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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Panchbhai, Ashutosh Vijay Raman, Ramakrishnan Thacker, Chintan Bhupeshbhai Muthumarilakshmi, S. Murugan, S. Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India S.A. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350357769

The available options for Electric Vehicles (EVs) charging need costly installation, restricted accessibility, and no remote management features. These obstacles could deter homeowners from switching to EVs and slow the pace at which EVs are adopted in the home sector. To solve these concerns, this work aims to create a cost-effective, user-friendly EV charging station (EVCS) that facilitates control, remote monitoring, and payment handling. Using IoT technology, it provides a unique solution to the difficulties of deploying an EVCS in a home environment. The EVCS is easy for EV owners to plug in and use, and it can be put in either an indoor or outdoor setting. A multiple-output charging device, a Raspberry Pi controller with Wi-Fi, and a simple mobile app are the core elements of this setup. The charging unit is connected to the Raspberry Pi controller, which then relays charging status and power usage information to the user's mobile device or an online server. © 2023 IEEE.

关键词： Charging station Electric vehicle Internet of things Raspberry Pi and Motor control

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Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

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Intelligent Automation & Soft Computing 2023年第4期36卷 929-955页

作者： Lamiaa A.Abou Elazm Walid El-Shafai Sameh Ibrahim Mohamed G.Egila H.Shawkey Mohamed K.H.Elsaid Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Microelectronics Electronics Research InstituteAlnozhaEgypt Department of Electronics and Electrical Communications Engineering Ain Shams UniversityCairoEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh 11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Biometric security is a growing trend,as it supports the authentication of persons using conﬁdential biometric *** of the transmitted data in multi-media systems are susceptible to attacks,which affect the security of these *** systems provide sufﬁcient protection and privacy for *** recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of *** addition,they have not been studied on different and large biometric *** point that deserves consideration is the hardware implementation of cancellable biometric recognition *** paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic *** rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates,and hence enhance the security and privacy of *** suggested cryptosystem adds signiﬁcant permutation and diffusion to the encrypted biometric *** introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against *** addition,a Field Programmable Gate Array(FPGA)implementation of the pro-posed system is *** obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes,such as Double Random Phase Encoding(DRPE)to reveal superiority,and hence high recogni-tion performance of the proposed cancellable biometric recognition *** obtained results prove that the proposed cryptosystem enhances the security and leads to better efﬁciency of the cancellable biometric recognition system in the presence of different types of attacks.

关键词： Information security cancellable biometric recognition systems cryptanalysis 3D chaotic map encryption FPGA

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Parkinson's Disease Early Detection and Classification based on EMG Signal using Spherical Convolutional Neural Network 8

Parkinson's Disease Early Detection and Classification based...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nandankar, Praful V. Kothiyal, Arnav Kiran Kumar, D. Patil, Anuradha Patil, Harshal Maranan, Ramya Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Bgs Institute of Technology Department of Information Science and Engineering Karnataka B G Nagar571448 India Sharnbasva University Faculty of Engineering and Technology Exclusively for Women Department of Electronics and Communication Engineering Karnataka Kalaburagi585103 India Iilm University School of Computer Science & Engineering Uttar Pradesh Greater Noida201 306 India Simats Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

Parkinson's Disease (PD) is a degenerative neurological condition that impairs motor function. Tremors, stiffness, sluggishness, and trouble balancing are its hallmarks. Degeneration of dopamine-producing neurons in the brain is the disease's primary cause, resulting in progressive motor and non-motor symptoms. But the existing methods have lot of disadvantages such as over fitting, quality control and limited data availability. To overcome the aforementioned problem, A Spherical Convolutional Neural Network with Mantis Search Algorithm (SCNN-MSA) is proposed for accurately classifying PD. In this input image is taken from two datasets such as ADL dataset and REMPARK dataset. The Wavelet Decomposition Threshold Anisotropic Filter is used for the pre-processing (WDTAF) is proposed, for the purpose of removing noises. Following that segmentation are done using Spike-Driven Transformer-based U-Net (SDT-Unet). After that classification and feature extraction are done using Spherical Convolutional Neural Network (SCNN) and optimization are done using Mantis Search Algorithm (MSA) for classifying the Parkinson's Disease. The efficiency of the proposed SCNN-MSA is analyzed using a dataset and attains 99.93% accuracy, 98.76% recall and attains better results compared with the existing methods. This demonstrates the approach's increased efficiency and potential advancement in the industry. The model's accuracy at identifying PD in EMG signals might allow doctors to treat patients considerably faster. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT Enabled Driver Compatible Cost-Effective System for Drowsiness Detection with Optimized Response Time 3rd

IoT Enabled Driver Compatible Cost-Effective System for Drow...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

作者： Sarkar, Argha Kundu, Mayuri Pareek, Prakash Gupta, Nishu Reis, Manuel J. Cabral S. School of Computer Science and Engineering REVA University Karnataka Bangalore560064 India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Electronic Systems Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Gjøvik2815 Norway Engineering Department UTAD/IEETA Vila Real5001-801 Portugal

ISBN: (纸本)9783031289743

The present work researches driver drowsiness, which constitutes a huge problem, and can turn into fatal incidents potentially involving the losing of lives, being it while driving in a highway (car, bus, truck, etc.), in the railway, or any other transportation mean (ship, airplane, etc.). Recent technology has been involved in the study of the fatigue behaviour. The purpose of this work is to identify the in-driver’s drowsiness, contributing to get rid of the accidents, mainly during the night, and to ensure better safety in train and on the highways. A camera is used to capture images from the driver, and face detection is executed in real-time to find out the exact position of driver’s face. Here, the blinking of the driver’s eye is under consideration. The fundamental concept lies in the closing of the driver’s eye for a specific duration of time. If the closing time exceeds that certain duration, drowsiness is detected and an alarm is sounded for awareness. Python and OpenCV (using Haarcascade library) are the fundamental programming platforms for sensing the facial attributes. In this work, a cost-effective system for the driver’s drowsiness detection is aimed. Further, the result also indicates the reduction of response and processing time, which is ideal for real time detection. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Python

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Investigating the Benefits of Adopting Secure Shell (SSH) in Wireless Network Security

Investigating the Benefits of Adopting Secure Shell (SSH) in...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Gavathri, C. Patil, Vivek D. Singh, Dhiraj Kumar Kumar, Ritesh Narmadha, T. Kalra, Hitesh Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

cozy Shell is an encrypted network protocol that has become increasingly popular in network security. Using SSH, organizations can defend their wireless networks from community penetration and malicious interest. SSH affords encryption for statistics transfers, thus stopping records robbery and interception of better-level protocols, which includes statistics confidentiality, integrity, and authentication. Similarly, complex SSH configurations can offer defense in opposition to assaults such as phishing, malware dissemination, and man-in-the-middle attacks. This paper will examine the feasibility and effectiveness of SSH for use in Wi-FiWi-Fi network safety. It will discuss the benefits of SSH for defensive a Wi-FiWi-Fi network and the demanding situations related to its implementation. Additionally, the paper will become aware of the metrics used to degree the overall performance of an SSH-blanketed community and suggest a secure framework for SSH implementation. In the end, this paper will explore the potential for further research within the region of wireless network protection using SSH. © 2023 IEEE.

关键词： Authentication

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Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

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computers, Materials & Continua 2021年第9期68卷 4109-4124页

作者： Kathiravan Srinivasan Lalit Garg Debajit Datta Abdulellah A.Alaboudi N.Z.Jhanjhi Rishav Agarwal Anmol George Thomas School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Faculty of Information and Communication Technology University of MaltaMsida2080MSDMalta School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India College of Computer Science Shaqra UniversityKingdom of Saudi Arabia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500Malaysia

According to various worldwide statistics,most car accidents occur solely due to human *** person driving a car needs to be alert,especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal *** though semiautomated checks,such as speed detecting cameras and speed barriers,are deployed,controlling human errors is an arduous *** key causes of driver’s distraction include drunken driving,conversing with co-passengers,fatigue,and operating gadgets while *** these distractions are accurately predicted,the drivers can be alerted through an alarm ***,this research develops a deep convolutional neural network(deep CNN)models for predicting the reason behind the driver’s *** deep CNN models are trained using numerous images of distracted *** performance of deep CNN models,namely the VGG16,ResNet,and Xception network,is assessed based on the evaluation metrics,such as the precision score,the recall/sensitivity score,the F1 score,and the specificity *** ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers’activities.

关键词： Deep-CNN ResNet Xception VGG16 data classification

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A Novel Transfer Learning Framework for Diagnosing Covid-19 from Chest X-Ray Images with Minimal Computational Resources

SSRN

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SSRN 2023年

作者： Abdelwahed, Ahmed Helmi, Ahmed M. Issa, Mohamed Computer and Systems Department Faculty of Engineering Zagazig University Egypt Computer Engineering Dept. Engineering and Information Technology College Buraydah Private Colleges Saudi Arabia Faculty of Computer Science and Information Technology Egypt−Japan University for Science and Technology Egypt

Deep learning proved effective in detecting COVID-19 from chest X-ray images. Although computing is the current bottleneck in training and deploying models, few studies focus on developing efficient frameworks while many aim only at performance. We propose a novel framework for detecting COVID-19 cases from chest X-ray images efficiently. Our study utilizes four high-performing architectures, including MobileNetV3, RegNetX, NasNetMobile, and EfficientNetV2, which use minimal resources, to extract features from X-ray images and classify COVID-19 cases using various statistical models and machine learning algorithms. We evaluated our proposed framework using metrics such as accuracy, precision, recall, specificity, f1-score, and AUC scores. The false negative rate for each feature extractor and classifier pair is reported, and the average inference time, to measure resource efficiency, is calculated. The results show that EfficientNetV2 was the best-performing feature extractor, while MobileNetV3 had the best average inference time and the lowest false negative rate. The Neural Network classifier performed the best, with state-of-the-art accuracy reaching 99.28%, followed by both RBF-SVM and XGBoost. The proposed framework is more robust, reliable, and resource-efficient for COVID-19 detection compared to existing literature. Our findings indicate the prospects of using the proposed framework in efficient clinical diagnosis. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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Performance Comparison of Object Detection Neural Network Models Based on Accuracy and Latency 2

Performance Comparison of Object Detection Neural Network Mo...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gomathy, B. Sengottaiyan, N. Aarthi, K. Thirumoorthy, P. Tamizharasu, K. Kalyanasundaram, P. Dr.NGP Institute of Technology Dept of Computer Science and Business Systems Tamilnadu Coimbatore India Alliance College of Engineering and Design Alliance University Faculty of Science and Technology Karnataka Anekal562106 India Kongu Engineering College Department of ECE Erode Tamilnadu Perundurai638 060 India Nandha Engineering College Department of CSE Erode Perundurai638 052 India KSR Institute for Engineering and Technology Tiruchengode Department of CSE 637215 India

ISBN: (纸本)9798350327533

Aim: The purpose of this study is to compare the object detection performance of You Only Look Once V4 (YOLOv4) and Single Shot Multibox Detector (SSD) algorithms with respect to metrics like accuracy and latency. Materials and method: Twenty sample photos in all, from different classifications and labels, were gathered. These samples were divided into training dataset (60 %) and test dataset (40 %). To measure the performance, values for accuracy and latency were computed for YOLOv4 and SSD with G power 0.8. Result: The accuracy in prediction of the object in the image was higher in the YOLOv4 algorithm (97 %) compared to the SSD algorithm (84 %). After running a t-test on an independent sample of the two groups under consideration. It is observed that YOLOv4 reported greater preference than the SSD algorithm having p value 0.166 (p>0.05). It was proven that the YOLOv4 reported greater preference than SSD in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks Machine Learning Novel Custom Dataset Object Detection Single Shot Multibox Detector You Only Look Once V4

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Automated Lip Syncing for Chatbots and Virtual Assistants: Enhancing Audio-Visual Interaction for Seamless Communication

Automated Lip Syncing for Chatbots and Virtual Assistants: E...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Rishi Shah Nisarg Chaudhari Tanvi Sheikh Dweepna Garg Parth Goel Department of Computer Engineering Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Changa Anand Gujarat Department of Computer Science & Engineering Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Changa Anand Gujarat India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This paper explores the integration of Wav2Lip lip-sync technology into virtual assistants and chatbots, enhancing their interactivity by turning static images into video with synchronized lip movements. By combining text-to-speech (TTS) and video synthesis, the system aims to create more engaging and human-like digital assistants. Current chatbots and virtual assistants, such as those found on websites and devices like Google Assistant and Alexa, lack personalized visual elements, limiting user connection and engagement. Our proposed solution utilizes the Bark AI library to generate personalized and vivid speech from text, converts images to video using MoviePy, and ensures accurate lip-sync with Wav2Lip technology. The final output is a video where the assistant's lips move in sync with the spoken audio, providing a more realistic and personalized user experience.

关键词： Visualization Limiting Virtual assistants Lips Chatbots Ubiquitous computing Turning User experience Text to speech Synchronization

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High-Level Deep Features and Convolutional Neural Network for Evaluating the Classification Performance of File Cluster Types 1

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Ali, Rabei Raad Dawd, Lahib Nidhal Mostafa, Salama A. Rachmawanto, Eko Hari Jubair, Mohammed Ahmed National University of Science and Technology Thi-Qar Nasiriyah Iraq Computer Techniques Engineering Department Dijlah University College Baghdad Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Department of Informatics Engineering Dian Nuswantoro University 207 Imam Bonjol Street Semarang50131 Indonesia Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna66002 Iraq

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Accurate file recovery of the corrupted or fragmented file is a very important task when missing the file system, since file recovery procedure involves methods, techniques, and tools that analyze and classify the contents of each data cluster including JPG file or non-JPG file. For classification problems, convolutional neural networks and high-deep learning have been applied to be very efficient and effective in file recovery. This study aims at finding a systematic comparison between a support vector machine (SVM) classifier method and an extreme learning machine (ELM) classifier method for classification issues. The classification methods automatically classify the files in a continuous series of data clusters based on three deep features which are rate of change (RoC), byte frequency distribution (BFD), and entropy. The methods are automatically assigning a class label of JEG file or non-JPG file for fragmented data clusters. The RABEI-2017 dataset was used for evaluating the performance of this study. The results demonstrate that the SVM method outperforms the ELM method with a high accuracy rate to classify fragmented JPG files. The accuracy of classification of the SVM method is 90.15% and of the ELM method is 96.21%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Support vector machines

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