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Transforming Sustainable Aquaculture: Synergizing Fuzzy Systems and Deep Learning Innovations

International Journal of Fuzzy Systems 2024年第8期26卷 2536-2552页

作者： Haobijam, Basanta Huang, Yo-Ping Chang, Yue-Shan Chang, Tsun-Wei Department of Electrical Engineering National Taipei University New Taipei City23741 Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan Department of Computer Science and Information Engineering HungKuo Delin University of Technology New Taipei City236302 Taiwan

Pisciculture encounters an array of intricate challenges that span disease management, preservation of water quality, prevention of genetic hybridization, ensuring the integrity of net systems, sourcing sustainable aquatic feed, and comprehending fish growth and reproductive dynamics. Addressing these multifaceted challenges necessitates a comprehensive research approach. This study employs an innovative synergy of fuzzy logic and deep learning techniques, resulting in a robust strategy to tackle these obstacles effectively. Fuzzy logic excels in assessing stressed fish conditions by handling inherent uncertainties. Simultaneously, YOLOv7 with fuzzy color enhancement (YOLOv7FCE) is used to detect damaged fish nets, thereby mitigating losses and upholding the integrity of the net infrastructure. This approach also leverages YOLOv7FCE for identifying Cobia fish within shoals, streamlining the identification process. Subsequently, DeepLabv3 is implemented to meticulously segment the recognized Cobia fish, facilitating precise measurements of their physical attributes. This comprehensive methodology yields profound insights into growth patterns and feeding tendencies within the confined aquatic environment. By embracing this approach, the research presents a versatile and adaptive framework that not only enhances our comprehension of piscine dynamics but also holds the potential to revolutionize the aquaculture industry. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Fish

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Organ Harbour: A Blockchain Solution for Organ Donation and Transplantation 2024

Organ Harbour: A Blockchain Solution for Organ Donation and ...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Bansal, Nikita Jain, Samyak Nishal, Ribha Pandey, Kavita Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India

ISBN: (纸本)9798400709722

Organ donation and transplantation have long been critical medical procedures, but they face numerous challenges, including scarcity, malpractices, and complex allocation processes. Also, they are vulnerable to the single point of failure problem due to centralization. Unlike existing blockchain solutions that only cover aspects like donor-recipient matching or medical record storage, our approach integrates hospitals into the system to ensure fair allocation of organs. The proposed organ donation decentralized app Organ Harbour utilizes Ethereum blockchain-based solution to automate this process in decentralized way, also it ensures transparency, privacy, security, and trustworthiness. This ensures secure and transparent registration of donor-patient information, including medical IDs, blood types, and organ details. Organ Harbour streamlines the process, eliminating third-party involvement and reducing transplantation costs. Moreover, by leveraging blockchain's immutability and smart contract capabilities, the system enhances data integrity and prediction accuracy. Overall, this research presents an approach to organ donation and transplantation, fostering security, transparency, and an efficient healthcare ecosystem. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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An intelligent approach for anomaly detection in credit card data using bat optimization algorithm

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Inteligencia Artificial 2023年第72期26卷 202-222页

作者： Haseena, S. Saroja, S. Suseandhiran, N. Manikandan, B. Department of Information Technology Mepco Schlenk Engineering College India Department of Computer Applications National Institute of Technology Trichy India

As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting fraud, identifying hidden correlations in data and reducing the time for the verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using the Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using a full center loss function to improve fraud detection performance and stability. The proposed model is tested with the Kaggle dataset and yields around 99% accuracy. © IBERAMIA and the authors.

关键词： Optimization

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Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 710-724页

作者： Dalvi, Prachi Kalbande, Dhananjay R. Rathod, Surendra Singh Dalvi, Harshal Agarwal, Amey Bharatiya Vidya Bhavans Sardar Patel Institute of Technology Department of Computer Engineering Mumbai400058 India Fr. Conceicao Rodrigues College of Engineering Department of Electronics and Computer Science Mumbai400050 India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai400056 India

Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issue is exacerbated in rural regions, like parts of India, where a notable dermatologist shortage exists, leading to overlooked skin diseases. In response, the use of medicinal plants for dermatological purposes has been a longstanding tradition. However, traditional plant identification often relies on a single attribute, such as leaves or flowers, which can be unreliable due to seasonal variations. This article introduces a novel approach for accurately identifying medicinal plants using a multiattribute deep convolutional neural network. This approach aims to bridge the gap in healthcare access by empowering individuals to recognize and utilize these plants effectively. Our objective is to develop a robust deep CNN model trained on a diverse dataset of images encompassing leaves, trunks, and seeds of medicinal plants associated with skin health. Our findings demonstrate that the model achieves high accuracy in plant identification, effectively addressing the limitations of single-attribute methods. This research not only contributes to the field of medicinal plant classification but also empowers individuals to make informed decisions about their skin health while preserving valuable traditional knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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Salp swarm algorithm using lens opposition-based learning and local search for constrained optimization problems

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Iran Journal of computer Science 2025年第2期8卷 659-686页

作者： Majumdar, Parijata Mitra, Sanjoy Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Jirania 799046 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh 799009 India

The Salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thus, we have proposed a novel variant of SSA (LOLSSA) by integrating lens opposition learning with the fundamental evolutionary techniques of SSA to increase the initial population variety. During exploration, the information about the local best position for each individual is effectively shared with the follower, using the salp position update process to avoid getting stuck at the local optimum. At the end of each iteration, the local search algorithm is utilized to increase exploitation. Additionally, the leader’s salp position incorporates inertia weight to speed up the convergence speed. Swarm intelligence algorithms have proven their efficacy in feature selection, since they search for an optimal set of features in a large search area that will have the best impact on the accuracy of the training system. LOLSSA is thus employed for feature selection to select informative features in 11 high-dimensional datasets, where it outperforms well-known metaheuristics in average fitness, accuracy, and average number of feature reductions. In the software-as-a-service cloud computing model, a task scheduling problem is solved to satisfy the QoS requirement of both the service provider and the user. Experiments show that LOLSSA optimizes this problem in terms of different performance metrics. LOLSSA also outperforms other metaheuristics regarding 23 well-known benchmark functions, IEEE CEC-C06 2019, and CEC-06 2020 benchmark functions, and in solving engineering design problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Convergence speed Feature selection Global optimization Lens opposition learning Salp swarm algorithm Task scheduling problem

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FLAG: frequency-based local and global network for face forgery detection

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Multimedia Tools and Applications 2025年第2期84卷 647-663页

作者： Zhou, Kai Sun, Guanglu Wang, Jun Wang, Jiahui Yu, Linsen School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Department of Information Engineering and Mathematics University of Siena Siena53100 Italy

Deepfake detection aims to mitigate the threat of manipulated content by identifying and exposing forgeries. However, previous methods primarily tend to perform poorly when confronted with cross-dataset scenarios. To address the above issue, we propose an innovative hybrid network called the Frequency-based Local and Global (FLAG) network to explore local and global information with the help of frequency-domain cues for better generalization capability. In consideration of the fact that forged faces often exhibit flaws in the frequency domain, we design a Frequency-based Attention Enhancement Module (FAEM) to enhance the aggregation of CNN and Vision Transformer (ViT). In this design, local features from CNN are attentively enhanced by selected frequency coefficients in FAEM, facilitating generalizable global features learning by the ViT module. The effectiveness of the proposed method is validated via numerous experiments and the generalization performance is improved under cross-dataset scenarios. Especially, the proposed method have obtained an AUC of 99.26% and an ACC of 96.56% using intra-dataset experimental results on FaceForensics++ (C23). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Face recognition

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Leveraging Journaling File System for Prompt Secure Deletion on Interlaced Recording Drives

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IEEE Transactions on Emerging Topics in Computing 2023年第3期11卷 619-634页

作者： Chen, Shuo-Han Huang, Kuo-Hao National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

With the growing awareness of secure computation, more and more users want to make their digital footprints securely deleted and irrecoverable after updating or removing files on storage devices. To achieve the effect of secure deletion, overwritten-based secure deletion techniques have been proposed to overwrite invalidated storage space with scrambled data content. Nevertheless, overwritten-based secure deletion requires explicit write requests for rewriting invalidated storage space with random data, thus incurring additional internal write traffic on storage devices. Recently, this inefficient aspect of secure deletion is exacerbated by the emergence of high-density interlaced magnetic recording (IMR) technology. IMR technology enhances the storage density of hard disk drives by interlacing narrower top tracks on wider bottom tracks. Owing to the interlaced track layout, securely erasing invalidated data on bottom tracks interferes with adjacent top tracks and track rewrites are required to preserve valid data on top tracks;thus, overwritten-based secure deletion on IMR drives significantly enlarges the internal write traffic and impairs the secure deletion efficiency. In this paper, instead of directly alleviating the constraint induced by the interlaced track layout, we propose leveraging the interlaced track layout of IMR drives in combination with the journaling data stream of journaling file systems to enable prompt secure data deletion while minimizing the write traffic of secure deletion operations. Experimental results suggest that the performance improvement achieve by the proposed prompt secure deletion (P-SD) strategy is 51.84% on average, when compared with the previous TrackLace approach on IMR drives. © 2013 IEEE.

关键词： Hard disk storage

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Designing an Ultrasonic-Assisted Machining System by Using a Network Mapping Fusion–Convolutional Neuro-Fuzzy Network Model

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IEEE Transactions on Industrial Informatics 2024年第7期20卷 1-12页

作者： Lin, Cheng-Jian Jhang, Jyun-Yu Lin, Xue-Qian the Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung 411030 Taiwan the Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung 404336 Taiwan

In the field of machining, product quality must meet customer specifications. In general, surface roughness is an essential indicator of machining quality. Low surface roughness correlates with increased fatigue strength and corrosion resistance. However, the main factor that affects surface roughness is the selection of the machining parameters. When different parameters are combined, the resulting machining quality varies. Therefore, to achieve the desired machining quality, appropriate machining parameters must be selected. In this study, an ultrasonic-assisted machining system (UAMS) was designed to help users determine the machining parameters and machine SiC materials. To establish a prediction model for surface roughness, a novel network mapping fusion (NMF) convolutional neuro-fuzzy network (CNFN) model was used in the designed UAMS. The differential evolution algorithm was then used to search for optimized machining parameters. To explain the prediction model, which can help analyze the factors that have the greatest influence on surface roughness, a Shapley additive explanations method is proposed. The proposed NMF–CNFN model was more accurate than were the other deep learning models and exhibited a MAPE of 1.98%. When optimized machining parameters were selected, the desired surface roughness was obtained, thereby confirming the effectiveness and accuracy of the proposed UAMS. Moreover, the proposed model was implemented in a field-programmable gate array (FPGA) to reduce its power consumption and increase its computational performance. Experimental results indicated that the computational speed of the FPGA was 99.64%and 99.16%higher than those of the CPU and GPU, respectively. IEEE

关键词： Convolutional neuro-fuzzy network (CNFN) differential evolution (DE) algorithm neuro-fuzzy network Shapley additive explanations (SHAP) ultrasonic-assisted machining system (UAMS)

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8 17

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. This system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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