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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: Meenakshi, K. | Revathi, M. | Harsha, Sanda Sri | Tamilarasi, K. | Shanthi, T.S. | Sugumar, D. | Suriyakrishnaan, K. | Uma Maheswari, B. | Rajaram, A.
Article Type: Research Article
Abstract: A new era in communication has been ushered in by MANET networks, in which users (nodes) interact with one another through a self-configuring network of handheld devices linked by wireless links. Nodes are capable of participating and enthusiastic about sending packets to other nodes. Consequently, the need for a routing protocol materializes. The most difficult aspect is dealing with the network’s dynamic topology as a result of node mobility. This is because limited resources like storage space, battery life, and bandwidth require a protocol that can quickly adapt to topology changes while periodically updating messages. On the other hand, security …is another important aspect of routing since the involvement of attackers will exhaust the network resources. This paper addresses the main issue of designing a routing protocol that handles all the adversaries and achieves better efficiency. For that, we proposed a Hybrid Machine Learning (HyML) model which evaluates the. Initially, the network is segregated by the Secure Stable Clustering (SSC) approach which first verifies the node’s legacy and forms clusters based on stability. The HyML is designed by combining two important ML techniques such as ANN and fuzzy-C Means (FCM) algorithm. The ANN model learns multiple attributes of the trust value and computes the cumulative trust score. Next, FCM determines the node position upon trust score. After the computation of the trust value, optimal route selection is performed by the Spider Monkey Optimization (SMO) technique. The overall work is evaluated through comprehensive simulations based on network longevity, throughput, energy usage, PDR, attack detection efficiency, and delay. Show more
Keywords: Hybrid machine learning, ANN, routing attacks, MANET
DOI: 10.3233/JIFS-231918
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3429-3445, 2024
Authors: Al-Qatf, Majjed | Hawbani, Ammar | Wang, XingFu | Abdusallam, Amr | Alsamhi, Saeed | Alhabib, Mohammed | Curry, Edward
Article Type: Research Article
Abstract: Visual attention has emerged as a prominent approach for improving the effectiveness of image captioning, as it enables the decoder network to focus selectively on the most salient regions in the image content, thereby facilitating the generation of precise and informative captions. Although visual attention achieves the improvement, the small numerical values of its input have a negative impact on its softmax, decreasing its effectiveness. To address this limitation, we propose a refined visual attention (RVA) framework that internally reweights visual attention by leveraging the language context of previously generated words. We first feed the language context into a fully …connected layer to obtain appropriate dimensions for the visual features. Then, we use a sigmoid function to obtain a probability distribution to reweight the softmax’s input by applying the multiplication process. Experiments conducted on the MS COCO dataset demonstrate that RVA outperforms traditional visual attention and other existing image captioning methods, highlighting its effectiveness in enhancing the accuracy and informativeness of image captions. Show more
Keywords: Visual attention, refined visual attention, image captioning
DOI: 10.3233/JIFS-233004
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3447-3459, 2024
Authors: Sathya, S. | Senthil Murugan, J. | Surendran, S. | Sundar, R.
Article Type: Research Article
Abstract: Oil spills in maritime areas pose a serious environmental risk, wreaking havoc on marine ecosystems, coastal habitats, and local residents. An accurate and timely evaluation of oil spill occurrences and extent is critical for effective pollution control and mitigation. In this study, we present a novel and cutting-edge approach for analyzing oil-spilled images using Deep Attention Transformer Nets (DATN) with Collective Intelligence (CI), with the goal of reducing pollution in the marine environment. This method takes advantage of deep learning capability, notably the incorporation of transformer-based attention processes, to improve the identification and measurement of oil spills in satellite and …aerial images. The DATN model is intended to learn complicated features from images automatically, capturing complex patterns associated with oil spills and their surrounding context. The model chooses focus on key regions and add spatial links by using attention mechanisms, allowing for a more comprehensive understanding of the environmental influence. We thoroughly test DATN performance using a variety of datasets encompassing various oil spill scenarios and environmental circumstances. The results show that DATN surpasses standard approaches and other deep learning models in recognizing oil spill regions, with excellent accuracy, precision, and recall rates. Furthermore, the model has strong generalization capabilities across a wide range of image sources and situations. Show more
Keywords: Oil spill detection, deep attention transformer nets, aerial imagery, pollution mitigation, neural networks
DOI: 10.3233/JIFS-235657
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3461-3473, 2024
Authors: Slimani, Hicham | El Mhamdi, Jamal | Jilbab, Abdelilah
Article Type: Research Article
Abstract: A significant concern is the economic impact of agricultural diseases on the world’s crop production. The disease significantly reduces agricultural production across the world. Loss of nutrients caused by parasite infection of leaves, pods, and roots–the pathogenic agent that causes fava bean rust disease–decreases crop health. This work addresses this requirement by offering an innovative deep-learning model approach for early identification and classification of fava bean rust disease. The suggested method uses the effectiveness of modern YOLO-based object detection architectures like You Only Look Once –Neural Architecture Search (YOLO-NAS) L, YOLO-NASM, and YOLO-NASS, Faster Region-based Convolutional Neural Network (Faster R-CNN), …and RetinaNet. An inclusive dataset of 3296 images of various lighting and background situations was selected for extensive model training. Each model underwent thorough training and adjusted parameters through careful experimentation. The models’ comparative studies found significant performance differences. The precision for YOLO-NASL was 82.10%; for YOLO-NASM, it was 84.80%; for YOLO-NASS, it was 83.90%; for Faster R-CNN, it was 75.51%; and for RetinaNet, it was 73.74%. According to the evaluation, model complexity and detection accuracy are directly correlated. YOLO-NASL, YOLO-NASM, and YOLO-NASS showed remarkable mean average precision values of 90.90%, 94.10%, and 92.60%, respectively, and became highly functional models. The fastest model was YOLO-NASS. Its satisfying recognition speed made real-time detection possible in particular applications. The YOLO-NASM model, which shows an extraordinary state-of-the-art performance, represents the pinnacle of our work. Its mean average precision (mAP@0.5) was 94.10%, with notable values of 90.84%, 96.96%, and 84.80% for the F1-score, Recall, and precision, respectively. This investigation addresses a critical need in agricultural disease management, aligning with broader global efforts toward sustainable agriculture. Our studies add to the knowledge about precision agriculture and inspire practical, long-lasting disease management techniques in the agricultural industry. The real-time performance of the system will need to be improved, and satellite imagery integration may be considered in the future to provide more comprehensive coverage. Show more
Keywords: Deep learning, YOLO-NAS, fava bean, rust disease, CNN, crops disease
DOI: 10.3233/JIFS-236154
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3475-3489, 2024
Authors: Yin, Xiang | Guan, Li | Li, Bing | Huang, Qing | Lin, Huijie
Article Type: Research Article
Abstract: We provide a strategy for minimizing losses and redistributing loads in distribution systems while emergency repairs are being made. The proposed approach takes advantage of the preexisting, network-accessible, and Power Companies’ Adoption of Residential Energy Storage Batteries devices. Batteries are expected to be used increasingly often to deal with a few of the growing challenges with renewable, among them the infamous duck curve difficulty, as renewable energy sources that are widely dispersed, like photovoltaic (PV) and wind turbines, become more popular. The proposed approach may be implemented using signals in reaction to demand. To demonstrate its value, we provide a …method for concurrent simulation for designing and analyzing strategies for optimizing a distribution that benefits from the synergy-connected smart grid, intelligent structures, and decentralized battery systems to reduce overall energy consumption and costs while enhancing power management. The suggested method is created and verified inside of the Smart Builds co-simulation environment. From what we can tell from simulations, energy storage devices provide interim relief for line-outage-affected distribution networks. Show more
Keywords: Distributed generation (DG), distribution system, power system, photovoltaic (PV), mat power, distributed energy resource (DER), battery energy storage systems (BESS), load balance
DOI: 10.3233/JIFS-236323
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3491-3503, 2024
Authors: Zhao, Hongyan
Article Type: Research Article
Abstract: China has now embraced the information era, which has had a significant impact on everyday life, employment, and educational practices. Information technology has also had a significant impact on the growth of the education sector, resulting in a fast-paced and resource-rich setting for student interaction. Through the network platform, various interactive software can improve students’ learning methods, especially language teaching software. English audio-visual speaking is software for training English language listening and speaking, which can carry out relevant oral activities and topic discussions according to the imported materials. As a result, you can assist pupils in using the vocabulary and …knowledge associated with the subject, which will increase their interest in learning. English teachers can fully prepare for speaking and listening tasks in the classroom by using audio-visual speaking. At the same time, through the learning of TV and movie trailers, English audio-visual speaking can provide readers with background knowledge, which is ready for readers to fully understand the language and content in the video materials. Based on information technology, this paper constructs English audio-visual and oral mobile teaching software, which depends on interactive digital media algorithms. Through the mobile teaching software for English audio-visual speaking, students can form good English listening and reading habits, which will provide important help for English language learning.First, this essay examines the value and benefits of mobile applications for providing English instruction orally and visually, which might help to illustrate the need for software development. The research then suggests various algorithms for English that are related to audio, visual, and oral input that can detect, assess, and correct students’ learning mistakes. Finally, this work develops the fundamental methodology of the audio-visual and verbal mobile software for instruction in English. Show more
Keywords: Interactive digital media algorithm, English audio-visual speaking, mobile teaching software
DOI: 10.3233/JIFS-233741
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3505-3515, 2024
Authors: Xu, Xiaosheng
Article Type: Research Article
Abstract: The current conventional water resources management planning method realizes the optimal allocation of water resources by constructing a function aiming at economic benefits; it causes poor model planning repercussions as a result of the disregard of comprehensive benefits. In this regard, a hydrological model-based water resource management planning method for climate change is proposed. By combining geological conditions, hydrological conditions and other climate change factors, a hydrological model is constructed to calculate watershed flows, and the hydrological model is used to divide the watershed scale and hydrological response units. A multi-objective function planning model is constructed with economic and ecological …benefits as the objective functions. The proposed approach is tested in trials and shown to provide advantages for thorough planning. The results of the study demonstrate that the algorithm has a high value of extensive benefit when the recommended strategy is utilized for the optimum allocation of water resources, and has a more preferable optimal allocation consequence. Show more
Keywords: Hydrological modeling, climate change, water resources, management planning, optimal allocation
DOI: 10.3233/JIFS-233939
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3517-3526, 2024
Authors: Hu, Jianbin
Article Type: Research Article
Abstract: This study intends to solve the problems brought on by regional differences in the distribution of educational resources, inadequate growth of schools, and various levels of informationization in university education. Because of the complicated functional framework that is now in place, university life is unsafe for both teachers and students. The issue is further complicated by challenges with maintaining several cards for one person, a lack of seamless software and educational platform integration, and multiple obstacles between data and users. Information inequality is exacerbated by inadequate learning resources, and development is hampered by the lack of efficient teacher-student feedback mechanisms. …It can also be difficult to accurately manage a huge group of people. This study uses the web and artificial intelligence (AI) technology to create comprehensive, succinct, effective, and high-performing college instruction information technology in order to address these difficulties. Irrespective of the time or day, the system seeks to serve teachers and students while managing a sizable influx of visitors. Throughout the development process, the system is actively optimized and improved by the research. The experiment’s findings illustrate a robust interface function via practical assessment. Usability assessments show that the feedback is better than the previous system, with response times being reduced. Additionally, the updated system shows a typical reduction in overall electrical usage. Show more
Keywords: Informationization, education, network, teacher-student feedback
DOI: 10.3233/JIFS-235050
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3527-3544, 2024
Authors: Li, Yajie | Cai, Xirui
Article Type: Research Article
Abstract: In recent years, significant progress has been made in the study of Chinese vocabulary acquisition, and the research content and scope have gradually expanded. Chinese vocabulary is the foundation for understanding and using language, and any language has developed on the basis of Chinese vocabulary. Many studies have shown that there are many factors that affect the acquisition of Chinese vocabulary, and the impact on Chinese vocabulary acquisition is also different. Among them, the influencing factors of Chinese vocabulary acquisition have gradually become a research hotspot, and a large number of related empirical studies have emerged. Based on the big …data technology and the random effect model, this paper comprehensively analyzes whether the influencing factors of Chinese vocabulary acquisition as a second language are significant and investigates the effects of internal and external influencing factors, delayed post-test, specific factors and experimental interval time on the learning effect. External factors significantly impact Chinese vocabulary acquisition, including learning style, input factors and input methods. Through the subgroup analysis of the experimental interval, we find that the effect of Chinese vocabulary acquisition decreases with the extension of the experimental interval. Therefore, this paper holds an inverse relationship between the experimental interval and the development of Chinese vocabulary acquisition. Show more
Keywords: Chinese, second language, Chinese vocabulary, big data technology, analysis of influencing factors
DOI: 10.3233/JIFS-235515
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3545-3556, 2024
Authors: Du, Pei
Article Type: Research Article
Abstract: To protect the historical and cultural heritage, the application of self-organizing mapping networks and genetic algorithms in the restoration of ancient architectural murals is studied. The results show that the average repair time for different types of mural paintings is less than 60 seconds, and the shortest repair time is only 17.81 seconds. The evaluation effect of the research model is good, and the comprehensive efficiency evaluation of the mural restoration work is improved by about 40.42%. The repair system has excellent performance, and the algorithm has high feasibility and effectiveness. The impact of restoring murals is substantial, and the …extent of restoration is highly consequential for the restoration of ancient architectural murals. Show more
Keywords: Ancient architectural murals, self-organizing networks, genetic algorithms, automatic annotation, restoration strategies
DOI: 10.3233/JIFS-235769
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3557-3568, 2024
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