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Computational Approach for Cardiac defect detection using CNN Approach 5

Computational Approach for Cardiac Defect Detection using CN...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Kanna, R. Kishore Anitha, R. Sundaramoorthy, K. Salau, Ayodeji Olalekan Sikarwar, Nirdesh Panda, Tuhina Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Information Technology Chennai India Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Itm University School of Nursing Science MP Gwalior India Hitech Institute of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542559

Cardiac disease is one of the leading causes of death worldwide. Increased cashew consumption in India has been identified as one of the major variables responsible for the increased prevalence of the disease among the elderly in this region. This study aims to establish a classification of cardiovascular disease policy that includes cashew as a risk. This was done using 18,500 magnetic resonance imaging (MRI) data as a complement, and then the convolutional neural network method was trained to build the classification model. The model results considering accuracy and loss functions were evaluated, which reported 98.77% and 0.0100567, respectively, after ten years. The model was tested with other state-of-the-art algorithms, and the results showed that the new system is more reliable compared to others. © 2024 IEEE.

关键词： Convolutional neural networks

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MlIdS: Revolutionizing of IoT based digital Security Mechanism with Machine learning Assisted Intrusion detection System

MLIDS: Revolutionizing of IoT based Digital Security Mechani...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Vijaya Vardan Reddy, S.P. Manonmani, S.Priskilla Anitha, C. Jaganathan, d. Reena, R. Suresh, M. R.M.K. Engineering College Department of Ece Tamil Nadu Chennai India Meenakshi Sundararajan Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha School of Engineering Simats Department of Cse Tamil Nadu Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350382723

It is becoming more critical to detect intrusions on these devices due to the exponential growth of the Internet of Things (IoT) and the accompanying explosion in the number of IoT devices. In order to create intrusion detection systems that really work, researchers are utilizing machine learning methods. Machine learning Assisted Intrusion detection System (MlIdS) is a new intrusion detection system that we provide in this study. It efficiently identifies network unusual traffic. A cross-validation test using the traditional learning model XGBoost allows for a transparent evaluation of the suggested algorithm's performance. Next, the preprocessed data is categorized using the suggested MlIdS and XGBoost methods. To get the best detection performance, the model's hyperparameters are tuned using optimization logic. The evolution of cybercrime has necessitated massive advancements in intrusion detection system (IdS) technology. In order to gain access to our computers' private data, hackers nowadays deploy a wide variety of techniques. To protect against these threats, there are a plethora of intrusion detection algorithms. There are growing worries over the secure communication and protection of digital information due to the exponential expansion and usage of the internet. To gain useful information, hackers nowadays deploy a wide variety of techniques. Those various assaults may be detected with the use of several intrusion detection algorithms, methods, and approaches. The overarching goal of this paper is to present a comprehensive analysis of intrusion detection systems, including but not limited to: different types of intrusion identification techniques, types of events, a number of approaches, and tools, future research requirements, difficulties and, subsequently the development of an IdS device for research purposes that can detect and prevent intrusions. © 2024 IEEE.

关键词： Internet of things

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Analysis of different Encryption Techniques used in Watermarking Algorithm for the Security of Medical Image 10

Analysis of Different Encryption Techniques used in Watermar...

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10th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Pulgam, Namita d. Shinde, Subhash K. Ramrao Adik Institute of Technology D y Patil Deemaed to Be University Computer Engineering Navi Mumbai India Lokmanya Tilak College of Engineering Computer Engineering Navi Mumbai India

ISBN: (纸本)9781665467414

Today, in the computerized age, the development and use of internet technology in healthcare has been increasing rapidly. Quick improvement is going on in the telemedicine field with a clear goal to design integrated applications in diagnostics and treatment. Hence medical images are preserved in databases for some time so that post diagnosis treatment will become easier for the patient and for doctors as well. during the process of the treatment patients tend to take experts' advice and that time images of the reports will be shared from one hospital to another. With the use of sharing and transmission technique post treatment cost gets reduced whereas increases the issue concerning the security of patients medical reports. It is important to keep track of images shared between two medical centers to identify sources of image generation as well as protecting patient's sensitive information from intruders or hackers against unauthorized access and modification. digital watermarking can be used to provide ownership and copyright protection. In the watermarking system image or text can be inserted in the cover image to get a watermarked image. The watermark image or secret image to be inserted can be protected with encryption technique. Hence this paper presents a digital watermarking system designed with encryption techniques to provide security to patients' medical records. The system is evaluated by implementing it with various encryption techniques like Advanced Encryption Standard (AES) with 256 bits, AES-GCM, Triple data Encryption Standard (TdES), Blowfish etc. The performance of the system is measured with PSNR and SSIM and assessment of the evaluation parameters shows that AES encryption is better in comparison with TdES and Blowfish. © 2022 IEEE.

关键词： Telemedicine Watermarking Information processing Copyright protection Market research Robustness Encryption

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data Visualization for Corona Patients Globally using Real-Time APIs 2

Data Visualization for Corona Patients Globally using Real-T...

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2nd International Conference on Sustainable Computing and data Communication Systems, ICSCdS 2023

作者： durai, S. Iqbal, M. Mohamed Kumar, S Niresh Alagappan, Chockalingam Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Dept. of Cse Chennai India VIT-AP University School of Computer Science and Engineering Amaravati India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9781665491990

All the nations' administrative units are concerned about the COVId-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVId-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVId-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.

关键词： COVId-19

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Harnessing Machine learning Approaches for Accurate Energy demand Forecasting in the Power Sector 2

Harnessing Machine Learning Approaches for Accurate Energy D...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Kadam, Swaraj Satish Karthick, l. Shaik, Khasimbee Waykar, Yashwant Vikram, G. Salyan, Srikanth Dr. D Y Patil Institute of Technology Department of Electrical Engineering Pune Pimpri411018 India Hindusthan College of Engineering and Technology Department of Mechanical Engineering Coimbatore641032 India Aditya College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India Dr. Babasaheb Ambedkar Marathwada University Department of Management Science Maharashtra India Karunya Institute of Technology and Sciences Karunya School of Management Coimbatore641114 India Dayananda Sagar College of Engineering Department of Aeronautical Engineering Karnataka Bengaluru560078 India

ISBN: (纸本)9798350367461

In the power sector, accurate energy demand forecasting is essential for effective resource allocation and decision-making. Conventional techniques frequently prove inadequate in comprehending the complex intricacies of energy consumption patterns, which has led to the investigation of machine learning (Ml) methodologies. This article provides an exhaustive examination of the utilization of Ml techniques to forecast energy demand with precision. An assortment of machine learning algorithms, such as ensemble methods, neural networks, regression models, and time series analysis, are evaluated within the framework of energy forecasting applications. This paper addresses crucial factors that enable the practical implementation of machine learning models, including feature engineering, data preprocessing, and model evaluation metrics. The efficacy of machine learning (Ml) in improving the precision of forecasts and facilitating more informed decision-making for energy providers and policymakers is substantiated utilizing case studies and empirical data. In addition to addressing challenges such as data quality, model interpretability, and scalability, the paper investigates future directions and emergent trends in the field. In its entirety, this study makes a valuable contribution to the progression of knowledge and implementation of machine learning in the domain of energy demand forecasting. As a result, it facilitates the development of power systems that are more robust and environmentally sustainable. © 2024 IEEE.

关键词： Adversarial machine learning

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Privacy-Preserving Human Activity Recognition in Smart Homes using deep learning and Edge Computing for Real-Time Processing 5

Privacy-Preserving Human Activity Recognition in Smart Homes...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mariappan, K. Gopal, Baskar Parthasarathy, J. Sreekanth, G.R. Nanmaran, R. Jegajothi, B. SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri India Information Technology Nandha Engineering College Erode52 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRS Tech Solutions Chennai India

ISBN: (纸本)9798331530013

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAR) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAR, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAR dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAR systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MRIs, to improve healthcare outcomes. © 2024 IEEE.

关键词： Smart homes

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Examining the Role of Guardian Communications Software design for Network Security 15

Examining the Role of Guardian Communications Software Desig...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Chaudhari, Madhuri Harshal Verma, Ashish dhabliya, dharmesh Kumar, l.Vinoth Jaison, B. Sunrise University DepartmenT of Computer Science & Engineering Alwar301028 India Dr.D.Y.Patil Science and Computer Science college Department of Computer Science pune Akurdi India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Technology University Department of Information Chennai India R.M.K.Engineering College Department of Computer Science and Engineering Kavarapettai601206 India

ISBN: (纸本)9798350370249

Network protection is a crucial region of generation and PC technology, and numerous equipment and techniques help ensure comfy conversation and records switch. Mum or dad Communications software program (GCS) is a product suite that gives customers an answer for securing their networks. GCS offers an interactive person interface to facilitate configuration, analysis, and optimization of a community's safety architecture. The suite carries three core components, supplying an excellent and specialized feature. These additives are the mum or dad Firewall, the guardian Host Intrusion Prevention System (HIPS), and the father or mother community protection appliance (NSA). Each component works independently and in concert with each other to provide comprehensive network security. The Guardian Firewall is an application-level firewall that provides a secure edge to the network by controlling all incoming and outgoing traffic and packet communications. It dynamically updates its rules database to recognize and block malicious traffic while allowing access to known secure applications and services. The Guardian HIPS is an internal network monitoring system that can detect and respond to unauthorized intrusions while providing the administrator with real-time alerts and intelligence about the nature of any malicious activity. Finally, the Guardian Network Security Appliance is a hardware-based appliance that can be configured with firewalls and. © 2024 IEEE.

关键词： Communications dynamically Guardian Security Unauthorized

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Blockchain Technology: Evolution and Future Prospects 5

Blockchain Technology: Evolution and Future Prospects

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Babu, A. Aalan Asha Judi, V. Jebamalar, G Belshia Prem Kumar, R. Nalini, M. Siva Subramanian, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of CSE Chennai India Sathyabama Institute of Science and Technology Dept of Computer Science and Engineering Chennai India S A Engineering College Department of CSE Chennai India RMK College of Engg and Tech Dept of CSE India S A Engineering College Dept of CSE India

ISBN: (纸本)9798331530013

This research study explores the new advancements in blockchain technology and its applicability across diverse fields. Starting with the general introduction to blockchain basics and components, and then following several trends including scalability, interoperability, privacy and security, green blockchain, and decentralized finance (deFi). The discussion explores further with the future developments and uses of blockchain technology such as in the field of finance and financial products, supply chain, healthcare, and governance structures, entertainment industries and so on. Further, this study analyses the major issues and obstacles related to the use of blockchain and their categorization into technical, legal, financial, and social. Some recommendations for further research are given focusing on the consensus algorithms, incorporation with AI technology, and interdisciplinary approaches. Finally, the survey provides an overview of the main findings on the integration of blockchain technology and its impact across different industries. This study gives a general understanding of the current state of development with significant implications for stakeholders, which supports the call for further research and cooperation in the field of blockchain. © 2024 IEEE.

关键词： decentralized finance

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Advancing Edge Computing Paradigms with Novel Architectures and Algorithms for Real-Time Processing 2

Advancing Edge Computing Paradigms with Novel Architectures ...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ramesh, P.S. Mathu Sudhanan, S.R. dineshkumar, R. Manigandan, K. Parthipan, V. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Mohan Babu University School of Liberal Arts & Sciences Department of English Andhra Pradesh Tirupati India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350364699

This paper focuses on the advancements of the edge computing paradigms regarding the creation of new emerging architectures and algorithms for improving real-time computing. looking at the results obtained, the proposed architecture provided 40% reduced latency rates, and 50% higher throughput than the baseline models while using 20% less energy. Key algorithms, including data compression, stream processing, and adaptive algorithms, were evaluated, with the hybrid approach showing the most significant improvements: The improvements were made in the latency where there was a 50% decrease in the latency and in the throughput where there was a 40% increase, and in the accuracy which is 30% enhanced. The above outcomes demonstrate the efficiency of offered solutions in the context of the discussed problems of real-time data processing;the recognition of the edge computing prospects regarding the further evolution of different applications. © 2024 IEEE.

关键词： Adaptive Algorithms Advanced Algorithms data Compression Edge Computing Energy Efficiency latency Reduction Novel Architectures Real-Time Processing Stream Processing Throughput Improvement

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Enhanced diabetes Prediction Using deep Autoencoder Framework and Electronic Health Records 2

Enhanced Diabetes Prediction Using Deep Autoencoder Framewor...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sivakumar, T.B. Malakar, Antara lekshmi, S. Shailaja, G. Kalaivani, E. Babu, dhananjaya K. Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science & Engineering The Assam Royal Global University Guwahati India Basic Science and Humanities Department Mangalam College of Engineering Kottayam Ettumanoor India Department IT CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Bannari Amman Institute of Technology sathyamangalam India Rajampet India

ISBN: (纸本)9798350383867

Type II diabetes, which nowadays has become one of the most common chronic metabolic illnesses, needs cloud-based information systems with great predictive models to ensure timely intervention and proper management. Given the fact that EHRs and deep learning are quite advanced serves as a solution to the given problem. The study is about the application of the improved diabetes prediction model that makes use of prevailing deep learning architecture and EHR data. The framework constitutes a three-step process: It encapsulates a preprocessing step, a feature extraction stage, and a training procedure for a deep autoencoders that learns levels of representation. demonstration of the system' s effectiveness becomes evident when it shows its improved ability of prediction compared to baseline models which in turn outperform in basic indicators such as accuracy, precision, recall, F1 score, and AUC-ROC. The result of the sensitivity check demonstrates that the model is able to perform well even under different hyperparameter arrangements. latent feature analysis shows some unequivocal ways of hidings that reflect diabetes risk factors thus making it easy to understand. The clinical validation in a healthcare setting presents high accuracy, the specificity and the predictive values that are known in indicating those who are at the risk of diabetes onset. This research aims at deepening the understanding for the diagnosis of diabetes through the implementation of deep learning methods and generating real-world medical data. It attempts to provide better prognosis, lower healthcare expenditures and alleviate diabetes burden on patients and society. © 2024 IEEE.

关键词： Electronic health record

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