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1st International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2023

作者： Angayarkanni, V. Rama, P. Vidhyalakshmi, M.K. SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu District Tamil Nadu 603203 India

ISBN: (纸本)9798350306637

Wireless networks employ cooperative transmission techniques to boost spectral efficiency. The interference between cells affects the performance of wireless devices in multi-cell environments. In this research work the downlink capabilities of edge users in a cellular network is analyzed to find whether base station collaboration improves spectral efficiency. For improving the Signal-to-Interference-Noise Ratio (SINR) and output the base stations need to synchronize their broadcast with the two cell-edge users. A novel Efficiency Improvement Selective Cooperation (EI-SC) system is applied, with throughput serving as the selection criterion in this paper. By working together, all cell-edge users share the cell's capacity. According to the results, the recommended hybrid EI-SC scheme outperforms traditional cooperation. In this work, 12% of improvisation has been observed. © 2023 IEEE.

关键词： Spectrum efficiency

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Real Time Motion Capture for Physical Rehabilitation Using Kinect

Real Time Motion Capture for Physical Rehabilitation Using K...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： Singireddy Srijay Reddy Nagumalla Yasash P. Rama V. Angayarkanni J. Kalaivani Faculty of Engineering and Technology Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Department of Computing Technologies Faculty of Engineering and Technology SRMIST Kattankulathur

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This Project is a Kinect-based rehabilitation system designed to assist patients with movement disorders by performing targeted physical exercises at home. This system enables the evaluation of whether patients are correctly executing the prescribed rehabilitation exercises. By utilizing the skeletal tracking capabilities of the Microsoft Kinect sensor, each movement of the patient during the exercises is recognized and *** research introduces a real-time motion capture system designed for physical rehabilitation using Microsoft's Kinect sensor. The system addresses the growing demand for remote, data-driven healthcare solutions that assist in rehabilitation exercises by tracking and analysing skeletal movements. The Kinect's ability to track 25 joints in real time provides a unique opportunity to offer non-intrusive monitoring, real-time feedback, and data collection for therapists and patients. While Kinect offers several advantages, including cost-effectiveness and ease of use, our study identifies several limitations in precision, especially for complex movements and occlusion scenarios. This paper details the system's design, testing, and evaluation, highlighting areas where future improvements are necessary.

关键词： Accuracy Target tracking Tracking Smart systems Motors Real-time systems Motion capture Monitoring System analysis and design Testing

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Implementation of ResNet-50 with the Skip Connection Principle in Transfer Learning Models for Lung Disease Prediction

Implementation of ResNet-50 with the Skip Connection Princip...

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International Conference on Intelligent Systems and Sustainable computing, ICISSC 2022

作者： Prabakaran, J. Selvaraj, P. Department of Networking and Communications College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819947164

The adoption of advanced technologies is crucially essential for medical progress. To identify or predict any diseases in humans, we must first have a proper diagnosis to predict the disease in that human body. Cancer is an illness that stands uncontrolled in cells. The illness begins in the lungs, lung node is also known as lung disease. Cancer of the pulmonary system may cause an infection or other organs, such as the brain and lungs. In addition, cancer from other organs can affect the lungs. An accurate diagnosis is essential to identify and anticipate the disease within a person's body. Machine learning, deep learning, and transfer learning models are frequently used to forecast and characterize lung disease utilizing data from chest X-ray, positron emission tomography scans, and magnetic resonance imaging. Residual neural network-50, MobileNet-V2, Visual Geometry Group-19, and DenseNet201 Cable News Network-based transfer learning algorithms reveal which image classification algorithm is better suited to forecast and categorize the classification problem of lung disease. ResNet-50 transfer learning enabled efficient lung disease categorization and prediction. The main point of this work is to utilize the ResNet-50 algorithm with the skip connection principle for lung disease identification and classification. This algorithm has been trained and evaluated on benchmark X-ray datasets. This methodology obtained 95.49 F1 measure, 97.45 accuracy, 93.41 precision, and 97.31 recall. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Ansh Tripathi Saransh Dubry P. Rama V. Angayarkanni J. Kalaivani Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Department of Computing Technologies Faculty of Engineering and Technology SRMIST Kattankulathur

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system’s capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system’s superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management.

关键词： Environmentally friendly manufacturing techniques Scalability Water quality Machine learning Market research Real-time systems Water pollution Indexes Environmental monitoring Public healthcare

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YOLO-based Traffic Signal Optimization for Intelligent Traffic Flow Management

YOLO-based Traffic Signal Optimization for Intelligent Traff...

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

作者： Joesam Dinesh C Shrinidhi S Sibi Amaran Sree Kumar K Karthikeyan U Dept of Computing Technologies SRM Institute of Science and Technology India Dept of Computing Technologies India Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

As cities grow in population and the number of vehicles increases, traffic congestion has become a major problem. Traffic jams lead to delays, driver stress, higher fuel consumption, and increased air pollution. Megacities are particularly affected. To manage traffic effectively, it's essential to calculate road traffic density in real-time. Optimizing traffic control is crucial for this. The system uses live images from traffic cameras and AI-based image processing to measure vehicle density and adjust traffic signals accordingly, reducing congestion, speeding up transit, and lowering pollution.

关键词： Urban areas Process control Traffic control Cameras Real-time systems Road traffic Delays Traffic congestion Stress Vehicles

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Face Recognition CCTV for Automated Attendance Tracking

Face Recognition CCTV for Automated Attendance Tracking

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Sai Sindhu Kola Harsha Kenchi P. Rama V. Angayarkanni J. Kalaivani S. Sivakumar Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Computing Technologies College of Engineering and TechnologyFaculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Department of Computing Technologies Faculty of Engineering and Technology SRMIST Kattankulathur

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This study investigates the design and execution of an automated attendance tracking system using facial recognition CCTV based . Facial recognition technology and CCTV cameras are integrated in this system to provide a seamless, frictionless way to track and record attendance in real time. In addition to boosting attendance record accuracy, the approach reduces the administrative burden associated with using traditional methods. The suggested system is designed to recognise people when they enter or leave a monitored area by leveraging developments in computer vision. It accomplishes this automatically and requires no interaction from humans. Furthermore, the system's use of the Internet of Things makes it flexible and scalable to a variety of business situations, including offices, schools, and large-scale events. This is made feasible via real-time data synchronising and reporting.

关键词： Computer science Electric potential Computer vision Accuracy Face recognition Cameras Real-time systems Internet of Things Monitoring Business

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Antisocial Behavior and the Dopamine Loop on Different Technological Platforms and Industries: An Overview 8th

Antisocial Behavior and the Dopamine Loop on Different Te...

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8th International Congress on Information and Communication technology, ICICT 2023

作者： Stupavský, Igor Dakić, Pavle Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Slovak University of Technology in Bratislava Bratislava Slovakia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (纸本)9789819932351

The aim of this review is to explore the relationship between the architecture of microservices and antisocial behavior. Microservice architecture is a software design approach that involves decomposing a system into small, independent services that can offer benefits such as flexibility and scalability. Expected result is to analyze dataset of statuses, which defines the length of time the status displays on the user’s device. Because people spend time on social networks at every opportunity, even for hours. When combined with the time-valued, we try to gain a deeper understanding of social networks and users behavior. They are forced into this behavior through the feedback loop mechanism of the neurotransmitter Dopamine. Main goal is to understand how to minimize the impact of antisocial behavior on social networks and what exactly makes users spend a disproportionate amount of time on these platforms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Software design

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API-GEN: Accelerator Tool for Generation of REST APIs 2

API-GEN: Accelerator Tool for Generation of REST APIs

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Senthil Kumar, T. Jayapradha, J. Roslee, Mardeni Bin Shaik, Hajna Garg, Sanskriti Sudhamani, Chilakala SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur India Multimedia University Faculty of Engineering Cyberjaya Malaysia

ISBN: (纸本)9798350355468

Application Programming Interfaces (APIs) are crucial for enabling seamless communication between software systems, allowing them to exchange data and perform tasks efficiently. They underpin essential operations across industries like e-Commerce, social media, and financial services. However, the traditional approach to API development is often labor-intensive, time-consuming, and prone to errors, diverting developers' focus from innovation to repetitive coding tasks. This paper explores the concept and functionality of APIs, with a focus on automated API generation using JSON and class-based models. By automating this process, developers can streamline access to data from databases, significantly reducing manual effort while improving efficiency and accuracy. The study compares existing methods of API development with automated approaches, highlighting how automation can save time, minimize costs, and enhance the overall software development lifecycle. With its ability to simplify data integration and accelerate development, automated API generation represents a transformative solution for addressing the challenges of modern software systems, making the process faster, error-free, and more efficient. © 2025 IEEE.

关键词： API generation automation class-based models data integration error reduction JSON REST API software development survey study time efficiency

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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Evaluating the Performance of an Incremental Classifier using Clustered-C4.5 Algorithm for Processing Big Data Streams 5

Evaluating the Performance of an Incremental Classifier usin...

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5th IEEE International Conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Banu, E. Afreen Robert, P. Shah & Anchor Kutchhi Engineering College Dept of Computer Engineering Maharashtra Mumbai India Srm Institute of Science & Technology Dept of Computing Technologies Tamilnadu Kattankulathur India

ISBN: (纸本)9798350379884

Currently, the inevitability of big data stream processing is increasingly becoming more critical and imperative due to the incremental nature of the heterogeneous and massive volume of data engendered by various sources. C4.5 is the most renowned algorithm to address the problem of data stream classification in online mode by constructing decision trees. But, the traditional C4.5 algorithm has a significant adverse impact on the speed of data stream processing in real-time scenarios. To handle this problem, we propose a new variant of the C4.5 classifier, named the Clustered-C4.5 (CC4.5) algorithm to classify data streams in online mode. This work integrates a Correlation-based Feature selection and Bat Optimization Technique (CFBOT) with the CC4.5 algorithm to improve the classification performance. The CFBOT is employed to measure the dependencies among selected features and to determine the ideal subset for training and testing tasks. Meanwhile, CC4.5 organizes the data into smaller clusters instead of building a single decision tree. The integration of the CC4.5 algorithm and CFBOT helps the system classify imbalanced and multi-class datasets effectively. The performance of the proposed classifier is cautiously studied on the real-world dataset (i.e., UCI-heart disease dataset) by relating its enactment with many state-of-the-art approaches regarding classification accuracy, sensitivity, specificity, the area under the receiver operating characteristic curve (AUC), and intersection over-union (IoU). Additionally, the Wilcoxon rank-sum test is employed to assess whether our classification technique offers a statistically significant improvement over other classifier methods. The experimental results demonstrate that the proposed classifier outperforms other approaches with superior accuracy, sensitivity, specificity, AUC, and IoU of 93.6%, 96.2%, 79.3%, 92.3%, and 90.3%, correspondingly. Accordingly, we can conclude that CC4.5 is the better data stream classific

关键词： Support vector machines

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