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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Reddy, Gajjala Venkat Rushith Abhiram, Prayaga Shashank Ashwanth, G.P.N. Narayana, G. Surya Thangam, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

Wireless Sensor Networks (WSNs) are widely implemented in environmental monitoring, disaster management, and smart cities. Sensor nodes, however, have limited power resources and require energy-efficient data aggregation and transmission protocols. This project compares WSN protocols, namely LEACH, PEGASIS, HEED, and TEEN, to identify the best approach for extension of network lifetime and energy conservation. To address existing limitations, a new integrated protocol, PT-Hybrid, is proposed, combining PEGASIS's chain-based communication with TEEN's threshold-based reporting. It employs dynamic leader election, multi-hop transmission, and event-driven reporting to optimi7.e energy usage and data accuracy. The PT-Hybrid protocol and standalone protocols were implemented and tested in MATLAB using 100 sensor nodes for 100 rounds. As shown by the results, PT-Hybrid outperforms LEACH and HEED in terms of energy efficiency and closely approaches PEGASIS with balanced energy dispersal. The hybrid approach consequently achieves well-balanced extension of network lifetime and minimizes unnecessary transmissions, hence making it practical for real-world WSN applications. © 2025 IEEE.

关键词： Sensor nodes

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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Enhancing Drug Candidate Generation: Comparing Genetic Algorithm and WGAN-GP Approaches 6th

Enhancing Drug Candidate Generation: Comparing Genetic Algor...

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6th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Krishnan, Aravind Dayanand, V. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031813382

The day-to-day developments in computer-aided drug design methods have improved the identification process of potential drugs. The de novo design creates novel chemical entities to increase the search space of potential drug-like candidates. Evolutionary-based algorithms and deep learning models like Generative Adversarial Networks (GANs) are extensively used in the de novo design. In this work, we are studying two models, a Genetic algorithm (GA) based on the evolutionary algorithm and WGAN-GP, which is an improved GAN model. The generated molecules have been further evaluated using drug-likeness, synthesizability, solubility and validity measures. It has been found that both models produced novel drug-like molecules and obtained better or similar scores compared to other baseline models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Pelican optimized extreme learning machine based prognosis of heart failure using textural patterns in CMR images

Pelican optimized extreme learning machine based prognosis o...

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2023 IEEE Technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Muthulakshmi, M. Ashwini, K. Jansi, R. Kiran, Batchu Bhanu Sai Mani Greeshma, Nimmagadda Keerthi, Pulimi Amrita School of Engineering Dept. of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Dept. of Electronics and Communication Engineering Kattankulathur Chennai603203 India Amrita School of Engineering Dept. of Computer and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350384659

One of the abnormal cardiovascular conditions with the greatest rate of increase is heart failure (HF). There is a lot of correlation in the exterior symptoms observed that are typically ignored, particularly when there is progression from one stage to another. Planning an efficient treatment requires grouping patients into different phenotypic categories. In CMR imaging, these conditions show damaged heart muscles. Understanding the classification performance of ELM aided by optimal texture features is the main aim of the proposed work. In this study, Krawtchouk moment and the co-occurrence of neighboring sparse local ternary pattern descriptors are used to extract texture and anatomical information of LV. However, an effective feature selection strategy is required to handle the high dimensionality of the feature vector. The primary benefit of the Pelican optimization algorithm (POA), which takes inspiration from nature, is its capacity to carry out both global and local searches effectively. This paper suggests usage of multi-objective POA to optimize the proportion of extracted features and improve the performance of the ELM classifier. The people with various HF stages have been diagnosed with the maximum accuracy of 95.7 % as a result of this inclusion. The enhanced traits have distinguished moderate and severe HF patients from control participants with significant performance. The proposed work further shows the impact of tissue characterization from CMR image and optimized features-based classification on the HF stage detection. © 2023 IEEE.

关键词： Magnetic resonance

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2D Wound Image Segmentation Using Deep Learning Algorithms

2D Wound Image Segmentation Using Deep Learning Algorithms

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Soubraylu Sivakumar V. S. V. Abhiram P. Rama Vikram Kallakrinda T. Rajesh Kumar P. Saravanan Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The purpose of wound segmentation is to accurately identify and delineate wound areas in images, enabling precise analysis of metrics like size, shape, and spread. This aids healthcare professionals in assessing wound severity and monitoring treatment progress. This research focuses on developing an automated wound image segmentation tool using the Deep learning Algorithms. The objective is to enhance wound assessment accuracy by precisely identifying and segmenting wound areas in digital images. Three models are employed to find the effectiveness in segmenting the wound which includes U- Net, DeepLabV3+ and Mask-CNN. The deep learning models are trained on a massive dataset of wound pictures and masks; they then provide segmented outputs, which are examined for wound characteristics and severity. The U-Net model has obtained better performance in segmenting the wound with an accuracy of $\mathbf{9 2. 3} \%$. This work seeks to incorporate advanced capabilities into a user-friendly interface for practical use in clinical settings. Initial results demonstrate promising segmentation accuracy, with plans for further enhancements in future.

关键词： Deep learning Image segmentation Visualization Accuracy Shape Standardization Medical services Wounds Object recognition Monitoring

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Dual-Plate Verification System for Vehicle Identifcation Using IoT and Embedded Systems

Dual-Plate Verification System for Vehicle Identifcation Usi...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： A V S S Sampath Nischal S Malagati V Kanapathi Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research uses a dual-plate verification system for improving vehicle security by using IoT and embedded sys-tems. The system compares front and rear license plates to detect any anomaly, which signals unauthorized modifications. Using real-time image processing and IoT-based data communication, this solution gives us an accurate plate matching system, making sure of reliable vehicle identity verification. Its possible applications include traffic monitoring, parking management, and detecting any unlawful activity. This project is unique because it integrates the dual plate verification system with an Arduino IoT setup. For hassle-free text recognition, this system uses deep learning frameworks combined with a wireless mobile setup that serves as a camera. The system's accuracy is shown through experiments, displaying its value in improving transportation security with automation. The number plates were detected, and the respective penalties for the vehicles were imposed accordingly.

关键词： Wireless communication Automation Accuracy Transportation Real-time systems Security Internet of Things Data communication Usability License plate recognition

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Comprehensive Web Analysis of Automobile Websites: Performance, SEO, and Accessibility Insights

Comprehensive Web Analysis of Automobile Websites: Performan...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Abhishek Busetty Ruchith Balam Hari Chillakuru Radha D Saranya Devi B Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The current research work proposes a difficultydriven comparison of three most visited automobile websites namely the Automobile Site1, Automobile Site2, Automobile Site3 on parameters including performance, Search engine optimization (SEO), accessibility and traffic. The research involves tool such as Yellow Labs, Google Page Speed Insight and Semrush with regard to the website's weight, loading time, user's interactions and search engine optimization. The findings reveal distinct strengths across the sites: From the three Automobile Site1 is the most optimized and accessibly friendly site, the factors of search engine friendliness is as follows. Looking at the traffic and users of Automobile Site2, it comfortably outclasses the other sites in terms of page views as well as the audience loyalty. At the same time, Automobile Site3 has relatively low first load time which indicates fast website browsing while it is still relatively weak in terms of its SEO and the usability of the site has to be improved in terms of theoretical potential. In conclusion, the realized comparison exposes the strengths and weaknesses of each site and is informative to future developments of the sites with an aim at better user interaction levels and competitive advantage.

关键词： Loading Search engines Internet Information and communication technology Automobiles Usability Optimization

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Image Compression Algorithms - Comparative Analysis

Image Compression Algorithms - Comparative Analysis

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： G. Niharika Reddy M. Namrutha Namratha Akshaya Sreebha Bhaskaran Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Due to the abundance of the new digital media data, the issue of image quality and volume of data requiring compression has become a significant factor of concern, especially in media storage and transmitting. This work affords a comparative analysis of different image compression techniques with focus on the compression ratio, quality preservation, and complexity. A new hybrid model of Predictive Coding, Run Length Coding and Quantum Entropy Coding (QEC) is proposed and shown to exhibit negligible quality loss with substantial space saving. The experimental outcomes show that the proposed method reduces space 80 percent and works better than previous methods for areas requiring high speed and relative accuracy. These insights are timely, as practical computing-communication trade-offs are paramount in the new generation of social networks, medicine, and multimedia streaming.

关键词： Integrated circuits Image quality Image coding Social networking (online) Streaming media Predictive coding Media Predictive models Prediction algorithms Entropy coding

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Enhanced Sensing for Automotive mmWave Radar: Super-Resolution Range and Angle Estimation With Coherent Extension and Advanced IAA

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IEEE Transactions on Consumer Electronics 2025年

作者： Wu, Zhenyu Ma, Xuxu Chang, Fei Li, Chuandong Luo, Zhen Leung, Man-Fai Southwest University College of Electronic and Information Engineering Chongqing400715 China Anglia Ruskin University School of Computing and Information Science Faculty of Science and Engineering Cambridge United Kingdom

This article delves into the super-resolution problem of range and angle for millimeter wave (mmWave) frequency-modulated continuous wave radar. First, a modified diagonal loading (MDL) is proposed for range estimation in practical scenarios to improve the convergence of the iterative adaptive approach (IAA). This loading term can be flexibly adjusted according to the actual noise level, and it has the potential to surpass the existing adaptive diagonal loading IAA under low signal-to-noise ratio. Second, the range estimation results are used for signal enhancement. A coherent extension technique is adopted to address the phase discontinuity between adjacent chirps, thus enabling the construction of a longer chirp signal. Finally, the extended signal is used to generate a super-resolution range-angle map. Practical and numerical examples demonstrate the feasibility of the above methodology. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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A Hybrid Feature Selection Model for Early Prediction of Metabolic Syndrome

A Hybrid Feature Selection Model for Early Prediction of Met...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： A S Sreepadh P Sheshu R G Reshiha T Vinay Kumar Gurusamy Jeyakumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Metabolic Syndrome (MetS) is a medical condition that increases the risk of cardiovascular diseases, stroke, and type 2 diseases. Some of the symptoms of MetS are high blood pressure, elevated blood sugar, excess body fat, and abnormal cholesterol levels. All these symptoms are interconnected, which has made early diagnosis of MetS critical. In current medical strategies, there is no personalized medication which leads to unfavorable health outcomes and further increased risks. This paper aims to address these challenges by identifying optimal feature subsets required for the early prediction of MetS. To get the optimal features a hybrid model is proposed in this paper. The proposed model utilizes both the filter method and the wrapper method. Firstly, features are selected using the correlation coefficient (CC) method which is a filter method, and then the selected features are further refined using genetic algorithm (GA) which is a wrapper method. This hybrid model increases the prediction accuracy and reduces computational cost. The methodology of the proposed model, its experimental setup, the results obtained, and the inferences gained are presented in this paper.

关键词： Correlation coefficient Sentiment analysis Accuracy Computational modeling Predictive models Filtering algorithms Stroke (medical condition) Feature extraction Medical diagnostic imaging Genetic algorithms

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