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

Authors: López-Jasso, Edgar | Felipe-Riverón, Edgardo M. | Valdez-Rodríguez, José E.

Article Type: Research Article

Abstract:  This study underscores the crucial role of image preprocessing in enhancing the outcomes of multimodal image registration tasks using scale-invariant feature selection. The primary focus is on registering two types of retinal images, assessing a methodology’s performance on a set of retinal image pairs, including those with and without microaneurysms. Each pair comprises a color optical image and a gray-level fluorescein image, presenting distinct characteristics and captured under varying conditions. The SIFT methodology, encompassing five stages, with preprocessing as the initial and pivotal stage, is employed for image registration. Out of 35 test retina image pairs, 33 (94.28%) were successfully …registered, with the inability to extract features hindering automatic registration in the remaining pairs. Among the registered pairs, 42.42% were retinal images without microaneurysms, and 57.57% had microaneurysms. Instead of simultaneous registration of all channels, independent registration of preprocessed images in each channel proved more effective. The study concludes with an analysis of the fifth registration’s resulting image to detect abnormalities or pathologies, highlighting the challenges encountered in registering blue channel images due to high intrinsic noise. Show more

Keywords: Image SIFT registration, microaneurysms counting, retina image analysis, multimodal registration, image processing

DOI: 10.3233/JIFS-219374

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Wei, Xiao | Lin, Yidian

Article Type: Research Article

Abstract: Legal judgment prediction(LJP) has achieved remarkable results. However, existing methods still face problems such as difficulties in obtaining key feature words for charges, which impose limitations on the improvement of prediction results. To this end, we propose a legal judgment prediction model with legal feature Word subgraph Label-Embedding and Dual-knowledge Distillation(WLEDD). Compared with traditional methods, our method has two contributions: (1) To mitigate the impact of overly sparse tail class data and high similarity text representations, we capture the critical features related to the charges by fusing LDA and legal feature word subgraphs. Then we encode them as label information …to obtain highly distinguished representations of legal documents. (2) To solve the problem of high difficulty in some subtasks in LJP, we perform subtask-oriented compression of models to construct a student model with lower complexity and higher accuracy through dual knowledge distillation. Moreover, we exploit the logical association between the subtasks to constrain the labels of articles by charge prediction results. It greatly reduces the difficulty of article prediction. Experimental results on four datasets show that our approach significantly outperforms the baseline models. Compared with the state-of-art method, the F1 value of WLEDD for charge prediction has increased by an average of 2.57% . For article prediction, the F1 value has increased by an average of 1.09% . In addition, we demonstrate its effectiveness through ablation experiments and analytical experiments. Show more

Keywords: Legal judgment prediction, knowledge distillation, label embedding, legal text mining

DOI: 10.3233/JIFS-237323

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Yang, Hong | Wang, Lina

Article Type: Research Article

Abstract: The paper focuses on how to improve the prediction accuracy of time series and the interpretability of prediction results. First, a novel Prophet model based on Gaussian linear fuzzy approximate representation (GF-Prophet) is proposed for long-term prediction, which uniformly predicts the data with consistent trend characteristics. By taking Gaussian linear fuzzy information granules as inputs and outputs, GF-Prophet predicts with significantly smaller cumulative error. Second, noticing that trend extraction affects prediction accuracy seriously, a novel granulation modification algorithm is proposed to merge adjacent information granules that do not have significant differences. This is the first attempt to establish Prophet based …on fuzzy information granules to predict trend characteristics. Experiments on public datasets show that the introduction of Gaussian linear fuzzy information granules significantly improves prediction performance of traditional Prophet model. Compared with other classical models, GF-Prophet has not only higher prediction accuracy, but also better interpretability, which can clearly give the change information, fluctuation amplitude and duration of a certain trend in the future that investors actually pay attention to. Show more

Keywords: Fuzzy number, gaussian linear fuzzy information granule, the prophet model, long-term prediction

DOI: 10.3233/JIFS-230313

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Borse, Rushikesh | Das, Rochishnu | Dash, Devasish | Yadav, Akshay

Article Type: Research Article

Abstract: In the wake of the contemporary competitive business landscape, the retention of employees has become one of the most important yet difficult tasks for any corporate. Retaining top-performing employees not only improves organizational performance but also reduces recruitment costs. In this study, the authors investigate the major drivers leading to employee attrition and using machine learning algorithms implemented on a well proven and validated IBM HR data set. Although the data set tags the samples for a target variable (attrited and non-attrited), the work presented in this paper comes up with another labelling (1. likely to leave, 2. On the …verge of leaving, 3. will stay). The data set is evaluated over top 10 Machine learning algorithms and a competitive analysis is made between them based on various factors. The best model has shown a prediction accuracy of over 85% +. Managers are provided with insights and recommendations at the end that will help companies to proactively identify at-risk employees and implement effective retention strategies. Show more

Keywords: Employee attrition, machine learning, early detection of attrition, artificial neural network

DOI: 10.3233/JIFS-219410

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024

Authors: Senthamil Selvi, M. | Senthamizh Selvi, R. | Subbaiyan, Saranya | Murshitha Shajahan, M.S.

Article Type: Research Article

Abstract: Accurate prediction of grid loss in power distribution networks is pivotal for efficient energy management and pricing strategies. Traditional forecasting approaches often struggle to capture the complex temporal dynamics and external influences inherent in grid loss data. In response, this research presents a novel hybrid time-series deep learning model: Gated Recurrent Units with Temporal Convolutional Networks (GRU-TCN), designed to enhance grid loss prediction accuracy. The proposed model integrates the temporal sensitivity of GRU with the local context awareness of TCN, exploiting their complementary strengths. A learnable attention mechanism fuses the outputs of both architectures, enabling the model to discern significant …features for accurate prediction. The model is evaluated using well-established metrics across distinct temporal phases: training, testing, and future projection. Results showcase Resulting in encouraging Figures for mean absolute error, root mean squared error, and mean absolute percentage error, the model’s capacity to capture both long-term trends and transitory patterns. The GRU-TCN hybrid model represents a pioneering approach to power grid loss prediction, offering a flexible and precise tool for energy management. This research not only advances predictive accuracy but also lays the foundation for a smarter and more sustainable energy ecosystem, poised to transform the landscape of energy forecasting. Show more

Keywords: Accurate prediction, grid loss, power distribution networks

DOI: 10.3233/JIFS-235579

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Singh, Pardeep | Lamsal, Rabindra | Singh, Monika | Shishodia, Bhawna | Sitaula, Chiranjibi | Chand, Satish

Article Type: Research Article

Abstract:  Social media platforms play a crucial role in providing valuable information during crises, such as pandemics. The COVID-19 pandemic has created a global public health crisis, and vaccines are the key preventive measure for achieving herd immunity. However, some individuals use social media to oppose vaccines, undermining government efforts to eliminate the virus. This study introduces the “GeoCovaxTweets” dataset, consisting of 1.8 million geotagged tweets related to COVID-19 vaccines from January 2020 to November 2022, originating from 233 countries and territories. Each tweet includes state and country information, enabling researchers to analyze global spatial and temporal patterns. An extensive set …of analyses are performed on the dataset to identify prominent topic clusters and explore public opinions across different vaccines and vaccination contexts. The study outlines the dataset curation methodology and provides instructions for local reproduction. We anticipate that the dataset will be valuable for crisis computing researchers, facilitating the exploration of Twitter conversations surrounding COVID-19 vaccines and vaccination, including trends, opinion shifts, misinformation, and anti-vaccination campaigns. Show more

Keywords: COVID-19 discourse, COVID-19 pandemic, sentiment analysis, social media, topic clustering, twitter dataset

DOI: 10.3233/JIFS-219418

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Tripathi, Diwakar | Reddy, B. Ramachandra | Dwivedi, Shubhra | Shukla, Alok Kumar | Chandramohan, D. | Dewangan, Ram Kishan

Article Type: Research Article

Abstract: Nature-inspired algorithms as problem-solving methodologies are extremely effective in discovery of optimized solutions in multi-dimensional and multi-modal problems. Because of qualities like “self-optimization”, “flexibility” and etc., nature-inspired algorithms for problem solving are effectively optimal. Feature selection is an approach to find approximate optimal subset of the features which are more relevant towards the particular outcome. In this study, we focused on how feature selection may improve the credit scoring model’s performance for prediction. Nature-inspired algorithms are applied for feature selection to improve the predictive performance of the credit scoring model. Additionally, four benchmark credit scoring datasets collected from the UCI …repository are used to test feature selection by several Nature-inspired algorithms aggregated with “Random Forest (RF)”, “Logistic Regression (LR),” and “Multi-layer Perceptron (MLP)” for classification and results are compared in terms of classification accuracy and G-measures. Show more

Keywords: Nature-inspired algorithms, credit score, feature selection, classification

DOI: 10.3233/JIFS-219413

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Faraz, Ansar Ali | Khan, Hina | Aslam, Muhammad | Albassam, Mohammed

Article Type: Research Article

Abstract: When data are hazy or uncertain, estimators given under classical statistics are ineffective. Given that it deals with uncertainty, neutrosophic statistics is the sole alternative. Due to the vast range of applications, extensive research has been done in this area. The objective of this study is to determine the most accurate predictions for the population mean with the least amount of mean square error. We have created neutrosophic ratio type estimators, when working with ambiguous, hazy, and neutrosophic-type data, the proposed estimation methods are very useful for computing results. These estimators produce findings that are not single-valued but rather have …an interval form, where our population parameter may lie more frequently. Since we have an estimated interval with the unknown population mean value given a minimal mean square error, it improves the estimators’ efficiency. Real life neutrosophic line losses data and simulation are both used to analyze the effectiveness of the proposed neutrosophic ratio-type estimators. Additionally, a comparison is made to show how helpful Neutrosophic ratio type estimator is in comparison to existing estimators. Show more

Keywords: Neutrosophic, conventional statistics, estimation, ratio estimators, mean square error

DOI: 10.3233/JIFS-240153

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Saravanan, Krithikha Sanju | Bhagavathiappan, Velammal

Article Type: Research Article

Abstract: The advancements in technology, particularly in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) can be advantageous for the agricultural sector to enhance the yield. Establishing an agricultural ontology as part of the development would spur the expansion of cross-domain agriculture. Semantic and syntactic knowledge of the domain data is required for building such a domain-based ontology. To process the data from text documents, a standard technique with syntactic and semantic features are needed because the availability of pre-determined agricultural domain-based data is insufficient. In this research work, an Agricultural Ontologies Construction framework (AOC) is proposed for …creating the agricultural domain ontology from text documents using NLP techniques with Robustly Optimized BERT Approach (RoBERTa) model and Graph Convolutional Network (GCN). The anaphora present in the documents are resolved to produce precise ontology from the input data. In the proposed AOC work, the domain terms are extracted using the RoBERTa model with Regular Expressions (RE) and the relationships between the domain terms are retrieved by utilizing the GCN with RE. When compared to other current systems, the efficacy of the proposed AOC method achieves an exceptional result, with precision and recall of 99.6% and 99.1% respectively. Show more

Keywords: Anaphora resolution, term extraction, relationships identification, RoBERTa model, regular expressions, graph convolutional network, domain ontology

DOI: 10.3233/JIFS-237632

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024

Authors: Jansi Rani, J. | Manivannan, A.

Article Type: Research Article

Abstract: This paper focuses on solving the fully fuzzy transportation problem in which the parameters are triangular Type-2 fuzzy numbers due to the instinctive of human imprecision. To deal with uncertainty more precisely, a triangular Type-1 fuzzy transportation problem is reformed as a transportation problem with triangular Type-2 fuzzy parameters in this paper. In order to compare triangular Type-2 fuzzy numbers, a new ranking(ordering) technique is proposed by extending the Yager’s function. However, two efficient algorithmic approaches namely, triangular Type-2 fuzzy zero suffix method (TT2FZSM) and triangular Type-2 fuzzy zero average method (TT2FZAM) are proposed to generate the initial transportation cost …of the fully triangular Type-2 fuzzy transportation problem. Both TT2FZSM and TT2FZAM are converging towards an optimal solution. In addition to TT2FZSM and TT2FZAM, the modified distribution method is applied to ensure optimality. Subsequently, we carry out a comprehensive discussion of the obtained results to establish the validation of the proposed approach. Show more

Keywords: Transportation problem, triangular type-2 fuzzy number, ranking function, optimal solution

DOI: 10.3233/JIFS-237652

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Yan, Huiming | Yan, Zilin | Wang, Weiling | Liu, Shuyue

Article Type: Research Article

Abstract: In recent years, the burgeoning imperative of energy-efficient building management practices has surged dramatically, underscoring an urgent mandate for comprehensive studies that integrate cutting-edge optimization algorithms with precise heating load forecasting techniques. These studies are not merely endeavors; they represent concerted efforts to increase building energy efficiency and address mounting concerns regarding sustainability and resource utilization. In the intricate domain of heating, ventilation, and air conditioning (HVAC) systems, energy optimization challenges are being meticulously confronted through rigorous exploration and the application of innovative problem-solving methodologies. This pioneering study introduces groundbreaking methodologies by seamlessly integrating two state-of-the-art optimization algorithms— the Red …Fox Optimization and the Golden Eagle Optimizer— with the Decision Tree model. This fusion is aimed at enhancing the accuracy of heating load predictions and streamlining HVAC system optimization processes, marking a significant leap toward achieving heightened energy efficiency and operational efficacy in building management practices. The study emphasizes the significance of precise heating load prediction in advancing energy efficiency, realizing cost savings, and fostering environmental sustainability in building management. Furthermore, it delves into the multifaceted impact of various building features on heating load, encompassing variables such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. These insights furnish actionable intelligence for refined decision-making processes in both building design and operation. Based on the results, the DT single model experienced the weakest performance among the three models, with R 2  = 0.975 and RMSE  = 1.608. The model DTFO (DT + FOX) achieves an extraordinary R 2 value of 0.996 and RMSE value of 0.961 for heating load prediction, surpassing the performance benchmarks set by other models. This achievement holds considerable promise for aiding engineers in crafting energy-efficient buildings, particularly within the swiftly evolving landscape of smart home technologies. Show more

Keywords: Decision tree, heating load, red fox optimization, golden eagle optimizer

DOI: 10.3233/JIFS-240283

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Sriraam, Natarajan | Chinta, Babu | Suresh, Seshadhri | Sudharshan, Suresh

Article Type: Research Article

Abstract: Assessing fetal growth and development requires accurate identification of the fetal area contour and measurement of the Crown-Rump Length (CRL). In this paper, we presented a unique method for autonomously segmenting the fetal region in ultrasound images and calculating the CRL based on the U-Net architecture. Because of its capacity to capture both global and local information, the U-Net model is a popular choice for image segmentation tasks. Our method employs the U-Net model to extract the fetal region contour and measure the CRL, resulting in a dependable and efficient prenatal evaluation solution.

Keywords: Fetal, segmentation, U-Net, ultrasound image

DOI: 10.3233/JIFS-219403

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024

Authors: Macias, Cesar | Soto, Miguel | Cardoso-Moreno, Marco A. | Calvo, Hiram

Article Type: Research Article

Abstract: Mental and cognitive well-being is of paramount significance for human beings. Consequently, the early detection of issues that may culminate in conditions such as depression holds great importance in averting adverse outcomes for individuals. Depression, a prevalent mental health disorder, can severely impact an individual’s quality of life. Timely identification and intervention are critical to prevent its progression. Our research delves into the application of Machine Learning (ML) and Deep Learning (DL) techniques to potentially facilitate the early recognition of depressive tendencies. By leveraging the cognitive triad theory, which encapsulates negative self-perception, a pessimistic outlook on the world, and a …bleak vision of the future, we aim to develop predictive models that can assist in identifying individuals at risk. In this regard, we selected The Cognitive Triad Dataset, which takes into account six different categories that encapsulate negative and positive postures about three different contexts: self context, future context and world context. Our proposal achieved great performance, by relying on a strict preprocessing analysis, which led to the models obtaining an accuracy value of 0.97 when classifying aspect contexts; 0.95 when classifying sentiment-aspects; and a value of 0.93 in accuracy was achieved under the aspect-sentiment paradigm. Our models outperformed those reported in the literature. Show more

Keywords: Cognitive triad inventory, depression detection, machine learning, deep learning, natural language processing

DOI: 10.3233/JIFS-219333

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Mundada, Shyamal | Jain, Pooja | Kumar, Nirmal

Article Type: Research Article

Abstract: Sustainable agriculture revolves around soil organic carbon (SOC), which is essential for numerous soil functions and ecological attributes. Farmers are interested in conserving and adding additional soil organic carbon to certain fields in order to improve soil health and productivity. The relationship between soil and environment that has been discovered and standardized throughout time has enhanced the progress of digital soil-mapping techniques; therefore, a variety of machine learning techniques are used to predict soil properties. Studies are thriving at how effectively each machine learning method maps and predicts SOC, especially at high spatial resolutions. To predict SOC of soil at …a 30 m resolution, four machine learning models—Random Forest, Support Vector Machine, Adaptive Boosting, and k-Nearest Neighbour were used. For model evaluation, two error metrics, namely R2 and RMSE have been used. The findings demonstrated that the calibration and validation sets’ descriptive statistics sufficiently resembled the entire set of data. The range of the calculated SOC content was 0.06 to 1.76 %. According to the findings of the study, Random Forest showed good results for both cases, i.e. evaluation using cross validation and without cross validation. Using cross validation, RF confirmed highest R2 as 0.5278 and lowest RMSE as 0.1683 for calibration dataset while without cross validation it showed R2 as 0.8612 and lowest RMSE as 0.0912 for calibration dataset. The generated soil maps will help farmers adopt precise knowledge for decisions that will increase farm productivity and provide food security through the sustainable use of nutrients and the agricultural environment. Show more

Keywords: Machine learning, remote sensing data, digital soil mapping, spatial predictions, precision farming

DOI: 10.3233/JIFS-240493

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Zheng, Danjing | Song, Xiaona | Song, Shuai | Peng, Zenglong

Article Type: Research Article

Abstract: This paper investigates an observer-based boundary controller design for interconnected nonlinear partial differential equation (PDE) systems. First, the Takagi–Sugeno (T–S) fuzzy model is adopted to accurately describe the target systems. Then, boundary measurements are employed to reduce the number of sensors. Next, considering the phenomenon of abnormal interference that may lead to measurement outliers and observer parameters’ uncertainties, an outlier-resistant non-fragile observer expressed by a saturation function is designed to guarantee the desired control objectives. Moreover, the boundary control approach is employed to trade-off the cost of system design and system performance. Furthermore, utilizing the membership function-dependent Lyapunov functions and …free-weight matrixes, sufficient conditions ensuring the closed-loop systems’ exponential stability are obtained while decreasing the conservativeness of the system stability analysis. Finally, the proposed method’s feasibility and effectiveness are validated by an example. Show more

Keywords: Boundary measurements, boundary control, interconnected nonlinear partial differential equation systems, membership function-dependent Lyapunov functions, outlier-resistant non-fragile observer

DOI: 10.3233/JIFS-238858

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Reka, S | Karthik Sainadh Reddy, Dwarampudi | Dhiraj, Inti | Suriya Praba, T

Article Type: Research Article

Abstract: Polycystic Ovary Syndrome (PCOS) is a hormonal condition that typically affects female during the time of their reproduction. It is identified by the disruptions in hormonal balance, particularly an increase in levels of androgen (male hormone) in the female body. PCOS can lead to various symptoms and health complications including irregular menstrual cycles, ovarian cysts, fertility issues, insulin resistance, weight gain, acne, and excess hair growth. The real-world PCOS detection is a challenging task whilst PCOS specific cause is unknown and its symptoms are unclear. Thus, accurate and timely diagnosis of PCOS is crucial for effective management and prevention of …long-term complications. In such cases, Machine learning based PCOS prediction model support diagnostic process, address potential errors and time constraints. Machine learning algorithms can analyze large set of patient data, including medical history, hormonal profiles, and imaging results, to assist in the diagnosis of PCOS. In particular, the performance of data analysis chore and prediction model is improved by ensemble feature selection strategies. These methods concentrate on selecting a subset of pertinent features from a broader range of features. The unstable nature of the outcome of feature selection algorithm is a frequent issue in practical applications, when it is applied multiple times on similar dataset or with slight modifications in the data. Thus, evaluating the robustness of feature selection algorithm is most important. To address these issues and quantify the robustness, this study uses Jenson-Shannon divergence, an information theoretic approach with ensemble feature selection method to handle the various findings, such as complete ranking, half ranking and top-k lists (without ranking). Furthermore, this article proposes a hybrid machine learning classifier with SMOTE – SVM for the prompt detection of PCOS and the performance of the model is compared with a number of other individual classifiers including KNN (K-Nearest Neighbour), Support Vector Machine (SVM), AdaBoost, LR –Logistic Regression, NB –Nave Bayes, RF –Random Forest, Decision Tree. The proposed SWISS-AdaBoost classifier surpassed other models with 97.81% of accuracy and AUC of 99.08%. Show more

Keywords: Polycystic ovary syndrome (PCOS), Jenson-shannon divergence, SVM (Support Vector Machine), K-nearest neighbour, logistic regression, decision tree, naive bayes and AdaBoost

DOI: 10.3233/JIFS-219402

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Ezhilarasie, R. | MohanRaj, I. | Ramakrishnan, Thiruvikram Gopichettipalayam | Madhavan, Vyas | Narayan, Keshav | Umamakeswari, A.

Article Type: Research Article

Abstract: Internet of Things (IoT) devices are major stakeholders of contemporary network bandwidth. The proliferation of IoT devices and the demand for latency-free communication in time-critical applications has proven the drawback of cloud-based solutions. Edge computing is an paradigm that reduces the application’s response time by utilizing computation and storage proximate to each devices. Privacy in cloud computing is attained by system virtualization, containerization, among other evolved technologies. As privacy remains a primary concern, there is a need to test the feasibility of resource-constrained edge devices. Hence, this work aimed to examine the usability of such devices in edge computing by …benchmarking on different runtime environments. The results reveal that a standard mechanism was achieved for defining the criteria to identify the suitable edge devices for computation offloading, particularly for a set of smart traffic surveillance use cases. Further, an optimization algorithm was designed to generate an optimum schedule that decides the best device to execute a particular task from the set of suitable edge devices to enhance energy and execution time in a global view. Based on the feasibility study and optimal schedule, a makespan that is nearly 11 times better than local execution for the considered traffic surveillance workflow was achieved. Show more

Keywords: Container, docker, edge computing, IoT, LXC, offloading, single board computer

DOI: 10.3233/JIFS-219424

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Bukya, Hanumanthu | Bhukya, Raghuram | Harshavardhan, A.

Article Type: Research Article

Abstract: Fog computing has several undeniable benefits, such as enhancing near-real-time response, reducing transmission costs, and facilitating IoT analysis. This technology is poised to have a significant impact on businesses, organizations, and our daily lives. However, mobile user equipment struggles to handle the complex computing tasks associated with modern applications due to its limited processing power and battery life. Edge computing has emerged as a solution to this problem by relocating processing to nodes at the network’s periphery, which have more computational capacity. With the rapid evolution of wireless technologies and infrastructure, edge computing has become increasingly popular. Nevertheless, managing fog …computing resources remains challenging due to resource constraints, heterogeneity, and distant nodes. For delay-sensitive intelligent IoT applications within the fog computing architecture, cooperation and communication processing resources in 6 G and future networks are essential. This study proposes a joint computational and optimized resource allocation (JCORA) technique to accelerate the processing of data from intelligent IoT sensors in a cell association environment. The proposed technique utilizes an uplink and downlink power allocation factor and the shortest job first (SJF) task scheduling system to optimize user fairness and decrease data processing time. This is a complex assignment due to several non-convex limitations. The suggested JCORA-SJF model simultaneously optimizes time partitioning, computing task processing mode selection, and target sensing location selection to maximize the weighted total of task processing and communication performance. The simulation results demonstrate the effectiveness of the proposed JCORA-SJF algorithms, and the system’s scalability is also examined. Show more

Keywords: Fog computing, Internet of Things (IoT), resource allocation, edge computing networks, optimized resource allocation (JCORA), shortest job first (SJF)

DOI: 10.3233/JIFS-219421

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Singh, Pardeep | Singh, Monika | Singh, Nitin Kumar | Das, Prativa | Chand, Satish

Article Type: Research Article

Abstract:  Social media platforms play vital roles in disseminating information during crisis situations. Many rescue agencies, media outlets, and volunteers regularly monitor this data to identify and analyze disasters, ultimately mitigating life risks. However, effectively categorizing these messages based on information types is crucial for enhancing the situational awareness of emergency responders. This paper addresses the challenge of analyzing informal crisis-related social media texts by classifying disaster event tweets into 10 humanitarian categories associated with 19 major natural disaster events. We fine-tune seven state-of-the-art pre-trained transformer models and compare their performance with the recently introduced domain-specific models, i.e., CrisisTransformers. We empirically …found that CrisisTransformers outperform seven strong baseline transformer models in classifying disaster-specific tweets from the HumAID dataset, achieving a macro-averaged F1 score of 0.77. Our work contributes to the crisis computing field by improving the classification of disaster-related tweets and enhancing the capabilities of emergency responders and disaster management organizations. Show more

Keywords: Transformers, crisis computing, disaster classification, Twitter, disaster response

DOI: 10.3233/JIFS-219419

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Muppavarapu, Vamsee | Ramesh, Gowtham

Article Type: Research Article

Abstract: The W3C linked building data group is working on modeling the information for integrating building information with building life cycle data using Semantic Web technologies. The community has proposed a set of semantic models such as ifcOWL and Building Topology Ontology (BOT), to model various applications across Architecture, Engineering, Construction, and Operation (AECO) domain. On the other hand, the Semantic Web of Things (SWoT) group proposed standard semantic models such as M3-lite and BOSH ontologies for describing the sensor networks, observations, and sensor measurements. Both the aforementioned domains have their own siloed applications and with the evolution of the smart …home domain, there is a need to combine the knowledge of building information with the sensor knowledge to develop cross-domain applications. However, in order to develop such downstream applications leveraging advantages from both domains requires interoperable knowledge. This paper proposes an interoperable ontology, Building Topology Ontology for Smart Homes (BOTSH), with the aim of aligning the building domain with sensors domain semantic models. The BOTSH ontology facilitates capturing knowledge from both domains and helps in developing cross-domain applications. The potential of the proposed model was demonstrated using a real-life building model based on the competency questions framed by the domain experts. Show more

Keywords: Semantic web of things, building information models, building topology, sensors and observations, smart homes, knowledge graphs, semantic applications

DOI: 10.3233/JIFS-219425

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Pillai, Leena G. | Muhammad Noorul Mubarak, D. | Sherly, Elizabeth

Article Type: Research Article

Abstract: Speech production is a complex sequential process which involve the coordination of various articulatory features. Among them tongue being a highly versatile active articulator responsible for shaping airflow to produce targeted speech sounds that are intellectual, clear, and distinct. This paper presents a novel approach for predicting tongue and lip articulatory features involved in a given speech acoustics using a stacked Bidirectional Long Short-Term Memory (BiLSTM) architecture, combined with a one-dimensional Convolutional Neural Network (CNN) for post-processing with fixed weights initialization. The proposed network is trained with two datasets consisting of simultaneously recorded speech and Electromagnetic Articulography (EMA) datasets, each …introducing variations in terms of geographical origin, linguistic characteristics, phonetic diversity, and recording equipment. The performance of the model is assessed in Speaker Dependent (SD), Speaker Independent (SI), corpus dependent (CD) and cross corpus (CC) modes. Experimental results indicate that the proposed model with fixed weights approach outperformed the adaptive weights initialization with in relatively minimal number of training epochs. These findings contribute to the development of robust and efficient models for articulatory feature prediction, paving the way for advancements in speech production research and applications. Show more

Keywords: Acoustic-to-articulatory inversion, smoothing techniques, articulatory features, weight initialization, bidirectional long short-term memory

DOI: 10.3233/JIFS-219386

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Sheshadri, Shailashree K. | Gupta, Deepa

Article Type: Research Article

Abstract: Non-Autoregressive Machine Translation (NAT) represents a groundbreaking advancement in Machine Translation, enabling the simultaneous prediction of output tokens and significantly boosting translation speeds compared to traditional auto-regressive (AR) models. Recent NAT models have adeptly balanced translation quality and speed, surpassing their AR counterparts. The widely employed Knowledge Distillation (KD) technique in NAT involves generating training data from pre-trained AR models, enhancing NAT model performance. While KD has consistently proven its empirical effectiveness and substantial accuracy gains in NAT models, its potential within Indic languages has yet to be explored. This study pioneers the evaluation of NAT model performance for Indic …languages, focusing mainly on Kashmiri to English translation. Our exploration encompasses varying encoder and decoder layers and fine-tuning hyper-parameters, shedding light on the vital role KD plays in facilitating NAT models to capture variations in output data effectively. Our NAT models, enhanced with KD, exhibit sacreBLEU scores ranging from 16.20 to 22.20. The Insertion Transformer reaches a SacreBLEU of 22.93, approaching AR model performance. Show more

Keywords: Neural machine translation, auto-regressive translation, non-autoregressive translation, Levenshtein Transformer, insertion transformer, knowledge distillation

DOI: 10.3233/JIFS-219383

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Cruz, Elsy | Santos, Lourdes | Calvo, Hiram | Anzueto-Rios, Álvaro | Villuendas-Rey, Yenny

Article Type: Research Article

Abstract: In recent years, multiple studies have highlighted the growing correlation between breast density and the risk of developing breast cancer. In this research, the performance of two convolutional neural network architectures, VGG16 and VGG19, was evaluated for breast density classification across three distinct scenarios aimed to compare the masking effect on the models performance. These scenarios encompass both binary classification (fatty and dense) and multi-class classification based on the BI-RADS categorization, utilizing a subset of the ABC-Digital Mammography Dataset. In the first experiment, focusing on cases with no masses, VGG16 achieved an accuracy of 93.33% and 90.00% for two and …four-class classification. The second experiment, which involved cases with benign masses, yielded a remarkable accuracy of 95.83% and 93.33% with VGG16, respectively. In the third and last experiment, an accuracy of 88.00% was obtained using VGG16 for the two-class classification, while VGG19 delivered an accuracy of 93.33% for the four-class classification. These findings underscore the potential of deep learning models in enhancing breast density classification, with implications for breast cancer risk assessment and early detection. Show more

Keywords: Mammography, breast tissue density, convolutional neural networks

DOI: 10.3233/JIFS-219378

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024

Authors: Vidhya, S.S. | Mathi, Senthilkumar | Anantha Narayanan, V. | Neelakanta Iyer, Ganesh

Article Type: Research Article

Abstract:  The Internet of Things lies in establishing low-power and lossy networks created by interconnecting many wireless devices with limited resources. Fascinatingly, an IPv6 routing protocol for low-power and lossy networks has become a common practice for these applications. Even though this protocol addresses the challenges of low-power networks, many issues concerning the quality of service and energy consumption are open to the research community. The protocol relies on a destination-oriented directed acyclic graph, and the root selection depends on some constraints and metrics associated with an objective function (OF). The conventional OFs select parents based on a single metric, such …as the expected transmission count or the number of nodes to travel. The current paper proposes an enhancement to the OF metric, aiming to decrease node energy and enhance the quality of service. This improvement is achieved by the factors, including the received signal strength indicator, node distance, power, link quality indicator, and expected transmission count, to select reliable communication links. The minimum power needed for reliable communication is predicted from the received signal strength indicator, node distance, receiver power, and link quality indicator using a nonlinear support vector machine. The OF value of the candidate node is computed from the power level and expected transmission count combined using the Takagi-Sugeno fuzzy model. The proposed OF is implemented in the Cooja simulator and compared against minimum rank with hysteresis OF and OF zero. A considerable improvement in the packet delivery ratio and a 37.5% reduction in energy consumption is obtained. Show more

Keywords: Classification, fuzzification, power prediction, received signal strength indicator, transmission power, link quality indicator, low power networks, TSK fuzzy model

DOI: 10.3233/JIFS-219420

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Mathi, Senthilkumar | Ramalingam, Venkadeshan | Sree Keerthi, Angara Venkata | Abhirup, Kothamasu Ganga | Sreejith, K. | Dharuman, Lavanya

Article Type: Research Article

Abstract: Long-term evolution in wireless broadband communication aims to provide secure communication for users and a high data rate for a fourth-generation network. Even though the fourth-generation network provides security, some loopholes lead to several attacks on the fourth-generation network attacks. The denial-of-service attack occurs when the user communicates with a rogue base station, and the radio base station in fourth-generation long-term evolution networks ensures that the user is attached to the rogue node assigned network. The location leak attack occurs when the packets are sniffed to find any user’s location using its temporary mobile subscriber identity. Prevention of rogue base …station and location leak attacks helps the system achieve secure communication between the participating entities. Earlier works in long-term evolution mobility management do not address preventing attacks such as denial-of-service, rogue base stations and location leaks and suffer from computational costs while providing security features. Hence, the present paper addresses the vulnerability of these attacks. It also investigates how these attacks occur and exposes communication in the fourth-generation network. To mitigate these vulnerabilities, the paper proposes a novel authentication scheme. The proposed scheme is simulated using Network Simulator 3, and the security analysis of the proposed scheme is shown using AVISPA –a security tool. Numerical analysis demonstrates that the proposed scheme significantly reduces communication overhead and computational costs associated with the fourth-generation long-term evolution authentication mechanism. Show more

Keywords: Authentication, long-term evolution, denial-of-service, attack, location leak, confidentiality

DOI: 10.3233/JIFS-219406

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Zheng, Lina | Wang, Yini | Wang, Sichun

Article Type: Research Article

Abstract: Due to the relatively high cost of labeling data, only a fraction of the available data is typically labeled in machine learning. Some existing research handled attribute selection for partially labeled data by using the importance of an attribute subset or uncertainty measure (UM). Nevertheless, it overlooked the missing rate of labels or the choice of the UM with optimal performance. This study uses discernibility relation and the missing rate of labels to UM for partially labeled data and applies it to attribute selection. To begin with, a decision information system for partially labeled data (pl-DIS) can be used to …induce two equivalent decision information systems (DISs): a DIS is constructed for labeled data (l-DIS), and separately, another DIS is constructed for unlabeled data (ul-DIS). Subsequently, a discernibility relation and the percentage of missing labels are introduced. Afterwards, four importance of attribute subset are identified by taking into account the discernibility relation and the missing rate of labels. The sum of their importance, which is determined by the label missing rates of two DISs, is calculated by weighting each of them and adding them together. These four importance may be seen as four UMs. In addition, numerical simulations and statistical analyses are carried out to showcase the effectiveness of four UMs. In the end, as its application for UM, the UM with optimal performance is used to attribute selection for partially labeled data and the corresponding algorithm is proposed. The experimental outcomes demonstrate the excellence of the proposed algorithm. Show more

Keywords: Partially labeled data, pl-DIS, uncertainty measure, attribute selection, the missing rate of labels, discernibility relation

DOI: 10.3233/JIFS-240581

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: Rao, Vishisht Srihari | Vinay, P. | Uma, D.

Article Type: Research Article

Abstract: A hazy image is characterized by atmospheric conditions that reduce the image’s clarity and contrast, thereby making it less visible. This degradation in image quality can hinder the performance of advanced computer vision tasks such as object detection and identifying open spaces which need to perform with high accuracy in important real world applications such as security surveillance and autonomous driving. In the recent past, the use of deep learning in image processing tasks have shown a remarkable improvement in performance, in particular, Convolutional Neural Networks (CNNs) perform superior to any other type of neural network in image related tasks. …In this paper, we propose the addition of Channel Attention and Pixel Attention layers to four state-of-the-art CNNs, namely, GMAN, U-Net, 123-CEDH and DMPHN, used for the task of image dehazing. We show that the addition of these layers yields a non-trivial improvement on the quality of the dehazed images which we show qualitatively with examples and quantitatively by obtaining PSNR and SSIM scores of 28.63 and 0.959 respectively. Through the experiments, we show that the addition of the mentioned attention layers to the GMAN architecture yields the best results. Show more

Keywords: Dehazing, deep neural network, convolutional neural network, attention

DOI: 10.3233/JIFS-219391

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Agrawalla, Bikash | Shukla, Alok Kumar | Tripathi, Diwakar | Singh, Koushlendra Kumar | Ramachandra Reddy, B.

Article Type: Research Article

Abstract: Software fault prediction, which aims to find and fix probable flaws before they appear in real-world settings, is an essential component of software quality assurance. This article provides a thorough analysis of the use of feature ranking algorithms for successful software failure prediction. In order to choose and prioritise the software metrics or qualities most important to fault prediction models, feature ranking approaches are essential. The proposed focus on applying an ensemble feature ranking algorithm to a specific software fault dataset, addressing the challenge posed by the dataset’s high dimensionality. In this extensive study, we examined the effectiveness of multiple …machine learning classifiers on six different software projects: jedit, ivy, prop, xerces, tomcat, and poi, utilising feature selection strategies. In order to evaluate classifier performance under two scenarios—one with the top 10 features and another with the top 15 features—our study sought to determine the most relevant features for each project. SVM consistently performed well across the six datasets, achieving noteworthy results like 98.74% accuracy on “jedit” (top 10 features) and 91.88% on “tomcat” (top 10 features). Random Forest achieving 89.20% accuracy on the top 15 features, on “ivy.” In contrast, NB repeatedly recording the lowest accuracy rates, such as 51.58% on “poi” and 50.45% on “xerces” (the top 15 features). These findings highlight SVM and RF as the top performers, whereas NB was consistently the least successful classifier. The findings suggest that the choice of feature ranking algorithm has a substantial impact on the fault prediction models’ predictive accuracy and effectiveness. When using various ranking systems, the research also analyses the trade-offs between computing complexity and forecast accuracy. Show more

Keywords: Software fault prediction, ensemble techniques, feature ranking, random forests, support vector machine

DOI: 10.3233/JIFS-219431

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Su, Xue | Chen, Lijun

Article Type: Research Article

Abstract: Incomplete real-valued data often misses some labels due to the high cost of labeling data. This paper investigates for partially labeled incomplete real-valued data and considers its application in semi-supervised attribute reduction. There are two decision information systems (DISs) in a partially labeled incomplete real-valued data DIS (p-IRVDIS): a labeled incomplete real-valued data DIS (l-IRVDIS) and a unlabeled incomplete real-valued data DIS (u-IRVDIS). The degree of importance on an attribute subset in a p-IRVDIS are defined using an indistinguishable relation and conditional information entropy. It is the weighted sum of l-IRVDIS and u-IRVDIS using the missing rate of label to …measure p-IRVDIS uncertainty. Based on the degree of importance, an adaptive semi-supervised attribute reduction algorithm in a p-IRVDIS is proposed. This algorithm can automatically adapt to various missing rates of label. The experimental results on 8 datasets reveal that the proposed algorithm performs statistically better than some state-of-the-art algorithms. Show more

Keywords: p-IRVDIS, the degree of importance, semi-supervised attribute reduction, indiscernibility relation, conditional information entropy

DOI: 10.3233/JIFS-239559

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Tahir Kidwai, Umar | Akhtar, Nadeem | Nadeem, Mohammad | Alroobaea, Roobaea Salim

Article Type: Research Article

Abstract: In recent years, the surge in online content has necessitated the development of intelligent recommender systems capable of offering personalized suggestions to users. However, these systems often encapsulate users within a “filter bubble”, limiting their exposure to a narrow range of content. This study introduces a novel approach to address this issue by integrating a novel diversity module into a knowledge graph-based explainable recommender system. Utilizing the Movie Lens 1M dataset, this research pioneers in fostering a more nuanced and transparent user experience, thereby enhancing user trust and broadening the spectrum of recommendations. Looking ahead, we aim to further refine …this system by incorporating an explicit feedback loop and leveraging Natural Language Processing (NLP) techniques to provide users with insightful explanations of recommendations, including a comprehensive analysis of filter bubbles. This initiative marks a significant stride towards creating a more inclusive and informed recommendation landscape, promising users not only a wider array of content but also a deeper understanding of the recommendation mechanisms at play. Show more

Keywords: Recommender system, explainable recommendations, filter bubble, knowledge graph, diversity

DOI: 10.3233/JIFS-219416

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Li, Xin | Hao, Miao | Ru, Changhai | Wang, Yong | Zhu, Junhui

Article Type: Research Article

Abstract: With the development of science and technology, people have higher and higher requirements for robots. The application of robots in industrial production is also increasing, and there are more applications in people’s lives. Therefore, robots must have a better ability to receive and process the external environment. Therefore, visual servo system appears. Pose estimation is a major problem in the current vision system. It has great application value in positioning and navigation, target tracking and recognition, virtual reality and motion estimation. Therefore, this paper put forward the research of robot arm pose estimation and control based on machine vision. This …paper first analyzed the technology of machine vision, and then carried out experiments. The accuracy and stability of the two methods for robot arm pose estimation were compared. The experimental results showed that when the noise of Kalman’s centralized data fusion method was 1 pixel, the maximum error of the X-axis angle was only 0.55, and the average error was 0.02. In Kalman’s distributed data fusion method, the average error of X-axis displacement was 0.06, and the maximum value was 17.66. In terms of accuracy, Kalman’s centralized data fusion method was better. In terms of stability, Kalman’s centralized data fusion method was also better. However, in general, these two methods had very good results, and could accurately control the position and posture of the manipulator. Show more

Keywords: Position and attitude estimation of manipulator, machine vision, kalman filter, world coordinate system

DOI: 10.3233/JIFS-237904

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Wang, Wei | Xu, Dehao | Lv, Jing | Rong, Jian | He, Donggang | Li, Shuangshuang

Article Type: Research Article

Abstract: The factors of water quality in the intensive marine stichopus japonicus aquaculture process are changing with seasons, so water temperature, salinity, pH value and nitrite were selected as auxiliary variables to measure the concentration of ammonia nitrogen. FCM (Fuzzy C-means) algorithm was adopted to classify them. Based on the EM (Expectation Maximization) algorithm, fuzzy sub-models of ammonia nitrogen concentration were constructed around each operating point, and finally the fuzzy sub-models were combined according to the posterior distribution of the characteristics of the sampling data. Based on the data collected at Xinyulong Marine Biological Seed Technology Co., Ltd, in Dalian China, …the ammonia nitrogen concentration prediction model was tested and verified. Show more

Keywords: Water quality, stichopus japonicus, expectation maximization, multi-model

DOI: 10.3233/JIFS-239032

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Shuangyuan, Li | Qichang, Li | Mengfan, Li | Yanchang, Lv

Article Type: Research Article

Abstract: With the development of information technology, the number and methods of cyber attacks continue to increase, making network security issues increasingly important. Intrusion detection has become a vital means of dealing with cyber threats. Current intrusion detection methods predominantly rely on machine learning. However, machine learning suffers from limitations in detection capability and the requirement for extensive feature engineering. Additionally, current intrusion detection datasets face the challenge of data imbalance. To address these challenges, this paper proposes a novel solution leveraging Generative Adversarial Networks (GANs) to balance the dataset and introduces an attention mechanism into the generator to efficiently extract …key feature information, the mechanism can effectively sort the key information of the data and quickly capture important features. Subsequently, a combination of 1D Convolutional Neural Networks (1DCNN) and Bidirectional Gated Recurrent Units (BiGRU) is employed to construct a classification model capable of extracting both spatial and temporal features. Furthermore, Particle Swarm Optimization (PSO) is utilized to optimize the input weights and hidden biases of the model, so as to further improve the accuracy and robustness of the model. Finally, the model is trained and implemented for network intrusion detection. To demonstrate the applicability of the model, experiments were conducted using the NSL-KDD dataset and the UNSW-NB15 dataset. The final results showed that the proposed model outperformed other models, achieving accuracies of 99.15% and 97.33% on the respective datasets. This indicates that the model improves the efficiency of network intrusion detection and better ensures the effectiveness of network security. Show more

Keywords: Intrusion detection, GAN, 1DCNN, BiGRU, PSO

DOI: 10.3233/JIFS-236285

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Liu, Xia | Zhang, Xianyong | Chen, Jiaxin | Chen, Benwei

Article Type: Research Article

Abstract:  Attribute reduction is an important method in data analysis and machine learning, and it usually relies on algebraic and informational measures. However, few existing informational measures have considered the relative information of decision class cardinality, and the fusion application of algebraic and informational measures is also limited, especially in attribute reductions for interval-valued data. In interval-valued decision systems, this paper presents a coverage-credibility-based condition entropy and an improved rough decision entropy, further establishes corresponding attribute reduction algorithms for optimization and applicability. Firstly, the concepts of interval credibility, coverage and coverage-credibility are proposed, and thus, an improved condition entropy is defined …by virtue of the integrated coverage-credibility. Secondly, the fused rough decision entropy is constructed by the fusion of improved condition entropy and roughness degree. By introducing the coverage-credibility, the proposed uncertainty measurements enhance the relative information of decision classes. In addition, the nonmonotonicity of the improved condition entropy and rough decision entropy is validated by theoretical proofs and experimental counterexamples, with respect to attribute subsets and thresholds. Then, the two rough decision entropies drive monotonic and nonmonotonic attribute reductions, and the corresponding reduction algorithms are designed for heuristic searches. Finally, data experiments not only verify the effectiveness and improvements of the proposed uncertainty measurements, but also illustrate the reduction algorithms optimization through better classification accuracy than four comparative algorithms. Show more

Keywords: Rough sets, Attribute reduction, Interval-valued decision systems, Algebraic measures and informational measures, Coverage-credibility-based rough decision entropy

DOI: 10.3233/JIFS-239544

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Tian, Wen | Zhang, Yining | Fang, Qin | Liu, Weidong

Article Type: Research Article

Abstract: In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route …network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling. Show more

Keywords: Air traffic flow management, resource allocation, collaborative trajectory options program (CTOP), multi-objective optimization, genetic algorithm

DOI: 10.3233/JIFS-233588

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Gong, Zengtai | Zhang, Yuanyuan

Article Type: Research Article

Abstract: In this paper, we focus on generalized fuzzy complex numbers and propose a straightforward matrix method to solve the dual rectangular fuzzy complex matrix equations C · Z ˜ + L ˜ = R · Z ˜ + W ˜ , in which C and R are crisp complex matrices and Z ˜ , L ˜ and M ˜ …are fuzzy complex number matrices. The existing methods for solving fuzzy complex matrix equations involve separately calculating the extended solution and the corresponding parameters of the real and imaginary parts, whereby we obtain the algebraic solution of the equations. By means of the interval arithmetic and embedding approach, the n  × n dual rectangular fuzzy complex linear systems could be converted into 2n  × 2n fuzzy linear systems, which are also equivalent to the 4n  × 4n real linear systems. By directly solving the 4n  × 4n real linear systems, the algebraic solutions can be obtained. The general dual rectangular fuzzy complex matrix equations and dual rectangular fuzzy complex linear systems are investigated by the generalized inverses of matrices. Finally, some examples are given to illustrate the effectiveness of method. Show more

Keywords: Fuzzy number, fuzzy complex number, rectangular fuzzy complex number, dual rectangular fuzzy complex matrix equations

DOI: 10.3233/JIFS-239305

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024

Authors: Aguilar-Canto, Fernando | Luján-García, Juan Eduardo | Espinosa-Juárez, Alberto | Calvo, Hiram

Article Type: Research Article

Abstract: Inferring phylogenetic trees in human populations is a challenging task that has traditionally relied on genetic, linguistic, and geographic data. In this study, we explore the application of Deep Learning and facial embeddings for phylogenetic tree inference based solely on facial features. We use pre-trained ConvNets as image encoders to extract facial embeddings and apply hierarchical clustering algorithms to construct phylogenetic trees. Our methodology differs from previous approaches in that it does not rely on preconstructed phylogenetic trees, allowing for an independent assessment of the potential of facial embeddings to capture relationships between populations. We have evaluated our method with …a dataset of 30 ethnic classes, obtained by web scraping and manual curation. Our results indicate that facial embeddings can capture phenotypic similarities between closely related populations; however, problems arise in cases of convergent evolution, leading to misclassifications of certain ethnic groups. We compare the performance of different models and algorithms, finding that using the model with ResNet50 backbone and the face recognition module yields the best overall results. Our results show the limitations of using only facial features to accurately infer a phylogenetic tree and highlight the need to integrate additional sources of information to improve the robustness of population classification. Show more

Keywords: Convolutional neural networks, deep learning, hierarchical clustering, phylogenetic tree

DOI: 10.3233/JIFS-219343

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-09, 2024

Authors: Li, Yuangang | Gao, Xinrui | Ni, Hongcheng | Song, Yingjie | Deng, Wu

Article Type: Research Article

Abstract: In this paper, an adaptive differential evolution algorithm with multi-strategy, namely ESADE is proposed to solve the premature convergence and high time complexity for complex optimization problem. In the ESADE, the population is divided into several sub-populations after the fitness value of each individual is sorted. Then different mutation strategies are proposed for different populations to balance the global exploration and local optimization. Next, a new self-adaptive strategy is designed adjust parameters to avoid falling into local optimum while the convergence accuracy has reached its maximum value. And a complex airport gate allocation multi-objective optimization model with the maximum flight …allocation rate, the maximum near gate allocation rate, and the maximum passenger rate at near gate is constructed, which is divided into several single-objective optimization model. Finally, the ESADE is applied solve airport gate allocation optimization model. The experiment results show that the proposed ESADE algorithm can effectively solve the complex airport gate allocation problem and achieve ideal airport gate allocation results by comparing with the current common heuristic optimization algorithms. Show more

Keywords: Differential evolution, multi-strategy, self-adaptive strategy, gate allocation, optimization

DOI: 10.3233/JIFS-238217

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Sowndeswari, S. | Kavitha, E. | Krishnamoorthy, Raja

Article Type: Research Article

Abstract: The development of tiny sensing nodes efficient for wireless communication in Wireless Sensor Networks (WSNs) can be attributed to the rapid advancements in processors and radio technology. Data transmission occurs through multi-hop routing in WSN, which relies on nodes’ cooperation. The collaboration between nodes has rendered these networks susceptible to various attacks. It is imperative to employ a security scheme to evaluate the dependability of nodes in distinctive malicious nodes from non-malicious nodes. In recent years, there has been a growing significance placed on security-based routing protocols with energy constraints as valuable mechanisms for enhancing the security and performance of …WSNs. A novel solution called the Deep Learning-based Hybrid Energy Efficient and Security System (DL-HE2S2) is introduced to address these challenges. The research workflow encompasses various essential stages, namely the deployment of nodes, the creation of clusters, the selection of cluster heads, the detection of malevolent nodes within each group, and the determination of optimal paths intra- and inter-clusters employing the routing algorithm for efficient packet transmission. The design of the algorithm is focused on achieving energy efficiency and enhancing network security while also taking into account various performance metrics, including a mean network lifetime of 187.244 hours, a throughput of 59.88 kilobits per second, an end-to-end latency of 11.939 milliseconds, a packet loss of 14.9%, a packet delivery ratio of 99.194%, network security at 92.026%, and energy usage of 19.424 J. This research examines the algorithm’s scalability and efficiency across various network sizes using a Network Simulator (NS-2). DL-HE2S2 offers valuable insights that can be applied to practical implementations in multiple applications. Show more

Keywords: Wireless sensor networks, energy efficiency, secured routing, cluster

DOI: 10.3233/JIFS-235322

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Xu, Liwen | Chen, Jiali

Article Type: Research Article

Abstract: Node classification in graph learning faces significant challenges due to imbalanced data, particularly for under-represented samples from minority classes. To address this issue, existing methods often rely on synthetic minority over-sampling techniques, introducing additional complexity during model training. In light of the challenges faced, we introduce GraphECC, an innovative approach that addresses numerical anomalies in large-scale datasets by supplanting the traditional CE loss function with an Enhanced Complementary Classifier (ECC) loss function’a novel modification to the CCE loss. This alteration ensures computational stability and mitigates potential numerical anomalies by incorporating a slight offset in the denominator during the computation of …the complementary probability distribution. In this paper, we present a novel training paradigm, the Enhanced Complementary Classifier (ECC), which offers “imbalance defense for free” without the need for extra procedures to improve node classification accuracy.The ECC approach optimizes model probabilities for the ground-truth class, akin to the cross-entropy method. Additionally, it effectively neutralizes probabilities associated with incorrect classes through a “guided” term, achieving a balanced trade-off between the two aspects. Experimental results demonstrate that our proposed method not only enhances model robustness but also surpasses the widely used cross-entropy training objective.Moreover, we demonstrate the versatility of our method by seamlessly integrating it with various well-known adversarial training techniques, resulting in significant gains in robustness. Notably, our approach represents a breakthrough, as it enhances model robustness without compromising performance, distinguishing it from previous attempts.The code for GraphECC can be accessed from the following link:https://github.com/12chen20/GraphECC . Show more

Keywords: Imbalanced node classification, trade-off optimization, enhanced complementary classifier (ECC), graph learning, minority classes

DOI: 10.3233/JIFS-239663

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Ali, Zeeshan | Yin, Shi | Yang, Miin-Shen

Article Type: Research Article

Abstract: In the context of fuzzy relations, symmetry refers to a property where the relationship between two elements remains the same regardless of the order in which they are considered. Natural language processing (NLP) in engineering documentation discusses the application of computational methods or techniques to robotically investigate, analyze, and produce natural language information for manufacturing contents. The NLP plays an essential role in dealing with large amounts of textual data normally recovered in engineering documents. In this paper, we expose the idea of a bipolar complex hesitant fuzzy (BCHF) set by combining the bipolar fuzzy set (BFS) and the complex …hesitant fuzzy set (CHFS). Further, we evaluate some algebraic and Schweizer-Sklar operational laws under the presence of BCHF numbers (BCHFNs). Additionally, using the above information as well as the idea of prioritized (PR) operators, we derive the idea of BCHF Schweizer-Sklar PR weighted averaging (BCHFSSPRWA) operator, BCHF Schweizer-Sklar PR ordered weighted averaging (BCHFSSPROWA) operator, BCHF Schweizer-Sklar PR weighted geometric (BCHFSSPRWG) operator, and BCHF Schweizer-Sklar PR ordered weighted geometric (BCHFSSPROWG) operator. Basic properties for the above operators are also discussed in detail, such as idempotency, monotonicity, and boundedness. Moreover, we evaluate the best way in which NLP can be applied to engineering documentations with the help of the proposed operators. Therefore, we illustrate the major technique of multi-attribute decision-making (MADM) problems based on these derived operators. Finally, we use some existing operators and try to compare their ranking results with our proposed ranking results to show the supremacy and validity of the investigated theory. Show more

Keywords: Fuzzy set (FS), hesitant FS, bipolar complex hesitant FS, Schweizer-Sklar prioritized aggregation operators, natural language processing, multi-attribute decision-making

DOI: 10.3233/JIFS-240116

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2024

Authors: Shi, Jing | Zhang, Xiao-Lin | Wang, Yong-Ping | Gu, Rui-Chun | Xu, En-Hui

Article Type: Research Article

Abstract: Deep neural networks (DNNs) are susceptible to adversarial attacks, and one important factor is that adversarial samples are transferable, i.e., adversarial samples generated by a particular network may deceive other black-box models. However, existing transferable adversarial attacks tend to modify the input features of images directly without selection to reduce the prediction accuracy in the alternative model, which would enable the adversarial samples to fall into the model’s local optimum. Alternative models differ significantly from the victim model in most cases, and while simultaneously attacking multiple models may improve transferability, gathering numerous different models is more challenging and expensive. We …simulate various models using frequency domain transformation to close the gap between the source and victim models and improve transferability. At the same time, we destroy important intermediate layer features that influence the decision of the model in the feature space. Additionally, smoothing loss is introduced to remove high-frequency perturbations. Extensive experiments demonstrate that our FM-FSTA attack generates more well-hidden and transferable adversarial samples, and achieves a high deception rate even when attacking adversarially trained models. Compared to other methods, our FM-FSTA improved attack success rate under different defense mechanisms, which reveals the potential threats of current robust models. Show more

Keywords: Deep neural networks, adversarial samples, transferable attacks

DOI: 10.3233/JIFS-234156

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Zhao, Xianhao | Wang, Mingyang | Xin, Chaoqun | Wang, Xianjie

Article Type: Research Article

Abstract: In the field of autonomous driving, driving systems need to understand and quickly respond to changes in road scenes, which makes it equally important to enhance the accuracy and real-time performance of semantic segmentation tasks in road scenes. This article proposes a lightweight road scene semantic segmentation model LR3S that integrates global contextual information based on the DeepLabV3+ framework. LR3S utilizes a lightweight GhostNetV2 network as the backbone to capture rich semantic information in images, and uses ASPP_eSE module to enhance the capture of multi-scale and detail level semantic information. In addition, a lightweight CARAFE upsampling operator is utilized to …upsample feature maps, taking advantage of CARAFE’s large receptive field and low computational cost to prevent the loss of fine-grained features and ensure the integrity of semantic information. Experimental results demonstrate that LR3S achieves an MIoU of 74.47% on the Cityscapes dataset and obtains an MIoU of 76.01% on the PASCAL VOC 2012 dataset. Compared to baseline semantic segmentation models, LR3S significantly reduces the parameter amount while maintaining segmentation accuracy, achieving a good balance between model accuracy and real-time performance. Show more

Keywords: Semantic segmentation, road scenes, attention mechanism, GhostNetV2, CARAFE

DOI: 10.3233/JIFS-239692

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Haennah, J.H. Jensha | Christopher, C. Seldev | King, G.R. Gnana

Article Type: Research Article

Abstract: Accurate SARS-CoV-2 screening is made possible by automated Computer-Aided Diagnosis (CAD) which reduces the stress on healthcare systems. Since Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is highly contagious, the transition chain can be broken through an early diagnosis by clinical knowledge and Artificial Intelligence (AI). Manual findings are time and labor-intensive. Even if Reverse Transcription-Polymerase Chain Reaction (RT-PCR) delivers quick findings, Chest X-ray (CXR) imaging is still a more trustworthy tool for disease classification and assessment. Several studies have been conducted using Deep Learning (DL) algorithms for COVID-19 detection. One of the biggest challenges in modernizing healthcare is extracting …useful data from high-dimensional, heterogeneous, and complex biological data. Intending to introduce an automated COVID-19 diagnosis model, this paper develops a proficient optimization model that enhances the classification performance with better accuracy. The input images are initially pre-processed with an image filtering approach for noise removal and data augmentation to extend the dataset. Secondly, the images are segmented via U-Net and are given to classification using the Fused U-Net Convolutional Neural Network (FUCNN) model. Here, the performance of U-Net is enhanced through the modified Moth Flame Optimization (MFO) algorithm named Chaotic System-based MFO (CSMFO) by optimizing the weights of U-Net. The significance of the implemented model is confirmed over a comparative evaluation with the state-of-the-art models. Specifically, the proposed CSMFO-FUCNN attained 98.45% of accuracy, 98.63% of sensitivity, 98.98% of specificity, and 98.98% of precision. Show more

Keywords: COVID-19 classification, deep Learning, U-Net, Convolutional Neural Network (CNN), Moth Flame Optimization (MFO)

DOI: 10.3233/JIFS-230523

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Zhan, Huawei | Han, Chengju | Li, Junjie | Wei, Gaoyong

Article Type: Research Article

Abstract: Aiming at the problems of slow speed and low accuracy of traditional neural network systems for real-time gesture recognition in complex backgrounds., this paper proposes DMS-yolov8-a gesture recognition method to improve yolov8. This algorithm replaces the Bottleneck convolution module in the backbone network of yolov8 with variable row convolution DCNV2, and increases the feature convolution range without increasing the computation amount through a more flexible feeling field. in addition, the self-developed MPCA attention module is added after the feature output layer of the backbone layer, which improves the problem of recognizing the accuracy of difference gestures in complex backgrounds by …effectively combining the feature information of the contextual framework, taking into account the multi-scale problem of the gestures in the image, this paper introduces the SPPFCSPS module, which realizes multi-feature fusion and improves real-time accuracy of detection. Finally, the model proposed in this paper is compared with other models, and the proposed DMS-yolov8 model achieves good results on both publicly available datasets and homemade datasets, with the average accuracy up to 97.4% and the average mAP value up to 96.3%, The improvements proposed in this paper are effectively validated. Show more

Keywords: Gesture recognition, yolov8, DCNV2, MPCA, feature fusion

DOI: 10.3233/JIFS-238629

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Meenakshi, A. | Bramila, M.

Article Type: Research Article

Abstract: Molecular structures are characterised by the Hosoya polynomial and Wiener index, ideas from mathematical chemistry and graph theory. The graph representation of a chemical compound that has atoms as vertices and chemical bonds as edges is called a molecular graph, and the Hosoya polynomial is a polynomial related to this graph. As a graph attribute that remains unchanged under graph isomorphism, the Hosoya polynomial is known as a graph invariant. It offers details regarding the quantity of distinct non-empty subgraphs within a specified graph. A topological metric called the Wiener index is employed to measure the branching complexity and size …of a molecular graph. For every pair of vertices in a molecular network, the Wiener index is the total of those distances. In this paper, discussed the Hosoya polynomial, Wiener index and Hyper-Wiener index of the Abid-Waheed graphs (AW)a 8  and (AW)a 10 . This graph is similar to Jahangir’s graph. Further, we have extended the research work on the applications of the described graphs. Show more

Keywords: Wiener index, Abid-Waheed, Hosoya polynomial, diameter, distance, connected graph

DOI: 10.3233/JIFS-236051

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024

Authors: Lin, Jiayi

Article Type: Research Article

Abstract: At this stage, network communication technology is increasingly mature, and intelligent wearable products are also widely used in human daily life. Wearable products are popular with users because of their numerous types, complete functions and convenient services. Wearable products integrate interaction technology, and users can interact with products. However, how to improve the user’s interaction experience and reduce the user’s cognitive burden on the interaction interface is an urgent problem in the current product interaction design. Therefore, based on the analysis of the types and related technologies of wearable products, this paper made a specific analysis of the interaction design …of wearable products, and established an interaction design model. At the same time, the wearable fall detection system was also tested by machine learning algorithm. The experimental results showed that the average test result of the algorithm in this paper was 87.39%, while the average test result of the traditional algorithm was 83.79%. In terms of the missed alarm rate of fall detection, the average test result of this algorithm was 6.4%, while the average test result of the traditional algorithm was 12.33%. In terms of fall detection sensitivity, the average test result of this algorithm was 92.50%, while the average test result of the traditional algorithm was 88.24%. Compared with traditional algorithms, this method performs better, with lower missed detection rate and higher sensitivity. Innovative combination of machine learning algorithm, through three-dimensional coordinate system, differentiation and vector sum formula, improves the accuracy and reliability of fall detection. In conclusion, the algorithm in this paper can effectively optimize the relevant performance of the system, thus improving the accuracy of the system’s fall detection. Show more

Keywords: 5 G network communication technology, wearable products, interaction design, wearable fall detection system

DOI: 10.3233/JIFS-237837

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Valadez-Godínez, Sergio | Sossa, Humberto | Santiago-Montero, Raúl

Article Type: Research Article

Abstract: The Associative Pattern Classifier (APC) was designed as an associative memory, focusing particularly on pattern classification. This implies that the training memory is constructed in a single operation and pattern classification also occurs in a single process. It is important to note that the APC translates the input patterns through a translation vector, which represents the average of all input patterns. Until now, there is no theoretical framework to explain the inner workings of the APC. Its relevance is inferred from the fact that several studies have been conducted using it as a foundation. This paper seeks to provide a …theoretical comprehension of the APC’s operation to facilitate future enhancements. We found the APC creates a system in static equilibrium through concurrent vectors at the origin (translation vector), resulting in a balanced separation of patterns. However, the APC cannot achieve complete pattern separation because of the presence of a neutral region. The neutral region is defined by all the points that define the separation hyperplanes. The points over the hyperplanes cannot be classified by the APC. Additionally, we discovered that the APC is unable to accurately classify the translation vector, which could be included as part of the input patterns. Our previous research showed that the APC is unsuccessful in achieving the linear separation of the AND function. In this research, we also broaden the examination of the AND function to illustrate that achieving linear separation is not feasible because the separation line represents a neutral region. The APC demonstrated exceptional performance when tested with artificial datasets where patterns were distributed over balanced regions, thus operating as an efficient multiclass and non-linear classifier. Nevertheless, the performance of the APC is lower when tested with real-world databases, making the APC inaccurate due to its restricted inner workings. Show more

Keywords: Classifier, pattern, associative memory, class, classification

DOI: 10.3233/JIFS-219347

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024

Authors: Zhang, Wei | Zheng, Hongxuan | Zhang, Runyu

Article Type: Research Article

Abstract: In this paper, a self-organizing RBF (SORBF) neural network with an adaptive threshold is proposed based on improved particle swarm optimization (IPSO) and neural strength (NS). The parameters and structure of SORBF can be optimized simultaneously and dynamically. Moreover, the tiresome problem of threshold setting is solved. Firstly, the network size and parameters of SORBF are mapped into the particle information of PSO. Secondly, an IPSO algorithm, based on diversity inertia weight and elite knowledge guiding, is proposed to reduce the probability of the population falling into the local optimum. Then, IPSO is used for optimizing the parameters of SORBF. …Based on neuron growth intensity and competition intensity, SORBF can realize the hidden neuron addition and deletion adaptively. Moreover, the thresholds during the structure adjustment can be provided adaptively based on the network scale and neuron strength, which avoids the subjectivity setting and can improve the adaptive ability. Finally, the convergence analysis of IPSO is provided to ensure the performance of SORBF. Experiment results show that the proposed SORBF has good self-organizing ability and compact network structure compared with other methods. Show more

Keywords: RBF neural network, PSO, self-organization, neural strength, adaptive threshold

DOI: 10.3233/JIFS-239569

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Pandiyarajan, Abinaya | Jagatheesaperumal, Senthil Kumar | Thayanithi, Manonmani

Article Type: Research Article

Abstract: This study explores how Electronic Health Records (EHR) might be transformed in the context of the rapid improvements in cloud computing and IoT technology. But worries about sensitive data security and access management when it moves to large cloud provider networks surface. Even if they are secure, traditional encryption techniques sometimes lack the granularity needed for effective data protection. We suggest the Secure Access Policy – Ciphertext Policy – Attribute-based Encryption (SAPCP-ABE) algorithm as a solution to this problem. This method ensures that only authorized users may access the necessary data while facilitating fine-grained encrypted data exchange. The three main …phases of SAPCP-ABE are retrieval and decoding, where the system verifies users’ access restrictions, secure outsourcing that prioritizes critical attributes, and an authenticity phase for early authentication. Performance tests show that SAPCP-ABE is a better scheme than earlier ones, with faster encryption and decryption speeds of 5 and 5.1 seconds for 512-bit keys, respectively. Security studies, numerical comparisons, and implementation outcomes demonstrate our suggested approach’s efficacy, efficiency, and scalability. Show more

Keywords: Attribute-based encryption, electronic health record, access policy, cloud providers, cloud computing

DOI: 10.3233/JIFS-240341

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Sageengrana, S. | Selvakumar, S.

Article Type: Research Article

Abstract: Distraction and fatigue are serious issues in online learning, and they directly impact educational outcomes. To achieve excellent academic achievement, students need to focus on their studies without being distracted or fatigued. Learners frequently overlook crucial information, directions, and concepts while they are passive and sleepy. They tend to miss important content, instructions, and concepts. Iris Angle Position (IAP) and electroencephalography (EEG) were used in this model to identify the behaviour of learners. Specifically, a Deep Convolutional Neural Network (DCNN) is constructed to extract IAP in order to accurately capture the learner’s facial area. EEG signals are effectively handled and …sorted using deep reinforcement learning (DRL). The learners’ facial landmarks are retrieved from a frame using the dlib toolbox. Only eye landmark points from face landmarks alone are focused on in order to determine the learner’s behaviour. When the learners EEG signals and Iris positions are monitored simultaneously, it’s helpful to identify the learner’s fatigue state (LFS) and the learner’s distraction state (LDS). The Brain Vision Algorithm (BVA) uses iris position and minimal facial landmarks, along with brain activity, to properly identify the learner’s level of distraction and exhaustion. When a student is detected as being preoccupied or sleepy, an alert goes off automatically, and the educator gets performance feedback. Iris position data and brain-computer interface-based EEG signal values are utilised to identify distraction and sleepiness. Comparative tests have demonstrated that this innovative method offers fast and high-accuracy student activity detection in virtual learning settings. Applying the suggested approach to different existing classifiers yields an F-Score of 91.92%, a recall of 93.87%, and a precision of 92.37% . The results showed that the detection rates for both distracted and sleepy phases were higher than those attained with other currently used techniques. Show more

Keywords: Drowsiness, online learning, iris position, EEG signals, distraction, brain vision algorithm

DOI: 10.3233/JIFS-237016

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: Canul-Chin, Miguel Angel | Moguel-Ordóñez, Yolanda Beatriz | Martin-Gonzalez, Anabel | Brito-Loeza, Carlos | Legarda-Saenz, Ricardo

Article Type: Research Article

Abstract: Yucatan has a variety of plant species of melliferous importance. The honey produced in Yucatan has several special properties that make it one of the most demanded internationally. Analyzing the pollen grains present in honey is essential to determine its quality and identify its plants of origin. This study is a time-consuming process that must be carried out by highly trained palynologists. In this work, we propose an improved model based on a fully convolutional neural network for the automatic detection of pollen grains in microscopic images of four plant species of Yucatan to contribute to the analysis of the …honey designation of origin. Show more

Keywords: Pollen analysis, object detection, palynology, deep learning

DOI: 10.3233/JIFS-219379

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024

Authors: Hashmi, Hina | Dwivedi, Rakesh | Kumar, Anil | Kumar, Aman

Article Type: Research Article

Abstract: The rapid advancements in satellite imaging technology have brought about an unprecedented influx of high-resolution satellite imagery. One of the critical tasks in this domain is the automated detection of buildings within satellite imagery. Building detection holds substantial significance for urban planning, disaster management, environmental monitoring, and various other applications. The challenges in this field are manifold, including variations in building sizes, shapes, orientations, and surrounding environments. Furthermore, satellite imagery often contains occlusions, shadows, and other artifacts that can hinder accurate building detection. The proposed method introduces a novel approach to improve the boundary detection of detected buildings in high-resolution …remote sensed images having shadows and irregular shapes. It aims to enhance the accuracy of building detection and classification. The proposed algorithm is compared with Customized Faster R-CNNs and Single-Shot Multibox Detectors to show the significance of the results. We have used different datasets for training and evaluating the algorithm. Experimental results show that SESLM for Building Detection in Satellite Imagery can detect 98.5% of false positives at a rate of 8.4%. In summary, SESLM showcases high accuracy and improved robustness in detecting buildings, particularly in the presence of shadows. Show more

Keywords: Object detection, image analysis, faster R-CNN, CNN, satellite imagery, object localization

DOI: 10.3233/JIFS-235150

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024

Authors: Huang, De Ling | Huang, Yi Fan | Yang, Yu Qiao

Article Type: Research Article

Abstract: Practical Byzantine Fault Tolerance (PBFT), the widest-used consensus algorithm in the alliance blockchain, suffers from high communications complexity and relatively low scalability, making it difficult to support large-scale networks. To overcome these limitations, we propose a secure and scalable consensus algorithm, Vague Sets-based Double Layer PBFT (VSDL-PBFT). Roles and tasks of consensus nodes are redesigned. Three-phase consensus process of the original PBFT is optimized. Through these approaches, the communication complexity of the algorithm is significantly reduced. In order to better fit the complexity of voting in the real world, we use a vague set to select primary nodes of consensus …groups. This can greatly reduce the likelihood of malicious nodes being selected as the primary nodes. The experimental results show that the VSDL-PBFT consensus algorithm improves the system’s fault tolerance, it also achieves better performance in algorithm security, communications complexity, and transaction throughput compared to the baseline consensus algorithms. Show more

Keywords: Blockchain, consensus algorithm, Byzantine fault tolerance, PBFT

DOI: 10.3233/JIFS-239745

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Rodriguez-Bazan, Horacio | Sidorov, Grigory | Escamilla-Ambrosio, Ponciano Jorge

Article Type: Research Article

Abstract:  Recently, Android device usage has increased significantly, and malicious applications for the Android ecosystem have also increased. Security researchers have studied Android malware analysis as an emerging issue. The proposed methods employ a combination of static, dynamic, or hybrid analysis along with Machine Learning (ML) algorithms to detect and classify malware into families. These families often exhibit shared similarities among their members or with other families. This paper presents a new method that combines Fuzzy Hashing and Natural Language Processing (NLP) techniques to find Android malware families based on their similarities by applying reverse engineering to extract the features and …compute fuzzy hashing of the preprocessed code. This relationship allows us to identify the families according to their features. A study was conducted using a database test of 2,288 samples from diverse ransomware families. An accuracy in classifying Android ransomware malware up to 98.46% was achieved. Show more

Keywords: Android malware analysis, android ransomware, cybersecurity, fuzzy hashing, natural language processing

DOI: 10.3233/JIFS-219367

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Arulmurugan, A. | Kaviarasan, R. | Garnepudi, Parimala | Kanchana, M. | Kothandaraman, D. | Sandeep, C.H.

Article Type: Research Article

Abstract: This research focuses on scene segmentation in remotely sensed images within the field of Remote Sensing Image Scene Understanding (RSISU). Leveraging recent advancements in Deep Learning (DL), particularly Residual Neural Networks (RESNET-50 and RESNET-101), and the research proposes a methodology involving feature fusing, extraction, and classification for categorizing remote sensing images. The approach employs a dataset from the University of California Irvine (UCI) comprising twenty-one groups of pictures. The images undergo pre-processing, feature extraction using the mentioned DL frameworks, and subsequent categorization through an ensemble classification structure combining Kernel Extreme Learning Machine (KELM) and Support Vector Machine (SVM). The paper …concludes with optimal results achieved through performance and comparison analyses. Show more

Keywords: Remote sensing, image scene classification, deep learning, feature extraction, RESNET- 101, ensemble

DOI: 10.3233/JIFS-235109

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

Authors: Yang, Yi | Huang, Huiling | Wu, FeiBin | Han, Jun | Ma, Mengyuan | Zhang, Yantong | Feng, Yanbing

Article Type: Research Article

Abstract: This paper introduces a novel neural network architecture and an enhanced data synthesis method that significantly boost the performance in removing complex smoke from images. The architecture features a multi-branch and multi-scale feature fusion design, which effectively integrates multiple feature streams and adaptively restores the background by identifying specific smoke characteristics within the image. A newly designed Fourier residual block is incorporated to capture frequency domain information, enabling the network to process and transform information across both spatial and frequency domains. To improve the network’s generalization ability and robustness, an in-depth analysis of the imaging process in smoky environments was …conducted, leading to an improved method for synthesizing smoke images. This methodology facilitates the creation of a more varied and realistic training dataset, substantially enhancing the neural network’s capabilities in image restoration. Experimental results show that this approach is highly effective on both synthetic and real-world smoke datasets, outperforming existing image de-smoking methods in terms of quantitative metrics and visual perception. The code for this method is available at https://github.com/Exiagit/MFSR. Show more

Keywords: Single image smoke removal, frequency domain learning, data synthesis method

DOI: 10.3233/JIFS-239146

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Nieves, Juan Carlos | Osorio, Mauricio | Rojas-Velazquez, David | Magallanes, Yazmín | Brännström, Andreas

Article Type: Research Article

Abstract: Humans have evolved to seek social connections, extending beyond interactions with living beings. The digitization of society has led to interactions with non-living entities, such as digital companions, aimed at supporting mental well-being. This literature review surveys the latest developments in digital companions for mental health, employing a hybrid search strategy that identified 67 relevant articles from 2014 to 2022. We identified that by the nature of the digital companions’ purposes, it is important to consider person profiles for: a) to generate both person-oriented and empathetic responses from these virtual companions, b) to keep track of the person’s conversations, activities, …therapy, and progress, and c) to allow portability and compatibility between digital companions. We established a taxonomy for digital companions in the scope of mental well-being. We also identified open challenges in the scope of digital companions related to ethical, technical, and socio-technical points of view. We provided documentation about what these issues mean, and discuss possible alternatives to approach them. Show more

Keywords: Conversational agents, well-being, mental health, trustworthy artificial intelligence

DOI: 10.3233/JIFS-219336

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Sánchez-Jiménez, Eduardo | Cuevas-Chávez, Alejandra | Hernández, Yasmín | Ortiz-Hernandez, Javier | Hernández-Aguilar, José Alberto | Martínez-Rebollar, Alicia | Estrada-Esquivel, Hugo

Article Type: Research Article

Abstract: Machine learning algorithms have been used in diverse areas among applications, including healthcare. However, to fit an effective and optimal machine learning model, the hyperparameters need to be tuned. This process is commonly referred to as Hyperparameter Optimization and comprises several approaches. We combined three Hyperparameter Optimization techniques (Bayesian Optimization, Particle Swarm Optimization, and Genetic Algorithm) with three classifiers (Random Forest, Support Vector Machine, and XGBoost) to identify the best combination of hyperparameters that maximize model performance. We use the Framingham dataset to test the proposal. For classifier performance, the Support Vector Machine obtained the best result in recall (96.40%) …and F-score (93.86%), while XGBoost obtained the best result in precision (96.30%) and specificity (96.36%). In the accuracy metric, both classifiers achieved 95%. Bayesian optimization had the best results in terms of accuracy, precision, specificity, and F-score metrics. Both Particle Swarm Optimization and Genetic Algorithm obtained the best result in the recall metric. Show more

Keywords: Bayesian optimization, framingham dataset, genetic algorithm, heart disease, hyperparameter default value, hyperparameter optimization, machine learning, particle swarm optimization, support vector machine, XGBoost

DOI: 10.3233/JIFS-219376

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Cosío-León, M.A. | Martínez-Vargas, Anabel | Rodríguez-Cortés, Gabriela

Article Type: Research Article

Abstract: It is well-known that tuning a metaheuristic is a critical task because the performance of a metaheuristic and the quality of its solutions depend on its parameter values. However, finding a good parameter setting is a time-consuming task. In this work, we apply the upper confidence bound (UCB) algorithm to automate offline tuning in a (1 + 1)-evolution strategy. Preliminary results show that our proposed approach is a less costly method.

Keywords: Upper confidence bound algorithm, meta-optimizer, bandit problems, reinforcement learning

DOI: 10.3233/JIFS-219362

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Akhmetova, Dilyara | Akhmetov, Iskander | Pak, Alexander | Gelbukh, Alexander

Article Type: Research Article

Abstract:  The paper focuses on the importance of coherence and preserving the breadth of content in summaries generated by the extractive text summarization method. The study utilized the dataset containing 16,772 pairs of extractive and corresponding abstractive summaries of scientific papers specifically tailored to increase text coherence. We smoothed the extractive summaries with a Large Language Model (LLM) fine-tuning approach and evaluated our results by applying the coefficient of variation approach. The statistical significance of the results was assessed using the Kolmogorov-Smirnov test and Z-test. We observed an increase in coherence in the predicted texts, highlighting the effectiveness of our proposed …methods. Show more

Keywords: Coherence, cohesion, extractive summary, abstractive summary, GPT2, summarization, seq2seq, random forest

DOI: 10.3233/JIFS-219353

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Ibarra Carrillo, Mario Alfredo | Montiel Pérez, Jesús Yaljá | Molina Lozano, Herón

Article Type: Research Article

Abstract: Today, it is the amount of data that defines the existence of mankind. Scientists respond to the large amount of required calculations by developing hardware in several directions. One of them is to increase the number of arithmetic elements. Another direction is to create new architectures that represent new algorithms for processing numerical data. We have chosen the second direction by developing a new systolic core architecture, which implies an improvement in efficiency, i.e. performing the same task with the same number of arithmetic elements but reducing the latency. Measurements are made in terms of computational capacity and the number …of arithmetic elements involved in the operations. The results of the tests are compared with data from a number of selected articles. Today, we have achieved 3.2GFlops with only two modules. In the future, we plan to integrate up to four of our cores in a system with its own memory and management processor and at a higher operating frequency. Show more

Keywords: Systolic array, systolic tensor core, accelerated matrix multiplication, accelerated convolution

DOI: 10.3233/JIFS-219361

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Tang, Ao | Wang, Xiaofeng | Peng, Qingyuan | Wang, Junxia | Yang, Yi | He, Fei | Hua, Yingying

Article Type: Research Article

Abstract: A CNF formula with each clause of length k and each variable occurring 4s times, where positive occurrences are 3s and negative occurrences are s , is a regular (3s  + s , k )-CNF formula (F 3s +s ,k formula). The random regular exact (3s  + s , k )-SAT problem is whether there exists a set of Boolean variable assignments such that exactly one literal is true for each clause in the F 3s +s ,k formula. By introducing a random instance generation model, the satisfiability phase transition of the solution is analyzed by …using the first moment method, the second moment method, and the small subgraph conditioning method, which gives the phase transition point s* of the random regular exact (3s  + s , k )-SAT problem for k ≥3. When s  < s* , F 3s +s ,k formula is satisfiable with high probability; when s  > s* , F 3s +s ,k formula is unsatisfiable with high probability. Finally, through the experimental verification, the results show that the theoretical proofs are consistent with the experimental results. Show more

Keywords: Random regular exact (3s + s, k)-SAT problem, first moment method, second moment method, small subgraph conditioning method, phase transition

DOI: 10.3233/JIFS-238254

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Kolesnikova, Olga | Yigezu, Mesay Gemeda | Gelbukh, Alexander | Abitte, Selam | Sidorov, Grigori

Article Type: Research Article

Abstract: Twitter has experienced a tremendous surge in popularity over recent years, establishing itself as a prominent social media platform with a large user base. However, with this increased usage, there has been a concerning rise in the number of individuals resorting to derogatory language and expressing their opinions in a demeaning manner toward others. This surge in hate speech has drawn significant attention to the field of sentiment analysis, which aims to develop algorithms capable of detecting and analyzing emotions expressed in social networks using intuitive approaches. This paper focuses on addressing the complex task of detecting hate speech and …aggressive behavior while performing target classification. We explored various deep-learning approaches, including LSTM, BiLSTM, CNN, and GRU. Each offers unique capabilities for capturing different aspects of the input data. We proposed an ensemble approach that combines the top three performing models. This ensemble approach benefits from the diverse strengths of each individual model showing F1 score of 0.85 for English-HS, 0.94 for English-TR, 0.92 for English-AB, 0.84 for Spanish-HS, 0.86 for Spanish-TR, 0.97 for Spanish-AB, 0.74 for multilingual-HS, 0.94 for multilingual-TR, and 0.88 for multilingual-AB. Show more

Keywords: Hate speech, aggressive behavior, target classification, ensemble learning, deep learning, target classification

DOI: 10.3233/JIFS-219350

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Valencia-Valencia, Alex I. | Gomez-Adorno, Helena | Stephens Rhodes, Christopher | Bel-Enguix, Gemma | Trueba, Ojeda | Fuentes Pineda, Gibran

Article Type: Research Article

Abstract: Social media platforms, such as Twitter (now X), are a major source of communication. Identifying communicative intentions is useful, as it encapsulates the latent motivations that drive text creation. This intention is also helpful in understanding the message, context, and audience. This study proposes a method for detecting communicative intentions in tweets using Jakobson’s language functions. We constructed a meticulously annotated dataset, drawing from the extensive RepLab2013 corpus. Our dataset underwent rigorous scrutiny by linguistic annotators who analyzed over 12,000 tweets individually. These experts identified the dominant language function within each tweet by employing diverse strategies to ensure precise labeling …quality. The outcome demonstrated a noteworthy Kappa agreement score of 0.6, reflecting a strong inter-annotator reliability. Subsequently, these functions were mapped to the corresponding intention categories. We employed logistic regression and support vector machines (SVM) algorithms to classify intention in tweets and explored various pre-processing techniques, incorporating n-grams and bag-of-words representations. Furthermore, we expanded our research using pre-trained large language models, incorporating the latest state-of-the-art techniques in natural language processing. Show more

Keywords: Intention, communicative intention, tweets, language functions, intention identification, n-grams, logistic regression, SVM, deep learning

DOI: 10.3233/JIFS-219357

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Rasham, Tahair | Kutbi, Marwan Amin | Hussain, Aftab | Chandok, Sumit

Article Type: Research Article

Abstract: The objective of this research is to propose some new fixed point theorems for fuzzy-dominated operators that satisfy a nonlinear contraction on a closed ball in a complete b -multiplicative metric space. Our strategy involves the use of a combination of two distinct kinds of mappings: one belongs to a weaker class of strictly increasing mappings, and the other is a class of dominated mappings. In order to demonstrate the validity of our new findings, we provide instances that are both illustrative and substantial. Finally, in order to illustrate the novelty of our findings, we provide applications that allow us …to derive the common solution to integral and fractional differential equations. Our findings have a significant impact on the interpretation of a large number of previously published studies, both present and historical. Show more

Keywords: Fixed point, b-multiplicative metric space, generalized nonlinear contraction, fuzzy dominated operators, graph contraction, ordered fuzzy mappings, integral equation, fractional differential equation

DOI: 10.3233/JIFS-238250

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Zhang, Xin-jie | Li, Jun-qing | Liu, Xiao-feng | Tian, Jie | Duan, Pei-yong | Tan, Yan-yan

Article Type: Research Article

Abstract: Enterprises have increasingly focused on integrated production and transportation problems, recognizing their potential to enhance cohesion across different decision-making levels. The whale optimization algorithm, with its advantages such as minimal parameter control, has garnered attention. In this study, a hybrid whale optimization algorithm (HWOA) is designed to settle the distributed no-wait flow-shop scheduling problem with batch delivery (DNWFSP-BD). Two objectives are considered concurrently, namely, the minimization of the makespan and total energy consumption. In the proposed algorithm, four vectors are proposed to represent a solution, encompassing job scheduling, factory assignment, batch delivery and speed levels. Subsequently, to generate high-quality candidate …solutions, a heuristic leveraging the Largest Processing Time (LPT) rule and the NEH heuristic is introduced. Moreover, a novel path-relinking strategy is proposed for a more meticulous search of the optimal solution neighborhood. Furthermore, an insert-reversed block operator and variable neighborhood descent (VND) are introduced to prevent candidate solutions from converging to local optima. Finally, through comprehensive comparisons with efficient algorithms, the superior performance of the HWOA algorithm in solving the DNWFSP-BD is conclusively demonstrated. Show more

Keywords: Distributed no-wait flow shop, batch delivery, hybrid whale optimization algorithm, path-relinking

DOI: 10.3233/JIFS-238627

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Fan, Zhou | Yanjun, Shen | Zebin, Wu

Article Type: Research Article

Abstract: In this article, a non-fragile adaptive fuzzy observer is proposed for nonlinear systems with uncertain external disturbance and measurement noise. Firstly, the nonlinear system is augmented by an output filtered transformation. The output with measurement disturbance is put into the state equation of the augment system. Then, we introduce fuzzy logic system (FLS) to approximate the measurement disturbance, and construct an augmented non-fragile adaptive fuzzy observer for the augment system. A Lyapunov function is constructed to reveal that the characteristic of estimation errors is uniformly ultimately boundedness (UUB). Finally, two experimental simulations are offered to confirm the validity of the …proposed design method. Show more

Keywords: Non-fragile, high-gain observer, adaptive observer, fuzzy logic system

DOI: 10.3233/JIFS-237271

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Rajesh Kannan, A. | Thirupathi, G. | Murali Krishnan, S.

Article Type: Research Article

Abstract: Consider the graph G , with the injection Ω from node set to the first p  + q natural numbers. Let us assume that the ceiling function of the classical average of the node labels of the end nodes of each link is the induced link assignment Ω * . If the union of range of Ω of node set and the range of Ω * of link set is all the first p  + q natural numbers, then Ω is called a classical mean labeling. A super classical mean graph is a graph …with super classical mean labeling. In this research effort, we attempted to address the super classical meanness of graphs generated by paths and those formed by the union of two graphs. Show more

Keywords: Labeling, super classical mean labeling, super classical mean graph

DOI: 10.3233/JIFS-232328

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024

Authors: Ihtisham, Shumaila | Mustafa, Ghulam | Qureshi, Muhammad Nauman | Manzoor, Sadaf | Alamgir, | Khan, Adnan

Article Type: Research Article

Abstract: This study explores the distribution of order statistics of the Alpha Power Pareto (APP) distribution. Alpha Power Pareto is a more flexible distribution proposed by adding an extra parameter in the well-known Pareto distribution. This paper focuses on the derivation of single and product moment of the APP order statistics. Additionally, a recurrence link for single moments of order statistics is established. Moreover, analytical formulas of Rényi and q-entropy for APP order statistics are obtained.

Keywords: Order statistics, q-entropy, rényi entropy, recurrence relation, single and product moments

DOI: 10.3233/JIFS-231873

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Shafi, Smd | Sathiya Kumar, C.

Article Type: Research Article

Abstract: Identifying diseases using chest X-rays is challenging because more medical professionals are needed. A chest X-ray contains many features, making it difficult to pinpoint the factors causing a disease. Moreover, healthy individuals are more common than those with illnesses, and various diseases occur at different rates. To diagnose the disease accurately using X-ray images, extracting significant features and addressing unbalanced data is essential. To resolve these challenges, a proposed ensemble self-attention-based deep neural network aims to tackle the problem of unbalanced information distribution by creating a new goal factor. Additionally, the InceptionV3 architecture is trained to identify significant features. The …proposed objective function is a performance metric that adjusts the ratio of positive to negative instances, and the suggested loss function can dynamically mitigate the impact of many negative observations by reducing each cross-entropy term by a variable amount. Tests have shown that ensemble self-attention performs well on the ChestXray14 dataset, especially regarding the dimension around the recipient’s characteristics curves. Show more

Keywords: Deep neural networks, cross-weighted entropy loss, data with discrepancies, feature extraction, X-ray

DOI: 10.3233/JIFS-236444

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Yang, Wenyang | Li, Mengdi

Article Type: Research Article

Abstract: The development of computer vision and artificial intelligence provides technical support for objective evaluation of classroom teaching, and promotes the implementation of personalized teaching by teachers. In traditional classroom teaching, due to limitations, teachers are unable to timely understand and evaluate the effectiveness of classroom teaching through students’ classroom behavior, making it difficult to meet students’ personalized learning needs. Using artificial intelligence, big data and other digital technologies to analyze student classroom learning behavior is helpful to understand and evaluate students’ learning situation, thus improving the quality of classroom teaching. By using the method of literature analysis, the paper sorts …out relevant domestic and foreign literature in the past five years, and systematically analyzes the methods of student classroom behavior recognition supported by deep learning. Firstly, the concepts and processes of student classroom behavior recognition are introduced and analyzed. Secondly, it elaborates on the representation methods of features, including image features, bone features, and multimodal fusion. Finally, the development trend of student classroom behavior recognition methods and the problems that need to be further solved are summarized and analyzed, which provides reference for future research on student classroom behavior recognition. Show more

Keywords: Behavior recognition, object detection, skeleton pose, deep learning

DOI: 10.3233/JIFS-238228

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Geetha, R. | Priya, E. | Sivakumar, Kavitha

Article Type: Research Article

Abstract: Purpose: Automated diagnosis of acute cerebral ischemic stroke lesions (ACISL) is an evolving science. Early detection and exact delineation of ACISL automatically from diffusion-weighted magnetic resonance (DWMR) images are crucial for initiating prompt treatment. Thus, this work aims to determine the appropriate slice out of 60 pieces using multi-fractal analysis (MFA) and to segment the lesions in DWMR images using a hybrid optimization method. Features extracted from the segmented images were clinically correlated with the modified Rankin Scale (mRS). Methods: Thirty-one real-time stroke patients’ images were collected from Rajiv Gandhi Government General Hospital, Chennai, India. Multiple …MRI slices were taken from each patient and filtered using an anisotropic diffusion filter (ADF). These filtered images were skull-stripped automatically by the maximum entropy thresholding technique incorporating mathematical morphological operations (MEM). The multi-fractal analysis (MFA) identifies the prominent slice with the significant infarct lesion. An isodata algorithm that integrated differential evolution with the particle swarm optimization method based on Kapur’s (IDPK) and Otsu’s (IDPO) approaches was attempted to segment the ACISL. Finally, the geometric and moment features extracted from the segmented lesions categorized the stroke severity and were correlated with the mRS. Results: The findings of the experimental work confirm that the suggested IDPK approach achieved usual normalized values for image similarity indices such as Sokal-Michener Coefficient (98.51%), Roger-Tanimoto Coefficient (90.16%), Sokel-Sneath-2 (91.04%), and Sorenson Index (90.04%) are superior to IDPO. Statistical significance proved that the segmented lesions’ area (r  = 0.820, p  <  0.0001) and perimeter (r  = 0.928, p  <  0.0001) were strongly correlated with the mild and moderate criteria of mRS. Conclusion: The proposed work effectively detected ischemic stroke lesions and their severity within the studied image groups. It could be a promising and potential tool to aid radiologists in validating their diagnosis. Show more

Keywords: Ischemic stroke lesion, magnetic resonance imaging, multi-fractal analysis, isodata algorithm, differential evolution with particle swarm optimization, modified Rankin Scale

DOI: 10.3233/JIFS-233883

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024

Authors: Lavanya, J. | Kavi Priya, S.

Article Type: Research Article

Abstract:  The paper addresses the optimization challenges in cloud resource task execution within the container paradigm, introducing the Multi-Objective Comprehensive Container Scheduling and Resource Allocation (MOCCSRA) scheme. It aims to enhance cost-effectiveness and efficiency by utilizing the Tuna Swarm Optimization (TSO) technique to optimize task planning and resource allocation. This novel approach considers various objectives for task scheduling optimization, including energy efficiency, compliance with service level agreements (SLAs), and quality of service (QoS) metrics like CPU utilization, memory usage, data transmission time, container-VM correlation, and container grouping. Resource allocation decisions are guided by the VM cost and task completion period factors. …MOCCSRA distinguishes itself by tackling the multi-objective optimization challenge for task scheduling and resource allocation, producing non-dominated Pareto-optimal solutions. It effectively identifies optimal tasks and matches them with the most suitable VMs for deploying containers, thereby streamlining the overall task execution process. Through comprehensive simulations, the results demonstrate MOCCSRA’s superiority over traditional container scheduling methods, showcasing reductions in resource imbalance and notable enhancements in response times. This research introduces an innovative and practical solution that notably advances the optimization field for cloud-based container systems, meeting the increasing demand for efficient resource utilization and enhanced performance in cloud computing environments. Show more

Keywords: Cloud container, task scheduling, resource allocation, DSTS, multi-objective optimization, tuna swarm optimizer, pareto optimality

DOI: 10.3233/JIFS-234262

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Su, Jiafu | Xu, Baojian | Liu, Hongyu | Chen, Yijun | Zhang, Xiaoli

Article Type: Research Article

Abstract: As an emerging concept in knowledge management (KM), green knowledge management plays a crucial role in the sustainable development of enterprises. A reasonable assessment of an enterprise’s green knowledge management capabilities can help the company effectively manage the embedded green knowledge within its operational processes, thereby achieving self-reinforcement of competitive advantages for the enterprise. Therefore, this paper proposes a multi-criteria classification method based on interval-valued intuitionistic fuzzy entropy weight method-TOPSIS-Sort-B (EWM-TOPSIS-Sort-B) to assess the green knowledge management capabilities of enterprises. In this method, expert assessments are expressed using interval-valued intuitionistic fuzzy sets. A new entropy weight method is introduced into …TOPSIS-Sort-B to determine the weights of various evaluation indicators, and TOPSIS-Sort-B is employed to classify and rate each evaluation scheme. It is worth noting that this paper has improved the TOPSIS-Sort-B method by not converting interval-valued intuitionistic fuzzy sets into precise values throughout the entire evaluation process, thus avoiding information loss. Finally, we applied a case of knowledge management capability assessment to validate the proposed method, and conducted sensitivity analysis and comparative analysis on this approach. The analysis results indicate that variations in the parameter ϑ of the interval-valued intuitionistic fuzzy aggregation operator lead to changes in criterion weights and the comprehensive evaluation matrix, resulting in unordered changes in the final classification results. Due to the absence of transformation of interval values in this study, compared to the four classification methods of TOPSISort-L, the classification results are more detailed, and the evaluation levels are more pronounced. Show more

Keywords: Interval-valued intuitionistic fuzzy set, TOPSIS-Sort-B, entropy weight method, green knowledge management capability

DOI: 10.3233/JIFS-239001

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024

Authors: Xiao, Le | Chen, Xiaolin | Shan, Xin

Article Type: Research Article

Abstract: News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using Large Language Model(LLM) with powerful natural language understanding and generative capabilities. We also designed News Summary Generator (NSG), …which aims to select and evolve the event pattern population and generate news summaries, so that using LLM extracts structured event patterns from events contained in news paragraphs, evolves the event pattern population using a genetic algorithm, and selects the most adaptive event patterns to input into LLM in order to generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability. Show more

Keywords: News summary generation, large language model, genetic algorithm, evolution

DOI: 10.3233/JIFS-237685

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Zheng, Quanchang

Article Type: Research Article

Abstract: We investigate the semi-online problem of MapReduce scheduling on two parallel machines. We aim to minimize the makespan. Jobs are released over-list, and each job includes a map task and a reduce task. The job’s map task can be preemptive and scheduled simultaneously onto different machines, however, the reduce task is non-preemptive. The job’s reduce task needs to wait for its map task to complete before starting. We consider the following two versions: Firstly, we know the processing time of the largest reduce task beforehand, and then design a 4/3-competitive optimal semi-online algorithm. Secondly, we know in advance the value …of the reduce task with the largest processing time and the the total sum of the processing times. Then we present a 4/3-competitive semi-online algorithm. We conclude that the algorithm is the best possible when the largest reduce task meets certain conditions. Show more

Keywords: MapReduce system, semi-online, scheduling, competitive ratio, makespan

DOI: 10.3233/JIFS-239276

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Cui, Jinrong | Sun, Haosen | Kuang, Ciwei | Xu, Yong

Article Type: Research Article

Abstract: Effective fire detection can identify the source of the fire faster, and reduce the risk of loss of life and property. Existing methods still fail to efficiently improve models’ multi-scale feature learning capabilities, which are significant to the detection of fire targets of various sizes. Besides, these methods often overlook the accumulation of interference information in the network. Therefore, this paper presents an efficient fire detection network with boosted multi-scale feature learning and interference immunity capabilities (MFII-FD). Specifically, a novel EPC-CSP module is designed to enhance backbone’s multi-scale feature learning capability with low computational consumption. Beyond that, a pre-fusion module …is leveraged to avoid the accumulation of interference information. Further, we also construct a new fire dataset to make the trained model adaptive to more fire situations. Experimental results demonstrate that, our method obtains a better detection accuracy than all comparative models while achieving a high detection speed for video in fire detection task. Show more

Keywords: Object detection, fire detection, efficient, multi-scale feature learning, interference immunity

DOI: 10.3233/JIFS-238164

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Lu, Mingzhen

Article Type: Research Article

Abstract: The idea of sustainable development has become more important in resolving environmental issues and fostering a healthy coexistence of human endeavors with the natural world. Internet of Things (IoT) technology is expanding across many industries, and it is also advancing in agriculture and the agricultural environment. The planning and design for intelligent gardens using a unique Sunflower Optimized-Enhanced Support Vector Machine (SFO-ESVM) is thoroughly analyzed and researched in this study. The development and plan of intelligent gardens are investigated using agricultural IoT technologies and agricultural landscapes. First, we used the SFO method to select the best garden plan inspired by …the mathematical patterns observed in sunflower seed groupings. Next, we use an ESVM model to assess how well each plant species fits into the planned garden. The SFO-ESVM considers several variables, such as soil qualities, climatic information, plant traits, and ecological requirements, to choose the best plants. Additionally, we create an intelligent control system that combines sensors, actuators, and IoT technologies to track and regulate the environmental parameters of the garden. The SFO-ESVM-based conceptual planning and design framework for smart gardens is proposed and systematically extended to give scientific direction for the agricultural IoT of smart gardens. The proposed method was then tested in a real-world garden environment. The outcomes show that the SFO-ESVM framework-based intelligent design and execution of the sustainable development-oriented garden combines ecological principles with innovative optimization methods. Show more

Keywords: Intelligent design and realization, garden, internet of things (IoT), sustainable development, sunflower optimized-enhanced support vector machine (SFO-ESVM)

DOI: 10.3233/JIFS-234540

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: He, Shun | Li, Chaorong | Wang, Xingjie | Zeng, Anping

Article Type: Research Article

Abstract: This paper proposes a watermarking method that can be used for the copyright protection of DNN models, utilizing learnable block-wise image transformation techniques and a secret key to embed a watermark. A black-box watermarking approach is used, which does not require a specific predefined training or trigger set, allowing for the remote verification of model ownership. As a result, this method can achieve copyright protection using authentication methods for DNN models. Results of experiments on established datasets [1, 2 ] indicate that the original watermark is not easily overwritten by pirated watermarks. Moreover, its performance in pruning attack experiments is …similar to that observed in the studies cited above. However, our approach demonstrates stronger robustness against fine-tuning attacks, while also achieving higher image classification accuracy. Show more

Keywords: DNN watermark, block-wise image transformation, black-box watermark, robustness

DOI: 10.3233/JIFS-240274

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Long, Huimin | Zheng, Hang | Chen, Ming | Liu, Chengjian

Article Type: Research Article

Abstract: The detection of communication signals in heterogeneous electromagnetic environments currently relies primarily on a one-dimensional statistical feature threshold method. However, this approach is highly sensitive to dynamic changes in the environment, fluctuations in signal-to-noise ratios, and complex noise. To address these limitations, this paper proposes a novel time-frequency diagram based on high-order accumulation for signal detection. Traditional time-frequency diagrams suffer from poor noise suppression ability and unclear features. However, higher-order cumulants can effectively overcome these shortcomings. Currently, methods based on higher-order cumulants are typically limited to one-dimensional signals. Yet, two-dimensional time-frequency signal diagrams can represent a broader array of features. …This paper employs higher-order accumulation to extract time-frequency features from the received signal, thereby transforming the conventional radio detection problem into an image recognition challenge. By merging the advantages of higher-order accumulations and time-frequency diagrams, we propose the use of higher-order accumulation time-frequency diagrams for signal detection. Extensive experimental simulations demonstrate that the proposed time-frequency diagram exhibits strong anti-noise performance and effectively suppresses frequency bias from multiple perspectives. The performance of the Higher-Order Cumulant-Time Frequency (HOC-TF) indicated lower Root Mean Square Error (RMSE) compared with the Short-Time Fourier Transform-Time Frequency (STFT-TF) and Wavelet Transform-Time Frequency (WT-TF). Additionally, compared to the STFT-TF and WT-TF methodologies, the novel time-frequency diagram introduced demonstrates superior stability using the Singular Value Decomposition (SVD) method. Moreover, by combining the new time-frequency diagram with the deep learning YOLOV5 network, signal detection and modulation identification of communication signals can be achieved. Show more

Keywords: Signal detection, higher-order cumulant, novel time-frequency diagram

DOI: 10.3233/JIFS-237988

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 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: 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: 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: 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: 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: Shivkumar, S. | Amudha, J. | Nippun Kumaar, A.A.

Article Type: Research Article

Abstract: Navigation of a mobile robot in an unknown environment ensuring the safety of the robot and its surroundings is of utmost importance. Traditional methods, such as pathplanning algorithms, simultaneous localization and mapping, computer vision, and fuzzy techniques, have been employed to address this challenge. However, to achieve better generalization and self-improvement capabilities, reinforcement learning has gained significant attention. The concern of privacy issues in sharing data is also rising in various domains. In this study, a deep reinforcement learning strategy is applied to the mobile robot to move from its initial position to a destination. Specifically, the Deep Q-Learning algorithm …has been used for this purpose. This strategy is trained using a federated learning approach to overcome privacy issues and to set a foundation for further analysis of distributed learning. The application scenario considered in this work involves the navigation of a mobile robot to a charging point within a greenhouse environment. The results obtained indicate that both the traditional deep reinforcement learning and federated deep reinforcement learning frameworks are providing 100% success rate. However federated deep reinforcement learning could be a better alternate since it overcomes the privacy issue along with other advantages discussed in this paper. Show more

Keywords: Federated deep reinforcement learning, navigation, path-planning, mobile robot, robotics

DOI: 10.3233/JIFS-219428

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Wu, Meiqin | Ma, Linyuan | Fan, Jianping

Article Type: Research Article

Abstract: This article proposes an expert-driven consensus and decision-making model that comprehensively considers expert behavior in Multi-criteria decision-making (MCDM) scenarios. Under the premise that experts are willing to adjust their viewpoints, the framework strives to reach group consensus to the utmost degree feasible. To tackle experts’ uncertainty during the evaluation process, this article employs the rejection degree in the picture fuzzy sets (PFS) to signify the level of ignorance while they deliver their evaluation opinions. Due to the diversity of expert views, reaching a group consensus is difficult in reality. Therefore, this article additionally presents a strategy for adjusting the weights …of experts who did not reach consensus. This approach upholds data integrity and guarantees the precision of the ultimate decision. Finally, this article confirms the efficiency of the aforementioned model by means of a case study on selecting the optimal carbon reduction alternative for Chinese power plants. Show more

Keywords: Picture fuzzy sets (PFS), weight of experts, behavior-driven, Multi-criteria decision-making (MCDM), Consensus reaching process (CRP)

DOI: 10.3233/JIFS-238151

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Jayswal, Hardik S. | Chaudhari, Jitendra | Patel, Atul | Makwana, Ashwin | Patel, Ritesh | Dubey, Nilesh | Ghajjar, Srushti | Sharma, Shital

Article Type: Research Article

Abstract: A nation’s progress is directly linked to the effective functioning of its agricultural sector. The detection and classification of plant disease is an essential component of the agricultural industry. Plant diseases may result in substantial financial losses due to decreased crop production. As per the Food and Agriculture Organization of the United Nations, it is estimated that plant diseases result in a reduction of approximately 10-16% in global crop yields annually. Farmers are traditionally relying on visual inspection, using naked eye observation, as the primary method for detecting plant diseases. This involves a meticulous examination of crops to identify any …visible signs of diseases. However, manual disease detection can lead to delayed identification, resulting in significant crop losses. Various methods, coupled with machine learning classifiers, were demonstrated effectiveness in scenarios involving manual feature extraction and limited datasets. However, to handle larger datasets, deep learning models such as Inception V4, ResNet-152, EfficientNet-B5, and DenseNet-201 were studied and implemented. Among these models, DenseNet-201 exhibited superior performance and accuracy compared to the previous methodology. Additionally, A Fine-tuning Deep Learning Model called SympDense was developed, which surpassed other deep learning models in terms of accuracy. Show more

Keywords: Plant diseases, classification, deep learning, SympDense

DOI: 10.3233/JIFS-239531

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Yuan, Chao | Zhao, Ziqi

Article Type: Research Article

Abstract: With the acceleration of urbanization, the concept of smart city is rising gradually. Wireless sensor network as an important technical support of smart city, its application in environmental monitoring and water resources management has a profound impact on economic growth. Water resource is one of the most dependent resources for human beings. With the growth of world population and the rapid development of economy, water resource crisis is constant, water pollution, water shortage and water waste coexist. How to build a perfect water resource economic policy is a worldwide problem at present. At present, the formulation of water resources policies …is often based on experience or the knowledge system of decision makers. Due to the dynamic nature of water resources utilization and the incomplete information of decision makers, there are often policy failures, which affect economic growth. Based on this, this paper uses system dynamics model to study the mechanism of water resources management policies affecting economic growth by taking Gansu, Tianjin and Zhejiang as three qualitatively representative arid areas, transitional areas and water-rich areas. The research results show that under the same water resources policy coupling, different regions also have different eco-economic effects. The effect of coupled water resources policy is better than that of single water resources management policy. Show more

Keywords: Smart city, environmental monitoring, water resources management, economic growth

DOI: 10.3233/JIFS-242195

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Ngo, Quoc Trinh | Nguyen, Linh Quy | Vu, Trung Hieu | Nguyen, Long Khanh | Tran, Van Quan

Article Type: Research Article

Abstract: Cemented paste backfill (CPB), a mixture of wet tailings, binding agent, and water, proves cost-effective and environmentally beneficial. Determining the Young modulus during CPB mix design is crucial. Utilizing machine learning (ML) tools for Young modulus evaluation and prediction streamlines the CPB mix design process. This study employed six ML models, including three shallow models Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Random Forest (RF) and three hybrids Extreme Gradient Boosting-Particle Swarm Optimization (XGB-PSO), Gradient Boosting-Particle Swarm Optimization (GB-PSO), Random Forest-Particle Swarm Optimization (RF-PSO). The XGB-PSO hybrid model exhibited superior performance (coefficient of determination R2  = 0.906, root mean square error …RMSE = 19.535 MPa, mean absolute error MAE = 13.741 MPa) on the testing dataset. Shapley Additive Explanation (SHAP) values and Partial Dependence Plots (PDP) provided insights into component influences. Cement/Tailings ratio emerged as the most crucial factor for enhancing Young modulus in CPB. Global interpretation using SHAP values identified six essential input variables: Cement/Tailings, Curing age, Cc, solid content, Fe2 O3 content, and SiO2 content. Show more

Keywords: Cemented paste backfill (CPB), young modulus, interpretable machine learning, cement/tailings, mix design

DOI: 10.3233/JIFS-237539

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: Hussain, Abrar | Zhang, Nan | Ullah, Kifayat | Garg, Harish | Al-Quran, Ashraf | Yin, Shi

Article Type: Research Article

Abstract: The q-rung orthopair fuzzy set (q-ROFS) is a moderate mathematical model, that has diverse capabilities to handle uncertain and ambiguous information of human opinion during the decision analysis process. The Aczel Alsina operations are more flexible and valuable aggregating tools with parameter values ϻ ⩾ 1, reflecting smooth and accurate information by aggregating awkward and redundant information. The theory of the Choquet integral operator is also used to express the interaction between preferences or criteria by incorporating certain values of preferences. The primary features of this article are to derive some dominant mathematical approaches by combining two different theories like Choquet integral …operators and operations of Aczel Alsina tools namely “q-rung orthopair fuzzy Choquet integral Aczel Alsina average” (q-ROFCIAAA), and “q-rung orthopair fuzzy Choquet integral Aczel Alsina geometric” (q-ROFCIAAG) operators. Some special cases and notable characteristics are also demonstrated to show the feasibility of derived approaches. Based on our derived aggregation approaches, a multi-attribute decision-making (MADM) technique aggregates redundant and unpredictable information. In light of developed approaches, a numerical example study to evaluate suitable safety equipment in the construction sector. To reveal the intensity and applicability of derived approaches by contrasting the results of prevailing approaches with currently developed AOs. Show more

Keywords: q-rung orthopair fuzzy values, choquet integral operators, aczel alsina operations, and decision support system

DOI: 10.3233/JIFS-240169

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Wu, Xiongyu | Yan, Yixuan | Zhu, Wenxi | Yang, Nina

Article Type: Research Article

Abstract: BACKGROUND: In recent years, Despite the proven economic growth brought by AI technology globally, the adoption of AI in the construction industry faces obstacles. To better promote the adoption of AI technology in the construction domain, this study, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, delves into the key factors influencing the adoption of AI technology in the construction industry. By introducing personal-level influencing factors such as AI anxiety and personal innovativeness, the UTAUT model is extended to comprehensively understand users’ attitudes and adoption behaviors towards AI technology. METHODOLOGY: The research …framework is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) with the added constructs of artificial intelligence anxiety and individual Innovativeness. These data were collected through a combination of online and offline surveys, with a total of 258 valid data collected and analyzed using structural equation modeling. RESULTS: The study found that Usage Behavior (UB) in adopting Artificial Intelligence (AI) is positively influenced by several factors. Specifically, Performance Expectancy (PE) (β= 0.266, 95%), Effort Expectancy (EE) (β= 0.262, 95%), and Social Influence (SI) (β= 0.131, 95%) were identified as significant predictors of UB. Additionally, Facilitating Conditions (FC) (β= 0.168, 95%) also positively influenced UB.Moreover, the study explored the moderating effects of Artificial Intelligence Anxiety and Individual Innovativeness on the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) with the Usage Behavior of AI technology. PRACTICAL IMPLICATIONS: This study lie in informing industry stakeholders about the multifaceted dynamics influencing AI adoption. Armed with this knowledge, organizations can make informed decisions, implement effective interventions, and navigate the challenges associated with integrating AI technology into the construction sector. Show more

Keywords: UTAUT, artificial intelligence, construction industry

DOI: 10.3233/JIFS-240798

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: AL-Qadri, Mohammed | Gao, Peiwei | Zhang, Hui | Zhao, Zhiqing | Chen, Lifeng | Cen, Feng | Zhang, Jun

Article Type: Research Article

Abstract: Crack detection in concrete buildings is crucial for assessing structural health, but it poses challenges due to complex backgrounds, real-time requirements, and high accuracy demands. Deep learning techniques, including U-Net and Fully Convolutional Networks (FCN), have shown promise in crack detection. However, they are sensitive to real-world environmental variations, impacting robustness and accuracy. This paper compares the performance of U-Net and FCN for concrete crack detection on bridges using raw images under various conditions. A dataset of 157 images (100 for training, 57 for testing) was used, and the models were evaluated based on Dice similarity coefficient and Jaccard index. …FCN slightly outperformed U-Net in accuracy (94.88% vs. 94.21%), while U-Net had a slight advantage in validation (93.55% vs. 92.99%). These findings provide valuable insights for automated infrastructure maintenance and repair. Show more

Keywords: Cracks detection, concrete buildings, deep learning, U-Net, Fully Convolutional Networks (FCN)

DOI: 10.3233/JIFS-239709

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Parthiban, P. | Vaisakhi, V.S.

Article Type: Research Article

Abstract: Wireless sensor network (WSN) collect and detect data in real time, but their battery life limits their lifetime. The CH selection process increases network overhead and reduces lifetime, but it considers node processing and energy limitations. To solve that problem this research methodology proposed Multi Objective Energy trust - Aware Optimal Clustering and Secure Routing (MOETAOCSR) protocol. At first, the trust factors such as direct and indirect factors are calculated. Thus, the calculated values are given as input to the SDLSTM to detect the malicious node and normal node. Here, the network deployment process is initially carried out and then …the cluster is formed by HWF-FCM. From the clustered sensor nodes, the cluster head is selected using Golden Jackal Siberian Tiger Optimization (GJSTO) approach. Then, the selection of CH the paths are learned by using the Beta Distribution and Scaled Activation Function based Deep Elman Neural Network (BDSAF-DENN) and from the detected paths the optimal paths are selected using the White Shark Optimization (WSO). From the derived path sensed data securely transferred to the BS for further monitoring process using FPCCRSA. The proposed technique is implemented in a MATLAB platform, where its efficiency is assessed using key performance metrics including network lifetime, packet delivery ratio, and delay. Compared to existing models such as EAOCSR, RSA, and Homographic methods, the proposed technique achieves superior results. Specifically, it demonstrates a 0.95 improvement in throughput, 0.8 enhancement in encryption time, and a network lifetime of 7.4. Show more

Keywords: Four point curve cryptographic and rivest shamir adleman (FPCCRSA), Haversine with weighted function based fuzzy c-means clustering (HWF-FCM), wireless sensor network, Cluster head (CH), sigmoid deep long short term memory (SDLSTM)

DOI: 10.3233/JIFS-236739

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Yu, Hongliang | Peng, Zhen | Wu, Zhaoliang | He, Zirui | Huang, Chun

Article Type: Research Article

Abstract: To address the existing shortcomings in the research on the coupling of safety risk factors in subway tunnel construction using the shallow-buried excavation method, this paper conducts a coupled analysis and dynamic simulation of the safety risks associated with this construction method. Firstly, by analyzing the mechanisms and effects of risk coupling in shallow-buried excavation construction of subway tunnels, this study divides the risk system into four risk subsystems (human, material, management, and environment), establishes an evaluation index system for the coupling of safety risks, calculates the comprehensive weight values of the risk indicators using the AHP-entropy weight method, and …constructs a risk coupling degree model by combining the inverse cloud model and efficacy function. Subsequently, based on the principles of system dynamics, a causal relationship diagram and a system dynamics simulation model for the coupling of “human-material” risks in construction are established using Vensim PLE software. Finally, the case study of the underground excavation section of Chengdu Metro Line 2 is employed to perform dynamic simulation using the established model. By adjusting the relevant risk coupling coefficients and simulation duration, the impact of the coupling of various risk factors on the safety risk level of the human-material coupling system is observed. The simulation results demonstrate that: 1) Heterogeneous coupling of human and material risks has a particularly significant effect on the system’s safety risks; 2) Violations by personnel and initial support structure defects are key risk coupling factors. The findings of this study provide new insights for decision-makers to assess the safety risk of shallow-buried excavation construction in subway tunnel. Show more

Keywords: Shallow-buried excavation method, risk coupling, coupling degree model, system dynamics, simulation analysis

DOI: 10.3233/JIFS-239674

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Saleh Mohamed Naser, Naser | Serte, Sertan | Al-Turjman, Fadi

Article Type: Research Article

Abstract: Deep learning has recently made great progress leading to revolutionizing image recognition, speech recognition, and natural language processing tasks that were previously challenging to make using traditional techniques. Image classification offers a lot of potential for architectural design, even though it is rarely used to uncover new techniques. It can be used to determine the client’s preferences and design a building that satisfies those preferences. The different architectural styles based on culture, region, and time are one of the main challenges for image classification in architecture. Hence, it can be challenging for untrained clients to recognize an architectural style, and …sometimes some buildings are made up of various types that are difficult to classify as a single style. This paper investigates the potential of employing state-of-art cutting-edge image classification algorithms in houses classification. In addition, the paper proposes the uses of Shifted Patch Tokenization (SPT) and Locality Self-Attention (LSA) in order to enhance the performance of Vision transformer (ViT) when trained to classify house images with a small dataset, opposed to the regular ViT which requires huge dataset in order to converge. Experimentally, these techniques proved to have a positive impact on the performance of the ViT, which reached 96.85% accuracy when SPT and LSA are employed. Show more

Keywords: Image recognition, house classification, vision transformer, ViT, shifted patch tokenization, locality self-attention

DOI: 10.3233/JIFS-230972

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Bai, Hao | Wang, Wubin | Tang, Hao | Li, Xin | Zhao, Yinting | Lv, Dongqin

Article Type: Research Article

Abstract: This study utilized several coupled approaches to create powerful algorithms for forecasting the compressive strength (C s ) of concretes that include metakaolin (MK ) and fly ash (FA ). For this purpose, three various methods were considered, named random forests (RF ), Categorical boosting model (CB ), and extreme gradient boosting (XGB ) by considering the seven most influential input variables. It was tried to divide the concrete components to binder value (B ) to achieve the non-dimensional input variables. Herein, the cutting-edge Tasmanian devil Optimization (TDO ) algorithm was linked with RF , XGB , and CB …for the purpose of determining the optimal values of hyperparameters (named TD  - CB , TD  - RF , and TD  - XG ). It is worth mentioning that developing the mentioned algorithms optimized with TD to estimate the mechanical properties of the concrete containing several important admixtures can be recognized as this study’s contribution to practical applications. The findings indicate that the algorithms possess a notable capacity to precisely forecast the C s of concrete, which includes MK and FA , with R 2 bigger than roughly 0.97. The lower value of OBJ comprehensive index belonged to the TD  - CB at 1.5762, followed by TD  - XG at 1.9943 and then 2.3317 related to TD  - RF with almost 70% reduction. The sensitivity analysis demonstrated that the prediction of C s is highly influenced by all input parameters, which are higher than 0.8659, but a higher influence from MK /B at 0.9548. Show more

Keywords: Modified concrete, metakaolin, fly ash, unary and binary mix, estimation, categorical boosting

DOI: 10.3233/JIFS-242189

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Atef, Shimaa | El-Seidy, Essam | Abd El-Salam, Salsabeel M.

Article Type: Research Article

Abstract: Relatedness is necessary and causal in the development of social life. Interlayer relatedness is a measure of how one player’s decisions affect the decisions of other players in the game. The relatedness can be positive or negative. We had to determine how effective each strategy was under specific conditions, and how the correlation between players affected their payoffs. In this paper, we analytically study the strategies that enforce linear payoff relationships in the Iterated Prisoner’s Dilemma (IPD) game considering both a relatedness factor. As a result, we first reveal that the payoffs of two players and three players can be …represented by the form of determinants as shown by Press and Dyson even with the factor. Show more

Keywords: Equalizer, iterated prisoner’s dilemma (IPD), relatedness, two-player, three-player, zero-determinant strategies (ZD)

DOI: 10.3233/JIFS-239406

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024