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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Indumathi, V. Ashokkumar, C. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed model is to enhance the prediction accuracy and fault detection capabilities of smart vehicles through advanced machine learning techniques. The EAE-Boost algorithm, an ensemble model integrating auto encoders and boosting, is compared with existing algorithms, namely K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Extreme Gradient Boosting (XGBoost), based on key simulation metrics, including accuracy, precision, prediction time, and fault detection rate. Simulation results demonstrate that the EAE-Boost algorithm outperforms the existing algorithms, achieving superior performance in all metrics. Notably, the proposed method showed significant improvements in accuracy and fault detection rate, with a reduction in prediction time, highlighting its potential for real-time maintenance applications in smart automotive systems. The findings underscore the effectiveness of the proposed deep learning model in enhancing vehicle reliability, safety, and operational efficiency. © 2025 IEEE.

关键词： Predictive maintenance

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Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1819-1835页

作者： Kangjia Qiao Jing Liang Kunjie Yu Xuanxuan Ban Caitong Yue Boyang Qu Ponnuthurai Nagaratnam Suganthan School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electronic and Information Zhongyuan University of TechnologyZhengzhou 450007China KINDI Center for Computing Research College of EngineeringQatar UniversityDoha 999043Qatar

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for *** all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto ***,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single ***,this paper attempts to identify promising single constraints and utilize them to help solve *** be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint ***,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance ***,an improved tentative method is designed to find and transfer useful knowledge among *** results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

关键词： Constrained multi-objective optimization(CMOPs) evolutionary multitasking knowledge transfer single constraint.

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Study of the Inhibition of Schumann Resonance-inspired Electromagnetic Field on Cancer Cell Proliferation

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Journal of Bionic engineering 2025年第1期22卷 341-353页

作者： Xianqiang Yan Xuelei Liu Shujun Zhang Zhenning Liu Luquan Ren Key Laboratory of Bionic Engineering(Ministry of Education) College of Biological and Agricultural EngineeringJilin UniversityChangchun130022China School of Computing and Engineering University of GloucestershireThe ParkCheltenhamGL502RHUK School of Mechanical Engineering Shandong University of TechnologyZibo255000China

Organisms on Earth evolve and coexist with natural Electromagnetic Fields(EMFs).Although many reports have suggested the potential anti-neoplastic effects of EMFs with specific parameters,the studies on the influence of natural EMFs on cancers are still ***,an EMF emitter has been developed to investigate the effects of the extremely-low frequency SR-mimicking EMF(SREMF)on cancer and normal cell *** numerical simulation has revealed that the emitter with specific parameters is able to enhance EMF intensity and uniformity on the designated plane above the *** importantly,honeycomb-like emitter array can generate a stronger EMF intensity on the 20 mm plane above the *** colony formation assays have demonstrated that SREMF generated by the honeycomb-like emitter array can significantly inhibit Hela cell proliferation in a cell-density-dependent *** morphological changes of SREMF-exposed Hela cells suggest that the anti-proliferative effect of SREMF may be caused by apoptosis *** contrast,no detrimental effect is observed for SREMF-treated normal cells,which probably can be explained by the evolutionary ***,this work can not only contribute to understanding the impact of natural EMF on creatures,but also afford a novel strategy to personalized cancer prevention and treatment.

关键词： Electromagnetic fields(EMFs) Schumann resonance EMF emitter Cancer Cell proliferation

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Deep Learning Approach for Identifying Red Lesions in Retinal Fundus Imagery 6th

Deep Learning Approach for Identifying Red Lesions in Retina...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Divyashrree, R. Dhawan, Samarth Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9789819774258

One dangerous side effect of diabetes that affects the eyes is called diabetic retinopathy. It happens as a result of alterations in the retina’s blood vessels, which can cause harm and even blindness. The development of red lesions in the retina is one typical sign of diabetic retinopathy. These red lesions may be a sign of microaneurysms or hemorrhages, which are important characteristics to identify and track as diabetic retinopathy progresses. This process becomes even more demanding in the early stages of the disease where symptoms are less pronounced in the images. Leveraging machine-based learning for medical image analysis, especially deep learning algorithms, has demonstrated its capability to enhance the early diagnosis of diabetic retinopathy, providing a more efficient and effective alternative to traditional manual assessments. The proposed work aims to improve the efficiency of early detection of diabetic retinopathy by utilizing Deep Learning (16-layer CNN) to determine a person’s likelihood of having diabetic retinopathy using a 02-Class model that compiles a variety of datasets, specifically IDRiD dataset. The entire dataset contains 18,590 fundus photographs, in which 3662 are allocated for training, 1928 for validation, and 13,000 for testing. All datasets had a similar distribution of output classes, which is a fundamental property of this type of data. Using the IDRiD dataset, the suggested Red Lesion detection system’s specificity and sensitivity were found to be 98% and 89%, respectively, with an accuracy of 95.32%. Using the MESSIDOR dataset, the Diabetic Retinopathy Severity Classification System’s specificity, sensitivity, and accuracy were found to be 93.8, 92.3, and 94%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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Image Fusion Based on NSCT and Sparse Representation for Remote Sensing Data

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Computer Systems Science & engineering 2023年第9期46卷 3439-3455页

作者： N.A.Lawrance T.S.Shiny Angel Department of Computational Intelligence School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyChengalpattu603203India

The practice of integrating images from two or more sensors collected from the same area or object is known as image *** goal is to extract more spatial and spectral information from the resulting fused image than from the component *** images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral *** study provides a novel picture fusion technique that employs L0 smoothening Filter,Non-subsampled Contour let Transform(NSCT)and Sparse Representation(SR)followed by the Max absolute rule(MAR).The fusion approach is as follows:first,the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing *** comes the fusion process,which uses an approach that combines NSCT and SR to fuse low frequency ***,the Max-absolute fusion rule is used to merge high frequency ***,the final image is obtained through the disintegration of fused low and high frequency *** terms of correlation coefficient,Entropy,spatial frequency,and fusion mutual information,our method outperforms other methods in terms of image quality enhancement and visual evaluation.

关键词： Remote sensing multispectral image pan chromatic image L0 smoothening filter non-sub sampled contourlet transform sparse representation

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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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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Tripathi, Ansh Dubry, Saransh Rama, P. Angayarkanni, V. Kalaivani, J. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Srmist Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies India

ISBN: (纸本)9798331529833

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. © 2025 IEEE.

关键词： Environmental monitoring

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Face Recognition CCTV for Automated Attendance Tracking

Face Recognition CCTV for Automated Attendance Tracking

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kola, Sai Sindhu Kenchi, Harsha Rama, P. Angayarkanni, V. Kalaivani, J. Sivakumar, S. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India Srm Institute of Science and Technology Computing TechnologiesCollege of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Faculty of Engineering and Technology Srmist Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331529833

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. © 2025 IEEE.

关键词： Face recognition

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Enhancing Asthma Diagnosis Through Machine Learning: A Stacking Ensemble Approach 1

Enhancing Asthma Diagnosis Through Machine Learning: A Stack...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Goel, Manas Gupta, Manya Jothikumar, C. Shanmugam, S. Ashokkumar, C. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Chennai603203 India

ISBN: (纸本)9798331518943

Asthma diagnosis remains a major clinical concern since it has diverse symptoms and is caused by various contributory causes. Machine learning was applied in this study to solve these complications in order to analyze such a broad dataset of 2,392 patients with the capture of important variables such as demographics, environmental exposures, allergies, medical history, and clinical indicators. Several machine learning models were implemented, including those such as Random Forest, XGBoost, Decision Tree, LGBM, CatBoost, and AdaBoost. Next, the stacking ensemble strategy was taken to further optimize predictive performance, using the Random Forest model meta-classifier. Our approach proves that stable ensemble learning takes place with an amazing 97% accuracy rate when it comes to difficult clinical data. Recall, precision, and F1-score represent some of the most important measures for model performance evaluation. These results might point out the machine learning potential in improving asthma diagnosis accuracy and might be helpful for a doctor in coping with this complicated disease. © 2024 IEEE.

关键词： Adversarial machine learning

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YOLOv4-Powered Traffic Intelligence: From Detection to Optimization

YOLOv4-Powered Traffic Intelligence: From Detection to Optim...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Sanjiv, Sinha Tharunn, E. Baskar, M. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (纸本)9798350382693

The Traffic Analysis System utilizes machine learning for real-time traffic insights at a junction. It provides instant stats like vehicle count, density, and FPS, while summarizing average vehicle crossings, densities, stop times, and identifying peak minutes. Machine learning techniques include object detection, tracking, and lane segmentation. The system offers a user-friendly interface with graphical representations, aiding traffic management decisions. It not only ensures efficient traffic control but also allows for predictive analytics and potential integration with traffic light systems for adaptive signal adjustments. Continuous model training and future enhancements promise improved accuracy and adaptability to evolving traffic scenarios. © 2024 IEEE.

关键词： Machine learning

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