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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jegadeesan, S. Hayvita, R.K. Nishath, A.G. Ramalakshmi, S. Krishnan, R. Santhana Essaki Muthu, A. Velammal College of Engineering And Technology Department of Information Technology Madurai India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

Data synchronization is a critical aspect in the deployment of Digital Twins within Fog-Edge Cloud environments, ensuring consistency and reliability across distributed systems. However, the security and efficiency of synchronization techniques remain significant challenges. The paper presents a novel algorithm named TwinCrypt, which is designed based on the combination of appropriate encryption algorithm called as Homomorphic Encryption and suitable computing mechanism scheme called as Secure Multi-Party Computation. The algorithm is aimed to achieve secure and efficient data synchronization for Digital Twins in Fog-Edge Cloud environments. The proposed algorithm aims to address vulnerabilities associated with data transmission and storage, offering robust protection against unauthorized access and data breaches. The performance effectiveness of TwinCrypt is analysed by conducting suitable simulations and the simulation results are compared with are conducted comparing it with the existing algorithms used for data synchronization viz. RSA Encryption, AES Encryption, and Zero-Knowledge Proofs in similar environments. Simulation metrics such as latency, throughput, and security overhead are employed to assess the performance of TwinCrypt against its counterparts. The results establish that TwinCrypt outperforms existing algorithms in both security and efficiency, showcasing its suitability for real- world deployment scenarios. © 2024 IEEE.

关键词： Fog computing

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Enhancing Health Record Security and Privacy with Blockchain-Based Access Management 1

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2nd EAI International Conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

作者： Prashanth, Mallellu Sai Karnati, Ramesh Velpuru, Muni Sekhar Venkateshwara Reddy, H. Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9783031660443

ISBN: (纸本)9783031660436

Access control is a critical component of medical health record management, ensuring that only authorized individuals can access sensitive patient data. Existing access control schemes suffer from limitations such as inefficiency, lack of patient control, and inadequate protection mechanisms. In this paper, we propose an advanced, efficient and enhancing Health Record Security and Privacy with Blockchain-based Access Management. Our proposed scheme leverages the benefits of blockchain technology to provide a decentralized, secure, and transparent access control framework that gives patients greater control over their data while also providing efficient protection mechanisms. The proposed scheme’s access control mechanisms include authentication, authorization, and auditing, while its protection mechanisms include data encryption, digital signatures, and privacy-preserving techniques. The patient control features of the proposed scheme include consent management and revocation. We evaluate the effectiveness and efficiency of the proposed scheme using performance metrics and benchmarking results. Our results Show that the suggested plan is more effective than the current ones in terms of efficiency, patient control, and protection mechanisms. The proposed scheme represents a significant step forward in the management of medical health records, providing a secure, efficient, and patient-centric access control framework that ensures privacy and confidentiality while also enabling effective healthcare delivery. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Electronic health record

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An Optimal Deep Learning for Cooperative Intelligent Transportation System

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computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.Lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

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Automated Detection of Infection in Diabetic Foot Ulcer Using Pre-trained Fast Convolutional Neural Network with U++net

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SN computer science 2024年第6期5卷 705页

作者： Murthy, S.V.N. Bhargavi, Kovvuri N. Isaac, Samson N.Ganesh, E. Department of Computer Science and Engineering SJC Institute of Technology Information Technology Aditya College of Engineering and Technology Biomedical Engineering Karunya Institute of Technology and Sciences Department of ECE St. Peter’s Institute of Higher Education and Research

A frequent consequence of diabetes and a significant contributor to morbidity and mortality is diabetic foot ulcer (DFU).Early detection and appropriate management of DFUs are essential to prevent complications such as infections and lower extremity amputations. In recent years, medical imaging and machine learning have emerged as promising tools for the automated detection and analysis of DFUs. We gathered a sizable foot imaging dataset, including DFU from multiple patients. This paper proposes a novel preprocessing technique based on the Shades of Gray color constancy algorithm to cope with noise and lighting variations in diabetic foot ulcer (DFU) images captured from different devices. The algorithm aims to enhance image quality, improve illumination normalization, and mitigate the impact of noise, thus providing more reliable and accurate DFU analysis and detection. Using the Diabetic Foot Infection Network with the Adam Optimizer (DFINET-AO), features were retrieved after the dataset had been preprocessed and divided. In order to comprehend the normal and pathological spectrum of diabetes, image data and numerical/text data are separated independently. Foot images of patients with aberrant diabetes coverage are separated from each other and classified using Pre-trained Fast Convolutional Neural Network (PFCNN), which has been trained on the U++network. Classification techniques, like foot ulcer analysis, forecast a etiology. This study's primary goal was to establish a novel method for evaluating the likelihood that diabetes individuals may acquire foot ulcers by imaging analysis of existing foot ulcers. The data was preprocessed and segmented after the researchers gathered a collection of foot photographs and medical information from historical records of diabetes patients. The amount of normal and pathological diabetes was then determined from numerical and textual data by extracting characteristics from the segmented data using DFINET-AO. To detect foot ulc

关键词： Diabetic foot infection network with the adam optimizer Diabetic foot ulcer Pre-trained fast convolutional neural network

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Enhanced Resource Allocation Strategy using Cox Regression for Wormhole Attack Mitigation in Routing Protocols 5

Enhanced Resource Allocation Strategy using Cox Regression f...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Praveenkumar, G.D. Ilango, M. Mohanasathiya, K.S. Naveen, N. Rakkimuthu, P. Kongu Engineering College-Erode Department of Computer Technology -UG India Government Arts and Science College Gudalur Department of Computer Science Tamilnadu The Nilgiris India VET Institute of Arts and Science Co-Education College- Erode Department of Computer Science India Excel Engineering college Department of Information Technology Tamilnadu India

ISBN: (纸本)9798350367171

MANETs, as self-configuring networks lacking a fixed infrastructure, are exceptionally vulnerable to a multitude of security threats. One such severe threat is the Wormhole Attack, where malicious nodes create a virtual link between two distant points in the network, creating a shortcut for communication. This deceptive behavior disrupts the routing protocols and leads to severe consequences, including data manipulation and network partitioning. The Wormhole Attack has had a significant impact on MANETs. This study stimulates COX Regression techniques that are used to calculate the regression coefficient for the association between mobile nodes to perform routing in the MANETs. Based on the calculation of residual energy, the delay and bandwidth are identified as optimal. Additionally, various mitigation techniques are proposed and evaluated to improve the network's security in the face of this sophisticated attack. According to the computation of residual energy, delay and bandwidth is detected as optimal one. Additionally, this study proposes and evaluates different mitigation techniques to enhance the network's security against this sophisticated attack. The proposed methods findings highlight the importance of proactive measures and vigilance in safeguarding MANETs from Wormhole Attacks. This helps to improve the rate of packet delivery with reduced latency and the lifetime of data transmission within the network. © 2024 IEEE.

关键词： Bandwidth

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Detection of communicable and non-communicable diseases using hyperparameter optimization with Bi-LSTM model in pathology images

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International Journal of Intelligent Computing and Cybernetics 2023年第4期16卷 649-664页

作者： Shiva Sumanth Reddy C.Nandini Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangaloreIndia Dayananda Sagar Academy of Technology and Management BangaloreIndia

Purpose:The present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early *** combination of LeNet and bidirectional long short-term memory(Bi-LSTM)model is used for the classification of heamoprotazoan samples into three classes such as theileriosis,babesiosis and ***,BreaKHis dataset image samples are classified into two major classes as malignant and *** hyperparameter optimization is used for selecting the prominent *** main objective of this approach is to overcome the manual identification and classification of samples into different haemoprotozoan diseases in *** traditional laboratory approach of identification is time-consuming and requires human *** proposed methodology will help to identify and classify the heamoprotozoan disease in early stage without much of human ***/methodology/approach:LeNet-based Bi-LSTM model is used for the classification of pathology images into babesiosis,anaplasmosis,theileriosis and breast images classified into malignant or *** optimization-based super pixel clustering algorithm is used for segmentation once the normalization of histopathology images is *** edge information in the normalized images is considered for identifying the irregular shape regions of images,which are structurally ***,it is compared with another segmentationapproach circularHough Transform(CHT).The CHT is used toseparatethe *** Canny edge detection and gaussian filter is used for extracting the edges before sending to ***:The existing methods such as artificial neural network(ANN),convolution neural network(CNN),recurrent neural network(RNN),LSTM and Bi-LSTM model have been compared with the proposed hyperparameter optimization approach with LeNET and *** results obtained by the proposed hyperparameter optimization-Bi-LSTM model showed the accuracy of

关键词： Breast cancer detection Convolution neural network Deep neural network Haemoprotozoan disease detection Hyper-parameter optimization LeNet Bi-LSTM

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An Efficient ODR for computer Night Vision Using Multi-Scale Retinex Network-Aided Enhanced Images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Charles Prabu, V. Pandiaraja, P. Sathiyamoorthi, V. Durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. The You Only Look Once Version 7 (YoloV7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： Deep learning

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Applications of Blockchain technology in Modern Education System - Systematic Review

Applications of Blockchain Technology in Modern Education Sy...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Chinnasamy, P. Ramani, D. Roja Ayyasamy, Ramesh Kumar Jebamani, B. Jency A Dhanasekaran, S. Praveena, V. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Faculty of Information and Communication Technology Kampar Malaysia KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Department of Information Technology TamilNadu Srivilliputtur India Dr.N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350348217

The development of new technologies and also the provision of great connections are becoming increasingly important in all aspects of our everyday situations. This advanced technology also brings a variety of flaws, most importantly security-related problems. Distributed ledger technology, amongst the most prominent technologies in the previous few years, is gaining traction due to its ability to just provide privacy in a variety of domains, including logistics management, transportation, as well as other industries. The educational industry should also make use of the important features blockchain technology offers. This technology is now being considered by schools and universities, particularly universities, as an approach to enhance the teaching-learning process and encourage the participation of all stakeholders including such undergraduates, professors, and even family members. E-transcripts, digitized certificates and credentials, data storage, plus access control will all benefit from it. This detailed research analysis discusses the adaptability of blockchain technology in the education sector. © 2023 IEEE.

关键词： Blockchain

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Advanced Double Input Layered Neural Network for medical diagnosis by using cloud computing technology 1

Advanced Double Input Layered Neural Network for medical dia...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Gowri, S. Jabez, J. Nirmalrani, V. Srinivasulu, Senduru Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnostics by solving pressing problems. It improves diagnostic precision by providing a single, unified platform for the examination of both organized and unstructured medical data. It provides real-time decision assistance and data management by utilizing the scalable, secure, and efficient data processing capabilities made possible by cloud computing. Improved patient care, more effective therapy, and higher-quality healthcare are some areas where Advanced Double Input Layered Neural Network can make a difference. Rapid, precise, and secure data analysis;the administration of multiple data sources;and realtime decision support are a few of the difficulties inherent in medical diagnosis. This research proposes an Advanced Double Input Layered Neural Network (ADILNN) with a double input layered neural community, enabling it to examine prepared and unstructured scientific data. This novel technique improves the community's mastering functionality from various data kinds. By centralizing statistics garage, processing, and analysis on the cloud, computing sources may be extra reliably accessed whilst wanted. The network's diagnostic precision and flexibility are each advanced through way of tool gaining knowledge of strategies (MLM). Because of its adaptability, ADILNN can be utilized in diverse medical fields, which include radiology, pathology, cardiology, and genetics. It permits examine genomic information, making recovery alternatives, and analyzing x-ray photos. The technique has numerous ability makes use of in healthcare, improving prognosis accuracy in diverse settings. Simulation analysis is used to gauge ADILNN's capacity by way of gauging its diagnostic accuracy, processing speed, scalability, and records protection. These research validate ADILNN's potential to improve clinical analysis, streamline facts management, and guarantee healthcare records's safe and effectiv

关键词： Diagnosis

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Edge Computational Integrated Improved Data Model for Adjustment and Restoration Strategy for Smart Grid Environment 1

Edge Computational Integrated Improved Data Model for Adjust...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Premalatha, J. Sarojini Abirami, G. Poornima, D. Sivakumar, Gayathri Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

In the current context, integrated distribution networks of the electricity grid to provide reliability during blackouts. Self-healing systems may keep functioning and reconfigure themselves in response to external breakdowns or attacks by using the efficient strategy of restoration. An intelligent decision-making framework is necessary to ensure the system as a whole can self-reconfigure in the face of external failures and attacks. Incorrect grid modifications can pose a significant threat to the power grid's security and cause delays in network traffic and packet loss. So, in this research, Edge Computational Integrated Improved Data paradigm (EC-IDM) to deal with failures and attacks in real time. By mitigating the consequences of security worries, packet loss, latency, and overcrowded networks, edge computing has greatly increased the smart grid's capacity for efficient operation and communication. Smart Grid's posterior intelligent model (SSPIM) integration enhances the system's functionality and productivity. This research introduces an EC-IDM that can detect blackout scenarios and automatically construct a restoration strategy in real time. In EC-IDM, an enhanced hierarchical coordination mechanism and a local object optimization method are combined to optimize failures and attacks in real time over many time and space axes. Differentiating features of EC-IDM include real-time data analysis, networked simulation, smart decision support, and a wealth of other useful features. The simulation results indicate that the recommended strategy achieves best in security concerns optimizes network congestion delay, and packet loss security, increases efficiency and performance compared to the other standard ways. © 2023 IEEE.

关键词： Smart power grids

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