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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: Yu, Peng | Song, Huxiong | Liu, Hui
Article Type: Research Article
Abstract: How to expand the variable domain and monotonicity of aggregation functions to generate new aggregation functions is an important research content in aggregation functions. In this work, the concept of interval-valued pre-(quasi-)grouping functions is given by relaxing the interval monotonicity of interval-valued (quasi-)grouping functions to interval directional monotonicity. Then, some basic properties of interval-valued pre-(quasi-)grouping functions and the relationship between interval-valued pre-(quasi-)grouping functions and pre-(quasi-)grouping functions are presented. Accordingly, several construction methods of interval-valued pre-(quasi-)grouping functions are proposed. Finally, the concept of ( I G , IN ) -interval-valued directional monotonic fuzzy implications and QL …-interval-valued directional monotonic operations are introduced on the basis of interval-valued pre-(quasi-)grouping functions I G , interval-valued overlap functions IO and interval-valued fuzzy negations IN . In addition, related studies were conducted on the basic properties of ( I G , IN ) -interval-valued directional monotonic fuzzy implications and QL -interval-valued directional monotonic operations. Show more
Keywords: Interval mathematics, Aggregation functions, Pre-(quasi-)grouping functions, Interval-valued directional monotonic fuzzy implications
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: Mild cognitive impairment (MCI) is a syndrome that occurs in the preclinical stage of Alzheimer’s disease (AD) and is also an early signal of the onset of AD. Early detection and accurate differentiation between MCI and AD populations, and providing them with effective intervention and treatment, are of great significance for preventing or delaying the onset of AD. In this paper, we propose a deep learning model, SE-DenseNet, that combines channel attention and dense connectivity networks and apply it to the field of magnetic resonance imaging (MRI) data recognition for the diagnosis of AD and MCI. First, to extract MRI …features with high quality, a slicing algorithm based on two-dimensional image information entropy is proposed to obtain AD brain lesion features with stronger representation ability. Second, in terms of model structure, SENet is introduced as a channel attention module and redistribute the weight of image features in the channel dimension; use DenseNet as the main architecture to maximize information flow, and each layer is directly interconnected with subsequent layers. It enables the network to learn and extract relevant features from the input data and improve the classification ability of the network. Finally, our proposed model is validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the results have shown that the accuracy for the four classification tasks of AD-NC, AD-MCI, NC-MCI, and AD-NC-MCI can reach 98.12%, 97.42%, 97.42%, and 95.24%, respectively. At the same time, the sensitivity and specificity have also achieved satisfactory results, exhibited a high performance in comparison with the classic machine learning algorithm and several existing state-of-the-art deep learning methods, demonstrating the proposed method is a powerful tool for the early diagnosis and detection of AD. Show more
Keywords: Alzheimer’s disease classification, computer aided diagnosis, medical image processing, megnetic resonance imaging, deep learning
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: Sarcasm is a rhetorical device commonly used in social media and is prevalent on some social platforms, such as Twitter and Reddit, to dismiss, criticize or ridicule people or events using metaphors and exaggeration. With the rapid growth of social media and internet technology, the way people express their emotions and feelings is not limited to text. Therefore, a multi-modal sarcasm detection task is crucial to understanding people’s real feelings and beliefs. However, most existing models often use implicit fusion and do not significantly align the emotions between modalities explicitly, neglecting the significant role of emotional words in sarcasm detection. …In this paper, a model was proposed based on emotion perception and cross-modality attention fusion for multi-modal sarcasm detection. Specifically, an external emotional knowledge was introduced for emotional information enhancement. In addition, the dual-channel BERT-based module and cross-modality interaction fusion were proposed based on an attention mechanism. The experimental results on a public multi-modal sarcasm detection dataset based on Twitter demonstrate the superiority of the proposed model. Show more
Keywords: Multimodality, sarcasm detection, emotion perception, attention
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
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