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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Anil Pandurang Shinde Ramandeep Singh Renuka Shantappa Anami Noor Mohammed R. Selvameena Ashwini Jotiram Patil Department of Computer Technology Bharati Vidyapeeth's Jawaharlal Nehru Institute of Technology (Poly) Pune Maharashtra Department of Computer Science Guru Nanak Khalsa College Daroli Kalan Jalandhar Jalandhar Punjab Department of Computer Technology Bharati Vidyapeeth's Jawaharlal Nehru Institute of Technology (Poly) Pune Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun Uttrakhand Department of Computer Science and Engineering Dr.M.G.R Educational and Research Institute Chennai TamilNadu India

An persistent eye infection called glaucoma causes irreversible vision loss. A crucial problem in the clinical field is glaucoma identification. There have been very few studies designed to detect glaucoma in its early stages. However, using current techniques to carry out glaucoma infection detection was ineffective. Moreover, the conventional glaucoma infection diagnosis method required additional time. Glaucoma identification in various fundus images is a challenging task that necessitates mastery and extensive training. Glaucoma is an ocular neuropathy characterized by mild retinal ganglion cell degeneration. There are several different types of glaucoma, each having a unique a etiology and set of pathogenic factors, but they all have alterations in the structure and function of the optic nerve that are essentially universal. Convolution organisations, a type of deep learning computation, have swiftly emerged as a method for making decisions while analyzing clinical images. A chronic eye condition called glaucoma causes irreversible vision loss. In this section, we discuss two state-of-the-art glaucoma detection methods in the light of deep learning. The glaucoma dataset is used in this deep learning computation proposed for glaucoma photo analysis. This finding contrasts with convolutional brain network grouping techniques in the context of deep learning. Each rating indicates that the newly proposed worldview is more favorable than the one currently in use.

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Blockchain and Deep Learning Development of Smart Charging of Electric Vehicles to Meet the Demand Side Management

Blockchain and Deep Learning Development of Smart Charging o...

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： C. P. Shirley S.V. Evangelin Sonia V. Sathya N. Manikandan M.K. Vidhyalakshmi Ch. Venkata Krishna Reddy Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Software Engineering Periyaar Maniammai Institute of Science and Technology Thanjavur Tamil Nadu India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Krishnankoil Srivilliputhur Tamil Nadu India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

The use of Electrical Vehicles (EVs) is increasing rapidly nowadays. This results in the efficient utilization of electrical energy. This leads to making the grid more economically reliable. Thus the smart charging of electric vehicles is done through blockchain technology with deep learning to meet the demand side management. This helps in the determination of the charging system with higher accuracy the systems. This indudes real-time implementation with higher precision control. This helps to obtain the exact optimal solution for dynamic functions. The deep earning helps to obtain real-time implementation in the system. This also helps to obtain the optimum charging cost consumed at a specific period. The charging station energy management system is implemented based on blockchain technology. This helps to meet the demand side management in the system. They are accompanied by centralized and decentralized energy models in the architecture. This also helps in the reduction of peer-to-peer transactions proceeding from the electricity grid to electric vehicles.

关键词： Deep learning Demand side management Electric vehicles Real-time systems Power grids Blockchains Security

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Integrating Multi-Dimensional Data for Advanced Domain Name Classification

Integrating Multi-Dimensional Data for Advanced Domain Name ...

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computer Communication and Artificial Intelligence (CCAI), International Conference on

作者： Xudong Liu Rui Xu Jing Ya Jing Zhao Wangjun Yao National Computer System Engineering Research Institute of China Beijing China School of Intelligence Science and Technology Peking University Beijing China Northking Information Technology Co. Ltd. Beijing China

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770

The rapid evolution of the digital world has elevated the task of domain name categorization to a critical challenge, essential for content filtering, fraud prevention, and cybersecurity. This paper introduces the “Dimensional Fusion Classifier” (DFC), a novel approach that integrates multi-dimensional data analysis with state-of-the-art deep learning techniques to enhance domain name categorization. Central to the DFC methodology is the construction of a rich dataset encompassing a wide range of domain attributes, including domain names, subdomains, titles, content, and Whois information. This multifaceted dataset lays the groundwork for our advanced classification model, which synergistically combines the contextual processing capabilities of BGE, the sequential data proficiency of BiLSTM, and the targeted precision of an Attention mechanism. This fusion enables a comprehensive interpretation and sophisticated classification of domain names, based on their complex attributes. Extensive experimental validation of DFC against established baseline models demonstrates its superiority across key performance metrics like accuracy, precision, recall, and F1 score. Our results reveal DFC's enhanced ability to interpret and classify intricate domain name data, outperforming traditional models. By fusing multi-dimensional data analysis with advanced deep learning algorithms, DFC sets a new standard in domain name categorization. This research marks a significant contribution to the field, offering an effective solution for domain name categorization and laying the groundwork for future advancements in web content analysis and internet security.

关键词： Deep learning Technological innovation Data analysis Attention mechanisms Accuracy Prevention and mitigation Data models

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Fisherman Location Store, Updating, and Alert System using Cloud AWS with Satellite

Fisherman Location Store, Updating, and Alert System using C...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： Kiruthiga Devi M Parthasarathy R Unnamalai K S. Perinba Athiban K. Manikandan N. Ramya Department of Information Technology Dr. MGR Educational and Research Institute Chennai India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science Anna Adarsh College for Women Artificial Intelligence & Robotics VIT Chennai Department of Biomedical Engineering Sona College of Technology Salem Department of CSE Sri Sai Ram Engineering College Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

he successful demonstration of their location tracking alert system for fishermen, Satellites will employ Amazon Web Services (AWS). Using cloud-based computing technology, the activities and whereabouts of fishermen are monitored in real time through this system. The ACS sat will send a vast amount of information in short always-shrinking cycle & store data very efficiently, only the location data for live updates on every moves by the fishermen. Here using this project we are stressing on the need to analyze data and push alerts according protocols, for timely raise alarm which definitely saves Fishermen life. The aim is to develop a Real Time Location Tracking and Monitoring GIS Application, which can trace the fishermen real-time location in marine activities. Analyze location information with the best edge of the sword such as cloud computing and AI /ML. The GIS App will have a simple user interface along with extensive monitoring abilities. Building a GIS App to aid marine safety by improving the tracking and monitoring of fishing operations using cloud technology. In the end, with these benchmarks in tow this could mark a step change and benchmark for maritime operations; as we finally get, safety & productivity combined.

关键词： Cloud computing Satellites Web services Sea measurements Benchmark testing Real-time systems Security Artificial intelligence Monitoring Meteorology

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Fluctuation and Dissipation in Quantum Nonlinear Optics

Fluctuation and Dissipation in Quantum Nonlinear Optics

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Conference on Lasers and Electro-Optics (CLEO)

作者： Shiekh Zia Uddin Nicholas Rivera Devin Seyler Yannick Salamin Jamison Sloan Shutao Xu Michelle Y. Sander Marin Soljačić Department of Physics Massachusetts Institute of Technology Cambridge MA USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA Department of Physics Harvard University Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering BU Photonics Center Boston University Boston MA USA

We present a new fluctuation-dissipation relation that shows how quantum noise transforms under engineered nonlinear dissipation found in many important nonlinear optical devices and validate the theory with experimen... 详细信息

ISBN: (纸本)9798350369311

关键词： Laser theory Fluctuations Laser noise Quantum mechanics Transforms Lasers and electrooptics Nonlinear optical devices Electro-optical waveguides Interferometers

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Optimizing Cost Efficiency in Cloud-Based Automation with DevOps Techniques

Optimizing Cost Efficiency in Cloud-Based Automation with De...

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Automation and Computation (AUTOCOM), International Conference on

作者： Avni Garg Tribhuvan Singh Asha Rajiv Dhamodaran S Girija Shankar Sahoo Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India School of Sciences Physics & ElectronicsJAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cloud automation has created a trend offering several advantages to businesses, including scalability and cost-effectiveness. Although deploying such automation in a cloud setting is fundamental, optimizing its cost efficiency proves challenging, particularly with DEVOPS techniques. DEVOPS integrates development and operations to promote collaboration and automate different parts of the software delivery process. It is also possible to elaborate the DEVOPS techniques by implementing them through cloud-based automation, which can greatly benefit both efficiency and cost. A key area for cost optimization is infrastructure management. By using DEVOPS techniques for the provisioning and deployment of resources, organizations can automate what have previously been tedious and manual processes, which can lead to lower infrastructure costs and more efficiency. Utilization of resources can be enhanced through DEVOPS techniques. Auto-scaling automatically adapts resources based on demand, while load balancing distributes workloads across resources to improve performance and costs. However, one area in which DEVOPS can dramatically enhance companies' cost efficiency is continuous monitoring and optimizing. Automated monitoring tools fire alerts notifying businesses about the most precise area where work is being done inefficiently in real-time, helping companies to save money.

关键词： Cloud computing DevOps Automation Costs Collaboration Manuals Companies Real-time systems Resource management Monitoring

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Vehicle Re-Identication Model Based on Optimized DenseNet121 with Joint Loss

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computers, Materials & Continua 2021年第6期67卷 3933-3948页

作者： Xiaorui Zhang Xuan Chen Wei Sun Xiaozheng He Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Department of Civil and Environmental Engineering Rensselaer Polytechnic InstituteTroyNY12180USA

With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public *** Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar *** existing methods focus on local attributes by marking local ***,these methods require additional annotations,resulting in complex algorithms and insufferable computation *** cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint *** model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in *** the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ***,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training *** results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,***,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.

关键词： Vehicle re-identication densenet joint loss focal loss SE block

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Silicon Solar Cell-Integrated Flexible Retinal Prosthesis for Artificial Vision

Silicon Solar Cell-Integrated Flexible Retinal Prosthesis fo...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Chaesung Kim Seung-Han Chung Yong-Jin Kim Hyeonhee Roh Seung-Ki Lee Yong-Kweon Kim Hyung-Min Lee Jae-Hyoung Park Min Gu Kang Maesoon Im School of Electrical Engineering Korea University Seoul South Korea Brain Science Institute Korea Institute of Science and Technology (KIST) Seoul South Korea Department of Electrical and Computer Engineering Seoul National University Seoul South Korea Photovoltaics Research Department Korea Institute of Energy Research (KIER) Daejeon South Korea Department of Electronics and Electrical Engineering Dankook University Yongin South Korea Division of Bio-Medical Science and Technology University of Science and Technology (UST) Seoul South Korea KHU-KIST Department of Converging Science and Technology Kyung Hee University Seoul South Korea

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

To date, no flexible silicon solar cell capable of directly converting visible light into electrical current has been developed for use in retinal prosthetic devices. In this study, we successfully fabricated silicon solar cells into small, pixelated structures and demonstrated their potential for retinal prostheses. To minimize surgical incisions during implantation and enhance device stability on the retina, we incorporated a flexible polymer substrate. Our results underscore the promise of silicon solar cells in biomedical engineering, especially for retinal prosthesis applications

关键词： Wet etching Shape Photovoltaic cells Surgery Rats Retina Silicon Stability analysis Substrates Prosthetics

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Hybrid Deep Learning Approaches for Data Security in Cloud Environments

Hybrid Deep Learning Approaches for Data Security in Cloud E...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： S. Rekha Manish Sharma Sathish Kannan Vijay Jagdish Upadhye Harshal Patil S. Rajkumar Department of Computer Science PSG College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Microbiology Research and Development Cell Parul Institute of Applied Sciences Parul University Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Faculty of Manufacturing Institute of Technology Hawassa University Ethiopia

More recent studies have concentrated on methods to improve the security and privacy of cloud operations for the benefit of user data. For example, in order to safeguard sensitive data kept by a cloud-deployed procedure, it is important to guarantee secure transmission of sensitive data across multiple stakeholders. researchers have investigated deeply into the subject of cloud security concerns, which include data breaches, denial of service, data loss, service rejection, and threats from malicious users. These issues typically stem from problems like multi-tenancy, lack of control over data, and trust issues. Cloud providers are primarily responsible for ensuring the security of the cloud platform. This study proposes a CNN-BiLSTM-AM hybrid model that combines Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and Attention Mechanism (AM). In order to determine how well our proposed hybrid model worked, this study tested it extensively against the traditional CNN and Long Short-Term Memory (LSTM) techniques. An established standard for evaluating intrusion detection systems, the CICIDS 2017 dataset was used in the study. The experimental results demonstrated that the proposed hybrid model outperformed the competition. The proposed hybrid model delivers the highest performance measure of accuracy (99.2%), detection rate (98.807 % ), and precision (99.59%).

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Named Entity Recognition using Knowledge Graph Embeddings and DistilBERT 21

Named Entity Recognition using Knowledge Graph Embeddings an...

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Proceedings of the 2021 5th International Conference on Natural Language Processing and Information Retrieval

作者： Shreyansh Mehta Mansi Radke Sagar Sunkle Computer Science and Engineering Visvesvsraya National Institute of Technology India and Developer Crowdstrike India Computer Science and Engineering Visvesvsraya National Institute of Technology India Research Lab Tata Consultancy Services India

ISBN: (纸本)9781450387354

Named Entity Recognition (NER) is a Natural Language Processing (NLP) task of identifying entities from a natural language text and classifies them into categories like Person, Location, Organization etc. Pre-trained neural language models (PNLM) based on transformers are state-of-the-art in many NLP task including NER. Analysis of output of DistilBERT, a popular PNLM, reveals that mis-classifications occur when a non-entity word is at a place contextually suitable for an entity. The paper is based on the hypothesis that the performance of a PNLM can be improved by combining it with Knowledge Graph Embeddings (KGE). We show that fine-tuning of DistilBERT along with NumberBatch KGE gives performance improvement over various Open-domain as well as Biomedical-domain datasets.

关键词： Contextualized Word Representation Knowledge Graph Embeddings Named Entity Recognition Neural Networks

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