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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: 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: Aiming at the existing UAV fire detection system with low small target detection accuracy, a high leakage rate, and a slow rate, an improved YOLOv5 UAV flame detection algorithm is proposed. First, the anchor box clustering is optimized using the K-mean++algorithm to reduce the classification error rate. Second, the original backbone network is enhanced with the CBAM attention mechanism, which scans the whole globe to obtain the target area with a high weighting proportion and needs to be focused on. Replace the PANet network with the BiFPN network in the neck and introduce jump connections when performing feature fusion, which …can better retain the semantic information of high-level and low-level features. Finally, the α-IoU loss function is added to achieve the regression accuracy of different levels of the bounding box by modulating α, which improves the detection accuracy of small datasets and the robustness to noise. According to the experimental results, using a randomly segmented dataset, the modified YOLOv5 algorithm obtains a mAP value of 80.2%, which is 6.7% higher than the original YOLOv5 method, while maintaining an FPS of 64 frames per second. The method helps to improve the accuracy of UAVs for fire monitoring, and the performance is better than the existing flame detection algorithms, which meet the requirements of practical applications. Show more
Keywords: YOLOv5, feature fusion, CBAM, unmanned aerial vehicle (UAV), α-IoU
DOI: 10.3233/JIFS-236836
Citation: Journal of Intelligent & Fuzzy Systems, vol. 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: The concept of intuitionistic fuzzy soft set is applied to generalize the theory of transversals in hypergraphs. The notion of transversals of an Intuitionistic Fuzzy Soft Hypergraphs (IFSHGs) and locally minimal transversals of IFSHGs are pioneered with some of its specifications. It is also proved that H ˜ is (μ, ν )-tempered IFSHGs if H ˜ is support simple, elementary and simply ordered. Then, an algorithm is developed and proposed to find the minimal transversals of IFSHGs. An application is also identified in selecting appropriate location for the …installation of wind turbines. Finally the proposed algorithm works in finding the suitable place for wind turbine installation. As a result the proposed algorithm is helpful in making decisions. Show more
Keywords: Transversals, locally minimal transversals, (μ, ν)-tempered IFSHGs
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: Knowledge tracing aims to model learners’ knowledge mastery based on their historical interaction records and predict their future performance. Due to its great potential in enabling personalized learning in intelligent tutoring systems, it has received extensive attention. However, most deep learning-based knowledge tracing methods have significant predictive performance. It is difficult to extract meaningful interpretations from the thousands of parameters in neural networks. The interpretability of knowledge tracing refers to the ability of learners to easily understand the predicted results.To address this problem, based on learning factors that influence the learner’s exercise performance, this paper proposes a novel knowledge tracing …model which is named Integrating L earning factors and B ayesian network for interpretable K nowledge T racing (LBKT). Firstly, meaningful learning factors, including knowledge mastery, learning ability, and exercise difficulty, are calculated from learners’ historical interaction records using deep learning and statistical methods. Then, Bayesian network is constructed to capture the causal relation between the three learning factors and exercise response. Finally, the Bayesian network is generated through structure and parameter learning to obtain interpretable prediction of future exercise performance. The proposed model named LBKT is evaluated on three public real-world educational datasets. The experiment results demonstrate that our approach achieves better predictive performance compared to baseline knowledge tracing methods, while also exhibiting significant superiority in model interpretability. Show more
Keywords: Interpretability, knowledge tracing, Bayesian networks, deep learning, personalized learning
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: Using the notion of the Łukasiewicz fuzzy set, we study the filter theory of Sheffer stroke Hilbert algebras. Here’s what we’re trying to do. 1. We first introduce the Łukasiewicz fuzzy filter of Sheffer stroke Hilbert algebras. 2. We provide an example to illustrates the Łukasiewicz fuzzy filter. 3. We examine the various properties of the Łukasiewicz fuzzy filter. 4. We discuss characterizations of the Łukasiewicz fuzzy filter. 5. We explore the conditions under which Łukasiewicz fuzzy set can be Łukasiewicz fuzzy filter. 6. We discuss the relationship between fuzzy filter and Łukasiewicz fuzzy …filter. 7. We use the given filter to creates a Łukasiewicz fuzzy filter. 8. We present conditions for the three subsets, called ∈-set, q -set and O -set, to be filters. Show more
Keywords: Sheffer stroke Hilbert algebra, Łukasiewicz fuzzy filter, ∈-set, q-set, O-set
DOI: 10.3233/JIFS-233295
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8231-8243, 2024
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