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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Veerapaneni, Madhu Latha Tamilselvan, P. Mercy, Masereka Sandeep, Jakka. Sai Siva Amrutha, Mandala Kusuma Manda, rabecca Koneru Lakshmaiah Education Foundation A.P Vaddeswaram India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798350384369

In the era of Logistics 4.0, the combination of Artificial Intelligence (AI) has emerged as a transformative force in optimizing Supply Chain Management (SCM). This study addresses the critical need for real-time decision-making in SCM by proposing the utilization of Backpropagation Neural Network (BPNN) within the Logistics 4.0 framework. The growing complexity and dynamism of supply chains pose challenges in timely decision-making, necessitating intelligent solutions. The research identifies a significant gap in current SCM practices, where traditional decision-making processes often struggle to cope with the speed and volume of data generated in the digitalized supply chain area. results will be evaluated based on the model effectiveness in improving decision accuracy, speed, and adaptability to dynamic supply chain scenarios. © 2024 IEEE.

关键词： Supply chain management

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Parental Control for techie Child Using Keylogger 11th

Parental Control for Techie Child Using Keylogger

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11th International Conference on Intelligent Computing and Applications, ICrTC 2023

作者： Begum, Almas Alex david, S. Vadla, deeksha Akshaya, K.V. Suchitra, B. Saveetha School of Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819717231

Now, everyone is leading in the twenty-first century where everyone gets any answer or any product from the Internet, anything required is directly available to us just by one click on systems, laptops, etc. As the technology has advanced, parents are very much worried about their children using the Internet and other upcoming technologies. Everyone witnesses many issues regarding cybercrimes in the news like playing games, the money from bank accounts vanishes and hackers taking all the money from the accounts, children are getting spoiled due to the technologies that they are using, mainly during Covid-19 where parents were into work from homes and children are into online classes. Children use this chance and surf all the websites for the good use as well the bad during the classes. But parents are not aware about the children and their usage of the gadgets or how to stop them after Covid-19 situation. Therefore, this paper presents an idea for parents to come up against the problems by recording the keystrokes and sending screenshots to the targeted devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Personal computing

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Assessment of Photoplethysmography Signals using Customized Wavelet and deep Learning Model 2

Assessment of Photoplethysmography Signals using Customized ...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： reddy, K. Naga Sankar Coumar, S. Oudaya Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Electrical and Communication Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388916

Photoplethysmography (PPG) is frequently influenced by interference, potentially resulting in inaccurate interpretation of physiological data. Hence, it is essential to conduct a quality assessment prior to extracting this information. This paper introduces a novel method for assessing PPG signal quality that combines multi-class features with multi-scale series information this approach overcomes the limitations of conventional machine learning methods, which tend to suffer from low accuracy, and deep learning methods that demand large training datasets. By extracting multi-class features, the reliance on sample size is reduced, while multi-scale series information is captured through a fusion of multi-scale wavelet transforms and deep learning techniques. The proposed method achieved the better accuracy of 98.3%, surpassing six other quality assessment methods across all metrics, including sensitivity, specificity, precision, and F1-score, based on 14,700 samples from seven experiments. This paper introduces a novel approach for quality assessment and information extraction in small PPG signal samples, showing potential for accurate monitoring and analysis of clinical and everyday PPG physiological data. © 2024 IEEE.

关键词： Photoplethysmography

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EPdTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel deep Transfer SubNet+-based Explainable Model (EPdTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MrI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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GlorotUniform Kernel Initializer based Conv2d Sequential Convolutional Neural Network for diabetic retinopathy detection

GlorotUniform Kernel Initializer based Conv2D Sequential Con...

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2023 International Conference on Evolutionary Algorithms and Soft Computing techniques, EASCT 2023

作者： Umanandhini, d. devi, M. Shyamala devi, S. Sri Vel Tech Rangarajan Dr. Sagunthala R&d Instituteof Science and Technology Computer Science & Engineering Chennai India

ISBN: (纸本)9798350313413

People with diabetes may develop diabetic retinopathy (dr), an sight illness that damages the eye nerve vessels in the retina and may end up in vision loss and blurred vision. retinal camera expenses, time restrictions, an absence of expertise in dr inspection, made it challenging to implement dr evaluation as a general practice, which led to the use of modern technology towards the detection of chronic ailments regardless of a firm faith in its effectiveness of controlling them. This paper offers suggestions by designing GlorotUniform Kernel Initializer Conv2d (GKIC) Sequential model for addressing this problem by experimenting the Indian dr Image dataset. The Indian dr dataset from KAGGLE, which contains 3363 retinal tissue scan images, has been implemented in the suggested GKIC model. The images are preprocessed and fitted with GKIC model for the detection of dr. The retina scan images are preprocessed with two convolutional layers accompanied max pooling layer followed by two dense layer that are weight initialized with GlorotUniform Kernel initializer to detect the existence of dr. The image files are broken down into training and evaluation images, and the training retinal scan images have been utilized to assess the efficiency of GKIC model and the prevailing conV2d paradigms. Implementation observations using Python on a Gtx Geforce Tesla V100 Nvidia GPU workstation using 64 and 250 training intervals reveals that the proposed GKIC model estimates accuracy of 98.67% when evaluated against existing CNN models. © 2023 IEEE.

关键词： accuracy ConV2d initializer kernel retina weight

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Identification of Breast Cancer Using Machine Learning Algorithm 2

Identification of Breast Cancer Using Machine Learning Algor...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： ramathilagam, A. Edwin raja, S. Vijai Chandar, P. Gopikannan, P. raghavan, P. Jayapriya, M. PSR Engineering College Tamil Nadu Sivakasi India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai60062 India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

In the modern era, the prevalence of various diseases is a significant concern, with cancer being among the most severe. Cancer, including types like lung, blood, nerve, cardiac, and breast cancer, contributes substantially to global mortality rates, accounting for approximately 1 in 6 deaths in 2018. The treatments for cancer, such as surgery and radiation therapy, play a crucial role in managing the disease and improving patient outcomes. However, there is a persistent need to reduce mortality rates further by leveraging advancements in machine learning techniques and algorithms, along with traditional medical interventions like medicines and surgical procedures. These innovations hold the potential to not only enhance cancer treatment but also improve diagnosis and patient satisfaction across a spectrum of diseases, highlighting the transformative impact of AI in healthcare. Furthermore, AI's ongoing applications in healthcare have demonstrated promising results, although they come with certain limitations and challenges. despite these challenges, the future possibilities of AI in disease management, personalized medicine, and patient care are vast. By exploring and integrating AI into healthcare systems, we can pave the way for more effective treatments better infection the executives procedures, and eventually, worked on understanding results and personal satisfaction across different ailments past disease. © 2024 IEEE.

关键词： diseases

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Energy Management System of Luminosity Controlled Smart City Using IoT

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-7页

作者： raaj, r. Sathesh Vijayprasath, S. Ashokkumar, S.r. Anupallavi, S. Vijayarajan, S.M. Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai India PSNA College of Engineering and Technology Dindigul India Sri Eshwar College of Engineering Coimbatore India VSB College of Engineering Technical Campus Coimbatore India N.P.R College of Engineering and Technology Natham India

INTrOdUCTION: With the escalating rates of urbanization, there is a pressing need for enhanced urban services. The concept of smart cities, leveraging digital technologies, offers a promising solution to elevate urban living. The integration of Internet-of-Things (IoT) in urban infrastructure, particularly on highways, opens avenues for novel services and cross-domain applications through Information and Communication technologies. However, the efficient functioning of an IoT-enabled smart city necessitates careful energy resource management. OBJECTIVES: Propose a Highway Lighting System (HWLS) integrating IoT technologies to enhance urban services, focusing on significant energy savings and real-time environmental parameter monitoring. METHOdS: To achieve the objective of enhancing urban services through the proposed Highway Lighting System (HWLS), the system was designed and implemented by integrating cutting-edge sensors, communication links, and the Blynk IoT app. The deployment involved incorporating IoT technologies for real-time monitoring of air quality, air moisture, and soil moisture, alongside a fault identification system using GSM and GPS modules. rESULTS: The proposed HWLS demonstrates significant energy savings, consuming only 37.6% of the original power consumption. The incorporation of IoT technologies facilitates real-time monitoring of environmental parameters, enabling informed decision-making for urban service optimization. The fault-finding system, utilizing GSM and GPS modules, enhances the reliability of the lighting system. CONCLUSION: In conclusion, the Highway Lighting System (HWLS) represents a novel approach to smart city infrastructure, particularly in the context of urban lighting. The integration of IoT technologies not only contributes to energy savings but also enhances the overall efficiency of urban services. The proposed system’s ability to monitor environmental parameters and identify faults demonstrates its potential for sustai

关键词： Internet of things

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Quantum-Secured Collaborative Machine Learning: Facilitating Privacy-Protecting Quantum Federated Learning 3rd

Quantum-Secured Collaborative Machine Learning: Facilitating...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： ravikumar, S. Chandralekha, E. Vijay, K. Antony Kumar, K. Pretty diana Cyril, C. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Tamil Nadu Chennai India SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819726707

Federated learning (FL) is an exciting new method for securing data privacy in distributed machine learning environments. Quantum computing conditions make it far more difficult to ensure strong privacy protection in FL. Quantum federated learning (QFL) approaches are the focus of this research in order to promote private, confidential, and effective group work. It is proposed that federated learning processes incorporate Quantum Secure Multi-Party Computation (SMPC) protocols. The benefits of QFL in terms of privacy protection and model performance have been empirically demonstrated. QFL’s computational efficiency, scalability, and security are highlighted in thorough performance studies in comparison to conventional FL approaches. The findings highlight the potential for quantum processing to improve the privacy and security of FL, paving the way for decentralized, privacy-preserving quantum-secured collaborative machine learning. In conclusion, empirical evidence supports the efficacy and scalability of QFL, making it a promising option for tackling the difficulties of privacy and security in collaborative machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Mobility-Aware rPL Protocol to Enhance the Quality of Service Metrics in the Internet of Things 3

Mobility-Aware RPL Protocol to Enhance the Quality of Servic...

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3rd International Conference on Electronics and renewable Systems, ICEArS 2025

作者： Arivubrakan, P. Kujani, T. Sathiya Priya, S. Thanga Selvi, r. Usha, V. Bhuvanya, r. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Tamilnadu Chennai India Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Tamilnadu Chennai India

ISBN: (纸本)9798331509675

Internet of Things (IoT) is an emerging technology in today's era, to connect and communicate with embedded sensors and actuators through the Internet. routing is essential for establishing the communication between the sender and receiver in a resource-constrained environment. rPL is the standardized routing protocol for low-power lossy networks, but it consumes more energy in their mobility environment. The proposed MT algorithm reduces the listen-only period and also optimizes the quality-of-service metric in the mobility environment by considering the objective function parameters as link quality and energy. The performance is simulated and analyzed in Contiki 3.0, Cooja simulator, where energy is reduced and the packet delivery ratio outperforms the proposed algorithm. © 2025 IEEE.

关键词： Metrics Mobility Quality of service routing rPL Trickle

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Enhancing Bitcoin Price Prediction Using LSTM and GrU Models 1

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12th International Conference on recent Trends in Computing, ICrTC 2024

作者： Anitha rajakumari, P. Karthick, S. Gupta, Shruti Gupta, Shreya Mahapatra, r.P. Vinoth Kumar, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad201204 India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

Abstract Bitcoin, as the pioneering cryptocurrency, has witnessed significant fluctuations in its price since its inception. Predicting these fluctuations is of great interest to investors, traders, and researchers alike. This paper presents a comparative study of Long Short-Term Memory (LSTM) and Gated recurrent Unit (GrU) models for predicting Bitcoin prices. Cryptocurrency markets, particularly Bitcoin, exhibit high volatility and non-linearity, making accurate price prediction challenging. recurrent Neural Networks (rNNs), including LSTM and GrU, have shown promise in capturing temporal dependencies and patterns in sequential data. In this study, we train and evaluate LSTM and GrU models separately on historical Bitcoin price data to forecast future price movements. Through comprehensive experimentation and analysis, we assess the performance of both models in terms of accuracy and effectiveness in Bitcoin price prediction. The performance of these models is evaluated based on various metrics such as Mean Absolute Error (MAE), root Mean Square Error (rMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrate the effectiveness of LSTM and GrU in predicting Bitcoin prices and offer insights into their comparative performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Long short-term memory

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