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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: Song, Xudong | Wang, Hao | Liu, Yifan | Wang, Zi | Cui, Yunxian
Article Type: Research Article
Abstract: Aiming at the inherent defects of BP neural network in the field of rolling bearing fault diagnosis, based on the optimization of particle swarm optimization algorithm, this paper uses a variety of optimization strategies to optimize the particle swarm optimization algorithm, and then uses the optimized particle swarm optimization algorithm to optimize the BP neural network. Therefore, a new fault diagnosis method (Dual Strategy Particle Swarm Optimization BP neural network, DSPSOBP) is proposed. DSPSOBP fault diagnosis method is mainly divided into two steps. The first step is EMD decomposition of vibration signal, and the second step is to classify rolling …bearing faults by using BP neural network optimized by Double Strategy Particle Swarm Optimization algorithm. Experiments show that DSPSOBP has stronger advantages than BP neural network basic fault diagnosis model. Show more
Keywords: Bearing fault diagnosis, BP neural network, optimization algorithm, particle swarm optimization
DOI: 10.3233/JIFS-213485
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5965-5971, 2022
Authors: Bennajeh, Anouer | Said, Lamjed Ben
Article Type: Research Article
Abstract: Studying driver behaviors has become a major concern for the transportation community, businesses, and the public. Thus, based on the simulation, we proposed an adaptive driving model in the car-following driving behavior and based on the normative behavior of the driver during decision-making and anticipation, whose intention is to ensure the objectives of imitation of ordinary human behavior and road safety. The presented model is based on a software agent paradigm to model a human driver and the Fuzzy Logic Theory to reflect the driver agent’s reasoning. To validate our model, we used the dataset from the program of the …US Federal Highway Administration. In this context, we notice an excellent homogeneity in the deviation of the adopted trajectory of the autonomous driver agent from the adopted trajectories by the human drivers. Moreover, the advantage of our model is that it works with different velocities. Show more
Keywords: Fuzzy logic, decision-making, anticipation, adaptive control, autonomous agent
DOI: 10.3233/JIFS-213498
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5973-5983, 2022
Authors: Apinaya Prethi, K.N. | Sangeetha, M.
Article Type: Research Article
Abstract: Network resources and traffic priorities can be utilized to distribute requested tasks across edge nodes at the edge layer. However, due to the variety of tasks, the edge nodes have an impact on data accessibility. Resource management approaches based on Virtual Machine (VM) migration, job prioritization, and other methods were used to overcome this problem. A Minimized Upgrading Batch VM Scheduling (MSBP) has recently been developed, which reduces the number of batches required to complete a system-scale upgrade and assigns bandwidth to VM migration matrices. However, due to poor resource sharing caused by suboptimal VM utilization, the MSBP was unable …to effectively ensure the global best solutions. In order to distribute resources and schedule tasks optimally during VM migration, this paper proposes the MSBP with Multi-objective Optimization of Resource Allocation (MORA) method. The major goal of this proposed methodology is to take into account different objectives and solve the Pareto-front problem to enhance lifetime of the fog-edge network. First, it formulates an NP-hard challenge for MSBP by taking into account a variety of factors such as network sustainability, path contention, network delay, and cost-efficiency. The Multi-objective Krill Herd optimization (MoKH) algorithm is then used to address the NP-hard issue using the Pareto optimality rule and produce the best solution. First, it introduces an NP-hard challenge for MSBP by accounting in network sustainability, path contention, network latency, and cost-efficiency. The Pareto optimality rule is then implemented to overcome the NP-hard problem and provide the optimum solution employing the Multi-objective Krill Herd optimization (MoKH) algorithm. This increases network lifetime and improves resource allocation cost efficiency. Finally, the simulation results show that the MSBP-MORA distributes resources more efficiently and hence increases network lifetime when compared to other traditional algorithms. Show more
Keywords: VM migration, MSBP, resource allocation, pareto-front issue, multi-objective Krill herd optimization algorithm, NP-hard challenge
DOI: 10.3233/JIFS-213520
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5985-5995, 2022
Authors: Chen, Liuxin | Wang, Yutai | Yang, Dongmei
Article Type: Research Article
Abstract: Picture fuzzy linguistic set is a vital solution to express complex and uncertain information, which has been applied in multi-attribute group decision-making (MAGDM). However, the credibility of decision-making information is unconsidered, which may give rise to the inaccuracy of final result. To solve this problem, the picture fuzzy Z-linguistic set (PFZLS) composed of linguistic term, picture fuzzy number, and credibility is proposed, which could express more complete decision-making information. Subsequently, operation rules, comparison methods, and distance measures of PFZLS are introduced. In addition, the weighted geometric average operator and the classical VIKOR method are extended and combined to solve the …MAGDM problem under the picture fuzzy Z-linguistic environment. Finally, an illustrative example about the emergency decision-making (EDM) problem of forest fire accident is proposed, and a series of comparative analyses are presented to verify the rationality and superiority of the PFZLS. Show more
Keywords: Multi-attribute group decision-making, picture fuzzy Z-linguistic set, weighted geometric average operator, VIKOR method
DOI: 10.3233/JIFS-213531
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5997-6011, 2022
Authors: Wang, Fan | Tian, Shengwei | Yu, Long | Long, Jun | Zhou, Tiejun | Wang, Bo | Wang, Junwen | Wang, Yongtao
Article Type: Research Article
Abstract: Human multi-modal emotions analysis includes time series data with different modalities, such as verbal, visual, and auditory. Due to different sampling rates from each modality, the collected data streams are unaligned. The asynchrony cross-modality increases the difficulty of multi-modal fusion. Therefore, we propose a new Cross-Modality Reinforcement model (CMR) based on recent advances in a cross-modality transformer, which performs multi-modal fusion in unaligned multi-modal sequences for emotion prediction. To deal with the long-time dependencies of unaligned sequences, we introduce a time domain aggregation to model the single modal, by aggregating the information in the time dimension, and enhance contextual dependencies. …Moreover, a CMR strategy is introduced in our approach.With the main and secondary modalities as inputs to the module, main modal features are strengthened through cross-modality attention and cross-modality gate, and the secondary modality information flows to the main modality potentially, while retaining main modality-specific features and complementing the missing cues. This process gradually learns the common contributing features between the main and secondary modalities and reduces the noise caused by the variability of the modal features. Finally, the enhanced features are used to make predictions about human emotions. We evaluate CMR on two multi-modal sentiment analysis benchmark datasets, and we report the accuracy of 82.7% on the CMU-MOSI and 82.5% and CMU-MOSEI, respectively, which demonstrates our method outperforms current state-of-the-art methods. Show more
Keywords: Cross-modality processing, multi-modal fusion, multi-modal unaligned sequences, multi-modal sentiment analysis
DOI: 10.3233/JIFS-213536
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6013-6025, 2022
Authors: Kong, Yuting | Qian, Yurong | Tan, Fuxiang | Bai, Lu | Shao, Jinxin | Ma, Tinghuai | Tereshchenko, Sergei Nikolayevich
Article Type: Research Article
Abstract: Data clustering has been applied and developed in all walks of life, which can provide convenience for enterprise service optimization. However, when the original data to be analyzed contains users’ personal privacy information, the clustering analysis process of the data holder may expose users’ privacy. Differential privacy k-means algorithm is a clustering method based on differential privacy protection technology, which can solve the privacy disclosure problem in the process of data clustering. In the differential privacy k-means algorithm, Laplacian noise controlled by privacy parameter ɛ is added to the center point of clustering to protect user sensitive information and clustering …results in the original data, but the addition of noise will affect the utility of clustering. In order to balance the availability and privacy of the differential privacy k-means clustering algorithm, the research on the improvement of the algorithm pays more attention to the selection of the initial clustering center or the optimization of the outlier processing, but does not consider the different contribution degree of each dimension data to the clustering. Therefore, this paper proposes a differential privacy CVDP k-means clustering algorithm based on coefficient of variation. The CVDP scheme first eliminates outliers in the original data through data density, and then designs weighted data point similarity calculation method and initial centroid selection method using variation coefficient. Experimental results show that CVDP k-means algorithm has some improvements in availability, performance and privacy. Show more
Keywords: Differential privacy, differential privacy k-means clustering, coefficient of variation, CVDP k-means
DOI: 10.3233/JIFS-213564
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6027-6045, 2022
Authors: Zhang, Shu | Wang, Yuhong
Article Type: Research Article
Abstract: This paper aims to improve the accuracy of software defect prediction by using a prediction model based on grey incidence analysis and Naive Bayes algorithm. The model employs the Naïve Bayes as the basic classifier of the software defect prediction model. The grey incidence analysis is used to analyze the relation between software modules and ideal modules. Then, the grey correlation degree is embedded into the Naive Bayes classification model as a feature attribute. According to the comparison and analysis of NASA’s public dataset, the prediction model in this paper improves the prediction accuracy.
Keywords: Naive Bayes, grey incidence analysis, software defect prediction
DOI: 10.3233/JIFS-213570
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6047-6060, 2022
Authors: Shi, Wen | Huang, Yongming | Zhang, Guobao | Yang, Wankou
Article Type: Research Article
Abstract: Degradation prognostic plays a crucial role in increasing the efficiency of health management for rolling element bearings (REBs). In this paper, a novel four-step data-driven degradation prognostics approach is proposed for REBs. In the first step, a series of degradation features are extracted by analyzing the vibration signals of REBs in time domain, frequency domain and time-frequency domain. In the second step, three indicators are utilized to select the sensitive features. In the third step, different health state labels are automatically assigned for health state estimation, where the influence of uncertain initial condition is eliminated. In the last step, a …multivariate health state estimation model and a multivariate multistep degradation trend prediction model are combined to estimate the residence time in different health status and remaining useful life (RUL) of REBs. Verification results using the XJTU-SY datasets validate the effectiveness of the proposed method and show a more accurate prognostics results compared with the existing major approaches. Show more
Keywords: Degradation prognostic, rolling element bearings (REBs), health state estimation, remaining useful life (RUL)
DOI: 10.3233/JIFS-213586
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6061-6076, 2022
Authors: Elmenshawy, Maha A. | Hamza, Taher | El-Deeb, Reem
Article Type: Research Article
Abstract: Due to the obvious significant expansion in the number of online Arabic textual information, Arabic Text Summarization has become a focus of intense research. Manual text summarization necessitates a large investment of time, effort, and money. Hence, Automatic Arabic Text Summarization (AATS) is currently necessary to create accurate and relevant summaries from the huge amount of accessible content. The developed techniques and methodologies for AATS are still in their immaturity because of the intrinsic complexity of the structure and morphology of the Arabic language. AATS methods could be categorized as extractive, abstractive, hybrid extractive to abstractive. The extractive method selects …and combines the most important sentences from the input document(s) to produce the summary. While the abstractive method needs deep understanding of the input document(s) for creating the summary with sentences that differ from the original ones. The extractive to abstractive method is a hybrid strategy for creating an informative and cohesive summary using the extractive summary as a first step. This paper provides a detailed explanation of the fundamental issues related to Arabic text summarization. It describes and analyzes the various methods and systems currently in use, traces their history and monitors their performance. The challenges and trends are explored. Show more
Keywords: Automatic text summarization, arabic text summarization, summarization approaches, extractive approaches, abstractive approaches, summary evaluation
DOI: 10.3233/JIFS-213589
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6077-6092, 2022
Authors: Rani, R.Jhansi | Vasanth, K.
Article Type: Research Article
Abstract: Latent fingerprint recognition plays an essential role for law enforcement agencies to detect criminals and security purposes. One of the key stages utilized in the latent fingerprint recognition model is to automatically learn consistent minutiae from fingerprint images. However, the existing state-of-the-art recognition approaches are not adequate since live-scan fingerprint images and enhancements are necessary for each step of the recognition process. Hence, an automated recognition system along with appropriate minutiae learning algorithm is required for matching the latent fingerprint exactly. In this paper, an efficient recognition system using dictionary learning and Local Context-Perception deep neural network (LCPnet) has been …proposed to enhance the accuracy of latent fingerprint recognition. Primarily, the Total Variation decomposition model is utilized to remove the smooth background noise and dictionary learning contributes to the extraction of multiple patches. Afterward, the LCPnet is trained for 12 patch types to develop a salient minutiae descriptor where every descriptor is trained using LCPnet with a particular patch size at a location surrounding the minutiae. The proposed detection system has been tested through two latent public datasets. Here, three different types of templates (LCPnet minutiae, LCPnet texture, and LCPnet minutiae+texture) are analyzed for evaluating the proposed fingerprint detection system. The performance results manifest that the proposed system acquires a superior recognition accuracy of 99.44% and 99.58% under two different datasets. Show more
Keywords: Latent fingerprint, dictionary learning, ridge enhancement, minutiae extraction, deep neural network
DOI: 10.3233/JIFS-220056
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6093-6108, 2022
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