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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: Sahu, Preeti Ranjan | Hota, Prakash Kumar | Panda, Sidhartha | Long, Hoang Viet | Allahviranloo, Tofigh
Article Type: Research Article
Abstract: This paper proposes adaptive fuzzy lead-lag controller structures for power system stabilizer and flexible AC transmission system based damping controllers to increase the stability of power system. The parameters of the proposed controller are tuned by a modified grasshopper optimization algorithm (MGOA). The new algorithm named MGOA accomplishes a proper balance between exploration and exploitation phases of original grasshopper optimization algorithm. This capability of MGOA is certified by using the benchmark functions by comparing with that of a grasshopper optimization algorithm, genetic algorithm, evolutionary strategies, particle swarm optimization, bat algorithm, population based incremental learning, flower pollination algorithm, monarch butterfly optimization …and improved monarch butterfly optimization. The proposed controller is optimized and verified under various loading circumstances using MGOA method. The results of MGOA are compared with grasshopper optimization algorithm, genetic algorithm, and particle swarm optimization. Additionally, the results of the proposed MGOA are compared with conventional lead-lag controller to demonstrate its superiority. Show more
Keywords: Modified grasshopper optimization algorithm, static synchronous series compensator, adaptive fuzzy lead-lag controller, power system stability
DOI: 10.3233/JIFS-212716
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5075-5094, 2022
Authors: Urquiza-Yllescas, José Fidel | Mendoza, Sonia | Rodríguez, José | Sánchez-Adame, Luis Martín
Article Type: Research Article
Abstract: Nowadays, chatbots have become popular tools in such a way that they are used in different sectors like commercial, elderly care, tourism, and education. The COVID-19 pandemic has forced many students and teachers to suspend face-to-face classes. Therefore, schools and governments have found it necessary to continue education remotely, using the resources provided by the Internet. This fact has created a greater interest in educational chatbots, so several projects have been proposed to develop these academic tools, each following its way of implementation and addressing issues from different points of view. This paper presents a proposal for chatbot classification, following …the Systematic Mapping Study and an iterative method to review and classify educational chatbots. We also discuss the resulting categories and their characteristics and limitations and possible uses by developers and researchers. Show more
Keywords: Chatbot, classification, chatbot-user interaction, education, scholar tools
DOI: 10.3233/JIFS-213275
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5095-5107, 2022
Authors: Tan, Fuxiang | Qian, Yurong | Kong, Yuting | Zhang, Hao | Zhou, Daxin | Fan, Yingying | Chen, Long | Xiao, Zhengqing
Article Type: Research Article
Abstract: Rain streaks severely affect the perception of the content and structure of an image so high-performance deraining algorithms are needed in order to eliminate the effects of various rain streaks for high-level computer vision tasks. Although much progress has been made with existing deraining methods, the task of single image deraining remains challenging. In this paper, we first point out that existing Transformers lack sufficient ability to capture channel attention which restricted the ability of models in deraining. To improve the performance of deraining model, we propose a dual branch deraining network based on Transformer. One branch uses dense connections …to connect Transformer modules which embed the attention of a composite channel. This branch captures channel attention more finely to learn the representation of rain streaks features. The other branch first obtains features at different scales by gradually expanding the receptive field, then uses these features to obtain attention for regional features, and finally uses the attention to guide the model to focus on areas of high rain streaks density and large scales. By fusing these two branches, the model is able to capture channel attention more finely and to focus on regions of high rain streaks density and large scales. The extensive experimental results on synthetic and real datasets demonstrate that the proposed method outperforms most advanced deraining methods. Show more
Keywords: Deraining, transformer, deep learning, image processing, channel attention
DOI: 10.3233/JIFS-220055
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5109-5123, 2022
Authors: Ming-Hui, Liao | Ge, You | Yuchen, Wei
Article Type: Research Article
Abstract: Small and medium-sized enterprises (SMEs) have been suffering from the problem of “difficult and expensive financing.” The few loans available are allocated unevenly to SMEs, resulting in a financing mismatch. To address this problem, this paper constructs a system dynamics model to simulate and calculate the theoretical loan amounts of SMEs. Compares the values between the theoretical and actual loan amount to find out the enterprises with extreme values of the difference. Then, it analyzes the characteristics of the index data of these enterprises to explore feasible solutions to solve the financing mismatch problem. The CRITIC and AHP weight determination …methods will be used to calculate weights and screen indicators. The data will be quantified and calculated according to the standardized scoring table of enterprise borrowing factor indicators. The results show that enterprises with actual borrowing amounts higher than the theoretical borrowing amount perform better than other sample enterprises in three indicators: growth rate of primary business income, the upper limit of the loan amount, and subsidy amount. The actual loan amount can be increased by improving these indicators. This research can help solve the problem of financing mismatch to realize effective resource allocation and provide significant guidance for the research on enterprises to obtain more loans. Show more
Keywords: Dual-Cycle, SME financing, system dynamics, borrowing amount, simulation analysis
DOI: 10.3233/JIFS-220091
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5125-5146, 2022
Authors: Wang, Xiaohan | Zhang, Zepei | Wang, Pei | Chen, Jiaqing | Wu, Junze
Article Type: Research Article
Abstract: Density peak clustering can be used in identifying high-density regions for urban hot spots detection. The distance matrix of each two position points needs to be calculated in the existing density peak clustering methods which causes inefficient clustering when processing large-scale data, and the traditional two-dimensional decision map cannot identify the coincident points. Thus, characteristic density peak clustering algorithm is proposed to avoid the influence of noise. At first, the location feature points and support index are defined to simulate the original locations. The number of feature points is adjusted by parameters to make density peak clustering no longer sensitive …to the amount of data to simplify the complexity to be solved. And then, the local density and the distance between the clustering centers of the feature points are proposed to construct three-dimensional decision map. Finally, the clustering center, basic clustering points, and noise data points are determined using the three-dimensional decision map combined with the support index of the feature points. Experiments are performed on real data set and the prototype system to verify that the method can significantly improve time efficiency while clustering accuracy is maintained. Show more
Keywords: Density peak clustering, three-dimensional decision map, location characterization, taxi trajectory, hot spots detection
DOI: 10.3233/JIFS-220327
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5147-5164, 2022
Authors: Liu, Siliang | Zhang, Wenyu | Yang, Song | Shi, Jiaxuan
Article Type: Research Article
Abstract: With the wide recognition of the potential commercial value of drone technology, the delivery method of trucks with drones has gradually been applied to the logistics field. This paper proposed a novel truck-drone collaborative service network for the special case of poor ground transportation. The proposed model uses the truck as an auxiliary tool at fixed non-customer locations to support drone deliveries, and develops the potential of drones to carry multiple commodities and undertake wide-range delivery missions in each flight. A modified variable neighborhood search algorithm with a new threedimensional coding scheme and five new neighborhood operators is presented to …represent the routes of multiple drones in collaborative service networks and solve the proposed model. The experiment was conducted under three types of customer distribution scenarios and experimental results illustrate that the presented algorithm effectively solves the proposed model compared with other hybrid heuristic algorithms. Show more
Keywords: Truck-drone, collaborative service networks, wide-range drone delivery, route optimization, Heuristic algorithms
DOI: 10.3233/JIFS-220378
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5165-5184, 2022
Authors: Arun, R. Arumuga | Umamaheswari, S.
Article Type: Research Article
Abstract: Traditional machine learning-based pest classification methods are a tedious and time-consuming process A method of multi-class pest detection based on deep learning and convolutional neural networks could be the solution. It automatically extracts the complex features of different pests from the crop pest images. In this paper, various significant deep learning-based object detection models like SSD, EfficientDet, Faster R-CNN, and CenterNet are implemented based on the Tensorflow Object Detection framework. Several significant networks like MobileNet_V2, ResNet101_V1, Inception_ResNet_V2, EfficientNet, and HourGlass104 are employed as backbone networks for these models to extract the different features of the pests. Object detection models are …capable of identifying and locating pests in crops. Initially, these models are pre-trained with the COCO dataset and later be fine-tuned to the target pest dataset of 20 different pest classes. After conducting experiments on these models using the pest dataset, we demonstrate that Faster R-CNN_ResNet101_V1 outperformed every other model and achieved mAP of 74.77%. Additionally, it is developed as a lightweight model, whose size is ∼9 MB, and can detect pest objects in 130 milliseconds per image, allowing it to be used on resources-constrained devices commonly used by farmers. Show more
Keywords: Deep learning, Convolutional Neutral Network, object detection, pest detection, transfer learning
DOI: 10.3233/JIFS-220595
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5185-5203, 2022
Authors: Hou, Yingan | Su, Junguang | Liang, Jun | Chen, Xiwen | Liu, Qin | Deng, Liang | Liao, Jiyuan
Article Type: Research Article
Abstract: In recent years, the number of stroke patients in China has been increasing and the development trend is not optimistic. In order to reduce the burden of doctors, improve the efficiency of clinical diagnosis and reduce the medical cost, the development of cerebral apoplexy imaging diagnosis is an inevitable trend. Taking stroke lesions in medical images as the object, a deep learning model 3D-SE ResNet10 is proposed which can distinguish whether stroke lesions are included in a given medical image with high accuracy. This model combines the attention mechanism with the residual learning network, and uses 3D convolution kernel to …utilize the continuous information between slices in the medical image sequence. The model achieves an average accuracy of 88.69%, an average sensitivity of 87.58% and an average specificity of 90.26% in multiple experiments based on the realistic dataset. Its classification effect is significantly higher than that of 2D convolutional neural networks and 3D convolutional neural networks without attention mechanism. The experimental results show that our model is effective and feasible, and has certain practical value. Show more
Keywords: Stroke, deep learning, medical image, 3D convolution, attention
DOI: 10.3233/JIFS-212511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5205-5214, 2022
Authors: Mobini, M. | Ahn, S.S. | Borzooei, R.A.
Article Type: Research Article
Abstract: Molodtsov, initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. In the paper entitled “The Category of Soft Sets”, we studied the category of soft sets, and we proved that the category of soft sets does not necessarily have products. But, there is a mistake in the proof of this result. In this short note, we give a correct proof for this result.
Keywords: Soft sets, category, product
DOI: 10.3233/JIFS-220762
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5215-5219, 2022
Authors: Kalaiarasan, D. | Ahilan, A.
Article Type: Research Article
Abstract: Securing image data from prying hackers is crucial in safeguarding the secrecy of data. Over the years, this was done by encrypting the image using an algorithm and a key, where the visible image was converted into a meaningless object. It is a difficult problem to design an image encryption technique based on chaotic systems with predictable cryptographic features. In this paper, the Quaternion, along with the Rossler attractor, was used to generate the key combination. The ciphering was done using the Least Square Approximation Algorithm (LSA). The algorithm was tested on a grayscale image database. The algorithm was initially …tested in software using MATLAB R2018b, and was implemented in the Cyclone II EP2C35F672C6 device FPGA. On average, for a cipher image, the Peak Signal to Noise ratio (PSNR) was 9.09303 dB and the entropy was 7.9990 bits. For the cipher image, the Number of Pixels Change Rate (NPCR) and Unified Average Change Intensity (UACI) were 99.6039 and 33.4980, respectively. This proved that the algorithm could effectively mitigate the statistical and differential attacks. The key space was 2 (M ×N ×7 ×8) , which was sufficiently high and mitigated the brute force attacks. The obtained results confirm that the cipher images resulting from the proposed ciphering scheme possess good cryptographic properties in terms of entropy, PSNR, UACI, NPCR, and keyspace analysis. Furthermore, the strength of the key is evaluated by the NIST test suite. Show more
Keywords: Quaternion, rossler attractor, Least Square Approximation, Pseudo random number generation, image encryption
DOI: 10.3233/JIFS-213600
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5221-5236, 2022
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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