Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Kumar, Ajay | Singh, Anuj Kumar | Garg, Ankit

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-233443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1259-1273, 2024

Authors: Jia, Xiaoying

Article Type: Research Article

Abstract: Under the premise of today’s socialist modernization construction, the development and progress of the country require more outstanding talents such as aspiring youth and intellectuals to participate, which puts forward higher requirements for education quality indicators and various aspects of operation. As the main institution and environment for implementing educational activities, the effectiveness of school management organization has a direct impact and even a decisive role on the quality of education. Therefore, how to improve the quality management of school education has become a hot topic in the education industry. The education quality management evaluation in higher education institutions is …viewed as the multiple-attribute decision-making (MADM) issue. In this paper, the interval-valued neutrosophic number cross-entropy (IVNN-CE) technique is built under interval-valued neutrosophic sets (IVNSs) based on the traditional cross-entropy technique. Then, combine traditional cross-entropy technique with IVNSs, the IVNN-CE technique is constructed for MADM under IVNSs. Finally, the numerical example for education quality management evaluation in higher education institutions was constructed and some comparisons is employed to verify advantages of IVNN-CE technique. The main contribution of this paper is constructed: (1) the cross-entropy model is extended to IVNSs; (1) the CRITIC technique is employed to construct the attribute weights under IVNSs; (3) the IVNN-CE technique is constructed to manage the MADM under IVNSs; (4) a case study about education quality management evaluation in higher education institutions is constructed to show the built technique; (5) some comparative algorithms are constructed to verify the rationality of IVNN-CE technique. Show more

Keywords: Multiple attribute decision making (MADM), interval-valued neutrosophic sets (IVNSs), cross-entropy technique, education quality management evaluation

DOI: 10.3233/JIFS-233481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1275-1286, 2024

Authors: Liu, Yayun | Ning, Kuangfeng

Article Type: Research Article

Abstract: The adaptive fusion module with an attention mechanism functions by employing a dual-channel graph convolutional network to aggregate neighborhood information. The resulting embeddings are then utilized to calculate interaction terms, thereby incorporating additional information. To enhance the relevance of fusion information, an adaptive fusion module with an attention mechanism is constructed. This module selectively combines the neighborhood aggregation and interaction terms, prioritizing the most pertinent information. Through this adaptive fusion process, the algorithm effectively captures both neighborhood features and other nonlinear information, leading to improved overall performance. Neighborhood Aggregation Interaction Graph Convolutional Network Adaptive Fusion (NAIGCNAF) is a graph representation …learning algorithm designed to obtain low-dimensional node representations while preserving graph properties. It addresses the limitations of existing algorithms, which tend to focus solely on aggregating neighborhood features and overlook other nonlinear information. NAIGCNAF utilizes a dual-channel graph convolutional network for neighborhood aggregation and calculates interaction terms based on the resulting embeddings. Additionally, it incorporates an adaptive fusion module with an attention mechanism to enhance the relevance of fusion information. Extensive evaluations on three citation datasets demonstrate that NAIGCNAF outperforms other algorithms such as GCN, Neighborhood Aggregation, and AIR-GCN. NAIGCNAF achieves notable improvements in classification accuracy, ranging from 1.0 to 1.6 percentage points on the Cora dataset, 1.1 to 2.4 percentage points on the Citeseer dataset, and 0.3 to 0.9 percentage points on the Pubmed dataset. Moreover, in visualization tasks, NAIGCNAF exhibits clearer boundaries and stronger aggregation within clusters, enhancing its effectiveness. Additionally, the algorithm showcases faster convergence rates and smoother accuracy curves, further emphasizing its ability to improve benchmark algorithm performance. Show more

Keywords: Graph representation learning, graph convolutional neural network (GCNN), attention mechanism, node classification

DOI: 10.3233/JIFS-234086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1287-1314, 2024

Authors: Lv, Jingjing

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-234212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1315-1328, 2024

Authors: Jiang, Zhujun | Zhou, Jieyong | He, Qixiang

Article Type: Research Article

Abstract: Fuzzy singular Lyapunov matrix equations have many applications, but feasible numerical methods to solve them are absent. In this paper, we propose an efficient numerical method for fuzzy singular Lyapunov matrix equations, where A is crisp and semi-stable. In our method, we transform fuzzy singular Lyapunov matrix equation into two crisp Lyapunov matrix equations. Then we solve the least squares solutions of the two crisp Lyapunov matrix equations, respectively. The existence of fuzzy solution is also considered. At last, two small examples are presented to illustrate the validate of the method and two large scale examples that the existing method …fails to slove are presented to show the efficiency of the method. Show more

Keywords: Fuzzy, singular Lyapunov matrix equations, semi-stable, Extension method, the least squares solutions

DOI: 10.3233/JIFS-230990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1329-1340, 2024

Authors: Geetha, M.P. | Karthika Renuka, D.

Article Type: Research Article

Abstract: A recommendation system serves as a distributed information filter, predicting customer preferences in reviews, ratings, and comments. Analysing customer behaviour aids in understanding needs and predicting intentions. E-commerce tracks product usage and sentiment to provide a personalized network based on consumer preference modelling. The challenge lies in optimizing item selection for suitable consumers to enhance performance. To address this, an imperative is the item recommendation approach for modelling future consumer behaviour. However, traditional machine learning methods often overlook dynamic product recommendations due to evolving user interests and changes in preferences reflected in customer ratings, causing cold-start issues. To overcome these …challenges, a comprehensive deep learning approach is introduced. This approach incorporates a deep neural network for consumer preference prediction, utilizing a multi-task learning paradigm to accommodate variations in consumer ratings. The research contribution lies in applying this network to predict consumer preference scores based on latent multimodal information and item characteristics. Initially, the architecture manages changing consumer aspects and preferences by extracting features and latent factors from customer review rating data. These latent factors include customer demographic information and other concealed features that signify preferences based on experiences and behaviours. Extracted latent features are processed using a sentiment analysis model to generate embedding latent features. A finely-tuned deep neural network with hyper-parameter adjustments serves as a prediction network, forming a customer performance-oriented recommendation system. It processes embedded latent features along with associated sentiments to achieve high prediction accuracy, reliability, and latency. The deep learning architecture, enriched with consumer-specific discriminative information, generates an objective function for item recommendations with minimal error, significantly enhancing predictive performance. Empirical experiments on Amazon review datasets validate the proposed model’s performance, showcasing its enhanced effectiveness and scalability in handling substantial data volumes. Show more

Keywords: Product recommendation, multitask learning, consumer buying behaviour analysis, user preference modelling

DOI: 10.3233/JIFS-231116

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1341-1357, 2024

Authors: Liu, Feng-Lang | Chien, Li-Chih | Chang, Ting-Yu | Ku, Cooper Cheng-Yuan | Chang, Ching-Ter

Article Type: Research Article

Abstract: Improving technological innovation (TI) capabilities is an integral component of government policies aimed at improving the competitiveness of small and medium enterprises (SMEs). This study aims to address implementation challenges arising from the use of Qualitative Forecasting Method (QFM) in new product development programs and proposes a novel method to aid decision makers (DMs) in their decision-making process. To tackle this issue, a hybrid method is proposed, incorporating Fuzzy Delphi method (FDM), Fuzzy Analytic Hierarchy Process (FAHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Multi-Choice Goal Programming with utility function (MCGP-U), while introducing prospect theory …as a novel approach. is proposed. The proposed method offers several advantages, including effective early planning, accurate identification of key success factors (KSFs), selection of the most suitable project leader, and estimation of the most reasonable resource investment, all of which are critical factors for success in TI for enterprises. The research results show that (1) the proposed method reduces project execution time by 20% compared to the original manual planning, (2) it facilitates the acquisition of KSFs using a rational approach to ensure project success, and (3) it increases the financial returns of the company by 17% compared to the company’s forecast. In summary, this paper makes a significant contribution to practical applications and additionally contributes to decision-making field by introducing prospect theory into the proposed hybrid method. Show more

Keywords: Technological innovation, decision-making model, fuzzy, MCGP

DOI: 10.3233/JIFS-234327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1359-1378, 2024

Authors: Liu, Die | Xu, MengDie | Li, ZhiTing | He, Yingying | Zheng, Long | Xue, Pengpeng | Wu, Xiaodong

Article Type: Research Article

Abstract: Concrete surface crack detection plays a crucial role in ensuring concrete safety. However, manual crack detection is time-consuming, necessitating the development of an automatic method to streamline the process. Nonetheless, detecting concrete cracks automatically remains challenging due to the heterogeneous strength of cracks and the complex background. To address this issue, we propose a multi-scale residual encoding network for concrete crack segmentation. This network leverages the U-NET basic network structure to merge feature maps from different levels into low-level features, thus enhancing the utilization of predicted feature maps. The primary contribution of this research is the enhancement of the U-NET …coding network through the incorporation of a residual structure. This modification improves the coding network’s ability to extract features related to small cracks. Furthermore, an attention mechanism is utilized within the network to enhance the perceptual field information of the crack feature map. The integration of this mechanism enhances the accuracy of crack detection across various scales. Furthermore, we introduce a specially designed loss function tailored to crack datasets to tackle the problem of imbalanced positive and negative samples in concrete crack images caused by data imbalance. This loss function helps improve the prediction accuracy of crack pixels. To demonstrate the superiority and universality of our proposed method, we conducted a comparative evaluation against state-of-the-art edge detection and semantic segmentation methods using a standardized evaluation approach. Experimental results on the SDNET2018 dataset demonstrate the effectiveness of our method, achieving mIOU, F1-score, Precision, and Recall scores of 0.862, 0.941, 0.945, and 0.9394, respectively. Show more

Keywords: Crack segmentation, U-NET, residual structure, attention mechanism

DOI: 10.3233/JIFS-231736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1379-1392, 2024

Authors: Zhang, Bin | Li, Jianqi | Li, Zewen | Sun, Jian | Xia, Yixiang | Zou, Pinlong

Article Type: Research Article

Abstract: The prediction of power demand for unmanned aerial vehicles (UAV) is an essential basis to ensure the rational distribution of the energy system and stable economic flight. In order to accurately predict the demand power of oil-electric hybrid UAV, a method based on variational mode decomposition (VMD) and Sparrow Search Algorithm (SSA) is proposed to optimize the hybrid prediction model composed of long-short term memory (LSTM) and Least Squares Support Vector Machine (LSSVM). Firstly, perform VMD decomposition on the raw demand power data and use the sample entropy method to classify the feature-distinct mode components into high-frequency and low-frequency categories. …Then, each modality component was separately input into the mixed model for rolling prediction. The LSSVM model and LSTM model were used to process low-frequency and high-frequency components, respectively. Finally, the predicted values for each modal component are linearly combined to obtain the final predicted value for power demand. Compared with the current models, the prediction model constructed in this paper stands out for its superior ability to track the changing trends of power demand and achieve the highest level of prediction accuracy. Show more

Keywords: UAV demand power, variational mode decomposition, sparrow search algorithm, long-short term memory, long-short term memory

DOI: 10.3233/JIFS-234263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1393-1406, 2024

Authors: Hou, Junjian | Xu, Yaxiong | He, Wenbin | Zhong, Yudong | Zhao, Dengfeng | Zhou, Fang | Zhao, Mingyuan | Dong, Shesen

Article Type: Research Article

Abstract: Fatigue driving is one of the primary causative factors of road accidents. It is of great significance to discern, identify and warn drivers in time for traffic safety and reduce traffic accidents. In this paper, a systematic review for the fatigue driving behavior recognition method is developed to analyze its research status and development trends. Firstly, the data information and its application scenarios related to fatigue driving is detailed. Three driving behavior recognition methods based on different types of signal data are summarized and analyzed, and this signal data can be divided into physiological signal characteristics, visual signal characteristics, vehicle …sensor data characteristics and multi-data information fusion. By summarizing and comparing the recognition effect of existing fatigue driving recognition methods, combined with deep learning technology, the paper concludes the fatigue driving behavior recognition method based on single data source has some shortcomings such as low accuracy and easy to be affected by external factors, but the recognition method based on multi-feature information fusion can achieve a exhilarated recognition result. Finally, some prospects are given to analyze the development trend of fatigue driving behavior recognition in the future. Show more

Keywords: Fatigue driving, information fusion, physiological signals, deep learning, vehicle sensors

DOI: 10.3233/JIFS-235075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1407-1427, 2024

Authors: Venkataramanan, K. | Arun, M. | Jha, Shankaranand | Sharma, Aditi

Article Type: Research Article

Abstract: This study delves into the development and analysis of a novel Embedded Fuzzy Type 2 PID Controller for Robot Manipulators, motivated by the increasing need for enhanced control systems in robotic applications to improve precision and stability. In the background section, the limitations of conventional PID controllers in addressing uncertainties and disturbances, especially in complex tasks performed by robot manipulators, are presented. The concept of fuzzy logic and the Type 2 fuzzy system is introduced, highlighting their potential to manage imprecise and uncertain information. Through rigorous analysis and simulation, the superior performance of the Embedded Fuzzy Type 2 PID Controller …is demonstrated when compared to traditional PID controllers and even Type 1 fuzzy controllers. The results showcase enhanced tracking accuracy, disturbance rejection, and adaptability, making it a promising solution for advanced robotic applications. In conclusion, this research provides a robust solution for improving the control of robot manipulators in uncertain and dynamic environments. The Embedded Fuzzy Type 2 PID Controller offers a new paradigm in control theory, ensuring stability and precision even in the face of unpredictable factors. This innovation holds great promise for advancing the capabilities of robotic systems and underlines the potential for further research in embedded fuzzy control systems. Show more

Keywords: Fuzzy type 2 PID controller, robot manipulator, embedded control, stability analysis, precision control

DOI: 10.3233/JIFS-235338

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1429-1442, 2024

Authors: Peng, Bo | Zhang, Tao | Han, Kundong | Zhang, Zhe | Ma, Yuquan | Ma, Mengnan

Article Type: Research Article

Abstract: Text classification is an important tasks in natural language processing. Multilayer attention networks have achieved excellent performance in text classification tasks, but they also face challenges such as high temporal and spatial complexity levels and low-rank bottleneck problems. This paper incorporates spatial attention into a neural network architecture that utilizes fewer encoder layers. The proposed model aims to enhance the spatial information of semantic features while addressing the high temporal and spatial demands of traditional multilayer attention networks. This approach utilizes spatial attention to selectively weigh the relevance of the spatial locations in the input feature maps, thereby enabling the …model to focus on the most informative regions while ignoring the less important regions. By incorporating spatial attention into a shallower encoder network, the proposed model achieves improved performance on spatially oriented tasks while reducing the computational overhead associated with deeper attention-based models. To alleviate the low-rank bottleneck problem of multihead attention, this paper proposes a variable multihead attention mechanism, which changes the number of attention heads in a layer-by-layer manner with the encoder, achieving a balance between expression power and computational efficiency. We use two Chinese text classification datasets and an English sentiment classification dataset to verify the effectiveness of the proposed model. Show more

Keywords: Text classification, BERT, Spatial attention, Multihead attention mechanism

DOI: 10.3233/JIFS-231368

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1443-1454, 2024

Authors: Sun, Xu | Zou, Qingyu

Article Type: Research Article

Abstract: Modern information technology has been constantly evolving, transforming the traditional power grid into a network that couples both power and information layers. Understanding the cascade failure behavior of such power communication interdependent networks is essential for effectively controlling catastrophic network failures, preventing system collapse, and ensuring normal network operation. This research can contribute to the development of tools to predict and prevent such failures, and restore normal network functions in a timely manner. This paper focuses on the modeling method and cascading fault analysis of the power-information double-layer coupling network. We construct power information interdependent networks based on IEEE30 system …and England39 system, and evaluate the cascade failure results using load distribution cascade failure model and HITS algorithm. The evaluation criteria include network efficiency, residual network size, and residual network load. By analyzing these parameters, we can gain insights into the performance of the power-information interdependent networks during cascade failures. Through simulation results, we demonstrate that the type i attack proposed in this paper renders the network structure unstable and less robust compared to the degree attack, intermediate attack, and random attack. These findings provide valuable references for developing strategies to mitigate the cascading failure of power-information interdependent networks. Show more

Keywords: Power network, information network, interdependent network, cascade failure, critical nodes

DOI: 10.3233/JIFS-232016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1455-1467, 2024

Authors: Li, Wenying | Guo, Qinghong | Wen, Ming | Zhang, Yun | Pan, Xin | Xiao, Zhenfeng | Yang, Shuzhi

Article Type: Research Article

Abstract: This research proposes a dynamic reconfiguration model (DRM) and method for the distribution network, considering wind power, photovoltaic distributed generation (DG), and demand-side response. The reconfiguration goal is to minimize the total operating cost of the distribution network. The electricity purchase costs, DG operation costs, participation in demand response programs, network losses, and voltage deviations are selected to construct the optimization function. The DRM is established by clustered load data segments. An improved backtracking search algorithm incorporating a differential evolution learning strategy and adaptive chaotic elite search strategy is adopted to solve the DRM. The viability of the proposed method …is validated by an IEEE 30-node simulation distributed system. Show more

Keywords: Active distribution network, distributed power sources, demand-side response, dynamic reconfiguration, backtracking search algorithm

DOI: 10.3233/JIFS-232993

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1469-1480, 2024

Authors: Amsaprabhaa, M.

Article Type: Research Article

Abstract: Vision-based Human Activity Recognition (HAR) is a challenging research task in sports. This paper aims to track the player’s movements and recognize the different types of sports activities in videos. The proposed work aims in developing Hybrid Optimized Multimodal SpatioTemporal Feature Fusion (HOM-STFF) model using skeletal information for vision-based sports activity recognition. The proposed HOM-STFF model presents a deep multimodal feature fusion approach that combines the features that are generated from the multichannel-1DCNN and 2D-CNN network model using a concatenative feature fusion process. The fused features are fed into the 2-GRU model that generates temporal features for activity recognition. Nature-inspired …Bald Eagle Search Optimizer (BESO) is applied to optimize the network weights during training. Finally, performance of the classification model is evaluated and compared for identifying different activities in sports videos. Experimentation was carried out with the three vision-based sports datasets namely, Sports Videos in the Wild (SVW), UCF50 sports action and Self-build dataset, which achieved accuracy rate of 0.9813, 0.9506 and 0.9733, respectively. The results indicate that the proposed HOM-STFF model outperforms the other state-of-the-art methods in terms of activity detection capability. Show more

Keywords: Bald eagle search optimizer, Gated recurrent unit, human activity recognition, multichannel-1DCNN, 2D-CNN

DOI: 10.3233/JIFS-233498

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1481-1501, 2024

Authors: Sun, Xianshan | Sheng, Yuefeng | Mao, Hongfei | Qian, Qingfeng | Cai, Qingnan

Article Type: Research Article

Abstract: In order to solve the problems of tedious, insufficient manpower, low efficiency, and easy to cause human errors in the verification of relay protection equipment settings with the development of the power grid, an automatic verification method of relay protection equipment settings combining cell image gray enhancement and AI recognition is studied. In this method, Gaussian mixture and particle swarm algorithm are used to enhance the gray level of the original image captured, and the binary method is used to further denoise the image; The histogram is used to segment the cells in the denoised constant value image one by …one; The OCR technology in AI technology uses the maximum width backtracking segmentation algorithm to segment a coherent text in a cell into multiple single words, and collects the 13 dimensional characteristics of the text to be detected to compare with the text in the database. The text with the smallest error is the detected text, which completes the text extraction in the cell; Store the extracted text data in the database, check the data in the notification constant value sheet and the device constant value sheet, and give an abnormal prompt of different data. The experimental results show that the image pre processed by this method is clear, the fixed value single cell segmentation is accurate, and the OCR text extraction efficiency is high. Through a large number of data experiments, the final verification accuracy can reach 99.8%. Show more

Keywords: Gray enhancement, OCR text extraction, cell segmentation, equipment constant value sheet, notify the fixed value sheet, automatic detection

DOI: 10.3233/JIFS-234457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1503-1515, 2024

Authors: Yang, Huailei

Article Type: Research Article

Abstract: The grid connected inverter is the core component of the photovoltaic grid connected power generation system, which mainly converts the direct current of the photovoltaic matrix into alternating current that meets the grid connected requirements, playing a key role in the efficient and stable operation of the photovoltaic grid connected power generation system.This paper uses fuzzy PI control model which to improve the performance of intelligent photovoltaic grid-connected inverter to simulate the intelligent photovoltaic inverter system, using mathematical analysis and reasoning methods for model analysis,adopts two-stage three-phase LCL grid-connected inverter, establishes mathematical models in two-phase synchronous rotating and two-phase static …coordinate systems, and adopts an active damping strategy based on grid-connected current. Based on existing research and empirical analysis,aiming at the disadvantage of poor dynamic response of repetitive control, an improved repetitive control strategy is adopted, and the controller is analyzed from two aspects of stability and dynamic performance, and the simulation model of photovoltaic grid-connected power generation system is built. Use experimental analysis method to verify the effectiveness of the model in this article,The experimental results show that the simulation system of intelligent photovoltaic grid-connected inverter considering fuzzy PI control proposed in this paper has certain effects. Show more

Keywords: Fuzzy PI control, intelligence, photovoltaic, grid connection, inverter

DOI: 10.3233/JIFS-234491

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1517-1529, 2024

Authors: Li, Yunzhi | Lei, Jingsheng | Shi, Wenbin | Yang, Shengying

Article Type: Research Article

Abstract: PCB defect detection aims to identify the presence of gaps, open circuits, short circuits, and other defects in the PCB boards produced in the industry. Designing effective deep learning algorithms is crucial to finding a solution. Previously proposed PCB defect detection algorithms are limited in detecting tiny objects in high-density. Directly applying previous models to tackle PCB defect detection tasks will cause serious issues, such as missed detection and false detection. In this paper, we present a detection algorithm for tiny PCB defect targets in high-density regions to solve the above-mentioned problems. We firstly propose a detection head to detect …tiny objects. Then, we design a four-channel feature fusion mechanism to fuse four different scale features and add an attention mechanism to find the attention region in scenarios with dense objects. Finally, we achieved accurate detection of tiny targets in high-density areas. Experiments were performed on the publicly available PCB defect dataset from Peking University. Our mAP@.5:.95 achieves 48.6%, while mAP@0.5 exceeds 90%. Compared with YOLOX and YOLOv5, our improved model can better localize tiny objects in high-density scenes. The experimental results certify that our model can obtain higher performance in comparison with the baseline and the state of the art. Show more

Keywords: defect detection, tiny objects, high density, detection head, feature fusion, print circuit board

DOI: 10.3233/JIFS-230150

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1531-1541, 2024

Authors: Pashikanti, Rajesh | Patil, C.Y. | Shinde, Amita

Article Type: Research Article

Abstract: Arrhythmia is the medical term for any irregularities in the normal functioning of the heart. Due to their ease of use and non-invasive nature, electrocardiograms (ECGs) are frequently used to identify heart problems. Analyzing a huge number of ECG data manually by medical professionals uses excessive medical resources. Consequently, identifying ECG characteristics based on machine learning has become increasingly popular. However, these conventional methods have some limitations, including the need for manual feature recognition, complex models, and lengthy training periods. This research offers a unique hybrid POA-F3DCNN method for arrhythmia classification that combines the Pelican Optimisation algorithm with fuzzy-based 3D-CNN …(F3DCNN) to alleviate the shortcomings of the existing methods. The POA is applied to hyper-tune the parameters of 3DCNN and determine the ideal parameters of the Gaussian Membership Functions used for FLSs. The experimental results were obtained by testing the performance of five and thirteen categories of arrhythmia classification, respectively, on UCI-arrhythmia and the MIT-BIH Arrhythmia datasets. Standard measures such as F1-score, Precision, Accuracy, Specificity, and Recall enabled the classification results to be expressed appropriately. The outcomes of the novel framework achieved testing average accuracies after ten-fold cross-validation are 98.96 % on the MIT-BIH dataset and 99.4% on the UCI arrhythmia datasets compared to state-of-the-art approaches. Show more

Keywords: Deep learning, optimization algorithm, ECG classification, cardiac arrhythmia, feature extraction, 3D-CNN, Pelican optimization algorithm

DOI: 10.3233/JIFS-230359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1543-1566, 2024

Authors: Saranya, D. | Bharathi, A.

Article Type: Research Article

Abstract: The interpretation of the electroencephalogram (EEG) signal is one method that can be utilized to diagnose epilepsy, which is one of the most prevalent brain illnesses. The length of an EEG signal is typically quite long, making it difficult to interpret manually. Extreme Learning Machine (ELM) is used to detection of Epilepsy and Seizure. But in ELM Storage space and training time is high. In order to reduce training time and storage space African Buffalo Optimization (ABO) algorithm is used. ABO is combined with Sparse ELM to improve the speed, accuracy of detection and reduce the storage space. First, Wavelet …transform is used to extract relevant features. Due to their high dimensionality, these features are then reduced by using linear discriminant analysis (LDA). The proposed Hybrid Sparse ELM technique is successfully implemented for diagnosing epileptic seizure disease. For classification, the Sparse ELM-ABO classifier is applied to the UCI Epileptic Seizure Recognition Data Set training dataset, and the experimental findings are compared to those of the SVM, Sparse ELM, and ELM classifiers applied to the same database. The proposed model was tested in two scenarios: binary classification and multi-label classification. Seizure identification is the only factor in binary classification. Seizure and epilepsy identification are part of multi-label classification. It is observed that the proposed method obtained high accuracy in classification with less execution time along with performance evaluation of parameters such as prediction accuracy, specificity, precision, recall and F-score. Binary classification scores 96.08%, while multi-label classification achieves 90.89%. Show more

Keywords: Extreme learning machine, african buffalo optimization, epilepsy and seizure detection, sigmoid activation, cost function optimization

DOI: 10.3233/JIFS-237054

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1567-1582, 2024

Authors: Tan, Guimei | Yu, Yuehai | Yu, Xichang

Article Type: Research Article

Abstract: Due to the complexity of the real world, randomness and uncertainty are ubiquitous and interconnected in the real world. In order to measure the research objects that contain both randomness and uncertainty in practical problems, and extend the entropy theory of uncertain random variables, this paper introduces the arc entropy of uncertain random variables and the arc entropy of their functions. On this basis, the mathematical properties of arc entropy and two key formulas for calculating arc entropy are also studied and derived. Finally, two types of the mean variance entropy model with the risk and diversification are established, and …the corresponding applications to rare book selection for the rare book market are also introduced. Show more

Keywords: Uncertainty theory, chance theory, uncertain random variable, arc entropy

DOI: 10.3233/JIFS-230995

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1583-1595, 2024

Authors: Nandhini, S.S. | Kannimuthu, S.

Article Type: Research Article

Abstract: It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which identifies itemsets with high utility from the transaction database. This gains popularity in data mining, because it identifies itemsets which have more value but the same was not identified as frequent by Frequent Itemset Mining. HUIM is generally referred to as Utility Mining. The utility of the items is measured based on parameters like cost, profit, quantity or any other measures …preferred by the users. Compared to high utility itemsets (HUIs) mining, high average utility itemsets (HAUIs) mining is more precise by considering the number of items in the itemsets. In state-of-the-art algorithms that mines HUIS and HAUIs use a single fixed minimum utility threshold based on which HAUIs are identified. In this paper, the proposed algorithm mines HAUIs from transaction databases using Artificial Fish Swarm Algorithm (AFSA) with computed multiple minimum average utility thresholds. Computing the minimum average utility threshold for each item with the AFSA algorithm outperforms other state-of-the-art HAUI mining algorithms with multiple minimum utility thresholds and user-defined single minimum threshold in terms of number of HAUIs. It is observed that the proposed algorithm outperforms well in terms of execution time, number of candidates generated and memory consumption when compared to the state-of-the-art algorithms. Show more

Keywords: Artificial fish swarm algorithm, data mining, frequent itemset mining, high average utility itemsets, itemset mining, utility mining

DOI: 10.3233/JIFS-231852

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1597-1613, 2024

Authors: Kumari, Ankita | Dutta, Sandip | Chakraborty, Soubhik

Article Type: Research Article

Abstract: M obile A d-Hoc Net works (MANET) are considered one of the significant and growing areas in today’s scenario of technological advancement. It is an infrastructure-less and dynamic ad-hoc network that requires a connection between nodes to deliver packets and data. However, its design adopts a connection-less approach, at the helm of which no monitoring node exists. Hence, the threat of maintaining the network’s security remains an uphill task. Many attacks have been attempted to breach the protection of the MANET. This paper discusses one of the most potent attacks in a MANET infrastructure, the Sinkhole Attack . We try …to minimize the possibility of a sinkhole attack using a Fuzzy Q-learning- based approach, a reinforcement learning technique. The results are encouraging, suggesting that sinkhole attacks can be minimized to a great extent after the adaption of the proposed approach. Show more

Keywords: Sinkhole attack, MANET, fuzzy Q –learning, security, cryptography

DOI: 10.3233/JIFS-232003

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1615-1626, 2024

Authors: Arif, Waqar | Khan, Waheed Ahmad | Khan, Asghar | Mahmood, Tariq | Rashmanlou, Hossein

Article Type: Research Article

Abstract: In this manuscript, we develop TOPSIS (Technique for order of preference by similarity to ideal solution) method in the setting of bipolar fuzzy environment which has the ability to deal the data while keeping in view the positive and negative aspects. By using bipolar fuzzy sets, we establish the novel concept of rating the numerous preferences of any object described through the connection number(CN) of set pair analysis(SPA). In this regard, we extend the TOPSIS method based on the connection number(CN) of set pair analysis(SPA) in the frame of bipolar fuzzy sets. For the sake of verification, effectiveness and superiority …of our method, we conduct the comparative study of some real life problem related to decision making theory. Moreover, we observe that our proposed method also fulfills the existing test criterions. Show more

Keywords: BFSs, SPA, CN, TOPSIS

DOI: 10.3233/JIFS-232838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1627-1635, 2024

Authors: Yin, Liru | Yang, Zhiwei

Article Type: Research Article

Abstract: When the traditional evolution model studies the regional distribution of agricultural parks, the relationship between regions is not clear enough, which leads to the lack of generality of regional distribution. In order to solve this problem, this study adopts the methods of topological division and cluster analysis, establishes the model of regional diversity and evolution of farms, and clusters the spatial information of agricultural parks. According to the feature factors, the images are classified, and the image dimension values are reduced. The data space is divided into cellular space by the method of network topology structure division, and the effective …coefficient of each cell is calculated, and the spatial structure and characteristics of agricultural parks are extracted to reveal the similarities and differences between different parks. The experimental results show that the evolution model of agricultural parks constructed by topological division and clustering method shows obvious clustering characteristics in space, and the relationships among the factors are good. It is proved that the model can describe the spatial differences and evolution trends of agricultural parks more accurately, so as to provide more targeted suggestions for the planning, management and sustainable development of agricultural parks. Show more

Keywords: Network partition, cluster method, agricultural park, regional difference, evolutionary model, collaborative

DOI: 10.3233/JIFS-234165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1637-1645, 2024

Authors: Zhu, Yimin | Gao, Qing | Shi, Hongyan | Liu, Jinguo

Article Type: Research Article

Abstract: Gestures have long been recognized as an interaction technique that can provide a more natural, creative, and intuitive way to communicate with computers. However, some existing difficulties include the high probability that the same type of movement done at different speeds will be recognized as a different category of movement; cluttered, occluded, and low-resolution backgrounds; and the near-impossibility of fusing different types of features. To this end, we propose a novel framework for integrating different scales of RGB and motion skeletons to obtain higher recognition accuracy using multiple features. Specifically, we provide a network architecture that combines a three-dimensional convolutional …neural network (3DCNN) and post-fusion to better embed different features. Also, we combine RGB and motion skeleton information at different scales to mitigate speed and background issues. Experiments on several gesture recognition public datasets show desirable results, validating the superiority of the proposed gesture recognition method. Finally, we do a human-computer interaction experiment to prove its practicality. Show more

Keywords: Multi-modal action recognition, body action, robot simulation

DOI: 10.3233/JIFS-234791

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1647-1661, 2024

Authors: Jia, Wanjun | Li, Changyong

Article Type: Research Article

Abstract: This study proposes a method to help people with different degrees of hearing impairment to better integrate into society and perform more convenient human-to-human and human-to-robot sign language interaction through computer vision. Traditional sign language recognition methods make it challenging to get good results on scenes with backgrounds close to skin color, background clutter, and partial occlusion. In order to realize faster real-time display, by comparing standard single-target recognition algorithms, we choose the best effect YOLOv8 model, and based on this, we propose a lighter and more accurate SLR-YOLO network model that improves YOLOv8. Firstly, the SPPF module is replaced …with RFB module in the backbone network to enhance the feature extraction capability of the network; secondly, in the neck, BiFPN is used to enhance the feature fusion of the network, and the Ghost module is added to make the network lighter; lastly, in order to introduce partial masking during the training process and to improve the data generalization capability, Mixup, Random Erasing and Cutout three data enhancement methods are compared, and finally the Cutout method is selected. The accuracy of the improved SLR-YOLO model on the validation sets of the American Sign Language Letters Dataset and Bengali Sign Language Alphabet Dataset is 90.6% and 98.5%, respectively. Compared with the performance of the original YOLOv8, the accuracy of both is improved by 1.3 percentage points, the amount of parameters is reduced by 11.31%, and FLOPs are reduced by 11.58%. Show more

Keywords: Machine vision, sign language recognition, YOLO, deep learning, lightweight

DOI: 10.3233/JIFS-235132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1663-1680, 2024

Authors: Sun, Qiong | Sun, Yu | Jiang, Jingjing

Article Type: Research Article

Abstract: As an important choice of strategic transformation of energy enterprises, digital transformation has a profound impact on the stock price fluctuation of enterprises. From the perspective of dynamic capacity and environmental regulation, analyzes influences of digital transformation upon energy companies’ share movement volatility, constructs a theoretical model that considers digital transformation and stock price volatility as the primary effects, dynamic capabilities as the mediator, and environmental regulation as the moderator. In addition, the study employs data from China’s A-share listed energy enterprises from 2013 to 2020, utilizing a fixed-effect model to perform an empirical test. The findings demonstrate a significant …positive correlation between the digital transformation of energy enterprises and the volatility of stock prices, indicating that the greater the extent of digital transformation, the higher the volatility of enterprise stock prices. Among the dimensions of dynamic capability, only adaptability and innovation ability appears to mediate the relation between digital transformation and stock price fluctuation. Moreover, environmental regulation positively moderates the relationship between digital transformation and the learning ability dimension. Finally, from the macro and micro levels, this study puts forward the policies and supportive measures to stabilize the stock price of energy enterprises, and suggestions on how to implement the digital transformation strategy reasonably according to their own development status and characteristics to provide valuable insights for encouraging the digital transformation among energy firms. Show more

Keywords: Energy enterprises, digital transformation, dynamic capability, stock price fluctuation, environmental regulation

DOI: 10.3233/JIFS-232161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1681-1695, 2024

Authors: Sumathi, S. | Balaji Ganesh, A.

Article Type: Research Article

Abstract: Arrhythmia disorders are the leading cause of death worldwide and are primarily recognized by the patient’s irregular cardiac rhythms. Wearable Internet of Things (IoT) devices can reliably measure patients’ heart rhythms by producing electrocardiogram (ECG) signals. Due to their non-invasive nature, ECG signals have been frequently employed to detect arrhythmias. The manual procedure, however, takes a long time and is prone to error. Utilizing deep learning models for early automatic identification of cardiac arrhythmias is a preferable approach that will improve diagnosis and therapy. Though ECG analysis using cloud-based methods can perform satisfactorily, they still suffer from security issues. It …is essential to provide secure data transmission and storage for IoT medical data because of its significant development in the healthcare system. So, this paper proposes a secure arrhythmia classification system with the help of effective encryption and a deep learning (DL) system. The proposed method mainly involved two phases: ECG signal transmission and arrhythmia disease classification. In the ECG signal transmission phase, the patient’s ECG data collected through the IoT sensors is encrypted using the optimal key-based elgamal elliptic curve cryptography (OKEGECC) mechanism, and the encrypted data is securely transmitted to the cloud. After that, in the arrhythmia disease classification phase, the system collects the data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database to perform training. The collected data is preprocessed by applying the continuous wavelet transform (CWT) to improve the quality of the ECG data. Next, the feature extraction is carried out by deformable attention-centered residual network 50 (DARNet-50), and finally, the classification is performed using butterfly-optimized Bi-directional long short-term memory (BOBLSTM). The experimental outcomes showed that the proposed system achieves 99.76% accuracy, which is better than the existing related schemes. Show more

Keywords: Internet of things, electrocardiogram, data security, arrhythmia disease classification, machine learning

DOI: 10.3233/JIFS-235885

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1697-1712, 2024

Authors: Zheng, Lina | Chen, Lijun | Wang, Yini

Article Type: Research Article

Abstract: Information amount has been shown to be one of the most efficient methods for measuring uncertainty. However, there has been little research on outlier detection using information amount. To fill this void, this paper provides a new unsupervised outlier detection method based on the amount of information. First, the information amount in a given information system is determined, which offers a thorough estimate of the uncertainty of this information system. Then, the relative information amount and the relative cardinality are proposed. Following that, the degree of outlierness and weight function are shown. Furthermore, the information amount-based outlier factor is constructed, …which determines whether an object is an outlier by its rank. Finally, a new unsupervised outlier detection method called the information amount-based outlier factor (IAOF) is developed. To validate the effectiveness and advantages of IAOF, it is compared to five existing outlier identification methods. The experimental results on real-world data sets show that this method is capable of addressing the problem of outlier detection in categorical information systems. Show more

Keywords: Outlier detection, CIS, Information amount, IAOF

DOI: 10.3233/JIFS-236518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1713-1734, 2024

Authors: Paul, Milner | Adhikari, Shuma | Singh, Loitongbam Surajkumar | Parekkattil, Adarsh V. | Athappilly, George

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-236583

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1735-1752, 2024

Authors: Chen, Tianwen | Zhou, Ronghu | Chen, Haoliang | Liu, Changqing

Article Type: Research Article

Abstract: The main purpose of this paper is to study the coordination, price and sales effort decisions of a dual channel supply chain under live streaming commerce mode. In nowadays’ e-commerce age, more and more people have interest in live streaming especially after the outbreak of COVID-19, but the research on live streaming supply chain is lacking. To fill this gap, a supply chain composed of a manufacturer and an internet celebrity is established, in which the demand is affected by the internet celebrity’s sales effort and personal influence. Considering different power structures of the supply chain, price and sales effort …decisions are studied in four models: Nash, manufacturer dominant (MD), internet celebrity dominant (KD) and cooperative game models. Subsequently, the feasible region of bargaining game is discussed in terms to share the extra profits and coordinate the supply chain. The manufacturer and the internet celebrity can be coordinated through bargaining problem in the cooperation model, and the extra profits sharing ratio is depend on each other’s bargaining power. Numerical analysis is further provided to test the propositions and show the impacts of market share rate, internet celebrity’s commission rate and personal influence on supply chain’s performance. Show more

Keywords: Supply chain, live E-commerce, internet celebrity, sales effort, personal influence

DOI: 10.3233/JIFS-231500

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1753-1769, 2024

Authors: He, Yu | Pan, Yigong | Hu, Xinying | Sun, Guangzhong

Article Type: Research Article

Abstract:  Concept prerequisite relation refers to the learning order of concepts, which is useful in education. Concept prerequisite learning refers to using machine learning methods to infer prerequisite relation of a concept pair. The process of concept prerequisite learning requires large amounts of labeled data to train classifier. Usually, the labels of prerequisite relation are assigned by specialists. The specialist labelling method is costly. Thus, it is necessary to reduce labeling expense. An effective strategy is using active learning methods. In this paper, we propose a pool-based active learning framework for concept prerequisite learning named PACOL. It is a …fact that concept u and concept v cannot be prerequisite of each other simultaneously. The idea of PACOL is to select the concept pair with the greatest deviation between the classifier’s prediction and the fact. Besides, PACOL can be used in two situations: when specialists assign three kinds of labels or two kinds of labels. In experiments, we constructed data sets for three subjects. Experimental results on both our constructed data sets and public data sets demonstrate that PACOL outperforms than existing active learning methods in all situations. Show more

Keywords: Educational data mining, prerequisite relation, active learning, Wikipedia

DOI: 10.3233/JIFS-231878

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1771-1787, 2024

Authors: Naveen, Palanichamy | NithyaSai, S. | Udayamoorthy, Venkateshkumar | Ashok kumar, S.R.

Article Type: Research Article

Abstract: In the current industry, quality inspection in semiconductor manufacturing is of immense significance. Significant achievements have been made in fault diagnosis in fabricated semiconductor wafer manufacturing due to the development of machine learning. Since real-time intermediate signals are non-linear and time-varying, the signals undergo various distortions due to changes in equipment, material, and process. This leads to a drastic change in information in intermediate signals. This paper presents a fault diagnosis model for semiconductor manufacturing processes using a generative adversarial network (GAN). The study aims to address the challenges associated with efficient and accurate fault identification in these complex processes. …Our approach involves the extraction of relevant components, development of a paired generator model, and implementation of a deep convolutional neural network. Experimental evaluations were conducted using a comprehensive dataset and compared against six existing models. The results demonstrate the superiority of our proposed model, showcasing higher accuracy, specificity, and sensitivity across various shift tasks. This research contributes to the field by introducing a novel approach for fault diagnosis, paving the way for improved process control and product quality in semiconductor manufacturing. Future work will focus on further optimizing the model and extending its applicability to other manufacturing domains. Show more

Keywords: Semiconductor manufacturing, GAN, fault diagnosis, quality inspection, wafer fabrication, deep CNN

DOI: 10.3233/JIFS-231948

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1789-1800, 2024

Authors: Pitchandi@Sankaralingam, R. | Arunachalaperumal, C. | Mary Anita, E.A.

Article Type: Research Article

Abstract: Source Location Privacy (SLP) in Wireless Sensor Networks (WSNs) refers to a set of techniques and strategies used to safeguard the anonymity and confidentiality of the locations of sensor nodes (SNs) that are the source of transmitted data within the network. This protection is important in different WSN application areas like environmental monitoring, surveillance, and healthcare systems, where the revelation of the accurate location of SNs can pose security and privacy risks. Therefore, this study presents metaheuristics with sequential assignment routing based false packet forwarding scheme (MSAR-FPFS) for source location privacy protection (SLPP) on WSN. The contributions of the MSAR-FPFS …method revolve around enhancing SLP protection in WSNs through the introduction of dual-routing, SAR technique with phantom nodes (PNs), and an optimization algorithm. In the presented MSAR-FPFS method, PNs are used for the rotation of dummy packets using the SAR technique, which helps to prevent the adversary from original data transmission. Next, the MSAR-FPFS technique uses an improved reptile search algorithm (IRSA) for the optimal selection of routes for real packet transmission. Moreover, the IRSA technique computes a fitness function (FF) comprising three parameters namely residual energy (RE), distance to BS (DBS), and node degree (ND). The experimental evaluation of the MSAR-FPFS system was investigated under different factors and the outputs show the promising achievement of the MSAR-FPFS system compared to other existing models. Show more

Keywords: Wireless sensor networks, metaheuristics, source location privacy preserving, fitness function, routing, false data forwarding

DOI: 10.3233/JIFS-233541

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1801-1812, 2024

Authors: Zhou, Bolong | Yu, Menghong | Guo, Jie

Article Type: Research Article

Abstract: Trailing suction hopper dredger is a kind of hydraulic dredger, it has the characteristics of self-propelled, selfloading, self-dredging, self-unloading, it is the main force in dredging and blowing works, it is widely used in the world, it can be said that where there is a big dredging project where there is a trailing suction hopper dredger’s figure. The loading optimization process of trailing suction hopper dredger contains a lot of dredging parameters related to soil type, and the soil type under different working conditions is not very clear. In this study, we present a hybrid optimization technique based on simulated …annealing and multi-population genetic algorithm to enhance the loading efficiency of a trailing suction hopper dredger and to examine the variation of dredged soil parameters. The soil parameters of the spoil hopper deposition model were estimated using this hybrid optimization algorithm. The experimental results show that the soil parameters are successfully estimated and verified by our measured construction data of a trailing suction hopper dredger. In addition, our proposed method has the highest accuracy of soil parameter estimation, the fastest algorithm convergence, and excellent robustness compared to the other three intelligent optimization methods. In addition, our method successfully avoids the phenomenon of premature convergence that usually occurs in traditional genetic algorithms, and the parameters show strong adaptability to different vessels under the same dredging area. Show more

Keywords: Trailing suction hopper dredger, spoil hopper deposition model, simulated annealing and multi-population genetic algorithm, soil parameters estimation

DOI: 10.3233/JIFS-233959

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1813-1831, 2024

Authors: Li, Xingge | Zhang, Shufeng | Chen, Xun | Wang, Yashun | Fan, Zhengwei

Article Type: Research Article

Abstract: The proliferation of artificial intelligence (AI) devices has generated an increasing demand for reliability in their utilization. Nevertheless, the significant concern persists regarding the absence of suitable assessment and testing techniques to evaluate the performance of these intelligent systems in real-world conditions. In response to these issues, this paper conducts research on the reliability testing and assessment of AI visual perception systems under vibration stress. The paper introduces the working mechanism of the visual perception system and the various testing methods for AI devices. Based on this, a reliability assessment method for intelligent devices is proposed, which uses the Fréchet …distance as the measurement function and environmental adaptability as the reliability metric. Additionally, a vibration test platform for the visual perception system is established, which offers a cost-effective and reliable solution to the high cost issue of field testing for AI devices. Finally, the reliability level of the visual perception system under various vibration conditions is tested through vibration testing. The research findings indicate that the reliability of AI models decreases as the degradation caused by vibration increases, following a normal distribution. Show more

Keywords: Reliability, fréchet distance (FD), visual perception system (VPS), environmental adaptability, vibration test

DOI: 10.3233/JIFS-234179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1833-1852, 2024

Authors: Wang, Feng

Article Type: Research Article

Abstract: A real-time sharing model of energy big data based on end cloud collaboration technology is built to safely and efficiently share energy big data in all fields. Through the collaboration between the client layer and the cloud platform layer in the end cloud collaboration module, combined with the vertical federation learning algorithm and the homomorphic encryption algorithm, the energy big data knowledge in various fields is extracted and encrypted, and the encrypted knowledge is stored in the cloud platform as shared data. After the blockchain module combines the smart contract identification coding and parsing of such shared ciphertext, the ciphertext …key is provided to the data user, and the shared energy big data plaintext is obtained after decryption, so as to realize the real-time security sharing of energy big data. According to the result analysis, the model performs well in data knowledge extraction and encryption, and has a good effect in ensuring the security and reliability of energy big data sharing. At the same time, the identification coding and analysis time of shared data knowledge is relatively short, making energy big data can be shared in real time. These results demonstrate the potential and feasibility of the model in facilitating big data sharing in the energy sector. Show more

Keywords: End cloud collaboration, energy big data, real-time sharing, client, cloud platform, blockchain

DOI: 10.3233/JIFS-234892

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1853-1865, 2024

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

Article Type: Research Article

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

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

DOI: 10.3233/JIFS-235118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1867-1879, 2024

Authors: Wang, Kejun | Zhang, Hebing

Article Type: Research Article

Abstract: With the ongoing evolution of the novel coronavirus pathogen and continuous improvements in our social environment, the mortality rate of COVID-19 is on a decline. In response to this, we introduce an adaptive control strategy known as intentional control, which offers cost-efficiency and superior control effectiveness. The classical SEIR model faces limitations in accurately representing close contacts and sub-close contacts and fails to distinguish their varying levels of infectivity. To address this, our study modifies the classical model by incorporating close contact (E) and a sub-close contact (E2) while reworking the infectious mechanism. Once the model is formulated, we employ …various statistical methods to identify crucial parameters, including R 2 , adjusted R 2 , and standard deviation. For disease control, we implement an intentional control program with four distinct grades. We develop and apply a scheme in MATLAB for our proposed model, generating diverse simulation results based on realistic parameter values for discussion. Additionally, we explore a range of strategy combinations to differentiate their effectiveness under various social conditions, aiming to identify an optimal approach. Comparing the intentional control strategy to random control, our findings consistently demonstrate the superiority of intentional control across all scenarios. Furthermore, the results indicate that our approach better aligns with the characteristics of the novel coronavirus, characterized by an “extremely low fatality rate and strong infectivity,” while offering detailed insights into the transmission dynamics among different compartments. Show more

Keywords: COVID-19, SEIR model, intentional control

DOI: 10.3233/JIFS-235149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1881-1898, 2024

Authors: Li, Zhaowen | Wei, Shengxue | Liu, Suping

Article Type: Research Article

Abstract: Outlier detection is critically important in the field of data mining. Real-world data have the impreciseness and ambiguity which can be handled by means of rough set theory. Information entropy is an effective way to measure the uncertainty in an information system. Most outlier detection methods may be called unsupervised outlier detection because they are only dealt with unlabeled data. When sufficient labeled data are available, these methods are used in a decision information system, which means that the decision attribute is discarded. Thus, these methods maybe not right for outlier detection in a a decision information system. This paper …proposes supervised outlier detection using conditional information entropy and rough set theory. Firstly, conditional information entropy in a decision information system based on rough set theory is calculated, which provides a more comprehensive measure of uncertainty. Then, the relative entropy and relative cardinality are put forward. Next, the degree of outlierness and weight function are presented to find outlier factors. Finally, a conditional information entropy-based outlier detection algorithm is given. The performance of the given algorithm is evaluated and compared with the existing outlier detection algorithms such as LOF, KNN, Forest, SVM, IE, and ECOD. Twelve data sets have been taken from UCI to prove its efficiency and performance. For example, the AUC value of CIE algorithm in the Hayes data set is 0.949, and the AUC values of LOF, KNN, SVM, Forest, IE and ECOD algorithms in the Hayes data set are 0.647, 0.572, 0.680, 0.676, 0.928 and 0.667, respectively. The advantage of the proposed outlier detection method is that it fully utilizes the decision information. Show more

Keywords: Rough set theory, outlier detection, outlier factor, conditional information entropy

DOI: 10.3233/JIFS-236009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1899-1918, 2024

Authors: Kahraman, Cengiz

Article Type: Research Article

Abstract: The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. The problem is that the expert cannot assign the same values to the degrees of membership, non-membership and hesitancy in decimal numbers for the same proposition in every attempt. Rather than the former, the assignment of proportional relationships between membership and non-membership degrees is more appropriate. We propose proportion-based models for intuitionistic fuzzy sets that include arithmetic and aggregation operators. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. These models will …make it easier to define intuitionistic fuzzy sets with more accurate data that better represents expert judgments. We transform AHP method, one of the traditional multi-criteria decision making methods, to PIF AHP using PIF sets. We compare the proposed PIF AHP method by interval-valued intuitionistic fuzzy AHP method existing in the literature. A wind turbine selection problem is handled to show the validity of the proposed PIF AHP method. Show more

Keywords: Proportional intuitionistic fuzzy sets, aggregation operators, multi-criteria decision making, AHP

DOI: 10.3233/JIFS-236035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1919-1933, 2024

Authors: Lai, Yibo | Fan, Libo | Sun, Zhiqing | Fang, Xiang | Shen, Bin | Tu, Yongwei

Article Type: Research Article

Abstract: Aiming at the problems of low convective heat transfer coefficient and high energy consumption in the air-cooled data center of immersed liquid cooling, an improved deep learning algorithm is proposed for the data center system of immersed liquid cooling equipment room. By improving the design of the immersed liquid cooling system, heat exchange is carried out between the immersed liquid cooling system and heating components such as the central processing unit of the server. The insulation coolant and cooling water achieve server heat dissipation through energy exchange, achieving data management of the immersed liquid cooling room. The proposed algorithm improves …data management efficiency while ensuring computational accuracy by conducting in-depth training and learning on the obtained immersed liquid cooling data, thus achieving the management of data in the immersed liquid cooling room. Through experiments, it has been proven that the immersed liquid cooling system in this study has high data management efficiency and low error, and can maintain server memory heat below 37 ° C, with a research accuracy of up to 92%. Show more

Keywords: Immersive liquid cooling, liquid cooling heat exchanger, deep learning, non relaxation hash algorithm, data management system

DOI: 10.3233/JIFS-233140

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1935-1944, 2024

Authors: Wu, Jian-Zhang | Zhang, Xue | Beliakov, Gleb

Article Type: Research Article

Abstract: Both the nonadditivity index and nonmodularity index have emerged as valuable indicators for characterizing the interaction phenomenon within the realm of fuzzy measures. The axiomatic representation plays a crucial role in distinguishing and elucidating the relationship and distinctions between these two interaction indices. In this paper, we employ a set of fundamental and intuitive properties related to interactions, such as equality, additivity, maximality, and minimality, to establish a comprehensive axiom system that facilitates a clear comprehension of the interaction indices. To clarify the impact of new elements’ participation on the type and density of interactions within an initial coalition, we …investigate and confirm the existence of proportional and linear effects in relation to null and dummy partnerships, specifically concerning the nonadditivity and nonmodularity indices. Furthermore, we propose the concept of the t -interaction index to depict a finer granularity for the interaction situations within a coalition, which involves subsets at different levels and takes the nonadditivity index and nonmodularity index as special cases. Finally, we establish and discuss the axiomatic theorems and empirical examples of this refined interaction index. In summary, the contributions of this work shed light on the axiomatic characteristics of the t -interaction indices, making it a useful reference for comprehending and selecting appropriate indices within this category of interactions. Show more

Keywords: Fuzzy measure, capacity, nonadditivity index, nonmodularity index, t-interaction index

DOI: 10.3233/JIFS-233196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1945-1956, 2024

Authors: Gobinath, C. | Gopinath, M.P.

Article Type: Research Article

Abstract: PURPOSE: Many researchers have found that the improvement in computerised medical imaging has pushed them to their limits in terms of developing automated algorithms for the identification of illness without the need for human participation. The diagnosis of glaucoma, among other eye illnesses, has continued to be one of the most difficult tasks in the area of medicine. Because there are not enough skilled specialists and there are a lot of patients seeking treatment from ophthalmologists, we have been encouraged to build efficient computer-based diagnostic methods that can assist medical professionals in early diagnosis and help reduce the amount of …time and effort they spend working on healthy situations. The Optic Disc position is determined with the help of the LoG operator, and a Disc Image map is projected with the help of a U-net architecture by utilising the location and intensity profile of the optic disc. After this, a Generative adversarial network is suggested as a possible solution for segmenting the disc border. In order to verify the performance of the model, a well-defined investigation is carried out on many retinal datasets. The usage of a multi-encoder U-net framework for optic cup segmentation is the second key addition made by this proposed work. This framework greatly outperforms the state-of-the-art in this area. The suggested algorithms have been tested on public standard datasets such as Drishti-GS, Origa, and Refugee, as well as a private community camp-based difficult dataset obtained from the All-India Institute of Medical Sciences (AIIMS), Delhi. All of these datasets have been verified. In conclusion, we have shown some positive outcomes for the detection of diseases. The unique strategy for glaucoma treatment is called ensemble learning, and it combines clinically meaningful characteristics with a deep Convolutional Neural Network. Show more

Keywords: Glaucoma, Cup-To-Disc Ratio (CDR), neuro-retinal rim (NRR) Loss, peripapillary atrophy (PPA), retinal nerve fiber layer (RNFL), deep convolutional neural network

DOI: 10.3233/JIFS-234363

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1957-1971, 2024

Authors: Li, Junwei | Liu, Huanyu | Jin, Yong | Zhao, Aoxiang

Article Type: Research Article

Abstract: Research on conflict evidence fusion is an important topic of evidence theory. When fusing conflicting evidence, Dempster-Shafer evidence theory sometimes produces counter-intuitive results. Thus, this work proposes a conflict evidence fusion method based on improved conflict coefficient and belief entropy. Firstly, the proposed method uses an improved conflict coefficient to measure the degree of conflict, and the conflict matrix is constructed to get the support degree of evidence. Secondly, in order to measure the uncertainty of evidence, an improved belief entropy is proposed, and the information volume of evidence is obtained by the improve entropy. Next, connecting with the support …degree and information volume, We get the weight coefficient, and use it to modify the evidence. Finally, using the combination rule of Dempster for fusion. Simulation experiments have demonstrated the effectiveness and superiority of the proposed method in this paper. Show more

Keywords: Evidence theory, conflict evidence, conflict coefficient, beleief entropy, combination rule

DOI: 10.3233/JIFS-221507

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1973-1984, 2024

Authors: Yapali, Reha | Korkmaz, Erdal | Çinar, Muhammed | Çoskun, Hüsamettin

Article Type: Research Article

Abstract: The idea of lacunary statistical convergence sequences, which is a development of statistical convergence, is examined and expanded in this study on L - fuzzy normed spaces, which is a generalization of fuzzy spaces. On L - fuzzy normed spaces, the definitions of lacunary statistical Cauchy and completeness, as well as associated theorems, are provided. The link between lacunary statistical Cauchyness and lacunary statistical boundedness with regard to L - fuzzy norm is also shown.

Keywords: ℒ-fuzzy normed space, lacunary double sequences, lacunary statistically convergence, lacunary statistical Cauchy, lacunary statistical boundedness

DOI: 10.3233/JIFS-222695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1985-1993, 2024

Authors: Prabaharan, P.

Article Type: Research Article

Abstract: Recent developments in wireless sensor networks (WSNs) have generated interest in the area of sensor tracking events. The proposed work aims to decrease energy usage by identifying functional relay nodes utilizing the enhanced energy proficient clustering (EEPC) method. To minimize long-distance interaction between CH and BS, a power-efficient relay chosen technique is proposed using improved Grasshopper Optimization algorithm (IGOA). The network is constructed using both mobile and fixed nodes. Mobile nodes first choose cluster head (CH) among fixed nodes after broadcasting information. Depending on the related positioning and power density, mobile nodes choose their CH. CH receives information from mobile …sensor nodes (SNs). Based on the nodes’ velocity and position, the EEPC method computes particle fitness value and chooses the relay nodes. Performance metrics include Throughput, End-to-End Delay, Packet Delivery Ratio (PDR), Quantity of Received Packets, Total Residual Energy, and Total Energy Consumption, network lifetime. The suggested technique enhances network lifetime and reduces energy consumption when compared to other existing protocols. After 200 simulation rounds, the suggested EEPC displays 98.87% PDR. However, during 200 simulation cycles, ANFISRS, ORNS and DTC-ORS show 97.82%, 96.03%, and 89.585% PDR, respectively. Show more

DOI: 10.3233/JIFS-231729

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1995-2008, 2024

Authors: Sandhu, Muhammad Abdullah | Amin, Asjad | Tariq, Sana | Mehmood, Shafaq

Article Type: Research Article

Abstract: Dengue mosquitoes are the only reason for dengue fever. To effectively combat this disease, it is important to eliminate dengue mosquitoes and their larvae. However, there are currently very few computer-aided models available in scientific literature to prevent the spread of dengue fever. Detecting the larvae stage of the dengue mosquito is particularly important in controlling its population. To address this issue, we propose an automated method that utilizes deep learning for semantic segmentation to detect and track dengue larvae. Our approach incorporates a contrast enhancement approach into the semantic neural network to make the detection more accurate. As there …was no dengue larvae dataset available, we develop our own dataset having 50 short videos with different backgrounds and textures. The results show that the proposed model achieves up to 79% F-measure score. In comparison, the DeepLabV3, Resnet achieves up to 77%, and Segnet achieves up to 76% F-measure score on the tested frames. The results show that the proposed model performs well for small object detection and segmentation. The average F-measure score of all the frames also indicates that the proposed model achieves a 76.72% F-measure score while DeepLabV3 achieves a 75.37%, Resnet 75.41%, and Segnet 74.87% F-measure score. Show more

Keywords: Dengue larvae, detection, tracking, semantic segmentation, image enhancement

DOI: 10.3233/JIFS-233292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2009-2021, 2024

Authors: Keikha, Abazar | Sabeghi, Narjes

Article Type: Research Article

Abstract: As the rapidly progressing applications of uncertainty theories, the need for modifications to some of their existing mathematical tools or creating new tools to deal correctly with them in various environments is also exposed. Hesitant fuzzy numbers (HFNs), as a particular case of fuzzy numbers, are not an exception to this rule. Considering the necessity of determining the distance between given HFNs in many of their practical applications, this article shows that the existing methods either do not provide correct results or are not able to meet the needs of users. This paper aims to present new methods for distance …measures of hesitant fuzzy numbers. To do them, three prevalent distance measures, i.e., the generalized distance measure, the Hamming distance measure, and the Euclidean distance measure, will be optimized into three distinct trinal categories. With the approach of reducing error propagation via reducing some unnecessary mathematical computations, new distance measures on HFNs will be introduced, first. The middle is the modification of the first category, which is more suitable when the given HFNs are equal-distance by the previous formula. Also, as the third category, the weighted form of these distance measures has been proposed, to be used where the real and membership parts of HFNs are not of equal importance. As an application of these, a TOPSIS-based technique for solving multi-attribute group decision-making problems with HFNs has been proposed. A numerical example will be implemented to describe the presented method. Finally, along with the validation of the proposed method, its numerical comparison with some other existing methods will be discussed in detail. Show more

Keywords: Hesitant fuzzy numbers, MAGDM, Hamming distance, Euclidean distance, TOPSIS

DOI: 10.3233/JIFS-234619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2023-2035, 2024