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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: Karthikeyan, M. | Colak, Ilhami | Sagar Imambi, S. | Joselin Jeya Sheela, J. | Nair, Sruthi | Umarani, B. | Alagusabai, Andril | Suriyakrishnaan, K. | Rajaram, A.
Article Type: Research Article
Abstract: This research paper introduces a cutting-edge approach to electric demand forecasting by incorporating the Temporal Fusion Transformer (TFT). As the landscape of demand forecasting becomes increasingly intricate, precise predictions are vital for effective energy management. To tackle this challenge, we leverage the sequential and temporal patterns in an extensive electric demand dataset spanning from 2003 to 2014. Our proposed Temporal Fusion Transformer model combines attention mechanisms with the transformer architecture, enabling it to adeptly capture intricate temporal dependencies. Thorough data preprocessing, including temporal embedding and external features, enhances prediction accuracy. Through rigorous evaluation, the TFT model surpasses existing forecasting techniques, …showcasing its capacity for accurate, resilient, and adaptive predictions. This research contributes to the advancement of electric demand forecasting, harnessing the TFT’s capabilities to excel in capturing diverse temporal patterns. The findings hold the potential to enhance energy management and support decision-making in the energy sector, bridging the gap between innovation and practical utility. Show more
Keywords: Electric demand forecasting, temporal fusion transformer, energy management, time-series analysis, transformer architecture
DOI: 10.3233/JIFS-236036
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Arenas Muñiz, Andrés Antonio | Mújica-Vargas, Dante | Rendón Castro, Arturo | Luna-Álvarez, Antonio | Vela-Rincón, Virna V.
Article Type: Research Article
Abstract: The selection of an appropriate trajectory for self-driving vehicles involves the analysis of several criteria that describe the generated trajectories. This problem evolves into an optimization problem when it is desired to increase or decrease the values for a specific criterion. The contribution of this thesis is to explore the use and optimization of another technique for decision-making, such as TOPSIS, with a sufficiently robust method that allows the inclusion of multiple parameters and their proper optimization, incorporating human experience. The proposed approach showed significantly higher safety and comfort performance, with about 20% better efficiency and 80% fewer safety violations …compared to other state-of-the-art methods, and in some cases outperforming in comfort by about 30.43%. Show more
Keywords: Decision-making, human experience, trajectory selection, self-driving
DOI: 10.3233/JIFS-219365
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Téllez-Velázquez, Arturo | Delice, Pierre A. | Salgado-Leyva, Rafael | Cruz-Barbosa, Raúl
Article Type: Research Article
Abstract: This paper performs an analysis comparing two evolutionary explainable fuzzy models that make inferences in a pipeline with a blood test data set for COVID-19 classification. Firstly, data is preprocessed by the following stages: cleaning, imputation and ranking feature selection. Later, we perform a comparative analysis between several clustering methods used in an Evolutionary Clustering-Structured Fuzzy Classifier (ECSFC) to solve this classification problem using the Differential Evolution (DE) algorithm. Complementarily, we find that the Fuzzy Decision Tree model produces similar performance when is tuned with the DE algorithm (EFDT). The obtained results show that, simpler models are easier to explain …qualitatively, i.e., increasing the number of clusters in ECSFC model or the maximum depth of the tree in EFDT model, does not necessarily help to obtain simplified and accurate models. In addition, although the EFDT model is by itself an intuitively explainable model, the ECSFC, with the help of the proposed Weighted Stacked Features Plot, generates more intuitive models that allow not only highlighting the features and the linguistic terms that defines a patient with COVID-19, but also allows users to visualize in a single graph and in specific colors the analyzed classes. Show more
Keywords: COVID-19, blood tests, fuzzy classifier, fuzzy decision tree, clustering, differential evolution
DOI: 10.3233/JIFS-219372
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Yao, Ziyang
Article Type: Research Article
Abstract: The traditional multi-task Takagi-Sugeno-Kang (TSK) fuzzy system modeling methods pay more attention to utilizing the inter-task correlation to learn the consequent parameters but ignore the importance of the antecedent parameters of the model. To this end, we propose a novel multi-task TSK fuzzy system modeling method based on multi-task fuzzy clustering. This method first proposes a novel multi-task fuzzy c-means clustering method that learns multiple specific clustering centers for each task and some common clustering centers for all tasks. Secondly, for the consequent parameters of the fuzzy system, the novel low-rank and row-sparse constraints are proposed to better implement multi-task …learning. The experimental results demonstrate that the proposed model shows better performance compared with other existing methods. Show more
Keywords: Multi-task fuzzy clustering, TSK fuzzy system, low-rank, row-sparsity, joint learning
DOI: 10.3233/JIFS-232312
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Gao, Lijun | Liu, Kai | Liu, Wenjun | Wu, Jiehong | Jin, Xiao
Article Type: Research Article
Abstract: As machine learning models become increasingly integrated into practical applications and are made accessible via public APIs, the risk of model extraction attacks has gained prominence. This study presents an innovative and efficient approach to model extraction attacks, aimed at reducing query costs and enhancing attack effectiveness. The method begins by leveraging a pre-trained model to identify high-confidence samples from unlabeled datasets. It then employs unsupervised contrastive learning to thoroughly dissect the structural nuances of these samples, constructing a dataset of high quality that precisely mirrors a variety of features. A mixed information confidence strategy is employed to refine the …query set, effectively probing the decision boundaries of the target model. By integrating consistency regularization and pseudo-labeling techniques, reliance on authentic labels is minimized, thus improving the feature extraction capabilities and predictive precision of the surrogate models. Evaluation on four major datasets reveals that the models crafted through this method bear a close functional resemblance to the original models, with a real-world API test success rate of 62.35%, which vouches for the method’s validity. Show more
Keywords: Model extraction, unsupervised learning, selection of strategies, active learning
DOI: 10.3233/JIFS-239504
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Yu, Xiaobing | Zhang, Yuexin | Wang, Xuming
Article Type: Research Article
Abstract: Sensors are often deployed in harsh environments, in which some threats may endanger the safety of sensors. In this paper, a sensor deployment model is developed in Wireless Sensor Networks (WSNs), in which the coverage rate and the threat risk are considered simultaneously. The model is established as an optimization problem. An adaptive ranking teaching learning-based optimization algorithm (ARTLBO) is proposed to solve the problem. Learners are divided into inferior and superior groups. The teacher phase is boosted by replacing the teacher with the top three learners, and the learner phase is improved by providing some guidance for inferior learners. …The experimental results show that the proposed ARTLBO algorithm can effectively optimize the model. The fitness values of the proposed model found by the proposed ARTLBO are 0.4894, 0.4886, which are better than its competitors. The algorithm can provide a higher coverage rate and lower threat risk. Show more
Keywords: WSNs, teaching-learning-based optimization, sensor deployment, coverage rate
DOI: 10.3233/JIFS-240215
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Zhang, Lixin | Yin, Hongtao | Li, Ang | Hu, Longbiao
Article Type: Research Article
Abstract: In large-scale scenes, how to quickly obtain paths while ensuring the shortest possible path length is a key issue. Rapidly-exploring Random Tree (RRT) have the characteristic of quickly exploring the state space, but it is often difficult to obtain a short path. To overcome this problem, this paper proposes an improved RRT algorithm based on equidistance retention strategy and A* local search(ERRRT-A*). First, RRT is used for large-step global fast exploration to obtain approximate paths. Then, an equidistance retention strategy is used to discard most of the points and retain a small number of key points. Finally, A* is used …to search between each segment to obtain a new path. The ERRRT-A* algorithm is compared with other commonly used algorithms on maps of different size in terms of path length and planning time. Simulation results indicate that compared with other algorithms, this algorithm achieves fast planning in large-scale scenes while obtaining short path length, which can effectively balance the path length and planning time. Show more
Keywords: Path planning, large-scale scenes, unmanned vehicles, RRT
DOI: 10.3233/JIFS-238695
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: López-Jasso, Edgar | Felipe-Riverón, Edgardo M. | Valdez-Rodríguez, José E.
Article Type: Research Article
Abstract: This study underscores the crucial role of image preprocessing in enhancing the outcomes of multimodal image registration tasks using scale-invariant feature selection. The primary focus is on registering two types of retinal images, assessing a methodology’s performance on a set of retinal image pairs, including those with and without microaneurysms. Each pair comprises a color optical image and a gray-level fluorescein image, presenting distinct characteristics and captured under varying conditions. The SIFT methodology, encompassing five stages, with preprocessing as the initial and pivotal stage, is employed for image registration. Out of 35 test retina image pairs, 33 (94.28%) were successfully …registered, with the inability to extract features hindering automatic registration in the remaining pairs. Among the registered pairs, 42.42% were retinal images without microaneurysms, and 57.57% had microaneurysms. Instead of simultaneous registration of all channels, independent registration of preprocessed images in each channel proved more effective. The study concludes with an analysis of the fifth registration’s resulting image to detect abnormalities or pathologies, highlighting the challenges encountered in registering blue channel images due to high intrinsic noise. Show more
Keywords: Image SIFT registration, microaneurysms counting, retina image analysis, multimodal registration, image processing
DOI: 10.3233/JIFS-219374
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Pradeepkumar, G. | Kavitha, S.
Article Type: Research Article
Abstract: To provide the best possible performance in precisely segmenting clinical images, several approaches are used. Convolutional neural networks are one method used in it to extract its features, which combine several models with several additional methods. It also improves the efficiency of generalisation between categorised and uncategorized image categories. The method proposed combines multi-style image fusion with two-dimensional fracture image representation. The photographs on this page have been updated with a variety of images to improve concentration sharing and achieve the desired visual look. The border detection algorithm is then used to extract the exact border of the image from …the contrast extended images. It will then be divided into basic and comprehensive layers. The fused image was then created using augmented end layers. Show more
Keywords: Segmenting, clinical images, extract features, categorized image, uncategorized image, multi style, border detection, image extraction
DOI: 10.3233/JIFS-239695
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Wei, Xiao | Lin, Yidian
Article Type: Research Article
Abstract: Legal judgment prediction(LJP) has achieved remarkable results. However, existing methods still face problems such as difficulties in obtaining key feature words for charges, which impose limitations on the improvement of prediction results. To this end, we propose a legal judgment prediction model with legal feature Word subgraph Label-Embedding and Dual-knowledge Distillation(WLEDD). Compared with traditional methods, our method has two contributions: (1) To mitigate the impact of overly sparse tail class data and high similarity text representations, we capture the critical features related to the charges by fusing LDA and legal feature word subgraphs. Then we encode them as label information …to obtain highly distinguished representations of legal documents. (2) To solve the problem of high difficulty in some subtasks in LJP, we perform subtask-oriented compression of models to construct a student model with lower complexity and higher accuracy through dual knowledge distillation. Moreover, we exploit the logical association between the subtasks to constrain the labels of articles by charge prediction results. It greatly reduces the difficulty of article prediction. Experimental results on four datasets show that our approach significantly outperforms the baseline models. Compared with the state-of-art method, the F1 value of WLEDD for charge prediction has increased by an average of 2.57% . For article prediction, the F1 value has increased by an average of 1.09% . In addition, we demonstrate its effectiveness through ablation experiments and analytical experiments. Show more
Keywords: Legal judgment prediction, knowledge distillation, label embedding, legal text mining
DOI: 10.3233/JIFS-237323
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
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