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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Nanthini, N. Shanmuga Prabha, P. Vidhyasri, R. Anand, Vruthi V. Sathyabama Institute of Science and Technology Dept. Computer Science and Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350365337

This study examines the design of fault tolerance mechanisms in cloud computing infrastructure, with particular emphasis on Amazon Web Services (A WS). Fault tolerance is critical to assure the continuous availability and reliability of cloud-based systems. Auto Scaling, an A WS feature, provides a dynamic way to handle demand changes while keeping the system flexible. This article presents a fault tracking management approach using Auto Scaling in A WS based on current research and real-world concerns. The importance of monitoring fault tolerance policies is emphasized, along with practical implementation methods. Furthermore, a case study demonstrating the success of this technique in real-world situations is presented, highlighting its importance for solving current challenges. © 2024 IEEE.

关键词： Websites

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Unmasking Frame Duplication: Comprehensive Approach to Detection in Digital Video Using Machine Learning Algorithms and Signal Processing Techniques

Unmasking Frame Duplication: Comprehensive Approach to Detec...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Praddipraj, N.N. Praveen Kumar, B. Sreeji, S. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-processing, enhancing video quality through temporal filtering and noise reduction. Next, feature extraction captures statistical and structural information from each frame. Deep neural networks and other machine learning methods are used to classify frames as original or duplicated using these features. To enhance detection accuracy, signal processing methods like block-based and key point-based approaches are applied. Block-based methods divide frames into segments for region comparison, while key point-based methods identify and match key points between frames. Our method's effectiveness and robustness are demonstrated through experiments on various digital videos. This combination of machine learning and signal processing offers a comprehensive solution to uncover frame duplication, ensuring video content's integrity and authenticity. © 2024 IEEE.

关键词： Learning algorithms

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Dynamic Charging on the Go: Optimizing Mobile Charging Stations for Electric Vehicle Infrastructure 11

Dynamic Charging on the Go: Optimizing Mobile Charging Stati...

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11th International Conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2025

作者： Jain, Suhas Gupta, Arobinda Dept. of Computer Science & Engineering Indian Institute of Technology WB Kharagpur721302 India

ISBN: (纸本)9789897587450

With the growing adoption of electric vehicles (EVs) as an eco-friendly and sustainable means of transportation, availability of adequate EV charging infrastructure has become very important. While fixed charging stations are the primary means for public charging, they can be effectively augmented by mobile charging stations. In this paper, we address the problem of planning and operating a mobile charging station fleet by an operator. We propose an algorithm for the operator to use for route planning of the MCSs and for scheduling charging requests of EVs to them to try to maximize the number of charging requests served. Detailed simulation results are presented in different realistic scenarios to show that the proposed algorithm works well. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Electric vehicles

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Sign Language Recognition by using Spatio-Temporal Features 5

Sign Language Recognition by using Spatio-Temporal Features

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Renjith, S. Rashmi, Manazhy Sumi Suresh, M.S. Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri690525 India Dept. of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amritapuri690525 India Dept. of Computer Science and Engineering University at Bufalo New York City United States

The recognition of sign language has significant importance as an application that addresses the communication challenges faced by those within the Deaf and hard-of-hearing populations. This study introduces a methodology for the identification of sign language using spatio-temporal attributes, specifically concentrating on two distinct sign language datasets: the CSL (Chinese Sign Language) alphabet and the ArSL (Arabic Sign Language) alphabet. The study's objective is to create a powerful recognition system capable of reliably identifying sign language motions. A combination of spatio-temporal characteristics collected from video sequences of sign language motions is used in the suggested technique. These characteristics capture the spatial layout as well as the temporal dynamics of the motions, allowing the model to recognise signals with more precision. Experiments were carried out on the CSL and ArSL alphabet datasets to validate the method's efficacy. The findings of the trials are encouraging. The suggested system recognized CSL alphabet signs with an accuracy of 90.87% and ArSL alphabet signs with an accuracy of 89.46%. These high accuracy rates show the power of the spatiotemporal feature-based method for sign language identification. The system's success on two different sign languages implies that it is adaptable and useful in a wide variety of sign language applications. This study contributes to the evolution of assistive technology, making sign language recognition more accessible and efficient for those who communicate using sign language. Furthermore, the findings pave the way for additional research into spatiotemporal feature-based approaches in sign language recognition, with potential applications in real-world settings such as sign language interpretation and communication support. © 2024 The Authors. Published by Elsevier B.V.

关键词： Audition

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A User-manageable Credential System based on Blockchain

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Transactions of the Korean Institute of Electrical Engineers 2022年第1期71卷 210-217页

作者： Hong, Seongho Kim, Heeyoul Dept. of Computer Science Engineering Kyonggi University Korea Republic of

Credentials are one of the most important things to prove oneself in modern society. As the world goes digital, credentials change its form from paper to digital. However, digital data are easy to be forged and to be duplicated. To make digital data secure, cryptographic digital signature is the key technology ensuring its authenticity and integrity. And it is an inevitable choice for secure credential management systems. Recently blockchain has been raised as another key technology to guarantee data transparency. It is a decentralized and public ledger of transactions that anyone can participate and execute programs called smart contract. In this paper, we propose a credential management system based on Ethereum blockchain. This system provides users with a means both to manage their credentials by themselves and to prove their credentials without the issuer. We implemented its prototype on Ethereum, and analyzed the proposed system compared to previous systems. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Smart contract

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Smart ANPR: A Checkpoint-Based Toll and Parking Management System Using Hybrid OCR and Blockchain 3

Smart ANPR: A Checkpoint-Based Toll and Parking Management S...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Samarth, M.N. Gopisetti, Sampriti Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Automated Number Plate Recognition is one of the most important applications of computer vision, employed in the enforcement of traffic law, vehicular management, security, and even at toll collection. This paper proposed an advanced system for ANPR that can move away from the traditional booth and establish a checkpoint. The strategically located cameras with checkpointing will automatically capture the plates of passing vehicles, permitting road tax and parking fees to be deducted without making contact. A hybrid approach that combines the capabilities of OpenCV, Tesseract OCR, and deep-learningbased OCR models helps tackle such challenges as stable images from live video feeds and highly accurate text recognition. To this end, since the system integrates high-resolution frame stabilization using OpenCV to ensure crystal clear captures, it further employs the use of YOLOv8 to boost the precision of real-time detection. The blockchain technology is further implemented to ensure that transactions are tamper proof, therefore ensuring secure and transparent billing systems. This makes the ANPR solution deployable on edge devices, which opens real-time processing capabilities for high-speed and high-volume environments, thereby offering a scalable and efficient approach for intelligent transportation and smart city applications. © 2025 IEEE.

关键词： Blockchain

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SYSTEMATIZATION OF TRUSTED I/O SOLUTIONS FOR ISOLATED EXECUTION ENVIRONMENTS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2022年第2期84卷 3-14页

作者： Stancu, Florin-Alexandru Mircea, Alexandru-Alin Rughiniș, Răzvan Chiroiu, Mihai Dept. of Computer Science and Engineering University POLITEHNICA of Bucharest Romania

Nowadays, operating systems have become increasingly complex, with codebases of millions of lines inadvertently containing security bugs. Modern CPUs promise a solution to this with Trusted Execution technologies (e.g., ARM TrustZone, Intel SGX) which can be employed to isolate critical applications from unauthorized access even from more privileged ac-tors (e.g., system kernel or hypervisor). However, as the traditional way to interface with I/O peripherals is by using drivers part an now-considered untrustworthy OS, separate solutions must be devised to secure user input or output to trusted environments by ensuring an authenticated pathway. Our paper provides a systematization of the available trusted I/O path solutions by reviewing the scientific works in this field and extracting common attributes and differences. We categorize them by supported peripheral type and trusted platform, comparing the usability, security and complexity of their implementations. Finally, we discuss the results and give future directions for improvement. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Virtualization

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Advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy 3

Advances in Deep Learning for Tuberculosis Detection in Ches...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jayakumar, K. Muthupandi, G. Dhayanidhi, R. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model. © 2025 IEEE.

关键词： Disease control

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Contrast-Enhanced Cardiac Magnetic Resonance Image Based on Enhanced Deep Residual Networks Modified 5

Contrast-Enhanced Cardiac Magnetic Resonance Image Based on ...

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5th IEEE International Conference on Communications, Information, Electronic and Energy systems, CIEES 2024

作者： Ulkareem Naser, Maysaa Abd Hassin Al-Asadi, Abbas H. University of Basrah Dept. of Computer Information Systems Basrah Iraq Kufa University Department of Computer Science Kufa Iraq

ISBN: (纸本)9798350352863

Contrast is very important in analyzing medical images, and some contrast enhances the methods that could be very helpful when the image contrast is not good. The enhancement of Deep Super-Resolution (EDSR) is the deep learning (DL) technique that improves the quality and resolution of low-resolution images. Within this study, a transfer learning method grounded in a convolutional neural network (EDSR-Modified) is leveraged for enhancing contrast in cardiac magnetic resonance imaging (CMR). The study conducts experiments at the S7 ISP and ACDC datasets, utilizing the S7 ISP dataset for training and the ACDC dataset for testing the assessment enhancement. This study aims to combine EDSR-Modified with transfer learning to improve CMR image quality, which is used to diagnose cardiovascular diseases. Qualitative comparisons are performed using mean squared error (MSE), peak signal-to-noise ratio (PSNR), and SSIM evaluation metrics. It is observed that the proposed technique outperforms the other techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification and Prediction of Stroke Disease Using Machine Learning Algorithms 7

Classification and Prediction of Stroke Disease Using Machin...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Lahari, Podutur Kumar, Akarsh Nandini, D. Usha Praveena, M. D. Anto Selvan, Mercy Paul Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Using text mining tools and machine learning algorithms, the paper presents a prototype for classifying strokes. The significance of machine learning extends across various domains, including surveillance, medicine, and data management, where appropriately trained algorithms prove invaluable. The study incorporates data mining techniques, offering a comprehensive understanding of information tracking from both semantic and syntactic perspectives. By analyzing medical case sheets, the proposed approach extracts symptoms and trains the system based on that information. A total of 507 case sheets are collected at "Sugam Multispecialty Hospital"in "Kumbakonam", Tamil Nadu, India, during the data collection phase. A tagging and maximum entropy approach was used to mine these case sheets. Stroke classification relies heavily on the suggested stemmer for distinguishing between common and special features. To handle the data that has been processed, support vector machines, XG Boost algorithms, SGD algorithms, decision trees, and random forests were used as machine learning algorithms. Random Forest demonstrated the highest accuracy among stroke classification algorithms evaluated. This integration of text mining and machine learning offers a promising framework for enhancing stroke classification based on patients' symptoms extracted from medical case sheets. The synergy between these technologies provides a robust methodology that can contribute significantly to improving the efficiency and accuracy of stroke diagnosis and classification in a medical. © 2024 IEEE.

关键词： Semantics

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