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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

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Computer Systems Science & engineering 2023年第9期46卷 3883-3899页

作者： Amarendra Reddy Panyala M.Baskar Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia Department of Information Technology MLR Institute of TechnologyHyderabad500043TelanganaIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia

The deep learning models are identified as having a significant impact on various *** same can be adapted to the problem of brain tumor ***,several deep learning models are presented earlier,but they need better classification *** efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this *** method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple *** noise-removed image has been equalized for its quality by using histogram ***,the features like white mass,grey mass,texture,and shape are extracted from the *** features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality *** texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution *** neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of *** on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关键词： CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture Gabor

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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A Hybrid Approach of Spider Monkey Optimization with Taylor Series for Optimal Routing in Wireless Sensor Network 7

A Hybrid Approach of Spider Monkey Optimization with Taylor ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sindhuja, M. Srm Institute of Science And Technology School of Computing College of Engineering And Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350359299

Clustering that facilitates routing protocols has been created in recent years to cut down on energy usage and increase the lifespan of WSNs. The LEACH cluster is well-known among WSNs. The LEACH selects a few sensor nodes at random from each cluster. Cluster heads (CHs) are the aforementioned nodes. Cluster members are the sensor nodes that make up each CH. CHs hold up extra load to coordinate diverse tasks during the cluster. Energy usage and overall network performance can both suffer if the wrong CHs are selected for the LEACH. Therefore, a significant factor in extending the WSN's lifetime is selecting an appropriate CH and an efficient routing algorithm. This work introduces a new approach to CH selection and optimal routing in WSN, combining the Spider monkey optimization technique with a Taylor-based gravitational search algorithm. The CHs are selected, and cluster-based routing algorithms are developed using Spider Monkey Optimization (SMO). The sensor nodes are first organized into clusters, with the maximum competent node being the cluster head. From there, communication is sent across several nodes in a multi-hop fashion. Taylor dependent gravitational search method (TDGSM) is introduced which uses the taylor series along with gravitational search method to find the optimal routing. The suggested algorithm has achieved superior results when compared to the existing methods. © 2024 IEEE.

关键词： Sensor nodes

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Intelligent patient monitoring through hybrid consensus algorithm based blockchain technology

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Multimedia Tools and Applications 2024年 1-22页

作者： Priyadharshini, A. Nithiya, S. Archana, H.A. Jaganathan, Subash Chandra Bose School of Engineering and Technology Bangaluru India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Agronomy SRM College of Agricultural Science Chengalpattu603201 India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India

This research aims to integrate IoT with blockchain technology to securely manage and monitor sensitive patient health data in critical care environments, thereby improving the reliability and efficiency of patient monitoring systems. A hybrid blockchain architecture is employed to store ICU patient data securely, linking each patient's information with their respective doctors' details. This system operates without third-party intermediaries, ensuring trusted communication channels between patients and doctors. The blockchain ensures authorized access and secure transmission of medical histories and treatment plans, even allowing for remote care by doctors outside hospital premises. The integration of blockchain technology with IoT devices facilitates rapid and secure data transmission, enhancing the speed and accuracy of critical care delivery. The decentralized nature of blockchain ensures data integrity and prevents unauthorized access, while the hybrid approach allows for efficient and trusted collaboration among healthcare professionals. By harnessing the power of hybrid blockchain technology, this system not only safeguards sensitive medical information but also improves the overall quality of critical care through timely and informed medical intervention. The proposed solution holds the promise of reducing patient mortality rates by ensuring secure, efficient, and collaborative healthcare delivery. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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An Optimized YOLO-based License Plate Recognition System with Integrated Privacy Safeguards 2

An Optimized YOLO-based License Plate Recognition System wit...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Putluru, Aravinda Reddy Pannuru, Vivekananda Reddy Ashokkumar, C. Kumar, C. Jothi School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India

ISBN: (纸本)9798331505745

Efficient and accurate license plate recognition (LPR) is crucial for intelligent transportation systems, surveillance, and security applications. This work proposes a Powered Smart License Plate Recognition System utilizing YOLOv8 for real-time detection and Optical Character Recognition (OCR) for character extraction. The system integrates publicly available datasets, including OpenALPR, CCPD, and UFPR-ALPR, along with custom-annotated data to enhance robustness. Data preprocessing involves normalization, augmentation, and privacy protection measures such as Gaussian blurring and encryption to ensure compliance with data protection regulations. The architecture is optimized using convolutional layers for feature extraction and attention mechanisms for improved character recognition. A privacy protection module ensures sensitive information masking, while Edge AI deployment on Jetson Nano or Raspberry Pi enables real-time inference with minimal latency. Model training employs transfer learning with pre-trained YOLOv8 weights, fine-tuned on license plate datasets. Optimizers such as Adam and SGD, along with IoU loss, enhance bounding box accuracy, ensuring precise detection. Performance evaluation demonstrates high mean Average Precision (mAP) exceeding 95% and OCR accuracy surpassing 90%, validating the system's efficiency across diverse environmental conditions. Applications include automated toll collection, traffic monitoring, vehicle tracking, and secure entry management in smart cities. Law enforcement agencies can utilize the system for stolen vehicle identification and traffic rule enforcement. The proposed approach offers a scalable, privacy-aware, and high-accuracy solution for modern LPR applications, ensuring compliance with security and regulatory standards. © 2025 IEEE.

关键词： Optical character recognition

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A Hybrid Approach of Black Widow Optimization with Firefly (FF) Algorithm for Efficient Routing in Wireless Sensor Network 2

A Hybrid Approach of Black Widow Optimization with Firefly (...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sindhuja, M. Srm Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

The sensor nodes that make up a Wireless Sensor Network (WSN) can be located anywhere and are linked wirelessly so that they can all keep tabs on the same set of physical characteristics in the area of interest. The nodes in a WSN run on internal batteries, thus when they run out of energy after repeated uses, the network has a shorter lifespan. Clustering and routing methods are frequently utilized in WSN to improve energy efficiency and network lifetime. For WSN's cluster-based routing, this work introduces the innovative black widow optimization with firefly algorithm (FFA). The suggested BWO-FF algorithm comprises BWO based clustering procedure to elect an optimal set of cluster heads (CHs). The BWO algorithm creates a fitness function utilizing input parameters. In addition, route selection for inter-cluster communication requires an FF-based routing method. This routing method aims to enhance network throughput and lifetime by calculating the ideal route from each node to the sink in an effort to ensure uniform energy consumption across the network. MATLAB simulations have been used to analyze the proposed approach from several perspectives. The BWO-FF algorithm's superiority to all other techniques studied is guaranteed by a selection of comparison evaluations. © 2024 IEEE.

关键词： Sensor nodes

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Graph-Theoretic Approaches in Semantic Web Service Orchestration and Discovery: A Comprehensive Literature Survey 5

Graph-Theoretic Approaches in Semantic Web Service Orchestra...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Vignesh, T.V. Vijaya, K. Minu, R.I. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies School of Computing Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Semantic Web Services (SWS) have become a powerful means for automating the discovery, composition, and orchestration of web services by providing them with semantic descriptions. This paper provides a comprehensive survey of graph-theoretic approaches in the orchestration and discovery of SWS, systematically categorizing and analyzing various methodologies. We examine how graph-based models contribute to enhancing the efficiency, accuracy, scalability, performance, reliability, and predictability of SWS processes. Furthermore, the paper discusses research gaps, challenges, and potential future directions, offering insights into emerging solutions and state-of-the-art techniques in the field. The survey also integrates recent advancements such as hybrid frameworks combining knowledge graphs and semantic web services, service clustering using graph embedding, and enhanced recommendation models. © 2024 IEEE.

关键词： Knowledge graph

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QIRA: A Quantum-Inspired Cluster Head Selection Algorithm for Wireless Sensor Networks 7

QIRA: A Quantum-Inspired Cluster Head Selection Algorithm fo...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sindhuja, M. Srm Institute of Science And Technology School of Computing College of Engineering And Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350359299

Cluster Head Selection (CHS) is one of the critical tasks in Wireless Sensor Networks (WSNs) as it directly impacts the network's performance and lifetime. In this manuscript, a Quantum-Inspired Recommendation Algorithm (QIRA) for optimal cluster head selection in WSNs has been proposed. QIRA utilizes the superposition and entanglement properties of quantum mechanics to explore the search space of potential cluster head candidates. The algorithm is designed to consider multiple features such as residual energy, distance to the base station, and node centrality during the process of Cluster Head Selection. The evaluation of the proposed QIRA algorithm, is has been done using wide-ranging simulations and compared with existing cluster head selection processes. The results demonstrate that QIRA is able to achieve significant improvements in terms of network lifetime, energy consumption, and data throughput. © 2024 IEEE.

关键词： Energy efficiency

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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