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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Nelmin, Bathlin N. Shaji, R.S. Hariprasad, S. Shettygari, Sudeshna Vasan, J.K. Vijayan, V. Department of Electronics and Communication Engineering University College of Engineering Nagercoil India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331522667

Approximate computing effectively impacts low-power VLSI circuits by reducing circuit overhead. It introduces minimal error in the output, which does not significantly affect the application. This paper proposes an approximate multiplier constructed using 5_3 and 5_2 counters. The proposed 5_2 counter delivers two outputs instead of three, thereby achieving 60% compression. The overhead of the 5_2 counter circuit is reduced, achieving a power delay product (PDP) improvement of 18%. This reduction comes with an addition of an 6% error rate. The newly designed multiplier has less delay, where the Mean error distance (MED) is kept as low as possible. The complexity of the 5_2 counter is also reduced since the counter uses only a full adder and three gates. This is achieved by applying various error optimization mechanisms. The error parameters, (i) Error Distance (ED), and (ii) Mean Error Distance (MED) are analyzed and compared with existing works. The proposed multipliers are involved in image contrast application with better Peak Signal to Noise Ratio (PSNR) value. © 2025 IEEE.

关键词： Delay circuits

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Predicting Heart Disease Using Gaussian Confidence Distance Algorithm with Extra Tree Classifier 2nd

Predicting Heart Disease Using Gaussian Confidence Distance ...

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2nd International Conference on Deep sciences for computing and Communications, IconDeepCom 2023

作者： Amudha, S. Satheesh Kumar, M. Amutha Devi, C. Sankar, Sasi Rekha Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India

ISBN: (纸本)9783031689079

To prevent fatal heart failure early detection of Heart attack is essential steps. Heart Disease prediction happened well in advance using Machine learning, Artificial algorithms in Information Communication Technology, and the use of a combination of multiple algorithms can further improve accuracy of predictions. However, it is important to note that the accuracy of algorithms in predicting heart disease depends on the quality and quantity factors. Important privacy concerns and potential biases are also very much essential in data. Reason for heart disease includes improper food habits and an increase in fat content due to less physical work. The diagnosis of heart illness is mainly related to symptoms, and clinical examination of patients. In this study, we try to classify and predict heart diseases at early stages on specifical features by fusing Various Machine Learning Algorithms. Machine learning when being used in health-care is able to detect disease earlier and accurately. This work yielded an accuracy of 88.5% in predicting heart disease using Random Forest Algorithm. To increase the prediction, many algorithms were combined with more tree classifiers to select features with domain-knowledge adding and physician expertise to the prediction models. This can lead to more accurate and reliable predictions, that will ultimately improve patient diagnosis and reduce health-care costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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A Machine Learning Model: Student Verification and Job Placement with Gradient Boosting Machines 15

A Machine Learning Model: Student Verification and Job Place...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Sakthivelan, R.G. Subha, S. Sree Gopirajan, P.V. Thandalam Chennai India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India

ISBN: (纸本)9798331300579

Every year, millions of students graduate, making it difficult to track and process their data. The One Point Student Verification Machine learning Model (OSVMLM) collects rich data on students, including biometric information, secondary education details, list of degrees completed, and detailed information on their last or current degree. In addition, the student's fingerprint is also collected and stored. The government's Aadhar database is used to verify the authenticity of the student. To ease the recruiter, the details collected by OSVMLM were assigned with ranks, information will be sent to the candidates autonomously based on the current job description. This makes it easy for students to identify and apply for available positions. OSVMLM application is built on Ionic, MongoDB, NodeJS and Gradient boosting machines (GBMs). OSVMLM is immensely useful for recruiters, as it makes data collection and processing effortless. This increases the visibility of various jobs open in different domains and suggests suitable job description to the job seekers. The key difference between OSVMLM and other platforms is that it is not limited to institutions and is accessible to all. This saves a lot of time for recruiters during initial screening. This proposed OSVMLM was validated with the real-time students dataset and achieved 91% prediction accuracy. © Grenze Scientific Society, 2024.

关键词： Students

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FADA: Flooding Attack Defense AODV Protocol to counter Flooding Attack in MANET 5

FADA: Flooding Attack Defense AODV Protocol to counter Flood...

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5th IEEE International Conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Kiruthika, M. Krishnasamy, Lalitha Samuel, Prithi Kanagaraj, Kaavya School of Computing SRM Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India School of Engineering and Technology Christ University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350305449

The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration. © 2023 IEEE.

关键词： Mobile ad hoc networks

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A New Sixth-Order WENO Scheme for Solving Hyperbolic Conservation Laws

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Communications on Applied Mathematics and Computation 2023年第1期5卷 3-30页

作者： Kunlei Zhao Yulong Du Li Yuan State Key Laboratory of Scientific and Engineering Computing(LSEC)and Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100190China School of Mathematical Sciences Beihang UniversityBeijing 100191China

In this paper,we develop a new sixth-order WENO scheme by adopting a convex combina-tion of a sixth-order global reconstruction and four low-order local *** the classical WENO schemes,the associated linear weights of the new scheme can be any positive numbers with the only requirement that their sum equals ***,a very simple smoothness indicator for the global stencil is *** new scheme can achieve sixth-order accuracy in smooth *** tests in some one-and two-dimensional bench-mark problems show that the new scheme has a little bit higher resolution compared with the recently developed sixth-order WENO-Z6 scheme,and it is more efficient than the classical fifth-order WENO-JS5 scheme and the recently developed sixth-order WENO6-S scheme.

关键词： Global smoothness indicator Linear weights Sixth-order accuracy WENO

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Development of A Multipurpose Healthcare Mobile App Using JAVA

Development of A Multipurpose Healthcare Mobile App Using JA...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Pongiannan, R.K. Bushra, S. Nikkath Lathif, Syed Ismail Abdul Pradhan, Saahil Brindha, R. Franklin, J. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Data Science and Business Systems School of Computing Srm Institute of Science and Technology Kattankulathur India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India

ISBN: (纸本)9798350315127

Health care has become an essential part of present human life. The preliminary requirement to select the basic information about medical facilities through a mobile app will serve the needs of the common people. This paper presents an integrated mobile app developed using JAVA. This app is user-friendly in terms of login, registration, and bookings. This caters to the facility to select information regarding doctors, medical lab facilities, pharmacy details, and health articles. This app will provide communication. © 2023 IEEE.

关键词： Medicine

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Predicting Average Tomato Prices Using Support Vector Machine with Polynomial Features

Predicting Average Tomato Prices Using Support Vector Machin...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Pongiannan, R.K. Agrawal, Swetanshu Banerjee, Samudra Brindha, R. Pravin A, Richard Franklin, J. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India

ISBN: (纸本)9798350315127

Every economy depends on agriculture, and research in this area is primarily focused on boosting food supply and quality while minimizing costs and maximizing profit. Numerous factors can have an impact on tomato pricing, which serve as a crucial indicator of market trends for vegetables. Making educated decisions could be facilitated by predicting tomato average prices. To forecast the typical prices of tomatoes, we used the Support Vector Machine (SVM) model together with polynomial feature modification in this study. For our projections, we used the 'Minimum' and 'Maximum' pricing attributes. To guarantee consistency and improved performance, the dataset was standardized. With crossvalidation, we used a Grid Search technique to refine the parameters of our SVM model. The Mean Squared Error (MSE) and R2 score metrics were used to assess the model's performance. Our findings were shown graphically by scatter plots that contrasted real and forecasted prices and a histogram that showed the prediction errors. The SVM model has the potential to forecast average tomato prices when used in conjunction with polynomial feature transformation and parameter optimization. The agricultural industry's stakeholders may use this technique to gather knowledge of price trends and guide their decisions. More research may be done to increase prediction accuracy by incorporating more features and using other algorithm. © 2023 IEEE.

关键词： Costs

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AI-Based Autonomous Paddy Farm using Smart and Precise Irrigation System

AI-Based Autonomous Paddy Farm using Smart and Precise Irrig...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Pongiannan, R.K. Brindha, R. Akbar, Sahil Ali Pravin, A Richard Franklin, J. Pemila, M. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India

ISBN: (纸本)9798350315127

With the rampant growth of the global population and the increasing need for agricultural products, farmers confront a critical hurdle of ensuring an adequate supply of water resources for irrigation to meet these escalating demands. Amid limited water resources, farmers must embrace innovative solutions and efficient water management practices in the era of Agriculture 4.0 turning this concept into a tangible reality to meet the growing demand for efficient farming practices. By integrating Artificial Intelligence (AI) and the Internet of Things (IoT) to gather and analyze agricultural data, we now have the capability to make more accurate and informed decisions in agriculture. In this, an innovative and adaptable irrigation system is introduced which prioritizes efficiency and cost-effectiveness by minimizing resource consumption. This strategy leverages the capabilities of Machine Learning (ML) algorithms to elevate the level of agricultural intelligence. By employing a suite of sensors, including those for soil moisture, temperature, and rainfall, within an environment conducive to prolonged plant growth, the collected data to construct SVM models findings indicate that SVM outperforms other models (LR, NN, NB) with an impressive recognition rate of 98.5% and a minimal root mean square error (RMSE) of 0.10. Through this system, crop irrigation is achieved with a high level of precision, entirely automated, and without the need for human intervention. As a result, this technology leads to a substantial reduction in water consumption, up to 50%. © 2023 IEEE.

关键词： Machine learning

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Methodology to Maximize the Output Voltage Harvested from Thermoelectric Generator by Using Luo Converter 4

Methodology to Maximize the Output Voltage Harvested from Th...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Sivagami, P. Harikrishnan, R. Srideviponmalar, P. Deshpande, Shripad V. Sathyabama Institute of Science & Technology Department of Electrical and Electronics Engineering Chennai India Symbiosis Institute of Technology Symbiosis International Deemed University Pune Campus Pune India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331508845

In this technical epoch we are dealing with sensible issues such as global warming, pollution, depletion of available resources, increase in power cost etc. Researchers are focusing to reduce the effects due to sensible issues in present time by looking out for neoteric energy harvesters, that is power generators. A physical phenomenon that converts heat energy into electrical energy is thermoelectric effect. Thermal energy is transformed into electrical energy by thermoelectric generators (TEGs) using the Seebeck effect. Energy harvested from TEG have no moving parts, can be integrated on other devices, produces no noise, can be positioned anywhere that is position independent. The operating life time is also high and has no maintenance. TEG can harness energy for 1-2K thermal discrepancy in the environment. Using any wearable application in the body thermo electric generator can forge power in microwatts and voltage in mv. TEG has low conversion efficiency that it produces voltage in millivolt. To mold this trifling amount of voltage to some electrical appliances mv is boosted using LUO converter. Luo-converters boosts the voltage in geometric progression fed from the thermoelectric generator. Thermoelectric generator fed Luo converter is controlled by PID controller to proliferate output volt-age at the LUO converter side. Thus, TEG has raised as emerging green technology to power devices. This paper discusses about TEG application for charging devices. © 2024 IEEE.

关键词： Energy harvesting

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