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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
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