Journal of Intelligent & Fuzzy Systems - Volume 46, issue 4

Authors: Huang, Yonggang | Teng, Teng | Li, Yuanyuan | Zhang, Minghao

Article Type: Research Article

Abstract: In order to avoid the risk of patients’ private information leakage, this paper puts forward a research on the protection of medical Internet private information based on double chaotic encryption algorithm. This paper analyzes the quantification of risk indicators for privacy information protection of medical Internet, establishes the risk quantification structure of health care big data according to the quantitative calculation results, and puts forward the strategy of controlling access to health care big data, configuring the risk level, describing the attributes of the system database, and realizing the privacy information protection of medical Internet under the double chaotic encryption …algorithm. The experimental results show that the real identity of patients is protected to a certain extent in the protection of private information of medical internet after applying this method. Moreover, this method has high storage integrity and small storage standard deviation, and the method in this paper can effectively resist network intrusion. Therefore, it shows that this method has a good effect of protecting private information of medical Internet. Show more

Keywords: Double chaotic encryption algorithm, medical internet, private information, information protection.

DOI: 10.3233/JIFS-237670

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7779-7789, 2024

Authors: Salem, Dina Ahmed | Hassan, Nesma AbdelAziz | Hamdy, Razan Mohamed

Article Type: Research Article

Abstract: Smart farming, also known as precision agriculture or digital farming, is an innovative approach to agriculture that utilizes advanced technologies and data-driven techniques to optimize various aspects of farming operations. One smart farming activity, fruit classification, has broad applications and impacts across agriculture, food production, health, research, and environmental conservation. Accurate and reliable fruit classification benefits various stakeholders, from farmers and food producers to consumers and conservationists. In this study, we conduct a comprehensive comparative analysis to assess the performance of a Convolutional Neural Network (CNN) model in conjunction with four transfer learning models: VGG16, ResNet50, MobileNet-V2, and EfficientNet-B0. Models …are trained once on a benchmark dataset called Fruits360 and another time on a reduced version of it to study the effect of data size and image processing on fruit classification performance. The original dataset reported accuracy scores of 95%, 93%, 99.8%, 65%, and 92.6% for these models, respectively. While accuracy increased when trained on the reduced dataset for three of the employed models. This study provides valuable insights into the performance of various deep learning models and dataset versions, offering guidance on model selection and data preprocessing strategies for image classification tasks. Show more

Keywords: Artificial intelligence, convolutional neural network, Fruit360, machine learning, transfer learning

DOI: 10.3233/JIFS-233514

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7791-7803, 2024

Authors: Hoang, Dinh Linh | Luong, Tran Thi

Article Type: Research Article

Abstract: The XOR operator is a simple yet crucial computation in computer science, especially in cryptography. In symmetric cryptographic schemes, particularly in block ciphers, the AddRoundKey transformation is commonly used to XOR an internal state with a round key. One method to enhance the security of block ciphers is to diversify this transformation. In this paper, we propose some straightforward yet highly effective techniques for generating t-bit random XOR tables. One approach is based on the Hadamard matrix, while another draws inspiration from the popular intellectual game Sudoku. Additionally, we introduce algorithms to animate the XOR transformation for generalized block ciphers. …Specifically, we apply our findings to the AES encryption standard to present the key-dependent AES algorithm. Furthermore, we conduct a security analysis and assess the randomness of the proposed key-dependent AES algorithm using NIST SP 800-22, Shannon entropy based on the ENT tool, and min-entropy based on NIST SP 800-90B. Thanks to the key-dependent random XOR tables, the key-dependent AES algorithm have become much more secure than AES, and they also achieve better results in some statistical standards than AES. Show more

Keywords: Random XOR table, AES, key-dependent block cipher, randomness, Shannon entropy

DOI: 10.3233/JIFS-236998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7805-7821, 2024

Authors: Aljanabi, Abdulqadir Rahomee Ahmed | Ghafour, Karzan Mahdi

Article Type: Research Article

Abstract: Buying decisions are influenced by a variety of factors that can give rise to impulsive, unplanned, or even irrational purchases. Research has examined the motivational factors that foster organic food consumption, but no study has explored the relative weights of these factors and whether their effects vary depending on the type of food. This study adopted the cognitive-affective perspective to examine the antecedents of online impulsive buying of organic food using a sample of 452 consumers living in Baghdad, Iraq. The fuzzy AHP and fuzzy TOPSIS methods were used to rank five organic food alternatives. The results revealed that the …effects of cognitive factors on organic food purchases differ from those of affective factors. Show more

Keywords: Impulsive buying behaviour, AHP, fuzzy TOPSIS, multi-criteria decision-making, organic food

DOI: 10.3233/JIFS-237400

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7823-7838, 2024

Authors: Xie, Wenhao | Lei, Lin | Liu, Xiangyi | Liu, Yuan

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-231747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7839-7857, 2024

Authors: Cao, Heling | Han, Dong | Chu, Yonghe | Tian, Fangchao | Wang, Yun | Liu, Yu | Jia, Junliang | Ge, Haoyang

Article Type: Research Article

Abstract: Automatic program repair (APR) is crucial to improve software quality. Recently, neural machine translation (NMT) based modeling for bug fixes has demonstrated great potential. However, these approaches still have two major challenges. One is that their search space is limited due to the out-of-vocabulary (OOV) problem. The other is that the NMT-based APR models tend to ignore past translation information, which often leads to over-translation and under-translation. To address the above challenges, we propose MNRepair, a new NMT-based APR approach that combines multiple mechanisms to fix bugs in source code. Specifically, we devise an encoder-decoder NMT framework with the attention …mechanism. Our framework combines the copy mechanism to overcome the OOV problem that occurs with source code. To deal with the over-translation and under-translation, we utilize a coverage mechanism to record past translation information. MNRepair is able to capture a wide range of repair operators and fix 26 bugs in Defects4J. Our evaluation shows the effectiveness of multiple mechanisms in the repair process. Show more

Keywords: Automatic program repair, neural machine translation, multiple mechanisms

DOI: 10.3233/JIFS-234037

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7859-7873, 2024

Authors: Zhang, Hongli | Wu, Guangyu | Zhao, Dongfang | Chen, Yesheng | Wei, Dou | Liu, Shulin | Jiang, Lunchang

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-234042

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7875-7886, 2024

Authors: Niu, Lu | Ye, Jun

Article Type: Research Article

Abstract: A consistency fuzzy set is composed of mean values and consistency degrees of fuzzy sequences in the transformation process of a fuzzy multiset (FM), but lacks confidence intervals in relation to a confidence level of fuzzy sequences, which shows its deficiency. To solve this deficiency, this paper aims to propose an improved transformation approach from FM to a confidence consistency fuzzy cubic set (CCFCS) and to develop an exponential similarity measure of CCFCSs for modeling piano performance evaluation (PPE) in a FM scenario. Consequently, this study includes the following context. First, a transformation approach from FM to CCFCS is proposed …in terms of mean values, consistency degrees (the complement of standard deviation), and confidence intervals of fuzzy sequences subject to a confidence level and normal distribution. Second, the exponential similarity measure of CCFCSs is proposed in the scenario of FMs. Third, a PPE model is developed based on the proposed similarity measure of CCFCSs in the FM scenario. Finally, the developed model is applied to a piano performance competition organized by Shaoxing University in China as an actual evaluation example, and then the rationality and validity of the proposed model in the scenario of FMs are verified through sensitivity and comparison analysis. Show more

Keywords: Fuzzy multiset, confidence consistency fuzzy cubic set, exponential similarity measure, confidence level, piano performance evaluation

DOI: 10.3233/JIFS-235084

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7887-7896, 2024

Authors: Yang, Lei | Li, Deqing | Zeng, Wenyi | Ma, Rong | Xu, Zeshui | Yu, Xianchuan

Article Type: Research Article

Abstract: Pythagorean fuzzy sets, as a generalization of intuitionistic fuzzy sets, have a wide range of applications in many fields including image recognition, data mining, decision making, etc. However, there is little research on clustering algorithms of Pythagorean fuzzy sets. In this paper, a novel clustering idea under Pythagorean fuzzy environment is presented. Firstly, the concept of feature vector of Pythagorean fuzzy number (PFN) is presented by taking into account five parameters of PFN, and some new methods to compute the similarity measure of PFNs by applying the feature vector are proposed. Furthermore, a fuzzy similarity matrix by utilizing similarity measure …of PFNs is established. Later, the fuzzy similarity matrix is transformed into a fuzzy equivalent matrix which is utilized to establish a novel Pythagorean fuzzy clustering algorithm. Based on the proposed clustering algorithm, a novel multiple attribute decision making (MADM) method under Pythagorean fuzzy environment is presented. To illustrate the effectiveness and feasibility of the proposed technique, an application example is offered. Show more

Keywords: Pythagorean fuzzy number, feature vector, similarity measure, Pythagorean fuzzy clustering analysis, multiple attribute decision making

DOI: 10.3233/JIFS-235488

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7897-7907, 2024

Authors: Guo, Xu

Article Type: Research Article

Abstract: The detection of tomato leaf diseases is crucial for agricultural sustainability, impacting crop health, yield optimization, and global food supply. Despite the advancements in deep learning methods, a pressing challenge persists— achieving consistently high accuracy rates, particularly in the context of rigorous agricultural requirements. This study addresses this problem directly, introducing a novel approach by employing the Yolov8 architecture in a deep learning model for tomato leaf disease detection. The identified research challenge is precisely targeted, and the model is developed using a meticulously curated custom dataset. Through comprehensive training, validation, and testing phases, the study ensures the robust performance …of the Yolov8 model. The novelty of this research lies in its focused solution to the specific accuracy challenge within deep learning-based tomato leaf disease detection. The proposed methodology is rigorously evaluated through extensive experimentation, showcasing its ability to surpass existing benchmarks and offering a highly effective solution. This innovative approach not only contributes a unique solution to the identified problem but also advances the field by providing a more accurate and reliable method for detecting tomato leaf diseases. Show more

Keywords: Tomato leaf disease detection, deep learning methods, agricultural sector

DOI: 10.3233/JIFS-236905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7909-7921, 2024

Authors: Kalaimathi, M. | Balamurugan, B.J. | Nagar, Atulya K.

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-231095

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7923-7932, 2024

Authors: Xiao, Yanjun | Pei, Eryue | Shi, Linhan | Peng, Kai | Liu, Weiling

Article Type: Research Article

Abstract: In order to solve the problem that Switched Reluctance Motor (SRM) generates torque pulsation phenomenon during operation, which reduces the stability of loom spindle operation, this paper proposes and designs a multi-algorithm fusion-based SRM control strategy from the point of view of control strategy research. Combined with the operating characteristics of the loom, the causes of SRM torque pulsation are analyzed from the point of view of SRM control strategy, and combined with the spindle control indexes, the voltage chopper control and torque distribution function are introduced to construct the SRM control strategy scheme for the loom. On this basis, …an optimization strategy based on the fusion of fuzzy control algorithm, particle swarm algorithm and simulated annealing algorithm is proposed to optimize the torque distribution function, and the algorithmic process of SRM control strategy is verified through comparative tests. The results show that the control strategy can make its torque pulsation reduced to less than 10%, the speed rise time is less than 0.1 s, and the relative error of the speed is less than 0.05%, which meets the index requirements of the spindle drive. This proves that the SRM torque pulsation can be reduced by the multi-algorithm fusion control strategy without increasing the hardware cost, which provides a useful reference for solving the SRM torque pulsation problem under the requirement of low cost. Show more

Keywords: Rapier loom, switched reluctance motor, torque distribution function, multi-algorithm fusion

DOI: 10.3233/JIFS-233138

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7933-7957, 2024

Authors: Zakaria, Aliya Syaffa | Shafi, Muhammad Ammar | Mohd Zim, Mohd Arif | Musa, Aisya Natasya

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-233714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7959-7968, 2024

Authors: Xiao, Yanjun | Li, Shifang | Zhang, Kun | Zhang, Yameng | Xiao, Yanchun

Article Type: Research Article

Abstract: Recovering low-quality waste heat using industrial waste heat is challenging, and the reuse technology needs to erupt. Moreover, the gas source of low-quality waste heat is relatively volatile, which makes it challenging to keep the actual working condition of the plant stable. Therefore, it is inspiring to research the robustness of root-waste heat power generation processed measurement and control system to improve the stability of the plant operation. Hence, in this paper, we have applied uncertainty theory to analyze it and formulate the uncertainty model based on the Bode diagram. We also proposed a control method based on the uncertainty …model, which combines robust control and internal model control to make the roots waste heat power generation system operate stably under the effect of external disturbances and changes of internal structure or parameters in actual operation. Experimental results show that the robust internal model control method has a speed deviation of no more than 7.9 r/min compared with the PID control method. The adjustment time to track the set value does not exceed 73.1 seconds within the allowed fluctuation range. The fluctuation variance is 30.95% of that of the PID controller. The dynamic performance is better, with strong anti-interference capability and significantly improved tracking performance. It ensures the stability of the roots-type waste heat utilization system, which is essential for future intelligent grid-connected power generation. Show more

Keywords: Waste heat power generation, uncertain theory, robust internal model control, roots power machine

DOI: 10.3233/JIFS-234416

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7969-7987, 2024

Authors: Lei, Fan | Cai, Qiang | Wei, Guiwu

Article Type: Research Article

Abstract: The development and application of blockchain provides technical support for supply chain technological innovation and industrial innovation. Integrating the decentralized, independent, open, traceable and tamper-proof features of the blockchain into the supply chain can effectively improve the problems of unstable supply chain structure, low security, low privacy, low collaboration ability and high operating costs. Establishing probabilistic double hierarchy linguistic multi-attribute decision-making (PDHL-MADM) model to evaluate the performance of blockchain is an effective measure to optimize blockchain performance and improve supply chain stability. Therefore, this thesis first takes the processing efficiency, cost, security performance, update and improvement ability as evaluation attributes. …Then the IDOCRIW weight method is used to calculate the objective weight of attributes. Based on Aczel-Alsina t-norm (AATN) and Aczel-Alsina t-conorm (AATCN), four operations of probabilistic double hierarchy linguistic term set (PDHLTS) are defined, and PDHLAAWA operator, PDHLAAOWA operator, PDHLAAHA operator, PDHLAAHM operator, PDHLAAWHM operator and their dual operators are proposed, and a series of corresponding PDHL operator models are constructed. In addition, the sensitivity and stability of this series of operator models are analyzed in depth. Finally, the new model proposed in this thesis is compared with the existing model to verify its scientific and superiority. Show more

Keywords: Probabilistic double hierarchy linguistic term set (PDHLTS), Multi-attribute decision-making (MADM), PDHLAAWA operator and PDHLAAWHM operator, evaluate the performance of blockchain

DOI: 10.3233/JIFS-235215

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7989-8024, 2024

Authors: Qiao, Junfeng | Peng, Lin | Zhou, Aihua | Pan, Sen | Yang, Pei | Xu, Min | Shen, Xiaofeng | Chen, Jingde | Gu, Hua

Article Type: Research Article

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

DOI: 10.3233/JIFS-236459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8025-8035, 2024

Authors: Kamala Devi, K. | Raja Sekar, J.

Article Type: Research Article

Abstract: Breast cancer has been life-threatening for many years as it is the common cause of fatality among women. The challenges of screening such tumors through manual approaches can be overcome by computer-aided diagnosis, which aids radiologists in making precise decisions. The selection of significant features is crucial for the estimation of prediction accuracy. This work proposes a hybrid Genetic Algorithm (GA) and Honey Badger Algorithm (HBA) based Deep Neural Network (DNN), HGAHBA-DNN for the concurrent optimal features selection and parameter optimization; further, the optimal features and parameters extracted are fed into the DNN for the prediction of the breast cancer. …It fuses the benefits of HBA with parallel processing and efficient feedback with GA’s excellent global convergent rate during the processing stages. The aforementioned method is evaluated on the Wisconsin Original Breast Cancer (WOBC), Wisconsin Diagnostic Breast Cancer (WDBC), and the Surveillance, Epidemiology, and End Results (SEER) datasets. Subsequently, the performance is validated using several metrics like accuracy, precision, Recall, and F1-score. The experimental result shows that HGAHBA-DNN obtains accuracy of 99.42%, 99.84%, and 92.44% for the WOBC, WDBC, and SEER datasets respectively, which is much superior to the other state-of-the-art methods. Show more

Keywords: Breast cancer prediction, DNN, feature selection, genetic algorithm, honey badger algorithm, parameter optimization

DOI: 10.3233/JIFS-236577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8037-8048, 2024

Authors: Hou, Yuntong | Shang, Shuye | Cao, Shengxi | Liu, Zhengjia

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-237293

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8049-8063, 2024

Authors: Tian, Huaqiang | Yu, Long | Tian, Shengwei | Long, Jun | Zhou, Tiejun | Wang, Bo | Li, Yuhuan

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-233077

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8065-8074, 2024

Authors: Ding, Huafeng | Shang, Junyan | Zhou, Guohua

Article Type: Research Article

Abstract: Emotional state recognition is an important part of emotional research. Compared to non-physiological signals, the electroencephalogram (EEG) signals can truly and objectively reflect a person’s emotional state. To explore the multi-frequency band emotional information and address the noise problem of EEG signals, this paper proposes a robust multi-frequency band joint dictionary learning with low-rank representation (RMBDLL). Based on the dictionary learning, the technologies of sparse and low-rank representation are jointly integrated to reveal the intrinsic connections and discriminative information of EEG multi-frequency band. RMBDLL consists of robust dictionary learning and intra-class/inter-class local constraint learning. In robust dictionary learning part, RMBDLL …separates complex noise in EEG signals and establishes clean sub-dictionaries on each frequency band to improve the robustness of the model. In this case, different frequency data obtains the same encoding coefficients according to the consistency of emotional state recognition. In intra-class/inter-class local constraint learning part, RMBDLL introduces a regularization term composed of intra-class and inter-class local constraints, which are constructed from the local structural information of dictionary atoms, resulting in intra-class similarity and inter-class difference of EEG multi-frequency bands. The effectiveness of RMBDLL is verified on the SEED dataset with different noises. The experimental results show that the RMBDLL algorithm can maintain the discriminative local structure in the training samples and achieve good recognition performance on noisy EEG emotion datasets. Show more

Keywords: Multi-frequency band, dictionary learning, electroencephalogram, noise data, low-rank representation

DOI: 10.3233/JIFS-233753

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8075-8088, 2024

Authors: Wei, Jiaxin | Yang, Jin | Liu, Xinyang

Article Type: Research Article

Abstract: Due to intensified off-balance sheet disclosure by regulatory authorities, financial reports now contain a substantial amount of information beyond the financial statements. Consequently, the length of footnotes in financial reports exceeds that of the financial statements. This poses a novel challenge for regulators and users of financial reports in efficiently managing this information. Financial reports, with their clear structure, encompass abundant structured information applicable to information extraction, automatic summarization, and information retrieval. Extracting headings and paragraph content from financial reports enables the acquisition of the annual report text’s framework. This paper focuses on extracting the structural framework of annual report …texts and introduces an OpenCV-based method for text framework extraction using computer vision. The proposed method employs morphological image dilation to distinguish headings from the main body of the text. Moreover, this paper combines the proposed method with a traditional, rule-based extraction method that exploits the characteristic features of numbers and symbols at the beginning of headings. This combination results in an optimized framework extraction method, producing a more concise text framework. Show more

Keywords: OpenCV, dilation operation, text structure extraction

DOI: 10.3233/JIFS-234170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8089-8108, 2024

Authors: Li, Wuke | Wang, Xingzhu | Tang, Minli

Article Type: Research Article

Abstract: Aiming at the problem of inaccurate transformer fault diagnosis in dissolved gas analysis, this paper proposes a novel diagnostic method that integrates an enhanced honey badger algorithm (EHBA) with an ensemble learning-based deep hybrid kernel extreme learning machine (DHKELM). First, kernel principal component analysis (KPCA) was deployed for feature fusion of the gas data, thus extracting more effective features. The DHKELM, combining polynomial and RBF kernel functions, was used as a base learning to build a powerful classifier with Adaboost framework. The EHBA introduces information sharing and firefly perturbation strategies based on HBA. This EHBA was harnessed to optimize the …DHKELM’s critical parameters, establishing the EHBA-DHKELM-Adaboost transformer fault diagnosis model. Finally, the features garnered by KPCA were fed into the model, simulating and validating various fault diagnosis models. The findings reveal that EHBA-DHKELM-Adaboost achieves 98.75% diagnostic accuracy in transformer faults, surpassing other models. Show more

Keywords: Transformer fault diagnosis, dissolved gas analysis, deep hybrid kernel extreme learning machine, adaboost, enhanced honey badger algorithm

DOI: 10.3233/JIFS-235563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8109-8121, 2024

Authors: Brintha, K. | Joseph Jawhar, S.

Article Type: Research Article

Abstract: Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 …consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively. Show more

Keywords: Railway track, object detection, fault detection, deep learning, Yolo network, fuzzy logic

DOI: 10.3233/JIFS-236445

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8123-8137, 2024

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

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-236836

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8139-8151, 2024

Authors: Achich, Nassira | Ghorbel, Fatma | Hamdi, Fayçal | Métais, Elisabeth | Gargouri, Faiez

Article Type: Research Article

Abstract: Dealing with temporal data imperfection is a crucial issue in several application domains. In fact, failure to handle these imperfections can have significant consequences and lead to incorrect analysis and decision-making. This is particularly true when handling imperfect temporal data inputs in applications for Alzheimer’s patients as a real example. In this context, there is a need for a global ontology that provides a semantic representation of temporal data imperfection. In the literature, there is a big number of ontologies that represent data. Some represent only perfect temporal data. Some others represent imperfect data but not temporal ones. To the …best of our knowledge, there is no ontology that represents temporal data imperfection. In this paper, we represent “TimeOntoImperfection”, a usable global ontology that represents four types of imperfection: imprecision, uncertainty, both uncertainty and imprecision and conflict. We describe the structure of “TimeOntoImperfection”, then we conduct a case a study in which we illustrate the usefulness of our ontology. Finally, we introduce the validation part in the context of CAPTAIN MEMO - an ontology based memory prothesis dedicated to alzheimer patients- and we discuss the encouraging results derived from the evaluation step. Show more

Keywords: Ontology, temporal data imperfection, temporal reasoning, uncertainty, imprecision, conflict, possibilistic ontology, fuzzy ontology, probabilistic ontology, probabilistic ontology

DOI: 10.3233/JIFS-237693

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8153-8168, 2024

Authors: Kang, Chen | Jin, Shuaizhen | Zhong, Zheng | Li, Kunyan | Zeng, Xiaoyu

Article Type: Research Article

Abstract: The quantification of the interplay between student behavior data and classroom teaching effectiveness using quantitative metrics has perennially posed a challenge in the evaluation of classroom instruction. Classroom activity serves as a reflection of student engagement, emotional ambiance, and other pertinent aspects during the pedagogical process. This article presents a methodology for quantifying student head posture during classroom instruction utilizing AI-driven video analysis technology, notably the Classroom Activity Index (CAI). A Classroom Activity Analysis System (CAAS) was designed and developed, integrating a multi-scale classification network based on ECA-ResNet50 and ECA-ResNet18. This network discerns and categorizes various head regions of students …situated in both the frontal and real rows of a lecture-style classroom, irrespective of their dimensions. The classification network attains exceptional performance, boasting F1 score of 0.91 and 0.92 for student head-up and head-nodding. Drawing on the live classroom instruction at a higher vocational college in Wuhan, Hubei Province, China, a comparative experiment was executed. The findings revealed that three factors: teacher-student verbal interaction, teacher body language, and utilization of digital resource, all exert an influence on CAI. Simultaneously, the degree of classroom activity as gauged by FIAS and manual analysis fundamentally aligns with the CAI indicators quantified by CAAS, validating the efficacy of CAI in the quantification of classroom activity. Consequently, the incorporation of CAAS in teaching, research, and oversight scenarios can augment the precision and scientific rigor of classroom teaching assessment. Show more

Keywords: Classroom activity index, multi-scale he.ad posture classification network, classroom activity analysis system, head-up rate, head-nodding rate

DOI: 10.3233/JIFS-237970

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8169-8183, 2024

Authors: Sun, Ping | Song, LinLin | Yuan, Ling | Yu, Haiping | Wei, Yinzhen

Article Type: Research Article

Abstract: News text is an important branch of natural language processing. Compared to ordinary texts, news text has significant economic and scientific value. The characteristics of news text include structural hierarchy, diverse label categories, and limited high-quality annotation samples. Many machine learning and deep learning methods exist to analyze various forms of news text. However, due to label imbalance, hierarchical semantics, and confusing labels, current methods have limitations. Therefore, this paper proposes a news text classification framework based on hierarchical semantics and prior correction (HSPC). Firstly, data augmentation is used to enhance the diversity of the training set and adversarial learning …is employed to improve the resistance of the model with its robustness. Then, a hierarchical feature extraction approach is employed to extract semantic features from different levels of news texts. Consequentially, a feature fusion method is designed to allow the model to focus on relevant hierarchical semantics for label classification. Finally, highly confusing label predictions are corrected to optimize the label prediction of the model and improve confidence. Multiple experiments are performed on four widely used public datasets. The experimental results indicate that HSPC achieves higher classification accuracy compared to other models. On the FCT, AGNews, THUCNews, and Ohsumed datasets, HSPC improves the accuracy by 1.03%, 1.38%, 2.55%, and 1.15%, respectively, compared to state-of-the-art methods. This validates the rationality and effectiveness of the designed mechanisms. Show more

Keywords: Text Categorization, hierarchical semantics, feature fusion, prior distribution, data enhancement

DOI: 10.3233/JIFS-238433

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8185-8203, 2024

Authors: Myithili, K.K. | Beulah, R.D.

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-222714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8205-8212, 2024

Authors: Diao, Xiu-Li | Zhang, Quan-Lei | Zeng, Qing-Tian | Duan, Hua | Song, Zheng-guo | Zhao, Hua

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-232189

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8213-8229, 2024

Authors: Borzooei, Rajab Ali | Ahn, Sun Shin | Jun, Young Bae

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-233295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8231-8243, 2024

Authors: Yu, Peng | Song, Huxiong | Liu, Hui

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-233318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8245-8272, 2024

Authors: Yuan, Weijin | Deng, Yunfeng

Article Type: Research Article

Abstract: This paper improves the visual change-based personnel evacuation model by considering the evacuees’ gravity. Specifically, first, the new model incorporates the gravity formula in the model’s mechanic part to consider the influence of gravity. Second, the new model involves rules for determining the visual range of personnel moving in the stairwell. Third, the proposed model investigates the influence of the angle and width of the stairwell, the number of people, and other factors during personnel evacuation under the influence of gravity. The model is developed in Python and is compared with actual results, revealing that the proposed model is more …realistic considering the evacuation time compared to current models. Indeed, under a fixed number of people, when the stairwell angle is less than 34°, the evacuation time decreases as the angle increases, and when the stairwell angle exceeds 34°, the evacuation time is almost unchanged. Additionally, under a fixed number of evacuees, the evacuation time decreases as the width of the stairwell increases, and due to stairwell width space redundancy, the evacuation time tends to stabilize. The results of the new model research provide reference for the design of building safety evacuation, thereby improving the safety of buildings. Show more

Keywords: Stair angle, stair width, view, pedestrian evacuation

DOI: 10.3233/JIFS-236008

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8273-8287, 2024

Authors: Shengbin, Liang | Haoran, Sun | Fuqi, Sun | Hongjian, Wu | Wencai, Du

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-236542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8289-8309, 2024

Authors: Teng, Wei | Li, Yan | Sun, Hongxing | Chen, Haojie

Article Type: Research Article

Abstract: In the present study, three hybrid models include support vector regression-salp swarm optimization (SVR-SSO), support vector regression-biogeography-based (SVR-BBO), and support vector regression-phasor particle swarm optimization (SVR- PPSO) was applied to forecast pond ash’s CBR value modified with lime sludge (LS) and lime (LI). In the developed models, five variables were selected as inputs. It can result that the developed integrated models have R2 bigger than 0.9952. It means the agreement between observed and forecasted values by hybrid models is mainly similar to represent the highest accuracy. In both the training and testing stages, PSO-SVR results from better performance than the …BBO-SVR model, with R2, RMSE, MAE, and PI equal to 0.9983, 0.6439, 0.3181, and 0.0081 for training data, and 0.9975, 0.7319, 0.4135, and 0.0141 for testing data, respectively. So, by considering the OBJ index, the OBJ value for PSO-SVR is 12.966, lower than BBO-SVR at 16.9957. Therefore, the PSO-SVR model outperforms another model to estimate the CBR of pond ash modified with LI and LS, consequently being recognized as the proposed model that makes it to be used for practical applications. Show more

Keywords: California bearing ratio, phasor particle swarm optimization, biogeography-based optimization, salp swarm optimization, support vector regression

DOI: 10.3233/JIFS-220745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8311-8327, 2024

Authors: Chen, Yong | Xie, Xiao-Zhu | Weng, Wei

Article Type: Research Article

Abstract: Graph-structured data is ubiquitous in real-world applications, such as social networks, citation networks, and communication networks. Graph neural network (GNN) is the key to process them. In recent years, graph attention networks (GATs) have been proposed for node classification and achieved encouraging performance. It focuses on the content associated on nodes to evaluate the attention weights, and the rich structure information in the graph is almost ignored. Therefore, we propose a multi-head attention mechanism to fully employ node content and graph structure information. The core idea is to introduce the interactions in the topological structure into the existing GATs. This …method can more accurately estimate the attention weights among nodes, thereby improving the convergence of GATs. Second, the mechanism is lightweight and efficient, requires no training to learn, can accurately analyze higher-order structural information, and can be strongly interpreted through heatmaps. We name the proposed model content- and structure-based graph attention network (CSGAT). Furthermore, our proposed model achieves state-of-the-art performance on a number of datasets in node classification. The code and data are available at https://github.com/CroakerShark/CSGAT. Show more

Keywords: Graph neural network, graph attention network, node classification, graph-structured data

DOI: 10.3233/JIFS-223304

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8329-8343, 2024

Authors: Li, Biao | Tang, Shoufeng | Li, Wenyi

Article Type: Research Article

Abstract: Pose estimation plays a crucial role in human-centered vision applications and has advanced significantly in recent years. However, prevailing approaches use extremely complex structural designs for obtaining high scores on the benchmark dataset, hampering edge device applications. In this study, an efficient and lightweight human pose estimation problem is investigated. Enhancements are made to the context enhancement module of the U-shaped structure to improve the multi-scale local modeling capability. With a transformer structure, a lightweight transformer block was designed to enhance the local feature extraction and global modeling ability. Finally, a lightweight pose estimation network— U-shaped Hybrid Vision Transformer, UViT— …was developed. The minimal network UViT-T achieved a 3.9% improvement in AP scores on the COCO validation set with fewer model parameters and computational complexity compared with the best-performing V2 version of the MobileNet series. Specifically, with an input size of 384×288, UViT-T achieves an impressive AP score of 70.2 on the COCO test-dev set, with only 1.52 M parameters and 2.32 GFLOPs. The inference speed is approximately twice that of general-purpose networks. This study provides an efficient and lightweight design idea and method for the human pose estimation task and provides theoretical support for its deployment on edge devices. Show more

Keywords: Pose estimation, multi-branch structure, lightweight network, context enhancement, attention mechanism

DOI: 10.3233/JIFS-231440

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8345-8359, 2024

Authors: Yu, Bengong | Ji, Xiaohan

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-233163

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8361-8374, 2024

Authors: Atef, Shimaa | El-Seidy, Essam | Reda, Naglaa M.

Article Type: Research Article

Abstract: Decisions in many dilemmas are based on a combination of factors, including as incentive, punishment, reputation, and memory. The impact of memory information on cooperative evolution in multi-round games is a decision-making process in group evolution. The iterated prisoner’s dilemma is an excellent model for the development of cooperation amongst the payoff-maximizing individuals. Since tit-for-tat proved successful in Axelrod’s repeated prisoner’s dilemma tournaments, there has been a great deal of interest in creating new strategies. Every iterative prisoner’s dilemma method bases its decision-making on a specific duration of past contacts with the opponent, which is referred to as the memory’s …size. This study examines the impact of strategy memory size on the evolutionary stability of n-person iterated prisoner’s dilemma strategies. In this paper, we address the role that memory plays in decision-making. We interested in the model of the Iterated Prisoner’s Dilemma game for three players with memory two, and we will look at strategies with similar behavior, such as Tit-For-Tat (TFT) strategies as well as Win Stay-Lose Shift (WSLS) strategies. As a result of this paper, we have shown that the effect of memory length is almost non-existent in the competitions of strategies that we studied. Show more

Keywords: Memory-Two, Tit-For-Tat strategies (TFT), three-players iterated prisoner’s dilemma game (3P-IPD), transition matrix, Win Stay-Lose Shift strategies (WSLS)

DOI: 10.3233/JIFS-233690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8375-8388, 2024

Authors: Chen, Jie | Yin, Chuancun

Article Type: Research Article

Abstract: Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, and several multi-criteria decision models based on PLTSs have been recently developed. In this framework, distortion risk measures are extensively used in finance and insurance applications, but are rarely applied in fuzzy systems. In this paper, distortion risk measures are applied to fuzzy tail decisions. In particular, three tail risk measurement methods are put forward, referred to as probabilistic linguistic VaR (PLVaR), expected probability linguistic VaR (EPLVaR), and Wang tail risk measure and extensively study their properties. Our novel methods help to clarify the connections between distortion …risk measure and fuzzy tail decision-making. In particular, the Wang tail risk measure is characterized by consistency and stability of decision results. The criteria and expert weights are unknown or only partially known during the decision making process, and the maximising PLTSs deviations are showed how to determine them. The theoretical results are showcased on an optimal stock fund selection problem, where the three tail risk measures are compared and analyzed. Show more

Keywords: Probabilistic linguistic term sets, probabilistic linguistic VaR, expected probability linguistic VaR, Wang tail risk measure, maximizing deviation method

DOI: 10.3233/JIFS-234218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8389-8409, 2024

Authors: Li, Yaqin | Zhang, Ziyi | Yuan, Cao | Hu, Jing

Article Type: Research Article

Abstract: Traffic sign detection technology plays an important role in driver assistance systems and automated driving systems. This paper proposes DeployEase-YOLO, a real-time high-precision detection scheme based on an adaptive scaling channel pruning strategy, to facilitate the deployment of detectors on edge devices. More specifically, based on the characteristics of small traffic signs and complex background, this paper first of all adds a small target detection layer to the basic architecture of YOLOv5 in order to improve the detection accuracy of small traffic signs.Then, when capturing specific scenes with large fields of view, higher resolution and richer pixel information are preserved …instead of directly scaling the image size. Finally, the network structure is pruned and compressed using an adaptive scaling channel pruning strategy, and the pruned network is subjected to a secondary sparse pruning operation. The number of parameters and computations is greatly reduced without increasing the depth of the network structure or the influence of the input image size, thus compressing the model to the minimum within the compressible range. Experimental results show that the model trained by Experimental results show that the model trained by DeployEase-YOLO achieves higher accuracy and a smaller size on TT100k, a challenging traffic sign detection dataset. Compared to existing methods, DeployEase-YOLO achieves an average accuracy of 93.3%, representing a 1.3% improvement over the state-of-the-art YOLOv7 network, while reducing the number of parameters and computations to 41.69% and 59.98% of the original, respectively, with a compressed volume of 53.22% of the previous one. This proves that the DeployEase-YOLO has a great deal of potential for use in the area of small traffic sign detection. The algorithm outperforms existing methods in terms of accuracy and speed, and has the advantage of a compressed network structure that facilitates deployment of the model on resource-limited devices. Show more

Keywords: Small target, deep learning, model compression, traffic sign detection

DOI: 10.3233/JIFS-235135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8411-8424, 2024

Authors: Zhou, Xiao-Guang | Chen, Ya-Nan | Ji, Jia-Xi

Article Type: Research Article

Abstract:  The multi-attribute decision-making (MADM) methods can deeply mine hidden information in data and make a more reliable decision with actual needs and human cognition. For this reason, this paper proposes the bipolar N -soft PROMETHEE (preference ranking organization method for enrichment of evaluation) method. The method fully embodies the advantages of the PROMETHEE method, which can limit the unconditional compensation between attribute values and effectively reflect the priority between attribute values. Further, by introducing an attribute threshold to filter research objects, the proposed method not only dramatically reduces the amount of computation but also considers the impact of the size …of the attribute value itself on decision-making. Secondly, the paper proposes the concepts of attribute praise, attribute popularity, total praise, and total popularity for the first time, fully mining information from bipolar N -soft sets, which can effectively handle situations where attribute values have different orders of magnitude. In addition, this paper presents the decision-making process of the new method, closely integrating theoretical models with real life. Finally, this paper analyses and compares the proposed method with the existing ones, further verifying the effectiveness and flexibility of the proposed method. Show more

Keywords: PROMETHEE method, bipolar N-soft set, attribute praise, attribute popularity, multi-attribute decision-making

DOI: 10.3233/JIFS-236404

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8425-8440, 2024

Authors: Dagal, Idriss | Akín, Burak | Dari, Yaya Dagal

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-224535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8441-8460, 2024

Authors: Gao, Shengxiang | He, Zhilei | Yu, Zhengtao | Zhu, Enchang | Wu, Shaoyang

Article Type: Research Article

Abstract: Cross-lingual event retrieval is an information retrieval task aimed at cross-lingual event retrieval among multiple languages to find text or documents related to a specific event. Specific to Chinese-Vietnamese cross-language event retrieval, it involves using Chinese as a query to retrieve Vietnamese documents related to the query event. The critical issue is how to efficiently align query and document representations with limited resources. Existing cross-language pre-training models are trained on large-scale multilingual corpora, but their training goals do not include explicit language alignment tasks. Due to the uneven distribution of training corpora between different languages, these models have The problem …of language bias. Therefore, this linguistic bias is also inherited in cross-lingual retrieval based on these models. To solve this problem, this paper proposes a Chinese-Vietnamese cross-lingual event retrieval method based on knowledge distillation. This approach enables the model to learn good query-document matching features from monolingual retrieval by transferring knowledge from high-resource to low-resource languages. By enhancing the alignment between queries and documents in different languages in a shared semantic space, the method improves the performance of Chinese-Vietnamese cross-lingual event retrieval. Show more

Keywords: Cross-lingual, event retrieval, knowledge distillation, language bias

DOI: 10.3233/JIFS-235749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8461-8475, 2024

Authors: Xu, Dongsheng | Chen, Chuanming | Jin, Qi | Zheng, Ming | Ni, Tianjiao | Yu, Qingying

Article Type: Research Article

Abstract: Abnormal-trajectory detection can be used to detect fraudulent behavior of taxi drivers transporting passengers. Aiming at the problem that existing methods do not fully consider abnormal fragments of trajectories, this paper proposes an abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition, which effectively detects abnormal sub-trajectories and further detects abnormal trajectories. First, each trajectory is reconstructed using the turning angles and is divided into multiple sub-trajectories according to the turning angle threshold and trajectory point original acceleration. The sub-trajectories are then classified according to the extracted directional features. Finally, the multivariate distances between angular adjacent segments are calculated …to obtain the outlier factor, and abnormal sub-trajectories are detected. The sum of the lengths of the abnormal sub-trajectories is used to calculate the outlier score and identify abnormal trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods. Show more

Keywords: Abnormal-trajectory detection, trajectory reconstruction, directional feature, outlier factor, sub-trajectory classification

DOI: 10.3233/JIFS-236508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8477-8496, 2024

Authors: Yu, Jie | Zhang, Jubin

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-237483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8497-8512, 2024

Authors: Wu, Guizhou | Wu, Junfeng | Zhang, Xinyu

Article Type: Research Article

Abstract:  Optimization of the routing represents an important challenge when considering the rapid development of Wireless Sensor Networks (WSN), which involve efficient energy methods. Applying the effectiveness of a Deep Neural Network (DNN) and a Gaussian Mixture Model (GMM), the present article proposes an innovative method for attaining Energy-Efficient Routing (EER) in WSN. When it comes to dealing with dynamic network issues, conventional routing protocols generally conflict, resulting in unsustainable Energy consumption (EC). By applying algorithms based on data mining to adapt routing selections in an effective procedure, the GMM + DNN methodology that has been developed is able to successfully address this …problem. The GMM is a fundamental Feature Extraction (FE) method for accurately representing the features of statistical analysis associated with network parameters like signal frequency, the amount of traffic, and channel states. By learning from previous data collection, the DNN, which relies on these FE, provides improved routing selections, resulting in more efficient use of energy. Since routing paths are constantly optimized to ensure dynamic adaptation, where less energy is used, networks last longer and perform more efficiently. Network simulations highlight the GMM + DNN method’s effectiveness and depict how it outperforms conventional routing methods while preserving network connectivity and data throughput. The GMM + DNN’s adaptability to multiple network topologies and traffic patterns and its durability make it an efficient EER technique in the diverse WSN context. The GMM + DNN achieves an EC of 0.561 J, outperforming the existing state-of-the-art techniques. Show more

Keywords: Sensor Node, WSN, gaussian mixture, CNN, energy consumption, routing

DOI: 10.3233/JIFS-238711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8513-8527, 2024

Authors: Allouche, Moez | Dahech, Karim | Gaubert, Jean-Paul

Article Type: Research Article

Abstract: This paper proposes a multi-objective H2 /H ∞ maximum power tracking control of a variable speed wind turbine to minimize the H2 tracking error and ensure the H ∞ model reference-tracking performance, simultaneously. The optimal condition is obtained via a boost converter use, which adapts the load impedance to the wind turbine generator. Thus, based on the fuzzy T-S model, a multi-objective Maximum Power Point Tracking (MPPT) controller is developed, ensuring maximum power transfer, despite wind speed variation and system uncertainty. To specify the optimal trajectory to follow, a TS reference model is proposed taking as input the optimal …rectified DC current. The conditions of stability and stabilization are expressed in terms of linear matrix inequality (LMI) for uncertain and disturbed T-S models leading to determining the controller gains. Finally, an example of MPP tracking applied to a Wind Energy Conversion System (WECS) illustrates the effectiveness of the proposed fuzzy control law. Show more

Keywords: Multi-objective fuzzy tracking control, maximum power point tracking (MPPT), linear matrix inequalities (LMIs), robust control, T-S fuzzy model

DOI: 10.3233/JIFS-222887

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8529-8541, 2024

Authors: Sharma, Itika | Gupta, Sachin Kumar

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-235275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8543-8562, 2024

Authors: Pandey, Vibha | Choubey, Siddhartha | Patra, Jyotiprakash | Mall, Shachi | Choubey, Abha

Article Type: Research Article

Abstract: Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic …License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. Show more

Keywords: Data security, secure image analysis, automatic license plate recognition, segmentation, image classification, convolution neural network, character recognition

DOI: 10.3233/JIFS-235400

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8563-8585, 2024

Authors: Sakthimohan, M. | Deny, J. | Rani, G. Elizabeth

Article Type: Research Article

Abstract: In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing …(SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The proposed one consumes a minimum energy of 49.67 mJ, achieves a better delivery rate of 99.92%, takes less lifetime of 5902 rounds, 0.057 s delay, and achieves a higher throughput of 0.99 Mbps when considering a maximum of 500 nodes in the network. Also, the proposed one achieves 99.76% accuracy for the intrusion detection. Thus, the simulation outcomes prove the superiority of the proposed SDLEER system over the existing schemes for routing and attack detection. Show more

Keywords: Wireless sensor networks, optimal cluster-based energy aware routing, intrusion detection system, cluster head selection, routing, dimensionality reduction, and deep learning

DOI: 10.3233/JIFS-235512

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8587-8603, 2024