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5th IEEE International Conference on Architecture, Construction, Environment and Hydraulics, ICACEH 2023

作者： Siao, Cheng-Yan Chang, Rong-Guey National Chung Cheng University Advanced Institute of Manufacturing with High-Tech Innovations Department of Computer Science and Information Engineering ChiaYi Taiwan

ISBN: (纸本)9798350303605

Many countries have set up ecological coastal preserves for sustainable oceans and ecological conservation and prohibiting people from angling along the shoreline. Although there are regular patrols by law enforcement officers, there are still many anglers who do not comply with the regulations, and intrude into the ecological coastal preserve. This requires a lot of human resources and time for law enforcement officers. Therefore, we developed a fishing detection system by collecting images of anglers' fishing behaviors. We used a deep learning model with object detection and posture recognition. When anglers engaged in fishing behaviors, the system immediately transmitted the anglers' geographic locations to the back-end database and notified the law enforcement officers. The results showed that the system's accuracy rate in determining angling postures was as high as 90%. © 2023 IEEE.

关键词： Convolutional neural networks

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Exploring the Potential Impacts of Wearable Technology in Reshaping Healthcare 15

Exploring the Potential Impacts of Wearable Technology in Re...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vishwakarma, Anish Kumar Ranjan, Rakesh Verma, Kavery Kumar, Rajnish University of Petroleum and Energy Studies School of Computer Science Dehradun India National Institute of Technology Department of Electronics and Communication Engineering Bihar Patna800005 India

ISBN: (纸本)9798350370249

Advancements in technology have facilitated the transformation of medical devices into wearable forms, providing smooth integration of healthcare solutions into daily life. These wearable devices serve diverse functions including remote patient monitoring, sports medicine applications, animal tracking, and activity monitoring. As these devices gain popularity, they are becoming the preferred option for individuals to monitor their health parameters and effectively track their belongings. This paper presents an extensive review of the evolution, applications, and impact of wearable healthcare solutions in the medical domain. It emphasizes the transformative role of wearable devices equipped with sensors and IoT technology in revolutionizing healthcare delivery. These devices, ranging from fitness bands to textile-based sensors, provide real-time physiological parameter monitoring, including blood pressure, respiration patterns, temperature, and medication adherence. Moreover, recent innovations extend their utility to sports tracking, animal monitoring, and livestock management. However, challenges of wearable devices such as accuracy, data interpretation, security, cost accessibility, clinical validation, and ethical considerations are also acknowledged. Despite these challenges, wearable devices hold immense potential to enhance healthcare by providing early detection, personalized monitoring, and improved accessibility to medical support, thus promising a healthier future. © 2024 IEEE.

关键词： and Brain Disorder Animal Tracking Internet of Things (IoT) Remote Patient Monitoring Smart Healthcare Wearable Devices

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Novel Plant Leaf Disease Detection Approach using Hybrid Deep Learning Strategy 5

Novel Plant Leaf Disease Detection Approach using Hybrid Dee...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Vamshi, Jalla Tanwar, Shashi Thatipudi, Jayendra Gopal Ponsangeetha, A. Mukherjee, Saptarshi Mandhare, Jagdish B. School of Agriculture Sr University Telangana Warangal India G D Goenka University Department- Computer Science and Engineering Haryana Gurugram India University of North Texas Dept. of Computer Science United States Velammal Institute of Technology Department of Computer Science and Engineering Panchetti India Goldsmith Institute of Advanced Research Department-Mtech Bioinformatics Giar West Bengal Kolkata India Bharati Vidyapeeth College of Engineering Department of Instrumentation Navi Mumbai India

ISBN: (纸本)9798350385649

The early detection and identification of plant diseases are pivotal for precision agriculture, aiming to mitigate crop losses and optimize yields. This study presents a novel approach to plant disease identification, leveraging Deep Learning (DL) techniques with a focus on transfer learning. Traditional methods often rely on complex image enhancement, disease region segmentation, and feature extraction, whereas our approach employs Convolutional Neural Networks (CNNs) for efficient and accurate disease detection. To enhance accessibility and diagnostic capabilities, we implement Hybrid DL Strategy, which consists of EfficientNetB0 and MobileNetV2 model, tailored for lightweight applications suitable for smartphone implementation. Efficient NetB0 model, which considers depth, width, and resolution during convolution, enhancing the model's capacity to capture intricate features critical for accurate disease diagnosis. The overall accuracy of proposed method is 98.44%, surpassing CNN, ResNet, and AlexNet. This high accuracy underscores the effectiveness of the proposed model in correctly classifying both diseased and healthy plant leaves. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Driving and Spectral Graph Theory for Automated Plant Leaf Disease Classification

Artificial Driving and Spectral Graph Theory for Automated P...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Bajpai, Chhavi Gaur, Manish Suganthi, J Roselin Vinayagam, S. Kaliappan, S. Manoj Senthil, K. Centre for Advanced Studies Department of Computer Science & Engineering Lucknow India Institute of Engineering and Technology Department of Computer Science & Engineering Lucknow India K. Ramakrishnan College of Engineering Department of Ece Trichirapalli India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Bioscience Chennai India Lovely Professional University Department of Research and Development Jalandhar India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350375442

Having a certified plant pathologist visually inspect the diseased leaves is now the gold standard for disease diagnosis in plants. This is because symptoms are present in the vast majority of plant illnesses. Because manual disease diagnosis is slow and results are highly dependent on the skill of the pathologist, this is an ideal domain for computer-aided diagnostic *** one of the suggested methods are preprocessing, step two is feature extraction, and step three is training the model. Pictures are acquired, then processed, enhanced, resized, segmented, and finally converted from RGB to HSI as part of the preprocessing phase. The topic of this study is the extraction of numerous features for the purpose of leaf disease categorization. Hexagons of colors are retrieved for classification purposes. The model was trained using Spectral Graph Theory. On average, this state-of-the-art technique achieves a higher accuracy rate of 92.48% than CNN and SVM. © 2024 IEEE.

关键词： Graph theory

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Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

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computers, Materials & Continua 2022年第7期72卷 1357-1372页

作者： Nguyen A.Tuan D.Akila Souvik Pal Bikramjit Sarkar Thien Khai Tran G.Mothilal Nehru Dac-Nhuong Le Faculty of Information Technology Ho Chi Minh City University of Foreign Languages-Information TechnologyHo Chi Minh City70000Vietnam Department of Information Technology Vels Institute of ScienceTechnology&Advanced StudiesChennai600117India Department of Computer Science and Engineering Sister Nivedita UniversityKolkata700156India Department of Computer Science and Engineering JIS College of EngineeringNadia741235India Institute of Research and Development Duy Tan UniversityDanang550000Vietnam School of Computer Science Duy Tan UniversityDanang550000Vietnam

This article presents a new scheme for dynamic data optimization in IoT(Internet of Things)-assisted sensor *** various components of IoT assisted cloud platform are *** addition,a new architecture for IoT assisted sensor networks is ***,a model for data optimization in IoT assisted sensor networks is proposed.A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization(MIDDO)algorithm for IoT assisted sensor network is proposed in this *** proposed algorithm considers every node data and utilized membership function for the optimized data *** proposed framework is compared with two stage optimization,dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio,coverage ratio and sensing *** optimization was performed based on the availability of cloud resource,sensor energy,data flow volume and the centroid of each *** was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%,reliability ratio of 94.74%,coverage ratio of 85.75%and sensing error of 0.154.

关键词： Data optimization IoT sensor network reliability membership sensor energy

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A Review on Zero Knowledge Proof Vulnerabilities in Zcash

A Review on Zero Knowledge Proof Vulnerabilities in Zcash

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Tyagi, Shobha Manav Rachna International Institute of Research and Studies School of Engineering and Technology Department of Computer Science and Engineering Haryana Faridabad India

ISBN: (纸本)9798350344387

The Zcash cryptocurrency is the first well-known application of zk-SNARKs in the blockchain. The role of Zk-SNARK is to provide privacy and confidentiality. As blockchain is incapable of providing privacy in the blockchain system, the researchers are banking at Zero Knowledge Proof protocols as the solution to the privacy problem. The Zero Knowledge Proof is a relatively new technique. Hence, currently, it comes with many risks and vulnerabilities. This paper mentions risks in cryptocurrency with Zero Knowledge Proof, primarily of Zcash. The discussed vulnerabilities are information leakage in transactions, trust risk, implementation vulnerability, and other security issues. © 2023 IEEE.

关键词： Blockchain Privacy Security Zero-Knowledge-Proof zk-SNARK

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Joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach 40

Joint Mobile Edge Caching and Pricing: A Mean-Field Game App...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Xu, Yin Zhang, Xichong Xiao, Mingjun Wu, Jie Liu, An Zhang, Sheng School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Temple University Department of Computer and Information Sciences Philadelphia United States School of Computer Science and Technology Soochow University Suzhou China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350317152

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines. © 2024 IEEE.

关键词： Game theory

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An Electrical Load Forecasting Method Using An Improved Online Training Strategy

An Electrical Load Forecasting Method Using An Improved Onli...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Wang, Xinlin Jin, Haizhen Papaefthymiou, Marios University of California School of Information and Computer Sciences Department of Computer Science Irvine United States Csiro Energy Business Unit NewcastleNSW Australia Korea Advanced Institute of Science and Technology Techno-Management Research Institute Korea Republic of

ISBN: (纸本)9781665471640

This work proposes a one-step-ahead electrical load forecasting strategy based on machine learning. The approach relies on a novel data-efficient online training data selection mechanism that uses an improved dynamic time warping method to dynamically subselect the most relevant training data from the entire dataset. It also uses an adaptive prediction result optimization method to further enhance forecasting accuracy. By continuously assessing the accuracy of prior forecasts, the proposed optimization method adjusts the real-time prediction outputs and the prediction framework to best reflect current consumption conditions. The effectiveness and robustness of the proposed strategy are assessed by two real-world datasets: a university campus demand dataset and an open-source dataset provided by Open Power System. Its performance is compared with nine alternative forecasting methods using six different evaluation metrics. Our experimental outputs show that the proposed forecasting approach consistently outperforms the alternative methods, yielding the most accurate forecasts across all metrics while using the smallest training data size. The proposed forecasting strategy, therefore, shows great potential to enhance the capabilities of ML-based prediction methods in smart grids. © 2023 IEEE.

关键词： data efficiency electrical load forecasting Machine learning online training smart grid

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Reinforcement Learning Technique Based Automated Feature Analysis of Gene Expression Data

Reinforcement Learning Technique Based Automated Feature Ana...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Jayakrishnan R S. Meera Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu Department of Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu

Immunohistochemistry and reverse transcription polymerase chain reaction, it is feasible to find molecular markers that are specific to cancer; nevertheless, there are no pathognomonic molecular markers available for the vast majority of solid tumors at this time. In this paper, we develop a Reinforcement learning Technique based automated feature analysis of gene expression data. This provides support for the idea that, on average, all the validation findings and training data from each epoch were utilized. This proposed method helps to acquiring knowledge at a more leisurely pace is, in the end, what makes episodic deep RL possible as a potential choice for students to pursue. To put it another way, the slow but steady accumulation of knowledge paves the path for the rapid extension of one existing body of information. The proposed methods provide 98 percent of the accuracy result than existing ANN and DNN. The proposed framework outperformed existing methods in terms of increase accuracy rate and reduced execution time than other methods

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A Comprehensive Framework to Enhance the Location Privacy for Geospatial Query Resolution

A Comprehensive Framework to Enhance the Location Privacy fo...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： D.S.Eunice Little Dani K. Rohini Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The rapid expansion of geospatial data analysis has led to an increasing need for systems that can respond quickly. Storing and analyzing extensive spatiotemporal datasets is crucial for extracting valuable information for location-aware applications like route planning and weather forecasts. Efficiently coordinating data sources and internet services is crucial for extracting results within time constraints, given the current surge in processing geographic searches. Privacy models that incorporate fog computing provide a significant advantage in terms of cost reduction and low latency. However, in order to address consumers' concerns about data exposure, recent efforts have been made to integrate fog computing for enhanced privacy. Through the use of simulated outcomes, it has been shown that the privacy protection offered by the Geo-Fog framework for localization services is highly effective. The system demonstrated average latency values as low as 23 ms for query loads of 200. Furthermore, the CPU usage remained below $37 \%$ even under the maximum query load of $\mathbf{5 0 0}$. Fog computing plays a crucial role in the advancement of location-based applications that prioritize efficiency and privacy. By enabling fog devices or servers to handle geospatial queries within the geographic environment, it ensures optimal management.

关键词： Privacy Data privacy Web and internet services Random access memory Weather forecasting Geospatial analysis Central Processing Unit Spatial resolution Protection Edge computing

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