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Underwater Sea Cucumber Target Detection Based on Edge-Enhanced Scaling YOLOv4

Journal of Beijing Institute of Technology 2023年第3期32卷 328-340页

作者： Ziting Zhang Hang Zhang Yue Wang Tonghai Liu Yuxiang He Yunchen Tian College of Computer and Information Engineering Tianjin Agricultural UniversityTianjin 300380China

Sea cucumber detection is widely recognized as the key to automatic *** underwater light environment is complex and easily obscured by mud,sand,reefs,and other underwater *** date,research on sea cucumber detection has mostly concentrated on the distinction between prospective objects and the ***,the key to proper distinction is the effective extraction of sea cucumber feature *** this study,the edge-enhanced scaling You Only Look Once-v4(YOLOv4)(ESYv4)was proposed for sea cucumber *** emphasizing the target features in a way that reduced the impact of different hues and brightness values underwater on the misjudgment of sea cucumbers,a bidirectional cascade network(BDCN)was used to extract the overall edge greyscale image in the image and add up the original RGB image as the detected ***,the YOLOv4 model for backbone detection is scaled,and the number of parameters is reduced to 48%of the original number of *** results of 783images indicated that the detection precision of positive sea cucumber samples reached *** improvement reflects that the algorithm is more effective to improve the edge feature information of the *** thus contributes to the automatic multi-objective detection of underwater sea cucumbers.

关键词： sea cucumber edge extraction feature enhancement edge-enhanced scaling You Only Look Once-v4(YOLOv4)(ESYv4) model scaling

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Research on full-cycle sharing method of natural resources big data based on middle platform architecture

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Multimedia Tools and Applications 2024年 1-18页

作者： Li, Qian College of Computer and Information Engineering Guangxi Vocational Normal University Nanning530007 China

Natural resources refer to the sum of natural environmental factors that exist naturally, have use value, and can be classified into current and future well-being. In order to realize the sharing of natural resources big data, this paper puts forward a full-cycle sharing method of natural resources big data based on the middle platform architecture. Build an analysis model of spatial distribution, time change and attribute characteristic parameters of natural resource big data response, obtain the characteristic quantity of cross-space coverage of natural resource change and state, establish the middle platform architecture system of resource big data by the method of natural resource collaborative perception, and realize the joint acquisition of natural resource big data by the method of aviation acquisition, ground observation and professional monitoring. Build a natural resources spatio-temporal database with business elements connected, rich thematic information and multi-scale integration, and solve the problems of interconnection, dynamic convergence and effective integration of multi-source business information of natural resources big data. Through high-level spatio-temporal cognition and processing, the spatio-temporal distribution law of natural resources can be obtained, and the full-cycle sharing of resource big data can be realized. The test results show that this method has good time–space coupling and strong conflict coordination and resolution ability for the full-cycle sharing of resource big data, and it has a good application value in the simulation, optimization and regulation of different scenarios of natural resources development, utilization and protection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

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Research on Plant Species Identification Based on Improved Convolutional Neural Network

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Phyton-International Journal of Experimental Botany 2023年第4期92卷 1037-1058页

作者： Chuangchuang Yuan Tonghai Liu Shuang Song Fangyu Gao Rui Zhang College of Computer and Information Engineering Tianjin Agricultural UniversityTianjin300392China

Plant species recognition is an important research area in image recognition in recent ***,the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition ***,ShuffleNetV2 was improved by combining the current hot concern mechanism,convolution kernel size adjustment,convolution tailoring,and CSP technology to improve the accuracy and reduce the amount of computation in this *** convolutional neural network models with sufficient trainable parameters were designed for differentiation *** SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local *** this paper,a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed,containing 3000 images of 10 plant species on the campus of Tianjin Agricultural ***,the improved model is compared with the baseline version of the model,which achieves better results in terms of improving accuracy and reducing the computational *** recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%,and the recognition precision reaches up to 93.6%,which is 5.1%better than the original model and reduces the computational effort by about 31%compared with the original *** addition,the experimental results were evaluated using metrics such as the confusion matrix,which can meet the requirements of professionals for the accurate identification of plant species.

关键词： Deep learning convolutional neural network plant identification model improvement

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Comparative Study and Analysis of Health Care System for Heart Patients: Formal Review 1

Comparative Study and Analysis of Health Care System for Hea...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Agrawal, Chanchal G. Uke, Nilesh J. Vishwakarma Institute of Information Technology Computer Engineering Pune India Indira College of Engineering and Management Computer Engineering Pune India

ISBN: (纸本)9798350355611

Nationwide in today's day to life the fundamental root causes of deaths and hospitalization is heart diseases which will put enormous economic burden on health care system and on the Human life. Prediction at early stage for this type of crucial diseases is one of the best results in morbidity which can be achieve by Clinical Data Analysis. The assessment of cardiovascular events relies on numerous risk factors. Central to effective cardiovascular disease prevention is risk evaluation. In medical systems, data mining and machine learning techniques play a crucial role in developing models to predict heart or cardiovascular disease. These techniques assist researchers in estimating the likelihood of heart disease in vulnerable patients and comparing various ML models. Researchers and practitioners have proposed multiple solutions at both data and algorithm levels over time. This study employs a formal literature review (FLR) approach to provide a comprehensive overview of existing literature and uncover challenges related to imbalanced data in heart disease predictions. Prior to writing, an extensive analysis was conducted using 100 reference papers from reputable journals published between 2017 and 2023. A thorough examination of 52 referenced papers was performed, considering factors such as heart disease type, algorithms, applications, and solutions across various scientific databases, including the Ryerson University Library and Archives (RULA) online system. The primary search databases accessed through RULA were PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, Web of Science, Hindawi, and Frontiers. The FLR study revealed that current approaches face several unresolved issues when handling heart disease datasets. © 2025 IEEE.

关键词： Cardiology

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Decentralized Ransomware Recovery Network: Enhancing Resilience and Security Through Secret Sharing Schemes 9

Decentralized Ransomware Recovery Network: Enhancing Resilie...

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9th International Conference on Internet of Things, Big Data and Security, IoTBDS 2024

作者： Ali, Sijjad Wang, Jia Leung, Victor Chung Ming Ali, Asad College of Computer Science and Software Engineering Shenzhen University Yuehai Campus Guangdong China College of Electronics and information Engineering Shenzhen University Yuehai Campus Guangdong China

ISBN: (纸本)9789897586996

Ransomware attacks present multiple threats to individuals such as businesses and organizations, causing data loss, financial stress, and operational interruptions. Traditional measures to mitigate ransomware threats usually include backups and secure applications. However, these countermeasures may not protect against sophisticated attacks. The purpose of this article is to explore a decentralized approach for recovering from multiple ransomware attacks. A decentralized secure approach is employed by the decentralized ransomware recovery network (DRRN) as a platform for sharing data privacy. Backup and restoration of encryption keys on shared domains are performed in the event of a ransomware attack. By paying for ransomware, users can encrypt their files. Additionally, the technical design of the DRRN and its management, as well as ransomware attacks are explored in our studies. A hybrid approach is utilized to evaluate its effectiveness and implications for cybersecurity and data protection. Finally, we assert that our proposed scheme is more secure and effective in the DRRN environment. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Malware

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Design of a Parkinson's Rehabilitation Management System Based on Multimodal Learning and Digital Twin Technology 10

Design of a Parkinson's Rehabilitation Management System Bas...

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10th IEEE Smart World Congress, SWC 2024

作者： Qin, Jing Cai, Yuecheng Wang, Zuming Jin, Jianqiang Huang, Xiaofei Pei, Lisha Dalian University College of Software Engineering Dalian China Dalian University College of Information Engineering Dalian China

ISBN: (纸本)9798331520861

Parkinson's disease (PD), a prevalent neurodegenerative disorder, significantly impairs motor functions, necessitating effective rehabilitation management. This paper presents the design of a Parkinson's rehabilitation management system leveraging multimodal learning and digital twin technology. By integrating data from diverse sources - such as speech signals, hand motion data, and gait videos - this system comprehensively captures PD symptoms, facilitating early detection and accurate diagnosis. Employing deep learning techniques, the system analyzes and fuses multimodal data, enhancing diagnostic precision and providing personalized rehabilitation plans. The digital twin component creates a virtual model of the patient, continuously monitoring motor functions, medication responses, and symptom changes, thus optimizing treatment plans and improving quality of life. Case studies demonstrate the system's efficacy in speech recognition, tremor identification, and tremor severity assessment, highlighting its role in enhancing patient self-management and rehabilitation outcomes. The proposed system not only advances PD rehabilitation management, but also sets a precedent for intelligent healthcare solutions by integrating multimodal data analysis with digital twin technology. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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Single-Frequency Self-Alignment RF Resonant Beam for information and Power Transfer

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IEEE Internet of Things Journal 2025年第11期12卷 16622-16636页

作者： Jiang, Qingwei Liu, Mingqing Xu, Mengyuan Fang, Wen Xiong, Mingliang Liu, Qingwen Zhou, Shengli Tongji University College of Computer Science and Technology Shanghai201804 China Tongji University College of Electronics and Information Engineering Shanghai201804 China University of Connecticut Department of Electrical and Computer Engineering CT06250 United States

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications. © 2025 IEEE.

关键词： Inductive power transmission

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Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

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computers, Materials & Continua 2024年第6期79卷 4127-4148页

作者： Abdulelah Alwabel Chinmaya Kumar Swain Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Institute of Management and Information TechnologyCuttackBPUTOdishaIndia

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data ***,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud *** approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing *** approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of *** this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing *** consider four cost-types for application deployment:Computation,communication,energy consumption,and *** proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the *** extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art *** results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

关键词： Placement mechanism application module placement fog computing cloud computing genetic algorithm flamingo search algorithm

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A Novel CNN-Transformer Framework for COVID-19 Diagnosis from ECG Images 7

A Novel CNN-Transformer Framework for COVID-19 Diagnosis fro...

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7th International Conference on computer information Science and Application Technology, CISAT 2024

作者： Huang, Xiaofei Xu, Qingchao Pei, Lisha Cai, Yuecheng Dalian University College of Information Engineering Dalian China Dalian University College of Software Engineering Dalian China

ISBN: (纸本)9798350375107

Research has demonstrated that COVID-19 impacts both the respiratory and cardiovascular systems. Timely and accurate detection of COVID-19 infection using electrocardiogram (ECG) images holds significant research importance. Most researchers have implemented convolutional neural networks (CNNs) to develop COVID-19 diagnostic models based on electrocardiograms (ECGs). However, such models primarily focus on local feature extraction and often lack understanding of inter-lead relationships. This study proposes a novel CNN-transformer framework that enhances the understanding and utilization of inter-lead features by leveraging CNN with CBAM for effective local feature extraction and Transformer encoders for generating rich, stable, and globally representative features. Finally, a classifier based on pre-trained DenseNet161 is employed for diagnosis. The proposed model achieves excellent performance across various evaluation metrics on the COVID-19 dataset, notably achieving an F1 score of 0.98, precision and specificity of 1.0, demonstrating robust diagnostic capabilities. © 2024 IEEE.

关键词： COVID-19

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