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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Patil, Nitin Jagannath Sharma, Avinash Prasad, K.D.V. Yogi, Kottala Sri Dankan Gowda, V. Department of Instrumentation Engineering D. N. Patel College of Engineering Shahada Maharashtra Untavad India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Pune India Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India

ISBN: (纸本)9789819751457

This study explores the capabilities of integrating the Internet of Things (IoT) with machine learning to monitor health in real time. Using IoT-powered devices like heart rate monitors, glucose meters, and blood pressure devices, health metrics were consistently gathered and sent to a cloud-based system. After refining the data for better quality, machine learning techniques were used to create health prediction models. The findings showed significant precision, especially when using neural networks, in predicting health-related events. This combined system can identify and notify healthcare experts about potential health concerns immediately. However, the promise of ongoing health tracking and early detection comes with concerns regarding data protection, data handling, and model reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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A Deep Neural Network for Image Classification Using Mixed Analog and Digital Infrastructure 3rd

A Deep Neural Network for Image Classification Using Mixed A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Kala, R. Punitha, M. Poomani Alias Banupriya, P.G. Veerasamy, B. Bharathi, B. Alzubi, Jafar Ahmad Abed Associate professor Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Information Technology Sethu Institute of Technology Kariapatti Puloor India Department of Computer Science and Engineering Karpagam Institute of Technology Tamil Nadu Coimbatore641032 India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Information Technology Coimbatore Institute of Engineering and Technology Tamil Nadu Coimbatore641109 India School of Engineering Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789819986453

Recently, Deep Learning (DL) methods are a well-known solution for image and object detection in computer vision applications. The software-based implementation of DL algorithms requires huge resources and consumes more power. To overcome these challenges, a hardware implementation of Deep Neural Network (DNN) using the integrated analog–digital architecture was proposed in this article. The Very Large-Scale Integration (VLSI)-based DNN implementation utilizes Pulse-Width Modulation (PWM), and analog–digital circuits. The integration of analog and digital architecture integrates the benefits of both circuits, which reduces power consumption and enhances processing speed and classification accuracy. The fabrication of the VLSI chip includes the DNN neuron circuit, PWM circuit, digital adders-subtracters, and SRAM memory. The experimental result verifies that the presented DNN VLSI architecture reduces the power consumption of the neural system, and enhances the image classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Shielding NLP Systems: An In-depth Survey on Advanced AI Techniques for Adversarial Attack Detection in Cyber Security

Shielding NLP Systems: An In-depth Survey on Advanced AI Tec...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： T. Sai Lalith Prasad K. B. Manikandan J. Vinoj Research Scholar Department of Computer Science and Engineering Vignan's Foundation for Science and Technology & Research (Deemed to be University) Guntur Andhra Pradesh India Assistant Professor Department of Artificial Intelligence and Data Science Vignan Institute of Technology and Science Telangana India Assistant Professor Department of CSE Vignan's Foundation for Science Technology and Research (Deemed to be University) Guntur Andhra Pradesh India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research investigates the challenges posed by adversarial perturbations to the robustness and reliability of Natural Language Processing (NLP) models. The study focuses on three key paradigms for enhancing the resilience of NLP systems: meta-learning, reinforcement learning, and self-supervised learning. A comprehensive evaluation of these strategies is conducted, analyzing their strengths and weaknesses in mitigating adversarial attacks. The study provides insights into the most promising approaches for enhancing the robustness of NLP models and offers directions for future research in this critical area. By advancing the resilience of NLP systems, this research contributes to the development of more robust and trustworthy AI applications across various domains, including healthcare, finance, and security.

关键词： Training Surveys Renewable energy sources Scalability Self-supervised learning Natural language processing Robustness Security Artificial intelligence Resilience

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Optimizing Resource Allocation in Healthcare Facilities Through IoT and Machine Learning Predictive Analysis 3rd

Optimizing Resource Allocation in Healthcare Facilities Thro...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Dankan Gowda, V. Sharma, Avinash Mukherjee, Saptarshi Rahman, Mirzanur Barshe, Shweta B. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Makaut Maulana Abul Kalam Azad University of Technology West Bengal Nadia Kolkata India Department of Information Technology Gauhati University Assam Guwahati India Department of Computer Engineering Bharati Vidyapeeth College of Engineering Mumbai Navi India

ISBN: (纸本)9789819751457

In the evolving landscape of health care, optimal resource allocation emerges as a critical determinant of patient outcomes and facility efficiency. This study explores the integration of Internet of Things (IoT) devices and machine learning (ML) predictive analysis in healthcare facilities to optimize resource allocation. With the omnipresence of IoT devices and the analytic prowess of ML, our research aims to develop a model that can predict real-time resource demands, ranging from patient care equipment to manpower distribution. Drawing data from wearable sensors, embedded devices, and facility infrastructure, the proposed ML model demonstrates significant improvement in predicting short-term resource needs. Preliminary results indicate a reduction in resource wastage, enhanced patient care, and increased operational efficiency in healthcare facilities that incorporated the proposed system. However, the research also acknowledges potential limitations, including data security concerns and system scalability. The findings presented highlight the transformative potential of IoT and ML in revolutionizing resource management in health care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Embedded devices Internet of Things (IoT) Machine learning (ML) Operational efficiency Wearable sensors

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Improving Speech Privacy with Fuzzy Logic-Based Encryption 2

Improving Speech Privacy with Fuzzy Logic-Based Encryption

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2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Yogeesh, N. Girija, D.K. William, P. Rashmi, M. Government First Grade College Assistant Mathematics Karnataka Tumkur572102 India Government First Grade College Assistant Computer Science Karnataka Madhugiri India Savitribai Phule Pune University Sanjivani College of Engineering Department of Information Technology Pune India GFGC T. Dasarahalli Faculty in Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350381979

Speech privacy is a critical concern in today's interconnected world, where speech processing technologies are becoming increasingly prevalent. Traditional encryption techniques may not be sufficient to protect sensitive information in speech data due to the unique characteristics of speech signals. This research paper explores the application of fuzzy logic-based encryption to enhance speech privacy. Fuzzy logic offers a flexible and adaptive approach to encryption, accommodating the inherent uncertainty and variability present in speech signals. The study investigates the effectiveness of fuzzy logic-based encryption in safeguarding speech data and compares it with conventional encryption methods. Experimental results demonstrate the superiority of fuzzy logic-based encryption in preserving speech privacy, highlighting its potential for securing speech processing systems and applications. © 2023 IEEE.

关键词： Cryptography

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Enhancing Digital Forensics: Machine Learning Techniques for Social Media Investigation

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Procedia computer science 2025年 258卷 2290-2301页

作者： Dhairya Vyas Milind Shah Ankita Kothari Jyoti Golakia Vruti Parikh Research Scholar The Maharaja Sayajirao University of Baroda Vadodara Gujarat India Assistant Professor Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (SVIT) Vasad Gujarat India

Due to the increasing growth of social media platforms, improved methods are needed to extract, examine, and interpret digital evidence. Due to the vastness and ever-changing nature of the data that is collected via social media platforms, traditional forensic procedures usually meet challenges. Specifically created for the purpose of conducting forensic analysis of material obtained from social media platforms, the purpose of this research is to build and analyze machine learning models and algorithms. Gathering information from various platforms, such as Kaggle, is the first step in the process. The succeeding processes involve data cleaning and categorization, which includes activities such as text normalization, entity recognition, and sentiment analysis. These steps come after the first data gathering has been completed. The extraction of relevant information, such as user behaviors, temporal trends, and network connections, is accomplished through the use of feature engineering. A significant portion of the investigation is centered on the application of supervised machine learning techniques for the purpose of detecting users and detecting offensive language. In order to determine whether or not the proposed approach is effective, comprehensive experiments are conducted utilizing datasets derived from social media activities. The findings indicate that there have been considerable improvements in the capabilities of forensic investigations, including enhanced accuracy, scalability, and automation. In this research we have investigated social media’s data and detected offensive language using various machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree, Random Forest, and Extra Tree. The support vector machine (SVM) achieved 86% accuracy where as KNN achieved only 77% accuracy which was far lower than other learning algorithms. However, the Decision Tree, Random Forest, and Extra Tree models achieved 99% a

关键词： Digital Forensics Machine Learning Social Media Investigation Cyber Crime Investigation Data Analysis

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Optimization of machining parameters in EDM using Taguchi based grey relational analysis

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Materials Today: Proceedings 2022年 82卷 43-46页

作者： Ganapathy S. Palanivendhan M. Susitra K. Balasubramanian P. Department of Mechanical Engineering Sri Sai Ram Engineering College Chennai 600044 India Centre for Automotive Materials Assistant Professor Department of Automobile Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu 603203 India Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai 600044 India Department of Mechanical Engineering A.V.C College of Engineering Mayiladuthurai 609305 India

The present investigation optimizes process parameters such tool size, peak current, pulse on time, and dielectric pressure in the EDM process. The material removal rate affects the machining operation's quality. The goal of the present work is to maximize material removal rate using Taguchi technique-based optimization of machining parameters. On an EDM, the experimental study for machining operations was carried out. For EN-31, machining operations are carried out using an orthogonal array with L9. The ANOVA technique was used to evaluate the experimental results. Using a technique called as grey relational analysis, the optimum conditions for material removal rate are determined. The optimum approach for a set of linked parameters is evaluated using the grey relation grade. © 2023 Elsevier Ltd. All rights reserved.

关键词： Anova EDM EN-31 GRA MRR

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Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture

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computers and Security 2022年 116卷

作者： M .P . , Ramkumar Reddy, P.V. Bhaskar Thirukrishna, J.T. Vidyadhari, Ch. Assistant Professor Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India Professor School of Computer Science and Engineering REVA University Karnataka Bengalore India Associate Professor Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bangalore560082 India Assistant Professor Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

With the rapid expansion of Internet services and increasing intrusion issues, conventional intrusion detection techniques cannot work well with several difficult intrusions. Though several intrusion detection methods have been introduced in the past years, developing an Intrusion Detection Systems (IDS) with prevailing intrusion detection strategy is still very much desirable. Hence, a robust and effective intrusion detection approach, named RV coefficient+Exponential Sea Lion Optimization-enabled Deep Residual Network (ExpSLO-enabled DRN) using spark is devised for the intrusion detection. Here, the unique features are selected using proposed RV coefficient-based hybrid feature fusion, which is designed by the incorporation of wrapper, class-wise information gain (CIG), and Canberra distance in slave node. With the distinctive features selected, the process of data augmentation is done using oversampling for making the data more appropriate to perform the further process in the slave node. Moreover, DRN classifier is utilized for detecting the intrusions in the master node where the DRN training is done using devised ExpSLO algorithm, which is the hybridization of Exponentially Weighted Moving average (EWMA) and Sea Lion Optimization (SLnO). Furthermore, the devised method obtained better performance by considering the evaluation metrics, such as precision, recall, and F-measure with the higher values of 0.8800, 0.8845, and 0.8822 based on without attacks using dataset-2. © 2022

关键词： Intrusion detection

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Revolutionizing COVID-19 Management: Block chain-Enabled Prediction and Secure Storage using Deep Learning Techniques

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Procedia computer science 2023年 230卷 853-863页

作者： B. Arulmozhi Dr. J I Sheeba S. Pradeep Devaneyan Research Scholar Department of Computer Science and Engineering Puducherry Technological University Puducherry-605014 India Associate Professor Department of Computer Science and Engineering Puducherry Technological University Puducherry-605014 India Professor Department of Mechanical Engineering Sri Venkateshwaraa College of Engineering and Technology Puducherry-605012 India

The proposed research presents a blockchain-based healthcare system that seeks to address the limitations of current data-sharing methods by providing secure and efficient data transfer, enhancing data privacy and security, enabling effective data sharing interoperability, and promoting data-driven healthcare decision-making. A blockchain platform, IPFS, and rule-based access control are among the technologies suggested for use in the proposed system. Implementing this system could result in more personalized and effective medication for patients, as well as lower healthcare costs and improved care quality. The suggested approach, known as the Homomorphic Zero-Knowledge Blockchain Algorithm (HZBA), employs a block chain technique linked with Proxy Re Encryption with Homomorphic with Zero proof knowledge. Finally, the Covid19-dataset has collected from Kaggle repository and HCDNN method has used to complete the classification. The proposed blockchain-based healthcare system has the potential to revolutionize healthcare by resolving data privacy and security concerns, increasing data accuracy, fostering more interoperability in data sharing, and enhancing data-driven healthcare decision-making. The proposed blockchain-based healthcare system provides potential answers to data sharing difficulties in the healthcare business. One potential disadvantage is the system's complexity and cost of installation. The experimental results and classification performance parameters have been thoroughly demonstrated

关键词： Blockchain healthcare data sharing deep learning COVID-19 security prediction privacy data transfer interoperability

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Comparative Analysis of ARIMA Time Series Model and Other Techniques for Cloud Workloads Performance Prediction 5th

Comparative Analysis of ARIMA Time Series Model and Other Te...

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Vishnu Kumar Mishra, Megha Dewangan, Bhupesh Kumar Amulya, P. Choudhury, Tanupriya Kotecha, Ketan Shri Shankaracharya Technical Campus Chhattisharh Bhilai India Department of Computer Science and Engineering OP Jindal University Raigarh India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana Secunderabad India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Maharashtra Pune412115 India Pune412115 India Research Professor CSE Dept. Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9789819997060

Accurate performance prediction of cloud workloads is essential for optimizing resource allocation, meeting service level agreements (SLAs), and ensuring efficient cloud service delivery. In this research paper, we conduct a comparative analysis of the ARIMA (Auto Regressive and Integrated Moving Average) time series model with other popular techniques for cloud workloads performance prediction. We evaluate the performance of ARIMA in comparison with other models, including machine learning algorithms and statistical methods, using real-world cloud workload performance data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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