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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: Ziquan, Xiang | Jiaqi, Yang | Naseem, Muhammad Hamza | Zuquan, Xiang
Article Type: Research Article
Abstract: In view of the extremely complex logistics level, long logistics cycle, high-risk coefficient, extremely fuzzy and uncertain evaluation information of cruise construction logistics allocation, an improved intuitionistic fuzzy TOPSIS method is proposed to evaluate the risk of cruise construction logistics allocation. Firstly, on the basis of empirical data analysis and research interviews, risk sources and risk assessment criteria are determined, and different weights are given to experts according to their importance. Then, in order to reduce the fuzziness and uncertainty of risk source information, an intuitionistic fuzzy weighted arithmetic average (IFWAA) operator is used to aggregate the experts’ opinions, and …the intuitionistic fuzzy aggregation decision risk matrix is obtained. The objective weight of risk assessment criteria is introduced to balance the impact of subjective weight on the final results, to improve the accuracy of the evaluation results. Finally, according to the relative closeness coefficient between each risk source and the positive-ideal solution, the priority of the risk sources of cruise construction logistics allocation is obtained, and the corresponding risk control measures are put forward. Compared with other methods and sensitivity analysis, the effectiveness and applicability of this method are verified. Show more
Keywords: Cruise construction, intuitionistic fuzzy number, TOPSIS method, risk assessment, logistics allocation
DOI: 10.3233/JIFS-211163
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5237-5250, 2022
Authors: Akila, K.
Article Type: Research Article
Abstract: Human action recognition encompasses a scope for an automatic analysis of current events from video and has varied applications in multi-various fields. Recognizing and understanding of human actions from videos still remains a difficult downside as a result of the massive variations in human look, posture and body size inside identical category. This paper focuses on a specific issue related to inter-class variation in Human Action Recognition. To discriminate the human actions among the category, a novel approach which is based on wavelet packet transformation for feature extraction. As we are concentrating on classifying similar actions non-linearity among the features …are analyzed and discriminated by proposed by Deterministic Normalized –Linear Discriminant Analysis (DN-LDA). However the major part of the recognition system relays on classification part and the dynamic feeds are classified by Hidden Markov Model at the final stage based on rule set. With a trained dataset and rules framed with the end user, our intelligent HAR system is capable of achieving the accuracy rate of 97.4% which is higher than the other state of art approaches. Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the inter-class variation. Show more
Keywords: Human action recognition, wavelet packet, video processing, feature extraction
DOI: 10.3233/JIFS-212088
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5251-5262, 2022
Authors: Geetha, M.P. | Karthika Renuka, D.
Article Type: Research Article
Abstract: In recent years, E-Commerce is globally increasing among online purchaser, in which customer post product related queries for finding the best product in online shopping. Manually answering the product related queries in real-time, cause online traffic and practically not possible. So, automatic answering system is helpful for answering product related queries. But, the product queries are always in product-explicit, so discovering related product queries and recovering its responds is distinctly be impractical. Accordingly, we propose Hierarchical Deep Neural Network (HiDeNN) model using MOQA framework to discern the appropriate reviews based on Mixtures of Opinions Question Answering (MOQA). The Hierarchical Deep …Neural Network provides discerning the most relevant review for queries and it also provides the relevant answer for specific product category queries. The proposed method is executed on Python and it provides 9.594% and 7.574% higher accuracy value for Discerning Appropriate Reviews compared with the existing method like Relevant Reviews for Answering Product-related Queries (MOQA-BERTQA+FLTR+PT) and IQA: Interactive Query Construction on Semantic Question Answering Systems (IQC-SQA). The experimental result indicates that the proposed MOQA- HiDeNN method can efficiently and accurately get the optimal global solutions for recognizing the appropriate discerning of most relevant review for queries and it also provides the relevant answer for specific product category queries. Show more
Keywords: E-Commerce, product-related queries, product, online shopping, mixtures of opinions, hierarchical deep neural network, accuracy.
DOI: 10.3233/JIFS-212485
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5263-5277, 2022
Authors: Prabaharan, L. | Raghunathan, A.
Article Type: Research Article
Abstract: The assisted method of fertilization has required an identification of sperm cells with normal morphological structure. The abnormal sperm cells cannot provide successful result in artificial fertilization. Nowadays the assessment of morphology of sperm cells is subjective and error prone hence creating automatic evaluation method for morphology assessment, it will improve the success ratio in infertility treatment. The first step in our proposed system is pre-processing where noise removal process is applied on microscopic medical images. In second step, adaptive alpha valued Havrda-Chavrat entropy-based threshold technique is proposed where the maximum probability distribution of foreground pixels or background pixels is …assigned to alpha value. Further, existing state-of-art threshold-based segmentation methods are implemented and obtained results on the input images. These segmentation results are compared with the proposed method in terms of supervised and unsupervised evaluation metrics, in which our proposed thresholding method has given optimum threshold value for the segmentation of spermatozoa cells. The outcome of the segmented images and their metric values are indicating better segmentation by our proposed method. Furthermore, this proposed method can be implemented in the mobile applications for diagnosis with artificial intelligence techniques. Show more
Keywords: Spermatozoa, spermatozoa segmentation, image processing, artificial intelligence, computer aided diagnosis
DOI: 10.3233/JIFS-213478
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5279-5292, 2022
Authors: Preethi Saroj, S. | Gurunathan, Pradeep
Article Type: Research Article
Abstract: Accurate segmentation of brain tumor regions from magnetic resonance images continues to be one of the active topics of research due to the high usability levels of the automation process. Faster processing helps clinicians in identification at initial stage of tumor and hence saves valuable time taken for manual image analysis. This work proposes a Cascaded Layer-Coalescing (CLC) model using convolution neural networks for brain tumor segmentation. The process includes three layers of convolution networks, each with cascading inputs from the previous layer and provides multiple outputs segmenting complete, core and enhancing tumor regions. The initial layer identifies complete tumor, …coalesces the discriminative features and the input data, and passes it to the core tumor detection layer. The core tumor detection layer in- turn passes discriminative features to the enhancing tumor identification layer. The information injection through data coalescing voxels results in enhanced predictions and also in effective handling of data imbalance, which is a major contributor in model viewpoint. Experiments were performed with Brain Tumor Segmentation (BraTS) 2015 data. A comparison with existing literature works indicate improvements up to35% in sensitivity, 27% in PPV and 28% in Dice Score, indicating improvement in the segmentation process. Show more
Keywords: Brain tumor segmentation, deep learning, CNN, cascaded layer-coalescing, auxiliary networks
DOI: 10.3233/JIFS-220167
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5293-5308, 2022
Authors: Qiao, Ruixiu | Guo, Xiaozhou | Mao, Wenyu | Li, Jixing | Lu, Huaxiang
Article Type: Research Article
Abstract: The location attention mechanism has been widely applied in deep neural networks. However, as the mechanism entails heavy computing workload, significant memories consumed for weights storage, and shows poor parallelism in some calculations, it is hard to achieve high efficiency deployment. In this paper, the field-programmable gate array (FPGA) is employed to implement the location attention mechanism in hardware, and a novel fusion approach is proposed to connect the convolutional layer with the fully connected layer, which not only improves the parallelism of both the algorithm and the hardware pipeline, but also reduces the computation cost for such operations as …multiplication and addition. Meanwhile, the shared computing architecture is used to reduce the demand for hardware resources. Parallel computing arrays are utilized to time-multiplex a single computing array, which can speed up the pipeline parallel computing of the attention mechanism. Experimental results show that for the location attention mechanism, the FPGA’s inference speed is 0.010 ms, which is around a quarter of the speed achieved by running it with GPU, and its power consumption is 1.73 W, which is about 2.89% of the power consumed by running it with CPU. Compared with other FPGA implementation methods of attention mechanism, it has less hardware resource consumption and less inference time. When applied to speech recognition tasks, the trained attention model is symmetrically quantized and deployed on the FPGA. The result shows that the word error rate is only 0.79% higher than that before quantization, which proves the effectiveness and correctness of the hardware circuit. Show more
Keywords: Attention mechanism, neural networks, FPGA, deep learning, hardware implementation
DOI: 10.3233/JIFS-212273
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5309-5323, 2022
Authors: Ali Abdu, Nail Adeeb | Wang, Zhaoshun
Article Type: Research Article
Abstract: Numerous academic and industrial research studies have focused on the feasibility of implementing blockchain-based healthcare systems data management that can transform the way data systems functioning, usage, and development can be effective. In this manuscript, the focus is on the development of a Grid-based blockchain structure formanaging the surgical process-related information in integrated systems. Considering the implications, scope of human error in the surgical processes, and the quantum of records integral to such process, the need for having robust records of surgical data is imperative. Aiming at improving the security of the information, in this manuscript, the Grid-block structure constituting …multiple grids framework is proposed, which can be optimal for transaction query processing, responsiveness for triggers, and security inheritance. The proposed system, if chosen for real-time implementation, can support interoperability of data among the distinct healthcare systems and support in minimizing the issues of human error inspections, prevention scope for errors whilst improving the quality of healthcare solutions. Show more
Keywords: Surgical procedure data, grid-framework blockchain system, enhanced surgical process data management system
DOI: 10.3233/JIFS-213414
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5325-5335, 2022
Authors: Dong, Xiaoqin | Sun, Xianbin
Article Type: Research Article
Abstract: In multi-attribute large group decision-making (MALGDM), the ideal state indicates a high degree of consensus among a set of decision-makers (DMs). It is complex to reach consensus because the number of decision attributes and DMs increases. Thus, we developed a novel consensus model to manage the decision-making in large group based on the non-cooperative behavior. The improved clustering method takes account of the similarities among different DMs. Similar DMs will be grouped into the same group. The consensus threshold is determined from an objective and subjective aspect to judge whether the consensus reaching process continues. With the introduction of three …non-cooperative behaviors, we investigated a non-cooperative behavior detection method under the change of consensus level. Base on the number of DMs who are willing to change their preliminary views and the change value of consensus level, the non-cooperative degree of subgroup can be computed. According to the non-cooperative degree, the subgroups’ weight can be modified to raise the consensus level. Meanwhile, the subgroup is allowed to change. Based on the adjustment amount of DMs’ opinions, whether decision maker (DM) belongs to this subgroup is recalculated. Finally, an emergency decision-making problem in flood disaster is applied to manifest the feasibility and distinctive features of the proposed method. Show more
Keywords: Large group consensus, comprehensive consensus threshold, non-cooperative subgroup, consensus level, consensus reaching model
DOI: 10.3233/JIFS-201805
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5337-5351, 2022
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