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2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Wang, Mini Han Fang, Xiaoxiao Lin, Zhiyuan Zeng, Peijin Yang, Yu Liu, Yunxiao Liu, Haoyang Hu, Wenhan Li, Xinyue Jiang, Xudong Chen, Guangshun Hou, Guanghui Chong, Kelvin K.L. Fang, Junbin The Faculty of Medicine Chinese University of Hong Kong 999077 Hong Kong City University of Macao The faculty of Data Sciences 999078 China Radiology and Imaging Department Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Guangdong Zhuhai519000 China AI and big data R&D department Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Guangdong Zhuhai519000 China AI and Ophthalmology R&D Laboratory Perspective Technology Group Guangdong Zhuhai519000 China The department of Dry Eye Disease Zhuhai Aier Eye Hospital Guangdong Zhuhai519000 China Beijing Institute of Technology Guangdong Zhuhai5190000 China School of Biomedical Engineering Guangdong Medical University Guangdong Dongguan523000 China Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Guangdong Zhuhai519000 China School of Optometry and Ophthalmology Tianjin Medical University Tianjin300000 China The School of Computer Zhuhai College of Science and Technology Guangdong Zhuhai519000 China Department of Optoelectronic Engineering Jinan University Guangdong Guangzhou510000 China

ISBN: (纸本)9781510675032

This study conducts a rigorous comparative assessment of YOLOv5 and YOLOv8 for the detection of demodex mites in microscopic examination images, leveraging crucial metrics such as accuracy, precision, recall, and F1-score. The investigation reveals the unequivocal superiority of YOLOv8, not only in quantitative measures but also substantiated by visual evidence, showcasing its applicability for real-time scenarios. YOLOv8 exhibits exceptional accuracy in overall detection and introduces a novel functionality for quantitative assessment of individual mites, providing essential granularity for precise diagnoses and therapeutic planning within dermatological and ophthalmological contexts. Positioned as a substantial advancement in object detection methodologies, YOLOv8 holds promise for significantly improving both accuracy and granularity in demodex mite detection within microscopic examination images. While acknowledging potential limitations associated with dataset-specific considerations, this research underscores the imperative for further validation across diverse clinical scenarios. Computational considerations for real-time processing prompt future investigations to explore optimization strategies, particularly in resource-constrained environments. These findings position YOLOv8 as a valuable tool for clinicians and researchers engaged in dermatological and ophthalmological studies, offering heightened accuracy and nuanced insights. Ongoing research, encompassing clinical validations and comparative assessments with other state-of-the-art models, is anticipated to contribute to a more exhaustive understanding of YOLOv8's potential and limitations in real-world applications based on microscopic examination images. © 2024 SPIE.

关键词： Object detection

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Advancing giga-strength and high-modulus aluminum matrix composites via nitrogen-induced self-forming process

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Journal of Materials research and technology 2025年 36卷 8876-8894页

作者： Kanhu C. Nayak Se-Hoon Kim Jin-Won Lee donghyun Bae Jae-Pyong Ahn Kon-Bae Lee Hyun-Joo Choi Department of Materials Science and Engineering Kookmin University Seoul 02707 Republic of Korea Advanced Materials R&D Department Korea Automotive Technology Institute (KATECH) Cheonan 31214 Republic of Korea Department of Materials Science and Engineering Yonsei University Seoul 03722 Republic of Korea Data Center Korea Institute of Science and Technology (KIST) Seoul 02792 Republic of Korea

Giga-strength Al/SiC composites with exceptional strength (>1 GPa) and stiffness (>200 GPa), yet low density (<3.0 g/cm 3 ), are produced through a simple yet transformative process that utilizes nitrogen's critical influence on the matrix-reinforcement interface, known as Nitrogen-Induced Self-Forming Aluminum Composites (NISFAC). The Al matrix, reinforced with 20–50 vol% microsized SiC particles, exhibited significant enhancements of mechanical properties, with a 471.93 % increase in compressive strength and a 189.33 % improvement in Young's modulus compared to monolithic aluminum. These improvements are attributed to the formation of aluminum nitride (AlN) and aluminum oxynitride (Al(O)N), which originate on the Al powder surface and integrate into the matrix and Al–SiC interface during the nitrogen-induced self-sintering process. detailed microstructural analysis revealed the critical role of these phases in load transfer from the matrix to the reinforcement. Mechanistic insights and novel predictive models further validated the contributions of reinforcements to the yield strength and Young's modulus. The Al/50 %SiC composite achieved compressive strength over 1 GPa and Young's modulus over 200 GPa, highlighting its potential for advanced structural applications.

关键词： Al/SiC composite Nitrogen-induced self-sintering Interface Giga-strength Nanoindentation Strengthening mechanism

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Multiple Target research of Construction Progress on the Basis of on BIM and decide Tree Algorithms 2

Multiple Target Research of Construction Progress on the Bas...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Selvakanmani, S. Yadav, C. Hazarathaiah Singh, Brijesh Manigandan, E. Kumar, Satheesh S. Karthik, B. Department of Information Technology RMK Engineering College RSM Nagar Chennai India Department of Chemistry Vel Tech Rangarajan Dr.Sagunthala R&D institute of Science and Technology Chennai India Department of MBA CMR Institute Of Technology Bangalore India Department of Computer Applications Saveetha College of Liberal Arts and Sciences Thandalam Tamilnadu Chennai602105 India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam Tamilnadu Erode India Department of Electrical and Electronics Engineering Sona college of technology Salem India

ISBN: (纸本)9798350305418

Because of India's growing urbanization, coupled with dwindling urban land supply and rising land costs, the construction of high-rise structures has emerged as the dominant method to urban planning and development. The rapid growth of India's economy and the rapid pace of urbanization have resulted in an increasing urban population, raising the requirement for effective land usage. High-rise structures have gradually been the main center of urban construction projects in response to these *** primary goal of this study is to look into the use of Building Information Modeling (BIM) in building projects with the goal of optimizing the use of information received from it. Numerous graphs and algorithmic formulas are developed during the study to examine and illustrate these features. According to the findings of the study, a significant amount, accounting for 28.2% of the total, is related to various focused research procedures. As a result, the interest and significance of this field of investigation are expected to endure and develop in the coming years.. © 2023 IEEE.

关键词： BIM and decide tree Algorithms Multiple target maximize of construction progress

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Energy Proficient and dependable Cluster routing in Wireless Sensor Network

Energy Proficient and Dependable Cluster Routing in Wireless...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Hemavathi, S. Jeya Ganesan, J. Geetha, G. Kishore Kumar, K. Kohila, r. Sri Sairam Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science Engineering Tamil Nadu Chennai India Tamil Nadu Namakkal India

ISBN: (纸本)9781665462631

Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed by an energy depletion observing system through the wireless sensor network (WSN). Conventional security approaches can face external attacks. Nevertheless, they cannot proficiently solve interior attacks triggered by the captured nodes. Energy-efficient and dependable Cluster routing (EPdC) in WSN. This approach aims to reduce energy expenditure and enhance reliable routing in WSNs. The highest energy and highest available bandwidth are selected as a cluster head (CH). In every cluster, the selected CH has received the observed information from the cluster member and then forwarded it to the BS through a relay node. The forwarder relay node is selected based on the average energy faith and transmission faith during data transmission. The whole faith is greater than the average value that node is selected as a data forwarder from cluster to BS in the WSN. Furthermore, the Path Optimization Algorithm (POA) is used to detect the obstacle and build the obstacle-aware route efficiently. The simulation results explain why the proposed method has better residual energy. In addition, it minimized both the network delay and the packet loss rate in the WSN. © 2023 IEEE.

关键词： Bandwidth

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Sustainable Smart Agriculture Advisor through Artificial Intelligence Approach

Sustainable Smart Agriculture Advisor through Artificial Int...

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2024 International Conference on distributed Systems, Computer Networks and Cybersecurity, ICdSCNC 2024

作者： Hariharan, Shanmugasundaram Srivani, Pogula Gogula, Sreenivasulu Venkateswaran, N. Kukreja, Vinay Jose, P. Vardhaman College of Engineering Dept. of Artificial Intelligence and Data Science Hyderabad India Cmr Technical Campus Department of H&s Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Jyothishmathi Institute of Technology & Science Nustulapur Department of Computer Science and Engineering Karimnagar India Centre for Research Impact & Outcome Chitkara University Rajpura India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9798350375442

Agriculture, constituting the backbone of India's economy and employing 70% of its population, faces critical challenges such as water scarcity, soil degradation, and unpredictable crop yields. Traditional farming methods lack efficiency, contributing to environmental impact and resource inefficiency. To address these issues on smart crop cultivation with artificial intelligence enabled crop advisor sustainable agriculture generation integrates machine learning and data analytic to offer real-time insights and recommendations. This advanced agricultural approach aims to optimize resource utilization, minimize environmental impact, and adapt to changing climatic conditions. By empowering farmers with actionable insights, Smart Crop seeks to revolutionize agriculture, promoting sustainable practices for increased resource efficiency and enhanced resilience against challenges like limited precision farming, climate variability, and yield maximization. This innovative solution endeavors to bridge the gap between traditional farming methods and modern challenges, fostering a more sustainable and productive future for Indian agriculture. The methods analyzed leads to promising results as compared with well known metrics. © 2024 IEEE.

关键词： Smart agriculture

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Enhancing Alzheimer's disease diagnosis Using Multi-relation Graph Convolutional Networks and Structural MrI data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (Ad) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MrI data and Multi-relation Graph Convolutional Networks (Mr-GCN) to improve Ad diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MrI features. Using a large dataset, the resusts are evaluated and contrasted in Mr-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of Ad. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how Ad progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Vehicle Prediction for BUS Identification Output from our route Testing real Time Algorithm

Vehicle Prediction for BUS Identification Output from our Ro...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Misba r. ramya Joel dickson L. Sharmila J. Kavitha K. Udayakumar Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's College of Engineering Chennai Bethlehem Institute of Engineering Karungal Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system.

关键词： Urban areas Software algorithms Memory Prediction algorithms real-time systems Software Maintenance Software reliability Public transportation Testing

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ACTIVE: A deep Network for Sperm and Impurity detection in Microscopic Videos

ACTIVE: A Deep Network for Sperm and Impurity Detection in M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ao Chen Feng-Lei Fan Jinghua Zhang Md Mamunur rahaman rui Li Jiang Tao Tieyong Zeng Marcin Grzegorzek Chen Li Northeastern University China Data and Intelligence R&D Center Zhuzhou CRRC Times Electric Co. Ltd. China Chinese University of Hong Kong China National University of Defense Technology China Chengdu University of Information Technology China University of Luebeck Germany

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities. So far, the detection of sperms and impurities still largely relies on the traditional image processing and detection techniques which only yield limited performance and often require manual intervention in the detection process, thus unfavorably escalating the time cost and injecting the subjective bias into the analysis. Encouraged by the success of deep learning methods in numerous object detection tasks, here we report a deep learning network: ACTIVE based on double Branch Feature Extraction Network (dBFEN) and Cross-conjugate Feature Pyramid Network (CCFPN). dBFEN extracts visual features from tiny objects with a double branch structure, and CCFPN fuses the features extracted by dBFEN to enhance the description of the position and high-level semantic information. Our work is the pioneer of introducing deep learning approaches to the detection of sperms and impurities. Experiments show that the highest AP 50 of the sperm and impurity detection is 91.13% and 59.64%, which lead its competitors by a substantial margin and establish the state-of-the-art results in this problem. Code is available for readers’ free evaluation at https://***/anheqiao-neu/ACTIVE.

关键词： deep learning Visualization Impurities Microscopy Semantics Morphology Object detection Manuals Feature extraction data mining

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Post-Quantum Encryption for Mathematical Model Using Quantum Machine Learning Approach

Post-Quantum Encryption for Mathematical Model Using Quantum...

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Women in Computing (InCoWoCo), International Conference for

作者： J. Kavitha V. Gayatri r. rajani L. Sharmila V.S Harilakshmi M. Misba School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Geethanjali Institute of Science and Technology Andhra Pradesh India Department of Artifical Intelligence and Data Science Holy Cross College (Autonomous) Nagercoil India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Because of the increasing emphasis put on the aviation industry in as a main industry of the global transportation industry, a number of organizations depend on different flight companies to get in touch with parts of the world which may be an effective measure to control the disease from spreading to other areas of the world. However, severe conditions can actually influence airline services in a given area of operation most significantly, flight delays. In this case, service encounters took place between the airline as a large organizational sector and the individual traveling as customers. The working for this issue is to accurately expect such flight delays though enables enhanced performers while in travel in order to be fit for destinations. disability which will be occasioned by the existence of the aforementioned impediment. Quantum machine learning is used to examine the measures that were used in building models to analyze probability of flights. Problems associated with late and early arrival and; delays due to unfavorable weather conditions. In the initial the process of project management, in which at the planned stage of the work, we study the feasibility of integration of Support Vector Machine with Python-based linear regression.

关键词： Support vector machines Accuracy Atmospheric modeling Transportation industry Project management Machine learning Mathematical models delays reliability Airline industry

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