiment lexicon, the study extracts comment data from Weibo related to epidemics made by users and calculates their textual scores. These two methods are integrated and applied to policy selection in epidemic management, along with the introduction of a new ranking function to compare the importance of alternative policies. Finally, a comparative analysis with …existing methods is conducted to validate the effectiveness of the proposed approach. Show more
Keywords: Matrix game, sentiment analysis, trapezoidal type-2 fuzzy linguistic intuitionistic fuzzy number, ranking function, pandemic management
DOI: 10.3233/JIFS-237319
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Mohammed Mustafa, M. | Kalpana Devi, S. | Althaf Ali, A. | Gunavathie, M.A.
Article Type: Research Article
Abstract: Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with …ZigBee and RA-TDMA & PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate. Show more
Keywords: Location points, real time scheduling, RATDMA, BSN
DOI: 10.3233/JIFS-234275
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Qu, Ying | Wang, Xuming
Article Type: Research Article
Abstract: In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of …the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention. Show more
Keywords: 24model, decision tree model, association rule, gas explosion
DOI: 10.3233/JIFS-234372
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Singh, Surender | Sharma, Sonam
Article Type: Research Article
Abstract: A Single-valued neutrosophic set (SVNS) has recently been explored as a comprehensive tool to assess uncertain information due to varied human cognition. This notion stretches the domain of application of the classical fuzzy set and its extended versions. Various comparison measures based on SVNSs like distance measure, similarity measure, and, divergence measure have practical significance in the study of clustering analysis, pattern recognition, machine learning, and computer vision-related problems. Existing measures have some drawbacks in terms of precision and exclusion of information and produce unreasonable results in categorization problems. In this paper, we propose a generic method to define new …divergence measures based on common aggregation operators and discuss some algebraic properties of the proposed divergence measures. To further appreciate the proposed divergence measures, their application to pattern recognition has been investigated in conjunction with the prominent existing comparison measures based on SVNSs. The comparative assessment sensitivity analysis of the proposed measures establishes their edge over the existing ones because of appropriate classification results. Show more
Keywords: Single-valued neutrosophic set, aggregation operator, pattern recognition, divergence measure
DOI: 10.3233/JIFS-232369
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Sakthimohan, M. | Deny, J. | Rani, G. Elizabeth
Article Type: Research Article
Abstract: In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing …(SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The proposed one consumes a minimum energy of 49.67 mJ, achieves a better delivery rate of 99.92%, takes less lifetime of 5902 rounds, 0.057 s delay, and achieves a higher throughput of 0.99 Mbps when considering a maximum of 500 nodes in the network. Also, the proposed one achieves 99.76% accuracy for the intrusion detection. Thus, the simulation outcomes prove the superiority of the proposed SDLEER system over the existing schemes for routing and attack detection. Show more
Keywords: Wireless sensor networks, optimal cluster-based energy aware routing, intrusion detection system, cluster head selection, routing, dimensionality reduction, and deep learning
DOI: 10.3233/JIFS-235512
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Liu, Jianping | Chu, Xintao | Wang, Jian | Wang, Meng | Wang, Yingfei
Article Type: Research Article
Abstract: Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations …in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model’s robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model’s generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability. Show more
Keywords: Machine reading comprehension, pre-trained model, adversarial training, Bi-GRU, multi-head self-attention mechanism
DOI: 10.3233/JIFS-234417
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Xiao, Yan | Liang, Jinqian
Article Type: Research Article
Abstract: In many real production scenarios, departmental organizations often exhibit a hierarchical structure, where departments cooperate with subordinate departments to optimize resource allocation and maximize their respective benefits. However, due to a lack of information or data, many model parameters in the allocation process cannot be precisely defined. In response to this challenge, an interval n -person hierarchical resource allocation model is proposed to achieve maximum economic benefit in uncertain environments. Based on the concepts of satisfactory degrees of comparing intervals and interval-valued cores of interval-valued n -person cooperative games, an auxiliary nonlinear programming model and method are developed to solve …the interval-valued cores of such cooperative games. The approach explicitly considers the inclusion and/or overlap relations between intervals, whereas the traditional interval ranking method may not guarantee the existence of interval-valued cores. The proposed method offers cooperative opportunities under uncertain conditions. Finally, the feasibility and applicability of the models and methods are demonstrated through a numerical example and comparison with other methods. Show more
Keywords: Hierarchical structure, resource allocation, uncertain environment, interval n-person cooperative game, nonlinear programming model
DOI: 10.3233/JIFS-191941
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Yu, Dan | Wu, Jun | He, Yongling
Article Type: Research Article
Abstract: The distributed robust optimal allocation method for multi-microgrid interconnected systems usually involves a large number of variables and constraints, and the computational complexity is high in practical applications, which makes it difficult to solve the problem. Therefore, a distributed robust optimal allocation method for multi-microgrid interconnection systems based on multi-objective swarm algorithm is proposed. A distributed robust optimization configuration constraint index model for multi-microgrid interconnection system is established. Considering the influence of energy storage technology operation characteristics on its service life, a micro-grid hybrid energy storage capacity optimization configuration model with the minimum annual comprehensive energy storage cost as the …objective function is established with charge and discharge power and residual power as the constraint conditions. The multi-objective swarm algorithm is used to realize the optimization model of distributed robust configuration microgrid interconnection system. By determining the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points, the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points are determined. The hybrid energy storage configuration model of multi-microgrid interconnection system is established with the minimum alternative operating cost as the objective function, so as to realize the distributed robust optimal configuration of multi-microgrid interconnection system. The simulation results show that the distributed configuration of multi-microgrid interconnection system with the proposed method has good robustness and strong optimization control ability. Show more
Keywords: Multi-objective bee colony algorithm, multi-microgrid, interconnection system, robust allocation
DOI: 10.3233/JIFS-235092
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Li, Guofa | Wang, Jinfu | He, Jialong | Wang, Jili | Hou, Tianwei
Article Type: Research Article
Abstract: The reliability of machine tool components, particularly the tool magazine manipulator, significantly affects the overall performance of the machine tool. To address the challenge of accurately evaluating the manipulator’s health status using a single performance indicator, this study proposes a method that combines Fuzzy Comprehensive Evaluation (FCE) and a Combined Weighting Method (CWM). By considering both subjective and objective factors, this method provides a comprehensive evaluation of the manipulator’s health status, enhancing the accuracy and reliability of the assessment. The method utilizes fuzzy distribution to construct membership matrices for different health levels and adopts the CWM that combines the Entropy …Weight Method (EWM) and Analytic Hierarchy Process (AHP) to determine the combined weights of the health evaluation indices. This approach improves the accuracy and reliability by considering multiple indicators and objectively weighting them based on their importance. The current health status of the manipulator is evaluated using the fuzzy weighted average operator and the maximum membership principle. Moreover, a fault prediction method based on Particle Swarm Optimization (PSO) and GM(1,1) is proposed to overcome the information gap and small sample problems. The proposed model’s prediction accuracy is verified by comparing it with other models, demonstrating its effectiveness and reliability. Show more
Keywords: Health status evaluation, fault prediction, fuzzy comprehensive evaluation, grey model, particle swarm optimization
DOI: 10.3233/JIFS-233028
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ajitha Gladis, K.P. | Srinivasan, R. | Sugashini, T. | Ananda Raj, S.P.
Article Type: Research Article
Abstract: Visual impairment people have many difficulties in everyday life, including communicating and getting information, as well as navigating independently and safely. Using auditory alerts, our study hopes to improve the lives of visually impaired individuals by alerting them to items in their path. In this research, a Video-based Smart object detection model named Smart YOLO Glass has been proposed for visually impaired persons. A Paddling - Paddling Squeeze and Attention YOLO Network model is trained with multiple images to detect outdoor objects to assist visually impaired people. In order to calculate the distance between a blind person and obstacles when …moving from one location to another, the proposed method additionally included a distance-measuring sensor. The visually impaired will benefit from this system’s information about around objects and assistance with independent navigation. Recall, accuracy, specificity, precision, and F-measure were among the metrics used to evaluate the proposed strategy. Because there is less time complexity, the user can see the surrounding environment in real time. When comparing the proposed technique to Med glasses, DL smart glass, and DL-FDS, the total accuracy is improved by 7.6%, 4.8%, and 3.1%, respectively. Show more
Keywords: Visual impairment, deep learning, outdoor object detection, wearable system, YOLO network
DOI: 10.3233/JIFS-234453
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Zhu, Meng-Meng | Mao, Jun-Jun | Xu, Wei
Article Type: Research Article
Abstract: Linguistic preference relations with self-confidence (LPRs-SC) are the preference relation that can reflect the decision maker’s (DM) confidence psychology and has received widespread attention for their simple form and multiple information. Currently, arithmetic studies of LPRs-SC are conducted separately for preference relations and self-confidence. In addition, personalized individual semantics (PIS) is an important tool in large-scale decision-making to reflect the differences in the semantic understanding of DMs. However, the confidence level in LPRs-SC limits the preference relation to a certain extent and the linguistic representations of these two components are usually different. This means that it is not only necessary …to propose an arithmetic rule that can express the restrictive relationship between the two but also to construct a model that can extract the PIS of preference relation and confidence respectively. Besides, we constructed a two-stage consensus reaching process (CRP) based on the specificity of the LPRs-SC structure when enhancing group harmony. The process takes self-confidence as an independent source of information, delineates the adjusted categories in detail, and builds an adjustment model accordingly. Finally, the example and comparative analyses verify the merits of the proposed PIS in terms of consistency enhancement and CRP in terms of speed and accuracy harmonization. Show more
Keywords: Personalized individual semantics, linguistic preference relations with self-confidence, consensus reaching process, large scale decision making
DOI: 10.3233/JIFS-236552
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Prabu Sankar, N. | Usha, D.
Article Type: Research Article
Abstract: This research paper presents a novel approach to improving healthcare services in rural areas by leveraging the potential of Fuzzy Intelligence Systems, Internet of Bodies (IoB) devices, and Blockchain technology. It begins by exploring the design and development of a Blockchain-based Patients Record System (BPRS), which ensures secure, transparent, and tamper-proof storage of patient medical records. The paper then delves into the fabrication of advanced IoB devices, specifically designed to study and monitor the health of rural populations. These devices, integrated with Fuzzy Intelligence Systems, provide efficient and reliable data capture, interpretation, and decision-making support. The highlight of the study …is the innovative integration of the IoB enabled Patient Monitoring System with the BPRS, which ensures real-time data synchronization and secure access to patient data for authorized personnel. The system collectively promotes efficient healthcare delivery, data privacy, and patient safety in rural areas. Show more
Keywords: Fuzzy intelligence systems, blockchain-based patients record system, internet of bodies devices, rural health monitoring, integrated healthcare system
DOI: 10.3233/JIFS-233752
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Kahraman, Cengiz
Article Type: Research Article
Abstract: Intuitionistic fuzzy sets aims at taking the hesitancy of an expert into account in assigning a membership degree or a non-membership degree. The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. Besides, the assigned degrees are generally composed of one digit or at most two digits after dot. This problem has not been addressed as much as it deserves in the literature. The hypothesis of the paper is that the determination of proportional relationships between membership and non-membership degrees is more appropriate than the direct assignment to obtain …the degrees. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. The accuracy of the results obtained with multi-criteria decision-making models definitely depends on how accurately the membership degrees are determined. In this paper, we extend Combinative distance-based assessment (CODAS) method by using single-valued proportional intuitionistic fuzzy sets. We compare the proposed PIF CODAS method with ordinary fuzzy CODAS method. A cloud service provider selection problem is handled to show the validity of the proposed PIF CODAS method. Additionally, a comparative analysis and a sensitivity analysis together with a discussion are presented. Show more
Keywords: Proportional intuitionistic fuzzy sets, aggregation operators, multi-criteria decision making, CODAS, Cloud service provider selection
DOI: 10.3233/JIFS-237389
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Zhan, Qiuyan | Saeid, A. Borumand | Davvaz, Bijan
Article Type: Research Article
Abstract: The aim of this paper is to investigate several operators on L -algebras. At first, closure (interior) operators on L -algebras are defined and some properties of them are obtained. Then, existential operators and universal operators on L -algebras are studied, a one-to-one correspondence between the set of all quantifier operators and the set of all relative complete subalgebras of CKL -algebras is constructed. Furthermore, very true operators on L -algebras are investigated and by giving a very true ideal of a very true L -algebra, quotient structures on very true L -algebras are established.
Keywords: L-algebra, closure (interior) operator, existential (universal) operator, very true operator
DOI: 10.3233/JIFS-234370
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Mohan, Prakash | Aishwarya, S.
Article Type: Research Article
Abstract: Price changes in construction materials have a significant impact on building construction projects. Such price variations occur at random and at varying rates over time. A system that can estimate the magnitude and quantity of the change in material prices with reasonable accuracy is required. The primary goal is to create a machine-learning model that can predict the type of building material chosen based on environmental factors. The compressive strength of concrete is critical in defining its mechanical qualities. Long laboratory testing is needed to determine the compressive strength of concrete. The capacity of powerful machine learning algorithms to forecast …concrete compressive strength speeds up these lengthy experimental methods while also lowering expenses. This study provides abilities to precisely anticipate and categorize numerous qualities and traits of distinct materials. The framework includes a broad dataset that details materials, composition, and performance characteristics. Machine learning algorithms such as logistic regression (LR), decision trees (DT), and random forests (RF) train models on the training data. The models are hyper-parameter tweaked and feature developed to achieve the most outstanding performance. The k-fold method is used throughout the training and assessment phase to guarantee robustness and reduce bias. The F1 score and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) curve are two performance measures used to measure how accurate and predictive the trained models are. The study findings provide insights into the qualities of the materials, facilitating improved material selection, quality assurance, and decision-making in the building sector. In the analyses, the best accuracy value was 99.92%, and the precision value was 88.83% using the LR algorithm. As a result, it was determined that the LR algorithm had the least execution 57.826 ms, and is thus the most suitable for use in concrete compressive strength estimation. Show more
Keywords: Building materials, machine learning algorithms, feature selection, model training, K-fold, performance evaluation
DOI: 10.3233/JIFS-236111
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Lin, Guangbo | Duan, Ninggui
Article Type: Research Article
Abstract: Integrating the E-commerce system with an enterprise resource planning tool can help the firm improve performance, maintain customers, and increase sales. In Enterprise Resource Planning, integration features can be provided either as developed features or as separate assignments and contributions. Problems with the online platform, improper addresses, rejected payments, and especially apparent transactions are frequent problems for online buyers. The enhanced Adaptive Ant Colony Optimization is utilized to optimize the rural E-commerce express of transportation. Several innovative routes can lower the downlink transportation cost and reach all collecting places with a fast delivery route. Convolutional Neural Networks were utilized to …increase the collective innovation of the E-commerce platform and simplify network communication. E-commerce is a mechanism used to market information services and products. Hence, ERP-AACO-CNN has been designed to integrate Enterprise Resource Planning and E-commerce, and business operations can stream smoothly from the front to the back of the business. Statistics on sales orders, customers, stock levels, price, and essential performance measurement systems. The automated invoices, frequent communications, financial report preparation, product and service delivery, and material requirements planning. The most significant results will likely finance businesses that employ it as a stimulant for a wide-ranging process improvement. In addition, E-commerce is a valuable innovation that connects buyers and sellers in various corners of the globe. Customer satisfaction is projected to be more significant than fault detection at 95.2 % accuracy for the proposed method’s E-commerce system with the superior value. According to client demand, an E-commerce system is the most accurate development at a given input level, and a future ERP is 64.9% efficient. The proposed approach has a 24.5% random error rate and a 13.2% mean square error rate. A comparison of E-commerce and enterprise ERP precision to the proposed technique yields 83.8% better results. Show more
Keywords: Adaptive ant colony optimization, enterprise resource planning, convolutional neural networks, E-commerce system
DOI: 10.3233/JIFS-237998
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: He, Fuyun | Feng, Huiling | Tang, Xiaohu
Article Type: Research Article
Abstract: The segmentation of neuronal morphology in electron microscopy images is crucial for the analysis and understanding of neuronal function. However, most of the existing segmentation methods are not suitable for challenging datasets where the neuronal structure is contaminated by noise or has interrupted parts. In this paper, we propose a segmentation method based on deep learning to determine the location information of neurons and reduce the influence of image noise in the data. Specifically, we adapt our neuron dataset based on UNet by using convolution with BN fusion and multi-input feature fusion. The method is named REDAFNet. The model simplifies …the model structure and enhances the generalization ability by fusing the convolution layer and BN layer. The noise interference in the data was reduced by multi-input feature fusion, and the ability to understand and express the data was enhanced. The method takes a neuron image as input and its pixel segmentation map as output. Experimental results show that the segmentation accuracy of the proposed method is 91.96%, 93.86% and 80.25% on the ISBI2012 dataset, U-RISC retinal neuron dataset and N2DH-GOWT1 stem cell dataset, respectively. Compared with the existing segmentation methods, the proposed method can extract more complete feature information and achieve more accurate segmentation. Show more
Keywords: Image segmentation, convolutional neural network, UNet, neuron image
DOI: 10.3233/JIFS-236286
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Du, Xueke | Li, Wenli | Wei, Xiaowen
Article Type: Research Article
Abstract: The fees of different certification services are charged in different ways: For example, T-mall.com (one of the leading e-commerce platforms in China) uses a total certification service , where each type of seller participating in the platform must purchase certification services; Pinduoduo.com (another Chinese e-commerce platform) uses an alternative certification service , where after paying a transaction fee, each seller participating in the platform can choose whether to purchase certification services. This paper studies how the choice of certification services affects the participation decisions of both sellers and buyers, as well as the revenue and quality level (the proportion of …high-quality sellers of all participating sellers) of a platform. According to previous research, network externalities also affect sellers’ and buyers’ participation strategies. Studies on the effectiveness of different certification services for e-commerce platforms have rarely considered both positive and negative network externalities. The results of constructed game-theoretic models show that both the certification capability and the certification cost play critical roles in determining which certification services can generate more revenue. If a platform provides certification services, the total certification service always generates a higher quality level than the alternative certification service. Furthermore, the applicable scope of certification services (defined as the certification strategy space), can be broadened by increasing both the profit ratio (the ratio between the profit of H-type sellers and L-type sellers) and the value ratio (the ratio between the value of H-type sellers and L-type sellers). Counterintuitively, a higher certification capability does not always yield a higher certification fee. Show more
Keywords: Certification services, E-commerce platforms, information asymmetries, network externalities, certification capability
DOI: 10.3233/JIFS-234621
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Wang, Hanpeng | Xiong, Hengen
Article Type: Research Article
Abstract: An improved genetic algorithm is proposed for the Job Shop Scheduling Problem with Minimum Total Weight Tardiness (JSSP/TWT). In the proposed improved genetic algorithm, a decoding method based on the Minimum Local Tardiness (MLT) rule of the job is proposed by using the commonly used chromosome coding method of job numbering, and a chromosome recombination operator based on the decoding of the MLT rule is added to the basic genetic algorithm flow. As a way to enhance the quality of the initialized population, a non-delay scheduling combined with heuristic rules for population initialization. and a PiMX (Precedence in Machine crossover) …crossover operator based on the priority of processing on the machine is designed. Comparison experiments of simulation scheduling under different algorithm configurations are conducted for randomly generated larger scale JSSP/TWT. Statistical analysis of the experimental evidence indicates that the genetic algorithm based on the above three improvements exhibits significantly superior performance for JSSP/TWT solving: faster convergence and better scheduling solutions can be obtained. Show more
Keywords: Improved genetic algorithm, total weight tardiness, minimum local tardiness, PiMX
DOI: 10.3233/JIFS-236712
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Zhang, Dabin | Yu, Zehui | Ling, Liwen | Hu, Huanling | Lin, Ruibin
Article Type: Research Article
Abstract: As CO2 emissions continue to rise, the problem of global warming is becoming increasingly serious. It is important to provide a robust management decision-making basis for the reductions of carbon emissions worldwide by predicting carbon emissions accurately. However, affected by various factors, the prediction of carbon emissions is challenging due to its nonlinear and nonstationary characteristics. Thus, we propose a combination forecast model, named CEEMDAN-GWO-SVR, which incorporates multiple features to predict trends in China’s carbon emissions. First, the impact of online search attention and public health emergencies are considered in carbon emissions prediction. Since the impact of different variables …on carbon emissions is lagged, the grey relational degree is used to identify the appropriate lag series. Second, irrelevant features are eliminated through RFECV. To address the issue of feature redundancy of online search attention, we propose a dimensionality reduction method based on keyword classification. Finally, to evaluate the features of the proposed framework, four evaluation indicators are tested in multiple machine learning models. The best-performed model (SVR) is optimized by CEEMDAN and GWO to enhance prediction accuracy. The empirical results indicate that the proposed framework maintains good performance in both multi-scenario and multi-step prediction. Show more
Keywords: Carbon emissions prediction, online search attention, machine learning, time series forecasting
DOI: 10.3233/JIFS-236451
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Luo, Zhenrong | Jiang, Lei
Article Type: Research Article
Abstract: In order to construct an evaluation index system suitable for tourism management classroom teaching, this article evaluates the teaching effectiveness of teachers and improves the teaching quality of tourism management courses. This article is based on developmental evaluation theory, using Analytic Hierarchy Process, Project Response Theory, and CIPP model to construct an indicator system suitable for tourism management classroom teaching. Then, based on the collected data of 5763 students, the reliability and effectiveness of the tool and indicator system were first verified. Then, the variable of teacher teaching style was introduced to construct an OLS regression model for empirical research. …The research will summarize teacher and student data collected through the platform and conduct reliability analysis in SPSS 22.0 software, using Cronbach α The credibility of coefficient testing and evaluation tools. Cronbach in Environmental Fundamentals α The cβoefficient value is 0.8350. Cronbach for resource allocation α The coefficient is 0.735, and the Cronbah of the implementation process α Cronb Bach with a coefficient of 0.7 47 for teaching performance α The coefficient is 0.7240, indicating that rat ings has high reliability. Research has found that among the four specific types, the holistic type has the greatest impact on the specific situation, the holistic type has the greatest impact on the environmental foundation and resource allocation, and the legislative type has the greatest impact on the implementation process and teaching performance. Show more
Keywords: Tourism management, AHP method, CIPP model, teaching style
DOI: 10.3233/JIFS-235844
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Vaikunta Pai, T. | Singh, Manmohan | Shaik, Nazeer | Ashokkumar, C. | Anuradha, D. | Gangopadhyay, Amit | Rao, Goda Srinivasa | Reddy, T.Sunilkumar | Nagaraju, D.
Article Type: Research Article
Abstract: As the demand for energy in India continues to surge, accurate forecasting becomes paramount for efficient resource allocation and sustainable development. This study proposes an innovative approach to forecasting Indian primary energy demand by integrating Artificial Intelligence (AI) techniques with Fuzzy Auto-regressive Distributed Lag (FADL) models. FADL models, incorporating fuzzy logic, allow for a nuanced representation of uncertainties and complexities within the energy demand dynamics. In this research, historical energy consumption data is analysed using FADL models with both symmetric and non-symmetric triangular coefficients, enhancing the model’s adaptability to the inherent uncertainties associated with energy forecasting. This study addresses the …urgent need for enhanced energy planning models in the context of sustainable development. Our research aims to provide a comprehensive framework for predicting future Total Final Consumption (TFC) in alignment with the Indian National Energy Plan’s net-zero emissions target by 2035. Recognizing the limitations of current models, our research introduces a novel approach that integrates advanced algorithms and methodologies, offering a more flexible and realistic assessment of TFC trends. The primary objective of this study is to develop an improved energy planning model that surpasses existing projections by incorporating sophisticated algorithms. We aim to refine Show more
Keywords: Auto-regressive, distributed lag, energy consumption, forecast, triangular coefficient
DOI: 10.3233/JIFS-240729
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ma, Chengfei | Yang, Xiaolei | Lu, Heng | He, Siyuan | Liu, Yongshan
Article Type: Research Article
Abstract: When calculating participants’ contribution to federated learning, addressing issues such as the inability to collect complete test data and the impact of malicious and dishonest participants on the global model is necessary. This article proposes a federated aggregation method based on cosine similarity approximation Shapley value method contribution degree. Firstly, a participant contribution calculation model combining cosine similarity and the approximate Shapley value method was designed to obtain the contribution values of the participants. Then, based on the calculation model of participant contribution, a federated aggregation algorithm is proposed, and the aggregation weights of each participant in the federated aggregation …process are calculated by their contribution values. Finally, the gradient parameters of the global model were determined and propagated to all participants to update the local model. Experiments were conducted under different privacy protection parameters, data noise parameters, and the proportion of malicious participants. The results showed that the accuracy of the algorithm model can be maintained at 90% and 65% on the MNIST and CIFAR-10 datasets, respectively. This method can reasonably and accurately calculate the contribution of participants without a complete test dataset, reducing computational costs to a certain extent and can resist the influence of the aforementioned participants. Show more
Keywords: Federated aggregation algorithm, contribution assessment, cosine similarity, Shapley value, equitable distribution
DOI: 10.3233/JIFS-236977
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Pandey, Sakshi Dev | Ranadive, A.S. | Samanta, Sovan | Dubey, Vivek Kumar
Article Type: Research Article
Abstract: Several methodologies have been proposed in the literature of graph theory for depicting collaboration among entities. However, in these studies, the measure of collaboration is taken based on the crisp graphical properties and discusses only its positive effects. In this manuscript, we discuss the simultaneous collaboration and competition that are observed among individuals, organizations, countries, communities and many others. The notion of bipolar fuzzy bunch graph (BFBG) is introduced in this study to effectively capture the positive and negative effects of both the terms collaboration and competition, which is jointly called coopetition. The goal of this paper is to introduce …an improved representation and analytical measure for coopetition. To further enrich the literature on competition graphs, the notion of survival and winning competition among species has been introduced and also provides its bipolar fuzzy competition degrees. We also introduce two types of coopetition measures to understand the ranking structure of entities (i.e. which node batter collaborates and competes with other nodes) in the network: a) bipolar fuzzy coopetition degree and b) bipolar fuzzy coopatition index. In the form of a bipolar fuzzy coopetition graph, we find evidence to validate our framework and computations. We gathered research articles on COVID-19 and their citations over a specific time period from a specific journal. To demonstrate our approach, we displayed bipolar fuzzy collaboration and competition of various countries on COVID-19 and classified their rankings based on their positive and negative coopetition indices. Show more
Keywords: Bipolar fuzzy bunch degree, communication potential effect (CPE), bipolar fuzzy mixed graph, winning and survival competition, coopetition degree, coopetition index
DOI: 10.3233/JIFS-234061
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Ding, Xiaomei | Ding, Huaibao | Zhou, Fei
Article Type: Research Article
Abstract: Given that cloud computing is a relatively new field of study, there is an urgent need for comprehensive approaches to resource provisioning and the allocation of Internet of Things (IoT) services across cloud infrastructure. Other challenging aspects of cloud computing include IoT resource virtualization and disseminating IoT services among available cloud resources. To meet deadlines, optimize application execution times, efficiently use cloud resources, and identify the optimal service location, service placement plays a crucial role in installing services on existing virtual resources within a cloud-based environment. To achieve load balance in the fog computing infrastructure and ensure optimal resource allocation, …this work proposes a meta-heuristic approach based on the cat swarm optimization method. For more clarity in the difference between the work presented in this research and other similar works, we named the proposed technique MH-CSO. The algorithm incorporates a resource check parameter to determine the accessibility and suitability of resources in different situations. This conclusion was drawn after evaluating the proposed solution in the ifogsim environment and comparing it with particle swarm and ant colony optimization techniques. The findings demonstrate that the proposed solution successfully optimizes key parameters, including runtime and energy usage. Show more
Keywords: Load balancing, cat swarm optimization, fog computing, resource allocation and IoT
DOI: 10.3233/JIFS-233418
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Zhou, Ruohan | Chen, Wei | Xie, Congjin
Article Type: Research Article
Abstract: The field of business management involves a large amount of data and information sources, including market data, customer data, supply chain data, etc. In order to quantify and analyze different resources, help enterprises better plan and allocate resources, and improve resource utilization efficiency, a clustering analysis based digital resource integration algorithm for business management is studied. Build a business management digital resource integration framework, including data layer, integration layer, and storage layer, to integrate and store data from different sources of business management databases, thereby facilitating unified management and utilization of digital resources by enterprises. The data layer collects data …from different business management databases and stores it in the database according to different sources; The integration layer preprocesses the collected data, simply fixes errors and missing information in the data, and improves data quality. Adopting a feature extraction method based on the projection direction uncorrelation strategy of the labeled power set conversion method, the useful feature information of digital resources in enterprise management can be effectively extracted; Based on the two-step clustering analysis method, business management digital resources are clustered according to similar characteristics to complete the classification and integration of business management digital resources, and improve the efficiency of resource utilization; The storage layer adopts the Security Information Diffusion Algorithm (IDA) storage model to store integrated and classified digital resources managed by enterprises, ensuring data security and effectively preventing data leakage and illegal access. The experimental results show that the digital resource structure of business management integrated by this algorithm is clear, with a data redundancy of less than 8% and a difference of less than 11% . The time consumption for data integration is less than 2.11 minutes, indicating good resource integration ability. Show more
Keywords: Cluster analysis, business administration, digitization, resource integration, data storage, resource sharing
DOI: 10.3233/JIFS-235573
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Rachamadugu, Sandeep Kumar | Pushphavathi, T.P.
Article Type: Research Article
Abstract: This paper introduces an innovative approach, the LS-SLM (Local Search with Smart Local Moving) technique, for enhancing the efficiency of article recommendation systems based on community detection and topic modeling. The methodology undergoes rigorous evaluation using a comprehensive dataset extracted from the “dblp. v12.json” citation network. Experimental results presented herein provide a clear depiction of the superior performance of the LS-SLM technique when compared to established algorithms, namely the Louvain Algorithm (LA), Stochastic Block Model (SBM), Fast Greedy Algorithm (FGA), and Smart Local Moving (SLM). The evaluation metrics include accuracy, precision, specificity, recall, F-Score, modularity, Normalized Mutual Information (NMI), betweenness …centrality (BTC), and community detection time. Notably, the LS-SLM technique outperforms existing solutions across all metrics. For instance, the proposed methodology achieves an accuracy of 96.32%, surpassing LA by 16% and demonstrating a 10.6% improvement over SBM. Precision, a critical measure of relevance, stands at 96.32%, showcasing a significant advancement over GCR-GAN (61.7%) and CR-HBNE (45.9%). Additionally, sensitivity analysis reveals that the LS-SLM technique achieves the highest sensitivity value of 96.5487%, outperforming LA by 14.2%. The LS-SLM also demonstrates superior specificity and recall, with values of 96.5478% and 96.5487%, respectively. The modularity performance is exceptional, with LS-SLM obtaining 95.6119%, significantly outpacing SLM, FGA, SBM, and LA. Furthermore, the LS-SLM technique excels in community detection time, completing the process in 38,652 ms, showcasing efficiency gains over existing techniques. The BTC analysis indicates that LS-SLM achieves a value of 94.6650%, demonstrating its proficiency in controlling information flow within the network. Show more
Keywords: Recommender Systems (RS), BagofWords (BoW), Pearson Correlation Co-efficient based Latent Dirichlet Allocation (PCC-LDA), Linear Scaling based Smart Local Moving (LS-SLM), Time Frequency and Inverse Document Frequency (TF-IDF), Community detection
DOI: 10.3233/JIFS-233851
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Zhang, Yonghong | Li, Shouwei | Li, Jingwei | Tang, Xiaoyu
Article Type: Research Article
Abstract: Electricity market violations affect the overall operations of the electricity market. This paper explores the evolutionary stability strategies of electricity generation enterprises and electricity consumers under two modes: traditional regulation and blockchain regulation to analyze blockchain technology’s mechanism and conditions in solving electricity market violations. The experimental results indicate that the likelihood of consumers accepting electricity and the regulatory capacity of regulatory agencies play a crucial role in determining the violation approach adopted by electricity generation enterprises. Under traditional regulatory models, due to information asymmetry, regulatory agencies may not be able to detect violations promptly. Meanwhile, electricity consumers may choose …to accept violations by power generation companies due to high appeal costs. Blockchain technology enables regulatory agencies to improve their regulatory capabilities by eliminating information asymmetry, reducing the cost of complaints from electricity consumers, thereby elevating the risk for enterprises engaging in market violations and optimizing the evolutionary game towards an optimum state. Show more
Keywords: Blockchain technology, electricity market, violation regulation, evolutionary game
DOI: 10.3233/JIFS-238041
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhang, Juwei | Wang, Jing | Liu, Mingjun | Li, Zhihui
Article Type: Research Article
Abstract: Assessing the effectiveness of physical education instruction, students’ learning, and the feedback received from the teaching process are all vital components of the physical education teaching process in colleges and universities. Improving the quality of physical education instruction in these settings is essential. With its ability to drive the digital revolution of physical education in schools, intelligent technology is bringing about significant changes in the field of education and drawing attention from people from all walks of life. To assess intelligent technology’s impact on physical education instruction in a scientific manner, this study utilizes the latest intelligent analysis and sensing …data mining to design an intelligent physical education measurement and evaluation model, which utilizes GPS positioning, built-in maps, and gravity sensing to provide real-time feedback on the trajectory, distance, and time of the movement, and then calculates the real-time and average speed of the movement, as different students’ body postures to achieve the the same effect when the required speed is not the same, this paper randomly selected students with different BMI index for empirical analysis. The experimental results show that the principal components of the factor analysis extracted four common factors with a cumulative contribution rate of 69.5%, and the test-retest reliability of the four dimensions is 0.665–0.862. Show more
Keywords: Intelligent analysis, sensor data mining, physical education, physical measurement and evaluation
DOI: 10.3233/JIFS-235410
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Lalitha, V. | Latha, B.
Article Type: Research Article
Abstract: The most valuable information of Hyperspectral Image (HSI) should be processed properly. Using dimensionality reduction techniques in two distinct approaches, we created a structure for HSI to collect physiological and diagnostic information. The tissue Oxygen Saturation Level (StO2 ) was extracted using the HSI approach as a physiological characteristic for stress detection. Our research findings suggest that this unique characteristic may not be affected by humidity or temperature in the environment. Comparing the standard StO2 reference and pressure concentrations, the social stress assessments showed a substantial variance and considerable practical differentiation. The proposed system has already been evaluated on …tumor images from rats with head and neck cancers using a spectrum from 450 to 900 nm wavelength. The Fourier transformation was developed to improve precision, and normalize the brightness and mean spectrum components. The analysis of results showed that in a difficult situation where awareness could be inexpensive due to feature possibilities for rapid classification tasks and significant in measuring the structure of HSI analysis for cancer detection throughout the surgical resection of wildlife. Our proposed model improves performance measures such as reliability at 89.62% and accuracy at 95.26% when compared with existing systems. Show more
Keywords: Hyperspectral Image, dimensionality reduction, stress tests, cancer detection, fourier coefficients
DOI: 10.3233/JIFS-236935
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Fan, Jianping | Chai, Mingxuan | Wu, Meiqin
Article Type: Research Article
Abstract: In this manuscript, we construct a Multi-Criteria Decision-Making (MCDM) model to study the new energy vehicle (NEV) battery supplier selection problem. Firstly, we select criteria to build an evaluation index system. Secondly, SAWARA and MEREC methods are used to calculate subjective and objective weights in the ranking process, respectively, and PTIHFS (Probabilistic Triangular Intuitionistic Hesitant Fuzzy Set) is employed to describe the decision maker’s accurate preferences in performing the calculation of subjective weights. Then, the game theory is used to find the satisfactory weights. We use TFNs to describe the original information in the MARCOS method to obtain the optimal …alternative. Finally, a correlation calculation using Spearman coefficients is carried out to compare with existing methods and prove the model’s validity. Show more
Keywords: PTIHFS, SWARA, MEREC, MARCOS, game theory
DOI: 10.3233/JIFS-231975
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Wang, Youwei | Feng, Lizhou
Article Type: Research Article
Abstract: A new bootstrap-aggregating (bagging) ensemble learning algorithm is proposed based on classification certainty and semantic correlation to improve the classification accuracy of ensemble learning. First, two predetermined thresholds are introduced to construct the long and short-text sample subsets, and different deep learning methods are compared to construct the optimal base classifier groups for each sample subsets. Then, the random sampling method employed in traditional bagging classification algorithms is improved, and a threshold group based random sampling method is proposed to obtain long and short training sample subsets of each iteration. Finally, the sample classification certainty of the base classifiers for …different categories is defined, and the semantic correlation information is integrated with the traditional weighted voting classifier ensemble method to avoid the loss of important information during the sampling process. The experimental results on multiple datasets demonstrate that the algorithm significantly improves text classification accuracy and outperforms typical deep learning algorithms. The proposed algorithm achieves the improvements of approximately 0.082, 0.061 and 0.019 on CNews dataset when the F1 measurement is used over the traditional ensemble learning algorithms such as random forest, M_ADA_A_SMV and CNN_SVM_LR. Moreover, it achieves the best F1 values of 0.995, 0.985, and 0.989 on the datasets of Spam, CNews, and SogouCS datasets, respectively, when compared with the ensemble learning algorithms using different base classifiers. Show more
Keywords: Ensemble learning, weak classifier, text classification, deep learning, random sampling
DOI: 10.3233/JIFS-236422
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Devi, Salam Jayachitra | Doley, Juwar | Gupta, Vivek Kumar
Article Type: Research Article
Abstract: Object detection has made significant strides in recent years, but it remains a challenging task to accurately and quickly identify and detect objects. While humans can easily recognize objects in images or videos regardless of their appearance, computers face difficulties in this task. Object detection plays a crucial role in computer vision and finds applications in various domains such as healthcare, security, agriculture, home automation and more. To address the challenges of object detection, several techniques have been developed including RCNN, Faster RCNN, YOLO and Single Shot Detector (SSD). In this paper, we propose a modified YOLOv5s architecture that aims …to improve detection performance. Our modified architecture incorporates the C3Ghost module along with the SPP and SPPF modules in the YOLOv5s backbone network. We also utilize the Adam and Stochastic Gradient Descent (SGD) optimizers. The paper also provides an overview of three major versions of the YOLO object detection model: YOLOv3, YOLOv4 and YOLOv5. We discussed their respective performance analyses. For our evaluation, we collected a database of pig images from the ICAR-National Research Centre on Pig farm. We assessed the performance using four metrics such as Precision (P), Recall (R), F1-score and mAP @ 0.50. The computational results demonstrate that our method YOLOv5s architecture achieves a 0.0414 higher mAP while utilizing less memory space compared to the original YOLOv5s architecture. This research contributes to the advancement of object detection techniques and showcases the potential of our modified YOLOv5s architecture for improved performance in real world applications. Show more
Keywords: Object detection, YOLO, convolutional neural networks, pig, and computer vision
DOI: 10.3233/JIFS-231032
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Abraham, Asha | Kayalvizhi, R. | Mohideen, Habeeb Shaik
Article Type: Research Article
Abstract: Nowadays, cancer has become more alarming. This paper discusses the most significant Ovarian Cancer, Epithelial Ovarian Cancer (EOC), due to the low survival rate. The proposed algorithm for this work is a ‘Multi classifier ShapRFECV based EOC’ (MSRFECV-EOC) subtype analysis technique that utilized the EOC data from the National Centre for Biotechnology Information and Cancer Cell Line Encyclopedia websites for early identification of EOC using Machine Learning Techniques. This approach increases the data size, balances different classes of the data, and cuts down the enormous number of features unrelated to the disease of interest to prevent overfitting. To incorporate these …functionalities, in the data preprocessing stage, OC-related gene names were taken from the Cancermine database and other OC-related works. Moreover, OC datasets were merged based on OC genes, and missing values of EOC subtypes were identified and imputed using Iterative Logistic Imputation. Synthetic Minority Oversampling Technique with an Edited Nearest Neighbors approach is applied to the imputed dataset. Next, in the Feature Selection phase, the most significant features for subtypes of EOC were identified by applying the Shapley Additive Explanations based on the Recursive Feature Elimination Cross-Validation (ShapRFECV) algorithm, preserving predefined features while selecting new EOC features. Eventually, an accuracy of 97% was achieved with Optuna-optimized Random Forest, which outperformed the existing models. SHAP plotted the most prominent features behind the classification. The Pickle tool saves much training time by preserving hidden parameter values of the model. In the final phase, by using the Stratified K Fold Stacking Classifier, the accuracy was improved to 98.9%. Show more
Keywords: Machine learning, Ovarian cancer, Pickle, multi classification, Random Forest
DOI: 10.3233/JIFS-236197
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ren, Shujun | Wang, Yuanhong
Article Type: Research Article
Abstract: Image segmentation is critical in medical image processing for lesion detection, localisation, and subsequent diagnosis. Currently, computer-aided diagnosis (CAD) has played a significant role in improving diagnostic efficiency and accuracy. The segmentation task is made more difficult by the hazy lesion boundaries and uneven forms. Because standard convolutional neural networks (CNNs) are incapable of capturing global contextual information, adequate segmentation results are impossible to achieve. We propose a multiscale feature fusion network (MTC-Net) in this paper that integrates deep separable convolution and self-attentive modules in the encoder to achieve better local continuity of images and feature maps. In the decoder, …a multi-branch multi-scale feature fusion module (MSFB) is utilized to improve the network’s feature extraction capability, and it is integrated with a global cooperative aggregation module (GCAM) to learn more contextual information and adaptively fuse multi-scale features. To develop rich hierarchical representations of irregular forms, the suggested detail enhancement module (DEM) adaptively integrates local characteristics with their global dependencies. To validate the effectiveness of the proposed network, we conducted extensive experiments, evaluated on the public datasets of skin, breast, thyroid and gastrointestinal tract with ISIC2018, BUSI, TN3K and Kvasir-SEG. The comparison with the latest methods also verifies the superiority of our proposed MTC-Net in terms of accuracy. Our code on https://github.com/gih23/MTC-Net. Show more
Keywords: Medical image segmentation, multi-scale features, detail enhancement, feature fusion, deep learning
DOI: 10.3233/JIFS-237963
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Chen, Sijia | Wang, Qingquan | Guo, Yuan
Article Type: Research Article
Abstract: Motivation: With the enhancement of people’s awareness of the protection of personal privacy information, how to provide better services on the premise of protecting users’ privacy has become an urgent problem to be solved. Therefore, it is a necessary motivation to build a network intelligent platform for privacy protection and integrated big data mining. Objective: In view of the existing network platform of data privacy leakage, low efficiency of data mining and user satisfaction is not high, this paper will adopt advanced privacy technology, to ensure the confidentiality of users’ personal information and security, to enhance the user …trust and use experience, to better meet the needs of users. Methods: In order to better protect the privacy of users, the network intelligent platform should adopt more advanced privacy protection technology. This paper uses the differential privacy algorithm to reduce the risk of data leakage and abuse, and ensure the accuracy and efficiency of data analysis and mining. In the design of the platform, the performance of the platform is fully taken into account to realize the secure storage and efficient processing of data, with good scalability and flexibility to meet the growing user needs and business needs. The performance of the network intelligent platform is also analyzed by experimental simulation. Result: The experimental results of this article indicated that in a network intelligent platform based on privacy protection and integrated big data mining, its data transmission encryption score was 9.5; the data storage encryption score was 9.8; the score of access control mechanism was 9.3; the privacy protection score was 9.6; the response time was 80 ms; the processing speed was 121GB/h; the user satisfaction rating was 6.6. Conclusion: This indicated that the network intelligent platform had good platform performance and user friendliness while ensuring data security and privacy protection. It could efficiently conduct data mining and ensure data security and privacy. Show more
Keywords: Construction of network intelligent platform, privacy protection, data mining, integrating big data
DOI: 10.3233/JIFS-236017
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Huang, Jui-Chan | Shu, Ming-Hung | Lin, Hsiang-Tsen | Day, Jen-Der
Article Type: Research Article
Abstract: With the fast advances of new energy vehicles, the EV battery technology needs to be further improved to follow the step. How to effectively diagnose the electric vehicle’s lithium battery fault becomes a hotspot in the academic circle. This study has proposed new method that uses the state of charge of the battery and self-coder depth to detect faults in the lithium battery group of electric vehicles. First, the study investigates the single lithium battery faults. Then, it builds a lithium battery group fault diagnosis model by integrating the battery charge state and denoising converter network. Finally, it uses a …dataset and retired battery group to validate the model’s performance. The results show that the proposed model achieves an accuracy of 93.18% and a recall rate of 93.73% in identifying the faults in the lithium batteries of the electric vehicles and its F1 value is as high as 0.95. Moreover, the modeling method has the lowest prediction error, indicating its high accuracy and robustness in diagnosing the faults of battery packs. This study aims to provide an effective solution for the fault diagnosis of lithium battery packs in electric vehicles. Show more
Keywords: Transformer framework, DAE, electric vehicle, lithium battery, fault diagnosis
DOI: 10.3233/JIFS-237796
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Jumde, Amol | Keskar, Ravindra
Article Type: Research Article
Abstract: With tremendous evolution in the internet world, the internet has become a household thing. Internet users use search engines or personal assistants to request information from the internet. Search results are greatly dependent on the entered keywords. Casual users may enter a vague query due to lack of knowledge of the domain-specific words. We propose a query reformulation system that determines the context of the query, decides on keywords to be replaced and outputs a better-modified query. We propose strategies for keyword replacements and metrics for query betterment checks. We have found that if we project keywords into the vector …space of word projection using word embedding techniques and if the keyword replacement is correct, clusters of a new set of keywords become more cohesive. This assumption forms the basis of our proposed work. To prove the effectiveness of the proposed system, we applied it to the ad-hoc retrieval tasks over two benchmark corpora viz TREC-CDS 2014 and OHSUMED corpus. We indexed Whoosh search engine on these corpora and evaluated based on the given queries provided along with the corpus. Experimental results show that the proposed techniques achieved 9 to 11% improvement in precision and recall scores. Using Google’s popularity index, we also prove that the reformulated queries are not only more accurate but also more popular. The proposed system also applies to Conversational AI chatbots like ChatGPT, where users must rephrase their queries to obtain better results. Show more
Keywords: Query reformulation, WordNet, word embedding, whoosh, TREC
DOI: 10.3233/JIFS-236296
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Seethappan, K. | Premalatha, K.
Article Type: Research Article
Abstract: Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to present a system that can automatically classify sarcastic phrases in multi-domain data. This multi-domain dataset consisting of 67850 sarcastic and non-sarcastic data is collected from various websites to identify sarcastic or non-sarcastic utterances. Multiple approaches are examined in this work to improve sarcasm identification: 1. A Combination of fasttext embedding, syntactic, semantic, lexical n-gram, and oxymoron features 2. TF-IDF feature weighting scheme 3. Three machine learning algorithms …(SVM, Multinomial Naïve Bayes, and Random Forest), three deep learning algorithms (CNN, LSTM, MLP), and one ensemble model (CNN + LSTM) The CNN + LSTM model achieves a Precision of 91.32%, Recall of 92.85%, F-Score of 92.08%, accuracy of 92.01%, and Kappa of 0.84 by combining the fasttext embedding, bigram, syntactic, semantic, and oxymoron features with TF-IDF method. These experimental results show CNN + LSTM with a combination of all features outperforms the other algorithms in classifying the sarcasm in both datasets. The sarcasm classification performance of our dataset and another sarcasm news dataset was compared while applying the above model. Show more
Keywords: Natural language processing, sarcasm, figurative language, deep learning, CNN, oxymoron
DOI: 10.3233/JIFS-224110
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Sangeetha, R. | Kuriakose, Bessy M. | Naveen, V. Edward | Jenefa, A. | Lincy, A.
Article Type: Research Article
Abstract: Classifying VoIP (Voice over Internet Protocol) traffic is vital for optimizing network performance and Quality of Service (QoS). This study introduces the Multivariate Statistical-Based Classification (MVSC) system, designed to classify network traffic with high accuracy and efficiency. As traditional methods struggle in the diverse and complex landscape of today’s network traffic, which includes voice, video, gaming, and data, the MVSC algorithm rises to the challenge. It employs Statistical Dissemination and leverages various statistical features such as Packet Size, Inter-Arrival Statistics, Packet and Data rates, Flow Length, and Five-tuple information to create nuanced profiles of network traffic packets. These packets are …then grouped into distinct clusters based on their statistical attributes through Application Flow Cluster Grouping. A unique aspect of the MVSC system is its approach to representing each application flow as points in a two-dimensional space, where distances to predefined application profiles are calculated. The nearest profile then determines the type of VoIP traffic. Experimental results using university traffic data (KU-IDS) underscore the system’s high accuracy, consistently around 98-99% . These findings affirm the system’s suitability for real-time deployment. In summary, the MVSC system offers a robust and efficient solution for VoIP traffic classification, significantly boosting network performance and QoS, and proving to be an invaluable asset in contemporary network management. Show more
Keywords: Statistical dissemination, artificial intelligence, clustering algorithms, semi-supervised models, statistical analysis, VoIP traffic
DOI: 10.3233/JIFS-231113
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ren, Zhenxing | Zhang, Jia | Zhou, Yu | Ji, Xinxin
Article Type: Research Article
Abstract: Over the past several decades, several air pollution prevention measures have been developed in response to the growing concern over air pollution. Using models to anticipate air pollution accurately aids in the timely prevention and management of air pollution. However, the spatial-temporal air quality aspects were not properly taken into account during the prior model construction. In this study, the distance correlation coefficient (DC) between measurements made in various monitoring stations is used to identify appropriate correlated monitoring stations. To derive spatial-temporal correlations for modeling, the causality relationship between measurements made in various monitoring stations is analyzed using Transfer Entropy …(TE). This work explores the process of identifying a piecewise affine (PWA) model using a larger dataset and suggests a unique hierarchical clustering-based identification technique with model structure selection. This work improves the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing Kullback-Leibler (KL) Divergence as the dissimilarity between clusters for handling clusters with arbitrary shapes. The number of clusters is automatically determined using a cluster validity metric. The task is formulated as a sparse optimization problem, and the model structure is selected using parameter estimations. Beijing air quality data is used to demonstrate the method, and the results show that the proposed strategy may produce acceptable forecast performance. Show more
Keywords: PWA model, prediction of air pollutants, spatial-temporal features, hierarchical clustering-based identification
DOI: 10.3233/JIFS-238920
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Wu, Hui-Yong | Zhou, Zi-Wei | Li, Hong-Kun | Yang, Tong-Tong
Article Type: Research Article
Abstract: In order to enhance the accuracy and reliability of fault diagnosis in chemical processes, this paper proposes a methodology for chemical process fault diagnosis based on an improved SE-ResNet-BiGRU neural network. Initially, the ResNet model is enhanced by incorporating the SENet mechanism, enabling the extraction of features from input data and selectively enhancing them, thereby strengthening the model’s ability to capture crucial features. Subsequently, the BiGRU model is employed to perform temporal modeling on the extracted features, allowing for better capture of dynamic changes in fault signals. In order to validate the effectiveness of this approach, experiments are conducted using …the TE chemical process dataset. The results are analyzed using methods such as ROC-AUC, confusion matrix, and t-SNE visualization. The improved SE-ResNet-BiGRU model achieves a testing accuracy of 97.78% and an average fault diagnosis rate of 97.24%. Compared to other deep learning methods, this methodology exhibits significant improvements in fault diagnosis rate and reliability. It holds promising potential as an essential tool for fault diagnosis in chemical processes, contributing to enhanced production safety, efficiency, and reduced risk of accidents. Show more
Keywords: Fault diagnosis, residual neural network, bidirectional gate recurrent unit, squeeze-and-excitation network, t-distributed Stochastic neighbor embedding
DOI: 10.3233/JIFS-236948
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Razzaque, Huzaira | Ahraf, Shahzaib | Sohail, Muhammad | Abdeljawad, Thabet
Article Type: Research Article
Abstract: Spherical q-linear Diophantine fuzzy sets (Sq-LDFSs) showed a significant improvement to handling uncertainty in multi-criteria decision-making (MADM). It is advantageous for two-parametric data as well as for data with three variable parameters. One of the most crucial functions of supply chain management is to increase competitive pressure. The study’s standout innovation, Multi-Attributive Ideal Real Comparative Analysis (MAIRCA), has been implemented to give powerful group decision-making. An ecological perspective is becoming more prevalent due to the competitive climate and customer perception. Green supplier selection (GSS) has become a significant issue. In this study, we address the problem of GSS, which aims …for flexibility, robustness, ecological sensitivity, leanness, and feasibility. The feasibility criteria in recycling, environmental, carbon footprints, and water consumption are different from those in standard supplier selection. The aim of our work is to introduced the weighted Average/Geometric aggregation operators based on Sq-LDFSs. For this we defined some operational rules as a foundation of aggregation operators. Secondly we proposed a MAIRCA approach for Sq-LDFSs to address these issues. The MAIRCA strategy, which uses multi-criteria group decision-making (MCGDM) to evaluate and choose traditional and environmental conventionalities, is used to reduce instability and ambiguity. The spherical q-linear Diophantine fuzzy MAIRCA approach provides comparative analysis of decision-makers and criteria. By merging Sq-LDFS and MAIRCA, a hybrid strategy is formed, successfully selecting the best provider among options based on the order of significance. These numerical examples demonstrate the suggested MCGDM approaches that were applied in actual situations, giving a realistic appreciation of their efficacy. The comparative study of the final ranking further supports the idea that these strategies are dependable in decision-making processes in addition to being practical and usable. Show more
Keywords: Spherical q-linear Diophantine fuzzy set, MAIRCA technique, Spherical q-linear Diophantine fuzzy weighted aggregation operators based on algebraic norms, decision making
DOI: 10.3233/JIFS-235397
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2024
Authors: Khan, Younas | Ashraf, Shahzaib | Farman, Muhammad | Abdallah, Suhad Ali Osman
Article Type: Research Article
Abstract: Achieving household food security is the tumbling issue of the century. This article explores the factors affecting household food security and solutions by utilizing a synergy of statistical and mathematical models. The methodology section is divided into two portions namely sociological and mathematical methods. Sociologically, 379 household heads were interviewed through structured questions and further analyzed in terms of descriptive and binary logistic regression. The study found that 4 independent variables (poverty, poor governance, militancy, and social stratification) showed a significant association (P = 0.000) to explain variations in the dependent variable (household FS). The Omnibus test value (χ2 = 102.386; P … = 0.000) demonstrated that the test for the entire model against constant was statistically significant. Therefore, the set of predictor variables could better distinguish the variation in household FS. The Nagelkerke’s R Square (R2 = .333) helps to interpret that the prediction variable and the group variables had a strong relationship. Moreover, 23% to 33% variation in FS was explained by the grouping variables (Cox and Snell R2 = 0.237 and Nagelkerke’s R2 = 0.333). The significant value of Wald test results for each variable confirmed that the grouping variables (poor governance P = 0.004, militancy P = 0.000, social stratification P = 0.021 and poverty P = 0.000) significantly predicted FS at the household level. Mathematically, all the statistics were validated further through the application of spherical fuzzy mathematics (TOPIS and MADM) to explore what factors are affecting household FS. Thus, the study found that F 3 (poverty ) > F 2 (militancy) > F 4 (social stratification) > F 1 (poor governance) respectively. Thus, it could be concluded from these findings that the prevalence of poverty dysfunctional all the channels of household FS at the macro and micro levels. Therefore, a sound and workable model to eradicate poverty in the study area by ensuring social safety nets for the locals was put forward some of the policy implications for the government are the order of the day. Show more
Keywords: Food security, militancy, poor governance, social stratification, poverty, logistic regression, TOPIS, MADM, spherical fuzzy set
DOI: 10.3233/JIFS-237938
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Rezaei, Reza | Shahidi, Seyed-Ahmad | Abdollahzadeh, Sohrab | Ghorbani-Hasansaraei, Azade | Raeisi, Shahram Naghizadeh | Hayati, Jamileh
Article Type: Research Article
Abstract: Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people’s health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a …multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry’s risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach. Show more
Keywords: Fuzzy cognitive map, beverage industry failures, risk evaluation, FMEA
DOI: 10.3233/JIFS-233277
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Madhubala, P. | Ghanimi, Hayder M.A. | Sengan, Sudhakar | Abhishek, Kumar
Article Type: Research Article
Abstract: The medical domain faces unique challenges in Information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Methods (KBM) have limitations, the integration of semantic knowledge bases and concept mapping techniques enhances data organization and retrieval. Addressing the growing demands in the biomedical field, a novel medical Information Retrieval System (IRS) is proposed that employs Deep Learning (DL) and KBM. This system comprises five core steps: pre-processing of texts, document indexing using DL (ELMo) and KBM, advanced query processing, a BiLSTM-based retrieval network for contextual representation, and a KR-R …re-ranking algorithm to refine document relevance. The purpose of the system is to give users improved biomedical search results through the integration of all of these techniques into a method that takes into consideration the semantic problems of medical records. An in-depth examination of the TREC-PM track samples from 2017 to 2019 observed an impressive leading MRR score of 0.605 in 2017 and a best-in-class rPrec score of 0.350 in 2019, proving how well able the system is to detect and rank relevant medical records accurately. Show more
Keywords: Biomedical information retrieval, BiLSTM, DL, accuracy, query semantics, kernel ridge regression
DOI: 10.3233/JIFS-237056
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Arunkumar, N. | Nagaraj, Balakrishnan | Keziah, M. Ruth
Article Type: Research Article
Abstract: Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because …the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination. Show more
Keywords: Parkinson diseases, EEG signals, wavelet transform, features, optimization, classifier
DOI: 10.3233/JIFS-236145
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Kexing, Zhang | Jiang, He
Article Type: Research Article
Abstract: Recent developments in wireless networking, big data technologies including 5G networks, healthcare big data analytics, the Internet of Things (IoT), sophisticated wearable technologies, and artificial intelligence (AI) have made it possible to design intelligent illness diagnostic models. In addition to its critical function in e-health applications, 5G-IoT is becoming a standard feature of intelligent software. Intelligent systems and architectures are necessary for e-health applications to counteract threats to the privacy of patients’ medical information. Using machine learning and IoMT, this research suggests a new approach to cloud data analysis using the 5G network in the context of a recommendation model. …This application of the 5G cloud network to the monitoring and analysis of healthcare data makes use of variational adversarial transfer convolutional neural networks. The treatment plan for abnormalities in a tolerant body is derived from this clustered outcome. Experiment analysis was performed for a number of healthcare datasets with respect to training precision, network efficiency, F-1 score, root-mean-squared error, and mean average precision as the metrics of interest. Show more
Keywords: 5G network, cloud data analysis, recommendation model, machine learning, internet of medical things (IoMT)
DOI: 10.3233/JIFS-235064
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Selvakumar, B. | Abinaya, P. | Lakshmanan, B. | Sheron, S. | Smitha Rajini, T.
Article Type: Research Article
Abstract: Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to …hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparatively more data than other recent works with lower Mean Square Error value of 4748.12 which is the main factor used to determine how well the data is hidden and cannot be interpreted easily. Also, it achieved a Peak Signal-Noise Ratio (PSNR) value of 71.34 dB, which is superior than other recent works, verifying that the image quality remains uncompromising even after being encrypted. Show more
Keywords: Hybrid image encryption, least significant bit algorithm, arithmetic division operation, canny edge detection algorithm, chaotic maps, DNA encoding
DOI: 10.3233/JIFS-236637
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Li, Chunling | Zhang, Yi
Article Type: Research Article
Abstract: The existing negative selection algorithms can not improve their detection performance by human intervention during the testing process. This paper proposes a negative selection algorithm with human-in-the-loop for anomaly detection. It uses self-sample clusters to train detectors with a nonrandom strategy. Its detectors and self-sample clusters fully cover state space without overlapping each other. It locally adjusts detectors and self-sample clusters with human intervention to improve its detection performance during the testing process. Experiments were performed on two synthetic datasets and the Iris dataset from the UCI repository to assess its performance. The results show that it outperforms the other …anomaly detection methods in most cases. Show more
Keywords: Negative selection algorithm, human-in-the-loop, anomaly detection, artificial immune algorithm, artificial immune system
DOI: 10.3233/JIFS-235724
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Zhong, Yu | Shen, Bo | Wang, Tao
Article Type: Research Article
Abstract: Document-level relation extraction aims to uncover relations between entities by harnessing the intricate information spread throughout a document. Previous research involved constructing discrete syntactic matrices to capture syntactic relationships within documents. However, these methods are significantly influenced by dependency parsing errors, leaving much of the latent syntactic information untapped. Moreover, prior research has mainly focused on modeling two-hop reasoning between entity pairs, which has limited applicability in scenarios requiring multi-hop reasoning. To tackle these challenges, a syntax-enhanced multi-hop reasoning network (SEMHRN) is proposed. Specifically, the approach begins by using a dependency probability matrix that incorporates richer grammatical information instead of …a sparse syntactic parsing matrix to build the syntactic graph. This effectively reduces syntactic parsing errors and enhances the model’s robustness. To fully leverage dependency information, dependency-type-aware attention is introduced to refine edge weights based on connecting edge types. Additionally, a part-of-speech prediction task is included to regularize word embeddings. Unrelated entity pairs can disrupt the model’s focus, reducing its efficiency. To concentrate the model’s attention on related entity pairs, these related pairs are extracted, and a multi-hop reasoning graph attention network is employed to capture the multi-hop dependencies among them. Experimental results on three public document-level relation extraction datasets validate that SEMHRN achieves a competitive F1 score compared to the current state-of-the-art methods. Show more
Keywords: Attention mechanism, document-level relation extraction, syntactic information, multi-hop reasoning
DOI: 10.3233/JIFS-237167
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Balasubramaniyan, M. | Navaneethan, C.
Article Type: Research Article
Abstract: Artificial intelligence has played a significant role in the expansion of the agriculture industry in recent times by evaluating data and making recommendations for better production. An automated method for determining significant information in seed quality analysis is the peanut maturity analysis in image processing through sensory images. The majority of the time, changes in picture intensity result in feature independence and precise maturity level determination. Therefore, agricultural precision in identifying essential features is low. To address this issue, we suggest employing a Cross-Layer Multi-Perception Neural Network (CLMPNN) for hyperspectral sensory image feature observation in order to determine the optimal …assessment of peanut maturity in agriculture. The sensing unit first determines the angular cascade projection’s (ACP) structural dependencies for the peanut pod structure. With the aid of color-intensive saturation, the entity projection of pod growth is found using the Slicing Fragment Segmentation (SFS) technique. This generates the various entity variations by integrating relational maturity and non-maturity findings with spectral values. Next, cross-layer multi-perception neural networks are trained with hyperspectral values optimized by LSTM to distinguish between mature and immature pods. In comparison to the other system, this one does exceptionally well in precision agriculture, with a 98.6 well recall rate, a 97.3% classification accuracy, and a 98.9% production accuracy. Show more
Keywords: Peanut maturity, feature selection and classification, deep learning, cascade projection, slicing segmentation
DOI: 10.3233/JIFS-239332
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Zhou, Ning | Liu, Bin | Cao, Jiawei
Article Type: Research Article
Abstract: Facial expression recognition has long been an area of great interest across a wide range of fields. Deep learning is commonly employed in facial expression recognition and demonstrates excellent performance in large-sample classification tasks. However, deep learning models often encounter challenges when confronted with small-sample expression classification problems, as they struggle to extract sufficient relevant features from limited data, resulting in subpar performance. This paper presents a novel approach called the Multi-CNN Logical Reasoning System, which is based on local area recognition and logical reasoning. It initiates the process by partitioning facial expression images into two distinct components: eye action …and mouth action. Subsequently, it utilizes logical reasoning based on the inherent relationship between local actions and global expressions to facilitate facial expression recognition. Throughout the reasoning process, it not only incorporates manually curated knowledge but also acquires hidden knowledge from the raw data. Experimental results conducted on two small-sample datasets derived from the KDEF and RaFD datasets demonstrate that the proposed approach exhibits faster convergence and higher prediction accuracy when compared to classical deep learning-based algorithms. Show more
Keywords: Facial expression recognition, logic reasoning, few-shot learning, local area recognition
DOI: 10.3233/JIFS-233988
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Vasanthamani, K. | Pavai Madheswari, S.
Article Type: Research Article
Abstract: This paper deals with a discrete-time Geo/G/1 queue with repeated attempts and starting failure. If the server fails to start, it is sent for repair. During a repair process, alterations in the repair times is permitted based on current requirements. Customers are served on priority by the pre-emptive resume queue discipline. The distributions of the various system states when the system is in stable are analysed using the generating function technique. Analytical expressions are supported by numerical illustrations to exhibit the influence of the various parameters of the system on the performancemeasures.
Keywords: Discrete-time retrial queues, general retrial times, unreliable server, impatience, priority, replacement in repair times
DOI: 10.3233/JIFS-233406
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Xiao, Yanjun | Li, Rui | Zhao, Yue | Wang, Xiaoliang | Liu, Weiling | Peng, Kai | Wan, Feng
Article Type: Research Article
Abstract: The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from “regular maintenance” and “post-event maintenance” to “predictive maintenance”. In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of “combining the real with the virtual and controlling the real”. Firstly, a digital twin system …structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom. Show more
Keywords: Digital twin, predictive maintenance, deep learning, rapier loom
DOI: 10.3233/JIFS-233863
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Wu, Xiaogang
Article Type: Research Article
Abstract: The similarity measure of intuitionistic fuzzy sets is a primary method for dealing with uncertainty and fuzzy problems and is commonly used in fuzzy decision-making and pattern recognition. The current mainstream similarity measure is based on the classical fuzzy set with only one negation, which often violates the intuitionistic problem in applications because the actual semantics of multiple negations are not considered. To solve the inconsistency and irrationality problems in the classical similarity methods, we introduce three negations (contradiction negation, opposition negation, and mediation negation) in the intuitionistic fuzzy set to obtain the generalized intuitionistic fuzzy set and prove its …related property theorem. On this basis, our similarity measure adopts a mediational negation to represent non-membership, which fully utilizes the multiple negation information of non-membership and hesitancy and avoids the loss of fuzzy information. We verify the method’s rationality, validity, and originality through pattern recognition experiments and numerical examples, which improves the performance of intuitionistic fuzzy set similarity in practical applications and provides a new approach for future research on intuitionistic fuzzy inference. Show more
Keywords: Generalized intuitionistic fuzzy sets (GIFS), three kinds of negation, similarity measure, fuzzy decision
DOI: 10.3233/JIFS-236510
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Zhang, Guili | Li, Pengxi | Zhang, Hanyue | Yu, Yinglong | Liang, Zhao
Article Type: Research Article
Abstract: Our society is being transformed by the technology emergence and the industrial revolution. The advances in the internet and artificial intelligence are reshaping the means of education, profoundly changing the ways of teaching and learning. This paper studies the pattern of how the new 5th generation blended campus network is applied to aid the new generation of intelligence teaching. This pattern is the implementation of national major policies and the measure of cultivating people. This paper introduces a new model for the intelligence teaching system. Based on the new model, distance interaction teaching system, VR practicing teaching system, intelligence testing …system, and higher education intelligence decision system are developed. This model can be the basis of the informatization of future education. Show more
Keywords: 5G, Blended campus network, intelligence teaching, VR practice teaching system
DOI: 10.3233/JIFS-237768
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Amiri-Bideshki, M. | Hoskova-Mayerova, S. | Ameri, R.
Article Type: Research Article
Abstract: The purpose of this paper is to study some properties of modular hyperlattices. We state and prove some propositions (theorems) of [2 ] with a stronger condition(modularity) than distributivity. We prove that if hyperlattice L with bottom element 0 is modular, then 0 ∨ 0 =0 and there exists no element in x ∨ x greater than x . Also, we study pentagonal hyperlattice that is non-modular. Finally, some results of fundamental relation are given.
Keywords: Hyperlattice, modular element, pentagonal hyperlattice
DOI: 10.3233/JIFS-237912
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-6, 2024
Authors: Chau, Vinh Huy | Vo, Anh Thu | Ngo, Huu Phuc
Article Type: Research Article
Abstract: This paper discusses the use of an improved random forest regression algorithm (RFRA) to predict the total score of powerlifters. The paper collected the age, weight, and total score of multiple powerlifters, and then used an improved RFRA to build a predictive model. The parameters of this model are optimized by a differential squirrel search algorithm. The experimental results show that our proposed method can effectively predict the total score of powerlifters with an error of less than 10%, which can provide a reference for experts and athletes before training or competition.
Keywords: Artificial intelligence, random forest, powerlifting, total score
DOI: 10.3233/JIFS-230032
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-6, 2024
Authors: Pan, Hongyan
Article Type: Research Article
Abstract: In addition to providing learners with a large amount of teaching resources, online teaching platforms can also provide learning resources and channels such as video courseware, Q&A tutoring groups, and forums. However, currently, there are still shortcomings in depth and dimensionality in mining student learning behavior data on the platform. In view of this situation, based on the learning interaction behavior, this study established the difficulty similarity model of knowledge points, and used spectral clustering to classify their difficulty. In addition, the study intended to use the maximum frequent subgraph under the Gspan framework to characterize learners’ implicit learning patterns. …The outcomes expressed that the algorithm put forward in the study achieved the highest accuracy index of 98.8%, which was 1.4%, 4.0%, and 8.6% higher than Apriori-based graph mining algorithms, K-means, and frequent subgraph discovery algorithms. In terms of F1 index, the convergence value of the algorithm proposed in the study was 95.5%, which was about 2.5% higher than the last three algorithms. In addition, learners of all three cognitive levels had the highest maximum number of frequent subgraphs with sizes above 100 when the minSup value was 60% . And when the number of clusters was 3, the clustering accuracy of the three learners was the highest. In similarity calculation, the calculation method used in the study was at the minimum in terms of root mean square error and absolute error average index, which were 0.048% and 0.01% respectively. This indicated that the model proposed by the research had better classification effect on the difficulty of knowledge points for learners of different cognitive levels, and had certain application potential. Show more
Keywords: Similarity, knowledge points, clustering, multidimensional, Gspan, accuracy
DOI: 10.3233/JIFS-234274
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Indupalli, Manjula Rani | Gera, Pradeepini
Article Type: Research Article
Abstract: Symptom-based disease identification is crucial to the diagnosis of the disease at the early stage. Usage of traditional stacking and blending models i.e., with default values of the models cannot handle the multi-classification data properly. Some of the existing researchers also implemented tuning with the help of a grid search approach but it consumes more time because it checks all the possible combinations. Suppose if the model has n estimators with k values it needs to check (n*k)! elements combination, this makes the learning time high. The proposed model chooses the estimators to train the model with in a considerable …amount of time using an advanced tuning technique known as “Bayes-Search” on an ensemble random forest and traditional, support vector machine. The advantage of this model is its capability to store the best evaluation metrics from the previous model and utilise them to store the new model. This model chooses the values of the estimator based on the probability of selection, which reduces the elements in search space i.e., (< (n-k)!). The proposed model defines the objective function with a minimum error rate and predicts the error rate with the selected estimators for different distributions. The model depending on the predicted value decides whether to store the value or to return the value to the optimizer. The Bayes search optimization has achieved +9.21% accuracy than the grid search approach. Among the two approaches random forest has achieved good accuracy and less loss using Bayes search with cross-validation. Show more
Keywords: Grid search, bayes search, objective function, error minimization, search space
DOI: 10.3233/JIFS-236137
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Kang, Xiaoqiang
Article Type: Research Article
Abstract: The Internet of Things (IoT) refers to a vast network of interconnected devices, objects, and systems powered by sensors, software, and connectivity capabilities. The interconnectivity of IoT devices has led to a substantial increase in data production. Efficiently managing and analyzing large data volumes is a significant challenge for IoT systems. To address this challenge, data aggregation is the primary process. IoT data aggregation aims to provide high-quality service by ensuring fast data transmission, high reliability, minimal energy consumption, and data priority consideration. Data aggregation involves collecting data from multiple sensors and devices and then integrating it using a function …to minimize system traffic. This paper thoroughly examines data aggregation techniques in the IoT context. Techniques are grouped according to underlying principles, and their potential applications, advantages, and limitations are discussed. Show more
Keywords: Internet of things, data aggregation, data transmission, energy consumption
DOI: 10.3233/JIFS-238284
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Hu, Yuanyuan
Article Type: Research Article
Abstract: Music education has a rich historical background. Nevertheless, the introduction of modern teaching methods is relatively delayed. In recent years, there has been a remarkable acceleration in the advancement of music education. A promising tool that has emerged to revolutionize education as a whole is Virtual Reality (VR) technology, which offers immersive and interactive experiences across various disciplines. At the university level, integrating VR technology into music education opens up exciting opportunities to enhance practical teaching methods and provide students with enriched musical experiences. Virtual Reality together with Internet of Things (IoT) demonstrates its capabilities in various tasks, but its …widespread availability in online learning remainders a pressing challenge that needs to be addressed. In pre-processing, it removes noise data using Dynamic Context-Sensitive Filtering (DCSF). VR technology creates an unparalleled learning environment, it transporting students to virtual concert halls, recording studios, or historical music venues. Hence the Multiscale deep bidirectional gated recurrent neural Network (MDBGNN) improves the practical teaching of music course concept, like Music theory, harmony, and rhythm can be visualized and experienced in VR. Finally, Dung Beetle Optimization Algorithm (DBOA) is employed to optimize the weight parameters of MDBGNN. The proposed MDBGNN-DBO-UMC-VRT is implemented in Python. The proposed method is analysed with the help of performance metrics, like precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time and RoC. The proposed MDBGNN-DBO-UMC-VRT method attains 13.11%, 18.12% and 18.73% high specificity, 11.13%, 11.04% and 19.51% lower computation Time, 15.29%, 15.365% and 14.551% higher ROC and 13.65%, 15.98%, and 17.15% higher Accuracy compared with existing methods, such as Enhancing Vocal Music Teaching through the Fusion of Artificial Intelligence Algorithms and VR Technology (CNN-UMC-VRT), Exploring the Efficacy of VR Technology in Augmenting Music Art Teaching (BPNN-UMC-VRT) and Implementing an Interactive Music-Assisted Teaching System Using VR Technology (DNN-UMC-VRT) respectively. Show more
Keywords: Dung Beetle optimization, Dynamic Context-Sensitive Filtering, multiscale deep bidirectional gated recurrent neural network, Virtual Reality, music course
DOI: 10.3233/JIFS-236893
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: de Oliveira, Heveraldo R. | Vieira, Antônio Wilson | Santos, Laércio Ives | Filho, Murilo César Osório Camargos | Ekel, Petr Ya. | D’Angelo, Marcos Flávio S.V.
Article Type: Research Article
Abstract: When providing patient care, healthcare professionals often rely on interpreting laboratory and clinical test results. However, their analysis is constrained by human capacity, leading to uncertainties in diagnoses. Machine learning has the potential to evaluate a larger amount of data and identify patterns and relationships that may otherwise go unnoticed. However, popular machine learning algorithms typically require abundant and labeled data, which is not always available. To address this challenge, the adoption of active learning allows for the selection of the most relevant instances for training, reducing the need for extensive labeling. Additionally, fuzzy logic offers the ability to handle …uncertainties. This paper proposes a novel approach that utilizes fuzzy membership functions to transform data as a pre-processing step for active learning. The objective is to approximate similar instances, specifically for the purpose of prediction, thereby minimizing the workload of human experts in labeling data for model training. The results of this study demonstrate the effectiveness of this approach in predicting heart disease and highlight the potential of using membership functions to enhance machine learning models in the analysis of medical information. By incorporating fuzzy logic and active learning, healthcare professionals can benefit from improved accuracy and efficiency in diagnosing and predicting pacients’ health conditions. Show more
Keywords: Active learning, fuzzy logic, cardiovascular diseases
DOI: 10.3233/JIFS-237047
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Geng, Xiuli | Du, Yuanhao | Cao, Shuyuan | Cheng, Sheng
Article Type: Research Article
Abstract: Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users’ opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are …employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers. Show more
Keywords: Low-carbon, customer satisfaction, importance performance analysis, latent dirichlet allocation, support vector machine, random forest
DOI: 10.3233/JIFS-235074
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Article Type: Research Article
Abstract: In order to ensure the safety of life and property in large buildings, the design of emergency evacuation routes for large buildings based on cloud computing and GIS big data is studied. Combining cloud computing and GIS big data, a command model for emergency evacuation of large buildings is built. Emergency evacuation functions are realized through the access layer, business logic layer, cloud computing layer and data layer. GIS big data of large buildings is stored in the model data layer. GIS geographic data is clustered through the MapReduce based parallel K-means clustering algorithm in the cloud computing layer. After …clustering, the emergency evacuation road network of large buildings is constructed through GIS in the business logic layer. On the road network, the emergency evacuation route selection method combining Dikstra algorithm and ant colony algorithm is used to realize the optimal route selection of emergency evacuation of large buildings. Experiments show that this method can effectively select the best evacuation path in large buildings, and the evacuation speed of the selected path is fast, which can ensure the safety of people in buildings. Show more
Keywords: Cloud computing, GIS big data, large buildings, emergency evacuation route, K-means clustering, ant colony algorithm
DOI: 10.3233/JIFS-237834
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Yu, Peng | Jing, Fengwei | Guo, Jin
Article Type: Research Article
Abstract: This paper studies the security control problem of semi-Markov jump systems. First, the parameter uncertainty, the time-varying delay, the nonlinear function and the cyber attack are considered in the system. Second, the nonlinear function is linearized by the fuzzy logic rule. A sliding mode surface is designed to obtain an equivalent controller and get a sliding mode dynamic system. By constructing Lyapunov functions of the mode dependence, a sufficient condition for H ∞ asymptotic stability of the system is obtained. Then, an adaptive sliding mode controller is established, and the original system reaches the sliding mode surface in a …finite time. Finally, two examples verify the correctness and practicality of the proposed theory. Show more
Keywords: Semi-Markov jump system, sliding mode control, cyber attack, fuzzy logic
DOI: 10.3233/JIFS-238994
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Pang, Yufeng | Li, Xiaojuan
Article Type: Research Article
Abstract: Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also …proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency. Show more
Keywords: Acoustic signal, bearing fault detection, fast-unoriented SIFT algorithm, feature extraction, word bag model
DOI: 10.3233/JIFS-237331
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Sung, Tien-Wen | Zhao, Baohua | Zhang, Xin | Lee, Chao-Yang | Fang, Qingjun
Article Type: Research Article
Abstract: Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of …different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm’s practicability. Show more
Keywords: QUATRE algorithm, swarm-based optimization, fixed dimension updating, 3D path planning, unmanned aerial vehicle
DOI: 10.3233/JIFS-230928
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2024
Authors: Zhang, Yongzhi | He, Keren | Ge, Jue
Article Type: Research Article
Abstract: Pedestrian detection plays a crucial role in ensuring traffic safety within the domain of computer vision. However, accurately detecting pedestrians in complex environments proves to be a challenge due to issues such as occlusion. To address this issue, this paper presents an end-to-end pedestrian detection model founded on the DEtection TRansformer (DETR) architecture, effectively managing occlusion scenarios. The proposed model comprises a backbone Convolutional Neural Network (CNN) and a Transformer network. The backbone CNN incorporates variable convolution and U-Net design principles to enhance feature extraction capabilities, particularly for occluded pedestrians. Additionally, our innovative Adaptive Occlusion-Aware Attention Mechanism (AOAM) is embedded …within the Transformer network, allowing the model to dynamically adjust attention weights and enhance the localization and identification of occluded pedestrians. Extensive experiments on the Caltech and ETH datasets demonstrate the superior performance of our model compared to state-of-the-art approaches across four key evaluation metrics. This study provides effective methodologies and theoretical foundations for pedestrian detection in complex environments. Show more
Keywords: Pedestrian detection, occlusion-aware, attention mechanism, DETR
DOI: 10.3233/JIFS-235386
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Liu, Zhanpeng | Xiao, Wensheng | Cui, Junguo | Mei, Lianpeng
Article Type: Research Article
Abstract: The drilling permanent magnet synchronous motor (DPMSM) contains multiple subsystems with identical structures and has a high probability of failure because the downhole working conditions are harsh. Therefore, the quick localization of faults is difficult to determine although the fault type may be identified in time. The system diagnostic model based on the Bayesian network (BN) can be used for fault diagnosis and localization for components in subsystems, but it is difficult to build and modify due to the complex system in practice. New methods are necessary to reduce the difficulty of building and modifying models. In this study, object-oriented …ideas are introduced into the BN to establish a system diagnostic model based on an Object-oriented Bayesian network (OOBN) for the DPMSM. First, the fault diagnostic models for subsystems based on BN are established, respectively. Then, submodels of forward and backward based on BN are instantiated as instance nodes. Next, instance nodes are connected through input nodes and output nodes to establish the OOBN-based system diagnosis model. Finally, the system diagnosis model is validated by sensitivity analysis and the effectiveness is discussed in Cases. The system diagnosis model can effectively reduce the difficulties of modeling and modifying. Show more
Keywords: Object-oriented Bayesian network, fault diagnosis, instance nodes, sensitivity analysis
DOI: 10.3233/JIFS-236850
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Cui, Wanqiu
Article Type: Research Article
Abstract: Graph data storage has a promising prospect due to the surge of graph-structure data. Especially in social networks, it is widely used because hot public opinions trigger some network structures consisting of massively associated entities. However, the current storage model suffers from slow processing speed in this dense association graph data. Thus, we propose a new storage model for dense graph data in social networks to improve data processing efficiency. First, we identify the public opinion network formed by hot topics or events. Second, we design the germ elements and public opinion bunching mapping relationship based on equivalence partition. Finally, …the Public Opinion Bunching Storage(POBS) model is constructed to implement dense graph data storage effectively. Extensive experiments on Twitter datasets demonstrate that the proposed POBS performs favorably against the state-of-the-art graph data models for storage and processing. Show more
Keywords: Graph data storage, social networks, topic cluster, equivalent partition
DOI: 10.3233/JIFS-233540
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Narendiranath Babu, T. | Kothari, Ayush Jain | Rama Prabha, D. | Mokashe, Rohan | Kagita, Krish Babu | Raj kumar, E.
Article Type: Research Article
Abstract: In the modern world, condition monitoring is crucial to the predictive maintenance of machinery. Gearboxes are widely used in machineries and auto motives to achieve the variable speeds. The major problem in gearbox is catastrophic failure due to heavy loads, corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The main purpose of this study to use Machine Learning (ML) algorithms and Artificial Neural Network (ANN) which are very powerful and reliable tool for fault detection and …its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. To do the vibration study, an experimental setup was created, and various faults were induced faults of various kinds that usually occurred in the gearbox. The gear in the gear train was subjected to vibration analysis which was captured via a sensor. Signal processing was carried out using MATLAB Toolbox. To automatically identify the flaws in the helical gearbox, an artificial neural network (ANN) and several machines learning methods, including KNN, decision tree, random forest, and SMV, were trained by creating a database from the experiment conducted. The outcomes showed potential in accurately classifying the faults. The results show that ANN has the highest accuracy of 99.6% with a 6.5662 seconds computational time while SVM has the lowest accuracy of 96% among them along with the highest computational time of 21.324 seconds. Show more
Keywords: Helical gearbox, vibration analysis, signal processing, fault diagnosis, artificial neural network, K-nearest neighbor, support vector machine, decision tree, random forest
DOI: 10.3233/JIFS-233602
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Rao, Bommaraju Srinivasa | Banerjee, Kakoli | Anand Deva Durai, C. | Balu, S. | Sahoo, Ashok Kumar | Priyadharshini, A. | Rama Krishna, Paladugu | Kakade, Revannath Babanrao
Article Type: Research Article
Abstract: In recent years, the Internet of Things (IoT) has rapidly emerged as an essential technology, enabling seamless communication between billions of interconnected devices. These devices generate a massive amount of data that requires efficient management to ensure optimum performance in IoT environments. Dynamic load balancing (DLB) is a crucial technique employed to distribute workloads evenly across multiple computing resources, thereby reducing latency and increasing the overall efficiency of IoT networks. This paper presents a novel DLB approach based on type-2 fuzzy logic systems (T2FLS) to enhance the performance and reliability of IoT environments. The proposed T2FLS-based DLB technique addresses the …inherent uncertainties and imprecisions in IoT networks by considering various parameters, such as workload, processing capability, and communication latency. A comprehensive performance evaluation is carried out to compare the proposed method with traditional DLB approaches. Simulation results demonstrate that the T2FLS-based DLB technique significantly improves the network’s response time, throughput, and energy efficiency, while also providing better adaptability and robustness to dynamic changes in IoT environments. This study contributes to the advancement of DLB techniques in IoT networks and lays the groundwork for further research in this field. Show more
Keywords: Dynamic load balancing, internet of things, type-2 fuzzy logic systems, performance evaluation, energy efficiency
DOI: 10.3233/JIFS-234105
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Alqudah, Rajaa | Al-Mousa, Amjed | Faza, Ayman
Article Type: Research Article
Abstract: Traffic on highways has increased significantly in the past few years. Consequently, this has caused delays for the drivers in reaching their final destination and increased the highway’s congestion level. Many options have been proposed to ease these issues. In this paper, a model of the highway drivers’ population was built based on several factors, including the behavioral patterns of the drivers, like drivers’ time flexibility to reach the destination, their carpool eligibility, and their tolerance to pay the toll price, in addition to the traffic information from the system. A fuzzy logic decision-making model is presented to emulate how …drivers would choose the lane to use based on the aforementioned factors and the current congestion levels of all the lanes on the highway. The presented model, along with the simulation results from applying the model to different simulation scenarios, show the usefulness of such a model in predicting an optimal toll value. Such optimal value would reduce congestion on the highway at one end while maximizing the revenue for the toll company. Show more
Keywords: Fuzzy logic, decision-making, probabilistic model, toll pricing, traffic management
DOI: 10.3233/JIFS-231352
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Mahmood, Tahir | Hussain, Kashif | Ahmmad, Jabbar | Shahab, Sana | ur Rehman, Ubaid | Anjum, Mohd
Article Type: Research Article
Abstract: The notion of a T-bipolar soft set (T - BS ft S ) is the structure that has the ability to discuss the two-sided aspects of certain situations like the effects and side effects of a medicine. Moreover, T - BS ft S has the ability to discuss the parametrization tool as well. Also, notice that a group is an algebraic structure that is the key tool in many branches of mathematics. In many decision-making situations, we have to discuss the two-sided aspects of a certain situation and we can see that T - BS ft S is …the only structure that can handle it. So based on a characteristic of T - BS ft S and groups theory there is a need to define the combined notion of T - BS ft S and group. So, based on these valuable structures, in this manuscript, we aim to introduce the notion of T-bipolar soft groups by generalizing T-bipolar soft sets. Based on this newly defined structure, we have defined the basic operational laws like extended union, extended intersection, restricted union, restricted intersection, AND product, and OR product for T-bipolar soft groups. Moreover, we have observed the impact of these newly defined notions on T-bipolar soft groups. Show more
Keywords: Soft set, soft groups, T-bipolar soft set, T-bipolar soft groups
DOI: 10.3233/JIFS-236150
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Shen, Xiaohui
Article Type: Research Article
Abstract: Analyzing Lexical Semantic Changes (LSC) in Educational Texts (ET) refers to examining how the meanings of words, terms, or phrases used in ET have evolved. It involves learning shifts in the semantic content, connotations, and language associations within educational resources such as textbooks, research articles, and instructional content. The analysis can reveal how educational models, pedagogical methods, and terminology have transformed in response to technological innovations, societal changes, and pedagogical developments. This analysis provides visions into the dynamic nature of educational discourse, helping researchers, educators, and policymakers understand how language has adapted to reflect changes in educational paradigms and the …broader educational context. This research investigates the semantic analysis and classification performance within ET, employing the innovative Decision Tree + Feed Forward Neural Networks (DT + FFNNs) framework. This research shows the dynamic semantic relationships inherent in educational terminology by diverse semantic similarity measures and contextualized embeddings. It looks at how educational language changes to reflect changes in society, technology, and pedagogy. The study uses a DT + FFNN framework for semantic analysis and classification. The study uses several embeddings and semantic similarity metrics, and Spearman’s Correlation Coefficient (SCC) is employed to evaluate their effectiveness. This study highlights the DT + FFNN framework’s capacity to capture complex semantics in an educational setting and offers insights into the adaptive nature of educational discourse. SCC serves as a guiding metric, offering insights into the efficiency of several embeddings and measures. The findings show the pivotal role of fine-tuning in significantly enhancing the accuracy of DT + FFNNs across measures, revealing its remarkable potential in capturing semantics within an educational context. Show more
Keywords: Semantic analysis, education, spearman correlation, machine learning, decision tree, and accuracy
DOI: 10.3233/JIFS-237410
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Gao, Yanbing | Ma, Rui
Article Type: Research Article
Abstract: With the deepening development of the financial market, the role of regulatory systems in ensuring green and safe financial environment is becoming increasingly prominent. The traditional intelligent financial regulatory systems on the market lack precise and effective real-time monitoring and recognition capabilities, making it difficult to effectively process and analyze large-scale financial data. In order to improve the real-time recognition of abnormal situations or potential risks, achieve automation and intelligence of supervision, this article combines deep learning technology to study the deep practice of IoT image recognition technology in intelligent financial supervision systems. In response to the “data silos” and …cross regional linkage issues faced by financial industry regulation, this article designs and implements an intelligent regulatory system based on IoT image recognition technology through deep learning. Using Convolutional Neural Network (CNN) algorithm to classify and analyze system images for regulatory and risk control purposes. The research results indicate that the intelligent financial regulatory system constructed in this article has high stability and responsiveness, which can effectively meet the real-time regulatory needs of finance and help promote the healthy development of the financial market. Show more
Keywords: Financial supervision system, internet of things, image recognition technology, deep learning, artificial intelligence
DOI: 10.3233/JIFS-237692
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Ma, Ping | Ni, Zhengwei
Article Type: Research Article
Abstract: Time series forecasting has a wide range of applications in various fields. To eliminate the need for time series data volume, a meta-learning-based few-shot time series forecasting method is proposed. This method uses a residual stack module as its backbone and connects the residuals forward and backward through a multilayer fully connected network so that the model and the meta-learning framework can be seamlessly combined. The Empirical knowledge of different time-sequence tasks is obtained through meta-training. To enable fast adaptation to new prediction tasks, a small meta-network is introduced to adaptively and dynamically generate the learning rate and weight decay …coefficient of each step in the network. This method can use sequences of different data distribution characteristics for cross-task learning, and each training task only needs a small number of time series to achieve sequence prediction for the target task. The results show that compared with the two baselines, the proposed method has improved performance on 67.07% and 58.53% of the evaluated tasks. Thus, this method can effectively alleviate the problems caused by insufficient data during training and has broad application prospects in the field of time series. Show more
Keywords: Time series forecasting, few-shot learning, meta learning, residual stack model
DOI: 10.3233/JIFS-233520
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Chen, Liang-Ching | Chang, Kuei-Hu | Wu, Chia-Heng | Chen, Shin-Chi
Article Type: Research Article
Abstract: Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of …extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data. Show more
Keywords: Corpus, natural language processing (NLP), technical word, advanced general-purpose word list (AGWL), COVID-19
DOI: 10.3233/JIFS-236635
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Zhang, Min
Article Type: Research Article
Abstract: Vehicle safety on roadsides is vital for preventing collisions, controlling failures and accidents, and ensuring driver and passenger wellness. Finite Element Analysis (FEA) in vehicle safety relies on the vehicle’s physical attributes for predicting and preventing collisions. This article introduces a Differential FEA (DFEA) model for predicting vehicle collisions regardless of the speed and direction for driver/ passenger safety. The proposed model induces a vehicle’s speed, direction, and displacement from two perspectives: self and approaching vehicle. The collision probability with the trailing or persuading vehicle is calculated by distinguishing the forward and rear features. The differential calculus for the point …of deviation and distance metrics are significantly estimated for a vehicle’s front and rear ends. Such calculus generates a maximum and minimum possibility for self and approaching vehicle contact. This contact is further split based on the collision threshold; the threshold is determined using the safe distance between two vehicles for collision-less driving. The threshold exceeding vehicles are alerted for their change in direction/ speed through contact point (rear/front) differential derivatives. This ensures collision detection under fewer contact errors, leveraging the safety of the duo vehicles. Show more
Keywords: Collision, contact threshold, differential equation, finite element analysis, vehicle safety
DOI: 10.3233/JIFS-233628
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Zhang, Weidong | Tan, Huadi
Article Type: Research Article
Abstract: Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for …ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation. Show more
Keywords: IoT, 5G, machine learning, smart farming, accuracy, plant disease prediction, WSN
DOI: 10.3233/JIFS-237482
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Selvy, R. | Vinod Kumar, P.B.
Article Type: Research Article
Abstract: It is observed that IFSs are defined based on the concept that the iterates take only an integer number of times. This work studies the dynamics of functions, where a function can iterate r times for every r ∈ R . Utilizing concepts from fuzzy sets, r -times iterates of a function are defined for r ∈ R . The study demonstrates that the chaotic property can be generalized to this new iterative concept. The chaotic behavior of a function is then extended using this iterative concept.
Keywords: Iterated function systems, fuzzy functions, chaotic functions
DOI: 10.3233/JIFS-236563
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Deepak Raj, D.M. | Arulmurugan, A. | Shankar, G. | Arthi, A. | Panthagani, Vijaya Babu | Sandeep, C.H.
Article Type: Research Article
Abstract: The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in an image serve as the object’s limits and can reveal crucial details about the object’s size, shape, and position. The pre-processing stage of edge detection is crucial because it can increase the precision and effectiveness of edge detection algorithms. As low-density or low-pixel values muddy the image, detecting edges in low-resolution images is difficult. This paper aims to introduce LRED, an improved edge detection model for low-resolution images based on Gaussian smoothing. Also used for …image pre-processing and smoothing is the Gaussian filter. The Gaussian smoothing method works well for spotting edges in images. Additionally, we have presented a comprehensive comparison of our proposed approach with three modern, cutting-edge detection approaches and algorithms. Investigations have been conducted on several images in addition to low-quality images to discover edges. RMSE and PSNR are two different evaluation metrics used to measure proposed methods. LRED achieved 90.25% MSE, which is slightly better than the other three approaches which show more reliable outcomes. Show more
Keywords: Edge detection, image pre-processing, image smoothing, low resolution image, metrics
DOI: 10.3233/JIFS-235332
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Niyasudeen, F. | Mohan, M.
Article Type: Research Article
Abstract: With the growing reliance on cloud computing, ensuring robust security and data protection has become a pressing concern. Traditional cryptographic methods face potential vulnerabilities in the post-quantum era, necessitating the development of advanced security frameworks. This paper presents a fuzzy-enhanced adaptive multi-layered cloud security framework that leverages artificial intelligence, quantum-resistant cryptography, and fuzzy systems to provide comprehensive protection in cloud environments. The proposed framework incorporates data encryption, access control, and intrusion detection mechanisms, with fuzzy logic systems augmenting the decision-making process for threat detection and response. The integration of artificial intelligence and quantum-resistant cryptographic techniques enhances the framework’s adaptability and …resilience against emerging threats. The implementation of fuzzy systems further improves the accuracy and efficiency of the security mechanisms, ensuring robust protection in the face of uncertainty and evolving attack vectors. The fuzzy-enhanced adaptive multi-layered cloud security framework offers a comprehensive, adaptable, and efficient solution for securing cloud infrastructures, safeguarding sensitive data, and mitigating the risks associated with the post-quantum era. Show more
Keywords: Cloud security, artificial intelligence, quantum-resistant cryptography, fuzzy systems, adaptive multi-layered framework
DOI: 10.3233/JIFS-233462
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023
Authors: Kandan, M. | Durai Murugan, A. | Ramu, Gandikota | Ramu, Gandikota | Gnanamurthy, R.K. | Bordoloi, Dibyahash | Rawat, Swati | Murugesan, | Prasad, Pulicherla Siva
Article Type: Research Article
Abstract: Privacy-Preserving Fuzzy Commitment Schemes (PPFCS) have emerged as a promising solution for secure Internet of Things (IoT) device authentication, addressing the critical need for privacy and security in the rapidly growing IoT ecosystem. This paper presents a novel PPFCS-based authentication mechanism that protects sensitive user data and ensures secure communication between IoT devices. The proposed scheme leverages error-correcting codes (ECC) and cryptographic hash functions to achieve reliable and efficient authentication. The PPFCS framework allows IoT devices to authenticate themselves without revealing their true identity, preventing unauthorized access and preserving users’ privacy. Furthermore, our PPFCS-based authentication mechanism is resilient against various …attacks, such as replay, man-in-the-middle, and brute-force attacks, by incorporating secure random nonce generation and timely key updates. We provide extensive experimental results and comparative analysis, demonstrating that the proposed PPFCS significantly outperforms existing authentication schemes in terms of security, privacy, and computational efficiency. As a result, the PPFCS offers a viable and effective solution for ensuring secure and privacy-preserving IoT device authentication, mitigating the risks associated with unauthorized access and potential data breaches in the IoT ecosystem. Show more
Keywords: Privacy-preserving, fuzzy commitment, IoT device authentication, error-correcting codes, cryptographic hash functions
DOI: 10.3233/JIFS-234100
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2023
Authors: Sitharamulu, V. | Mahammad Rafi, D. | Naulegari, Janardhan | Battu, Hanumantha Rao
Article Type: Research Article
Abstract: In this study, we investigate the viability of applying fuzzy reinforcement learning (FRL) to Internet of Things-based robots for purposes of autonomous navigation and collision avoidance. The proposed approach utilises FRL, IoT, and a sensor network to give the robot the ability to learn from its environment and act in accordance with those policies. The authors used FRL to train a mobile robot with wheels to move around and avoid obstacles, and then they put the robot through its paces in a virtual world. Results showed that the FRL-based technique improved the robot’s navigation and collision avoidance performance compared to …traditional rule-based approaches. The results of this study indicate that FRL may be a viable technique for enabling autonomous navigation and obstacle avoidance in IoT-based robotics. Show more
Keywords: Fuzzy reinforcement learning, IoT-based robotics, autonomous navigation, collision avoidance, sensor network
DOI: 10.3233/JIFS-233860
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Zhang, Hongli | Wu, Guangyu | Zhao, Dongfang | Chen, Yesheng | Wei, Dou | Liu, Shulin | Jiang, Lunchang
Article Type: Research Article
Abstract: Mechanical fault diagnosis is currently a highly trending topic, facing two significant challenges. Firstly, the acquisition of an ample number of fault samples proves to be difficult, thereby limiting access to sufficient data samples. Secondly, intricate and non-mathematically describable associations often exist among different faults. Most algorithms treat fault samples as isolated entities, consequently impacting the accuracy of fault diagnosis. This paper proposes a novel machine learning framework called Domain Graph Attention Neural Network (DGAT), which leverages the topological structure of graphs to effectively capture the interrelationships among fault samples. Additionally, this framework incorporates domain information during node updates …to obtain richer embeddings, particularly in scenarios with limited available samples. It effectively overcomes the fixed receptive field limitation of the original Graph Attention Network (GAT). In order to validate the effectiveness of the model, we conducted extensive comparative experiments on diverse datasets, which demonstrated the superior performance of the proposed model. Show more
Keywords: Classification, graph attention neural network, small-sample, mechanical fault diagnosis
DOI: 10.3233/JIFS-234042
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Article Type: Research Article
Abstract: Compared with large enterprises, the development scale and organizational structure of small and medium-sized enterprises are insufficient, which brings certain limitations to the development of small and medium-sized enterprises in China. In order to promote the long-term development of small and medium-sized enterprises in the new era, it is necessary to require enterprise leaders to innovate marketing plans, strengthen risk management of enterprises, and enhance their strength in market competition. The market risk evaluation of small and medium sized enterprises (SMSEs) in the new era is a multiple-attribute decision-making (MADM). The IVIFSs are employed as the tool for portraying uncertain …information during the market risk evaluation of SMSEs in the new era. In this paper, the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive power geometric (IVIFHIPG) technique is addressed based on IVIF Hamacher interactive weighted geometric (IVIFHIWG) technique and power geometric (PG) technique. Some properties of IVIFHIPG technique were addressed. Then, the IVIFHIPG technique is employed to manage MADM under IVIFSs. Finally, an example for market risk evaluation of SMSEs in the new era is employed to verify the IVIFHIPG technique. Thus, the main contributions of this paper are addressed: (1) the IVIFHIPG technique is addressed based on IVIFHIWG technique and PG technique; (2) the IVIFHIPG technique is came up with to manage the MADM under IVIFSs; (3) a numerical example for market risk evaluation of SMSEs in the new era has been came up with to show the IVIFHIPG technique; and (4) some comparative analysis is addressed to verify the I IVIFHIPG technique. Show more
Keywords: Multiple-attribute decision-making (MADM), IVIF sets (IVIFSs), IVIFHIPG technique, market risk evaluation
DOI: 10.3233/JIFS-238763
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Yadav, Ravindra Kumar | Bhadoria, Vikas Singh | Hrisheekesha, P.N.
Article Type: Research Article
Abstract: The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous …for the power industry’s pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer’s cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context. Show more
Keywords: Demand side management, ant colony optimization, solar photovoltaic energy, utility peak load, consumer’s cost of energy
DOI: 10.3233/JIFS-234281
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Zhan, Huawei | Li, Junjie | Wei, Gaoyong | Han, Chengju
Article Type: Research Article
Abstract: Aiming at the existing UAV fire detection system with low small target detection accuracy, a high leakage rate, and a slow rate, an improved YOLOv5 UAV flame detection algorithm is proposed. First, the anchor box clustering is optimized using the K-mean++algorithm to reduce the classification error rate. Second, the original backbone network is enhanced with the CBAM attention mechanism, which scans the whole globe to obtain the target area with a high weighting proportion and needs to be focused on. Replace the PANet network with the BiFPN network in the neck and introduce jump connections when performing feature fusion, which …can better retain the semantic information of high-level and low-level features. Finally, the α-IoU loss function is added to achieve the regression accuracy of different levels of the bounding box by modulating α, which improves the detection accuracy of small datasets and the robustness to noise. According to the experimental results, using a randomly segmented dataset, the modified YOLOv5 algorithm obtains a mAP value of 80.2%, which is 6.7% higher than the original YOLOv5 method, while maintaining an FPS of 64 frames per second. The method helps to improve the accuracy of UAVs for fire monitoring, and the performance is better than the existing flame detection algorithms, which meet the requirements of practical applications. Show more
Keywords: YOLOv5, feature fusion, CBAM, unmanned aerial vehicle (UAV), α-IoU
DOI: 10.3233/JIFS-236836
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Luan, Fei | Tang, Biao | Li, Ye | Liu, Shi Qiang | Yang, Xueqin | Masoud, Mahmoud | Feng, Baoyu
Article Type: Research Article
Abstract: As environmental contamination becomes more and more severe, enterprises need to consider optimizing environmental criteria while optimizing production criteria. In this study, a multi-objective green flexible job shop scheduling problem (MO_GFJSP) is established with two objective functions: the makespan and the carbon emission. To effectively solve the MO_GFJSP, an improved chimp optimization algorithm (IChOA) is designed. The proposed IChOA has four main innovative aspects: 1) the fast non-dominated sorting (FDS) method is introduced to compare the individuals with multiple objectives and strengthen the solution accuracy. 2) a dynamic convergence factor (DCF) is introduced to strengthen the capabilities of exploration and …exploitation. 3) the position weight (PW) is used in the individual position updating to enhance the search efficiency. 4) the variable neighborhood search (VNS) is developed to strengthen the capacity to escape the local optimum. By executing abundant experiments using 20 benchmark instances, it was demonstrated that the developed IChOA is efficient to solve the MO_GFJSP and effective for reducing carbon emission in the flexible job shop. Show more
Keywords: Multi-objective green flexible job shop scheduling, meta-heuristics, improved chimp optimization algorithm, variable neighborhood search
DOI: 10.3233/JIFS-236157
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Salem, Dina Ahmed | Hassan, Nesma AbdelAziz | Hamdy, Razan Mohamed
Article Type: Research Article
Abstract: Smart farming, also known as precision agriculture or digital farming, is an innovative approach to agriculture that utilizes advanced technologies and data-driven techniques to optimize various aspects of farming operations. One smart farming activity, fruit classification, has broad applications and impacts across agriculture, food production, health, research, and environmental conservation. Accurate and reliable fruit classification benefits various stakeholders, from farmers and food producers to consumers and conservationists. In this study, we conduct a comprehensive comparative analysis to assess the performance of a Convolutional Neural Network (CNN) model in conjunction with four transfer learning models: VGG16, ResNet50, MobileNet-V2, and EfficientNet-B0. Models …are trained once on a benchmark dataset called Fruits360 and another time on a reduced version of it to study the effect of data size and image processing on fruit classification performance. The original dataset reported accuracy scores of 95%, 93%, 99.8%, 65%, and 92.6% for these models, respectively. While accuracy increased when trained on the reduced dataset for three of the employed models. This study provides valuable insights into the performance of various deep learning models and dataset versions, offering guidance on model selection and data preprocessing strategies for image classification tasks. Show more
Keywords: Artificial intelligence, convolutional neural network, Fruit360, machine learning, transfer learning
DOI: 10.3233/JIFS-233514
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Qiao, Junfeng | Peng, Lin | Zhou, Aihua | Pan, Sen | Yang, Pei | Xu, Min | Shen, Xiaofeng | Chen, Jingde | Gu, Hua
Article Type: Research Article
Abstract: This paper proposes a method of beforehand prediction of electric equipment faults based on chain-linked recurrent neural network algorithm, which takes the operating parameters of power equipment and other relevant environmental factors as inputs, and takes the fault characteristics as output judgment marks, and constructs a machine learning training model to realize the prediction of power equipment faults. The neural network algorithm adopted in this paper adopts a tree structure. Each sub-node can transfer information with its multiple superior nodes, so that the correlation between the data of the front and back nodes can be obtained, which meets the needs …of the equipment fault prediction model. Considering that the occurrence of power transformer faults is sudden and greatly affected by changes in the surrounding environment, the input of prediction algorithms should consider more environmental factors. This method takes the historical data of various parameters including meteorological phenomena, geography data, and temperature of adjacent equipment and facilities as the training sample set, improves the learning model, gives the trend curve of each index, and gives a prompt at its threshold to ensure the prediction accuracy and give the index prediction. Show more
Keywords: Recurrent neural network, power equipment fault prediction, index trend curve, fault feature sample set, power supply reliability
DOI: 10.3233/JIFS-236459
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Achich, Nassira | Ghorbel, Fatma | Hamdi, Façal | Métais, Elisabeth | Gargouri, Faiez
Article Type: Research Article
Abstract: Dealing with temporal data imperfection is a crucial issue in several application domains. In fact, failure to handle these imperfections can have significant consequences and lead to incorrect analysis and decision-making. This is particularly true when handling imperfect temporal data inputs in applications for Alzheimer’s patients as a real example. In this context, there is a need for a global ontology that provides a semantic representation of temporal data imperfection. In the literature, there is a big number of ontologies that represent data. Some represent only perfect temporal data. Some others represent imperfect data but not temporal ones. To the …best of our knowledge, there is no ontology that represents temporal data imperfection. In this paper, we represent “TimeOntoImperfection”, a usable global ontology that represents four types of imperfection: imprecision, uncertainty, both uncertainty and imprecision and conflict. We describe the structure of “TimeOntoImperfection”, then we conduct a case a study in which we illustrate the usefulness of our ontology. Finally, we introduce the validation part in the context of CAPTAIN MEMO - an ontology based memory prothesis dedicated to alzheimer patients- and we discuss the encouraging results derived from the evaluation step. Show more
Keywords: Ontology, temporal data imperfection, temporal reasoning, uncertainty, imprecision, conflict, possibilistic ontology, fuzzy ontology, probabilistic ontology, probabilistic ontology
DOI: 10.3233/JIFS-237693
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Sharma, Amit | Naga Raju, M. | Hema, P. | Chaitanuya, Morsa | Jagannatha Reddy, M.V. | Vignesh, T. | Chandanan, Amit Kumar | Verma, Santhosh
Article Type: Research Article
Abstract: Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their wide range of applications, such as environmental monitoring, smart agriculture, and structural health monitoring. With the increasing volume of data generated by WSNs, efficient data analytics techniques are crucial for improving the overall performance and reducing energy consumption. This paper presents a novel distributed data analytics approach for WSNs using fuzzy logic-based machine learning. The proposed method combines the advantages of fuzzy logic for handling uncertainty and imprecision with the adaptability of machine learning techniques. It enables sensor nodes to process and analyze data locally, reducing …the need for data transmission and consequently saving energy. Furthermore, this approach enhances data accuracy and fault tolerance by incorporating the fusion of heterogeneous sensor data. The proposed technique is evaluated on a series of real-world and synthetic datasets, demonstrating its effectiveness in improving the overall network performance, energy efficiency, and fault tolerance. The results indicate the potential of our approach to be applied in various WSN applications that demand low-energy consumption and reliable data analysis. Show more
Keywords: Wireless sensor networks, distributed data analytics, fuzzy logic, machine learning, energy efficiency
DOI: 10.3233/JIFS-234007
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Kumar, Manoj | Sharma, Sukhwinder | Mittal, Puneet | Singh, Harmandeep | Singh, Sukhwinder
Article Type: Research Article
Abstract: The rapid expansion of Internet of Things (IoT) applications and the increasing complexity of Wireless Sensor Networks (WSNs) have created a critical need for efficient load balancing strategies. This paper proposes a dynamic load balancing approach for IoT-enabled WSNs using a fuzzy logic-based control mechanism. The proposed method aims to optimize energy consumption, reduce latency, and enhance network lifetime by intelligently distributing the workload among sensor nodes. The fuzzy logic controller takes into account various parameters, such as energy levels, communication distances, and node density, to make adaptive load balancing decisions. The control mechanism allocates tasks to the most suitable …nodes, ensuring efficient utilization of resources and preventing overloading of individual nodes. Simulations are conducted in diverse network scenarios to validate the performance of the proposed approach. Results demonstrate significant improvements in energy efficiency, latency reduction, and overall network lifetime compared to traditional load balancing techniques. The fuzzy logic-based control mechanism proves to be a promising solution for addressing the dynamic and resource-constrained nature of IoT-enabled WSNs, paving the way for more robust and resilient networks in various IoT applications. Show more
Keywords: IoT, Wireless Sensor Networks, load balancing, fuzzy logic, network lifetime
DOI: 10.3233/JIFS-234075
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Aljanabi, Abdulqadir Rahomee Ahmed | Ghafour, Karzan Mahdi
Article Type: Research Article
Abstract: Buying decisions are influenced by a variety of factors that can give rise to impulsive, unplanned, or even irrational purchases. Research has examined the motivational factors that foster organic food consumption, but no study has explored the relative weights of these factors and whether their effects vary depending on the type of food. This study adopted the cognitive-affective perspective to examine the antecedents of online impulsive buying of organic food using a sample of 452 consumers living in Baghdad, Iraq. The fuzzy AHP and fuzzy TOPSIS methods were used to rank five organic food alternatives. The results revealed that the …effects of cognitive factors on organic food purchases differ from those of affective factors. Show more
Keywords: Impulsive buying behaviour, AHP, fuzzy TOPSIS, multi-criteria decision-making, organic food
DOI: 10.3233/JIFS-237400
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
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
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