Aswath Selvaraj

@veltech.edu.in

Assistant Professor
Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology

Aswath Selvaraj


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https://researchid.co/aswathselva

RESEARCH INTERESTS

Artificial Intelligence, Machine Learning, Bio Signal Processing
22

Scopus Publications

73

Scholar Citations

6

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Hybrid net powered large scale audit of dataset licensing and attribution practices for enhanced transparency and compliance
    Velmurugan Ayyamperumal, S. Aswath, S. Vignesh, T. Thamaraimanalan
    Scientific Reports, 2026
    In the era of data-driven research and artificial intelligence, proper dataset licensing and attribution practices are crucial for legal compliance and ethical data usage. Open-access datasets are often shared under various licensing schemes, such as Creative Commons (CC) or MIT, each with distinct usage and attribution requirements. However, ensuring adherence to these requirements across vast repositories poses a significant challenge. This study presents a HybridNet-powered system combining RoBERTa and InceptionV3 models to audit large-scale dataset licensing and attribution practices for enhanced transparency and legal compliance. The system leverages RoBERTa for natural language processing (NLP) to classify licensing terms and detect attribution requirements in textual metadata, while InceptionV3 handles visual attribution embedded in images. A comprehensive dataset audit was conducted on 5,000 datasets from the OpenML repository, resulting in an overall accuracy of 95% for detecting and classifying licenses and attributions. Cross-validation with OpenML metadata showed 94% consistency in license classification and 90% consistency in attribution detection. License violations were identified in 5% of the datasets, while attribution violations were flagged in 6%, leading to an overall compliance violation rate of 11%. The system’s ensemble approach significantly outperformed traditional models, such as Logistic Regression (accuracy: 72%) and Support Vector Machines (accuracy: 75%), demonstrating its effectiveness in auditing multi-modal dataset content. By flagging datasets with missing or inconsistent license and attribution information, the system enables corrective action, improving the transparency of dataset repositories.
  • Evolutionary reinforcement learning framework for energy-efficient fault resilience and topological stability in WSNs
    S. Lakshmi, S. Aswath, A. Swaminathan, Anandakumar Haldorai
    Scientific Reports, 2026
    Wireless Sensor Networks (WSNs) form the technological foundation for several modern applications, including smart cities, environmental monitoring, healthcare surveillance, and industrial automation. However, the performance and long-term reliability of WSNs continue to be constrained by persistent challenges such as communication faults, accelerated energy depletion, reduced network lifetime, increased latency, and inconsistent throughput. These challenges are further intensified by the growing scale, density, and dynamic behavior of contemporary WSN deployments. Existing techniques frequently optimize one dimension-such as energy conservation or communication quality-while neglecting equally important aspects like fault recovery, adaptability, and real-time responsiveness. To address these limitations, this study introduces EvoGenRL, an Evolutionary Reinforcement Learning framework that integrates Reinforcement Learning (RL), Differential Evolution (DE), and Generative Adversarial Networks (GANs) for robust WSN management. In EvoGenRL, RL is utilized to learn adaptive routing and fault-tolerant decision policies; DE optimizes hyperparameters to improve convergence stability and policy efficiency; and GANs generate diverse, realistic fault scenarios to enrich the training environment and enhance model generalization. This combined strategy enables WSNs to operate efficiently under uncertain, heterogeneous, and failure-prone conditions. Experimental evaluations confirm that EvoGenRL delivers substantial improvements compared to conventional optimization and routing schemes. The proposed method successfully reduces energy consumption to 2.2 J, extends network lifetime to 1700 cycles, increases packet delivery ratio to 99.7%, lowers latency to 3.2 ms, and boosts throughput to 350 kbps. These advancements demonstrate the capability of EvoGenRL to simultaneously enhance energy efficiency, communication performance, and fault resilience. Overall, this research provides a comprehensive and scalable solution for next-generation WSN optimization. The EvoGenRL framework not only addresses current operational limitations but also establishes a foundation for future developments in intelligent, adaptive, and power-efficient sensor network control.
  • Adoption of Machine Learning Techniques in Smart Applications based on Blockchain Technology
    Machine Learning and Blockchain Challenges Future Trends and Sustainable Technologies, 2026
  • Novel ML-based Detection System for Identifying Malicious Pdf Documents in Financial Cloud
    Dr. Prerna Mahajan
    Journal of Internet Services and Information Security, 2025
    The growing dependability of financial institutions on cloud-based services for document management has given rise to significant safety concerns over the potential for malicious PDF documents. In response, this research suggests a Machine Learning (ML) based detection technique for spotting erroneous PDF files in a financial cloud environment. The recommended solution leverages modern technologies to enhance the security posture of financial institutions by efficiently recognizing and mitigating potential hazards. The methodology analyzes the attributes of malicious PDF documents and the final relevant qualities that indicate malicious intent using the Sparse K Nearest Neighbor (SKNN) algorithm. The PDF files are pre-processed to ascertain whether the samples have errors or duplicate content. Principal Component Analysis (PCA) is used to extract the features of PDF files. A user employs files that can avoid detection by a security system and send a variety of payloads that have the potential to do serious damage. Comprehensive testing and assessment in terms of ROC-AUC (98.7%), F1-score (98%), recall (97%) and detection accuracy (98.5%) demonstrate the effectiveness of the recommended strategy. The finding's outstanding performance demonstrates how ML techniques can be used to improve cloud financial system cybersecurity safeguards.
  • Advanced Beamforming and Multi-Access Edge Computing: Empowering Ultra-Reliable and Low-Latency Applications in 6G Networks
    K. Gunasekaran, S. Dhanasekaran, R. Vinod Kumar, S. Aswath
    International Journal of Communication Systems, 2025
    6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.
  • Enhanced Early Brain Tumor Detection Crossing Blood–Brain Barrier through MRI Images Using Berkeley Wavelet-Transformation-Based Segmentation
    D. Santhakumar, R. Prasanna, M. Sivakumar, S. Aswath, P. S. Arthy, R. Rajesh Kanna
    Critical Reviews in Therapeutic Drug Carrier Systems, 2025
    Brain tumor is one of the reasons for several mortality cases in hospitals. Early detection and diagnosis of brain tumors are necessary to cure the disease early. The extraction of the tumor from the brain's magnetic resonance image (MRI) is considered to be a difficult task when done by clinical experts, and it is also pretty time-consuming. These drawbacks can be overcome by using computer vision-based technologies. The proposed method detects brain tumor crossing the blood-brain barrier (BBB) through MRI images by using Berkeley wavelet transformation (BWT) for segmenting the affected areas. Support vector machine (SVM) is used for classification purpose by which the classification process is divided into two different categories namely, the tumor affected and tumor non-affected parts. Initially, the acquired image is converted to a greyscale from RGB. Then, image segmentation is done. During the image segmentation, morphological operations are carried out. Two morphological operations have been used in the proposed system. They are erosion and dilation. Both these techniques are used for edge detection. In erosion, the pixels are removed from the edges of the tumor image. In dilation, pixels are added at the edges of the tumor images. After the morphological operation, feature extraction is carried out. The features like homogeneity, contrast of the image and the energy might be determined. Then, the image is classified using the SVM classification algorithm. The experimental results have been tabulated and depicted using graphical representations. Comparing to the existing approaches the proposed method is proved to be better in accuracy and efficiency.
  • Optimizing federated learning approaches with hybrid Convolutional Neural Networks - Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks
    Raja Lakshminarayanan, Selvaraj Dhanasekaran, Rangarajan Vinod Kumar, Aswath Selvaraj
    International Journal of Communication Systems, 2024
    SummaryWireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network–bidirectional encoder representations from transformers (CNN‐BERT) model. Effectively applying min–max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10; Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12; and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL‐optimized method in improving localization accuracy. The outcomes demonstrate the FL‐optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings.
  • Modified Box Filter Design and Noise Analysis on Two-Dimensional Images
    R. Nanmaran, S. Aswath, K. Vishalatchi, S. Srimathi, P. Banu Priya
    Lecture Notes in Networks and Systems, 2024
  • Deep Learning -Enhanced Image Segmentation for Medical Diagnostics
    V Malathy, Niladri Maiti, Nithin Kumar, D. Lavanya, S. Aswath, Shaik Balkhis Banu
    Proceedings 3rd International Conference on Advances in Computing Communication and Applied Informatics Accai 2024, 2024
    Picture segmentation is an essential instrument in medical diagnostics, as it enables the precise identification and examination of anatomical characteristics and pathological regions. Modern picture segmentation algorithms have been considerably improved in comparison to their predecessors as a result of recent developments in deep learning. This paper offers a succinct overview of the most recent developments in medical diagnostic image segmentation, which have been facilitated by the application of deep learning algorithms. U-Nets, CNNs, and FCNs are deep learning architectures that are frequently implemented in medical imaging. The precision and efficacy of automated image analysis have been improved by integrating these models into medical diagnostics. Furthermore, we investigate the challenges that arise when applying deep learning models to healthcare, including the necessity for a more comprehensive comprehension of the models and the restricted availability of data.
  • AI-Augmented Decision-Making in Management Using Quantum Networks
    Brijesh Goswami, Monika Dixit, V. Asha, V. Chandra Jagan Mohan, S. Aswath, Joshuva Arockia Dhanraj
    Multidisciplinary Applications of AI and Quantum Networking, 2024
    The convergence of artificial intelligence with massive computing allows for the delivery of real-time analytics, the optimization of complicated decision-making scripts, and the recycling of large datasets, all of which are previously unknown capabilities. It is possible to considerably improve the efficiency, delicacy, and rigidity of operation methods by employing artificial intelligence models and algorithms that increase the quantum of data. The purpose of this study is to investigate the theoretical foundations of artificial intelligence and quantum networks, investigate the combined eventuality of these concepts, and display practical operations in a variety of operation disciplines. The authors explain, by means of an exhaustive study of case studies and experimental data, how artificial intelligence-driven decision-making fabrics have the potential to rewrite conventional operational procedures.
  • EFFECTIVE BIOMEDICAL SYSTEM FOR DETECTING, TRACKING, AND PREVENTING ASYMPTOMATIC COVID-19 PATIENTS NON-INVASIVELY USING IoT AND MIXED REALITY
    R. Prasanna, T. Ragupathi, N. Ganesh Kumar, Banu Priya Prathaban, S. Aswath, R. Rajesh Kanna
    International Journal for Multiscale Computational Engineering, 2024
  • Artificial Bee-Optimized CNN for Osteoporosis Detection using Leg X-ray Images
    Bakiya A, Vetrivel V, Kamalanand K, Anitha A, Aswath S, Valliammai M, Sridevi S
    Proceedings of the 2024 10th International Conference on Biosignals Images and Instrumentation Icbsii 2024, 2024
  • An Adaptive Sleep Apnea Detection Model Using Multi Cascaded Atrous-Based Deep Learning Schemes With Hybrid Artificial Humming Bird Pity Beetle Algorithm
    Selvaraj Aswath, Valarmathi Ravichandran Shanmuga Sundaram, Miroslav Mahdal
    IEEE Access, 2023
  • An Enhanced Analysis of Blood Cancer Prediction Using ANN Sensor-Based Model
    Althaf Ali A, K. Hemalatha, N. Mohana Priya, S. Aswath, Sushma Jaiswal
    Engineering Proceedings, 2023
  • A Review on Effective Open-Source Web-Based Tools to Teach Signals and Systems Course in Online Mode
    S. Aswath, Anandan P, R.S. Valarmathi, CH. Mohan Sai Kumar, D. Bharathi Dhasan
    Proceedings of the 5th International Conference on Inventive Research in Computing Applications Icirca 2023, 2023
  • Obstructive Sleep Apnea Severity Prediction Model GUI using Anthropometrics
    Aswath S, Valarmathi S
    Ssrg International Journal of Electrical and Electronics Engineering, 2022
  • Modified Spotted Hyena Optimizer Based Leukemia Microscopic Images Classification
    S. Aswath, N. Bharanidharan, R. S. Valarmathi, Harikumar Rajaguru
    Ifmbe Proceedings, 2022
  • Highly Secured Steganography Method for Image Communication using Random Byte Hiding and Confused & Diffused Encryption
    S. Aswath, R. S. Valarmathi, C. H. Mohan Sai Kumar, M. Pandiyarajan
    Lecture Notes on Data Engineering and Communications Technologies, 2022
  • DNA Sequence Classification with Improved Performance of Supervised Classifiers using Nature Inspired Algorithms
    S. Aswath, CH.Mohan Sai Kumar, V.Hima Deepthi, S.Imran Javeed, SVN. Rupesh
    2022 2nd International Conference on Intelligent Technologies Conit 2022, 2022
  • Enhancing the Performance of Classifiers in Detecting Abnormalities in Medical Data using Nature Inspired Optimization Techniques
    S. Aswath, Ch. Mohan Sai Kumar, K. Reethi, B. Deepthi, K. Chikitha, Svn. Rupesh
    2022 International Conference for Advancement in Technology Iconat 2022, 2022
  • An Empirical Review on Image Dehazing Techniques for Change Detection of Land Cover
    CH.Mohan Sai Kumar, R.S. Valarmathi, S. Aswath
    2021 Asian Conference on Innovation in Technology Asiancon 2021, 2021
  • Multiscale image segmentation using cascade MultiResUNet proposal masking convolutional network
    Ch. Mohan Sai Kumar, R. S. Valamathi, S. Aswath
    2021 2nd International Conference for Emerging Technology Incet 2021, 2021

RECENT SCHOLAR PUBLICATIONS

  • Novel Isolation Enhanced Broadband MIMO Antenna Design for 5G Communications: Extended Mid-Microwave Band of 6 GHz to 13 GHz
    IJ Settu, D Muthukumaran, R Dileepan, M Pandiyarajan, S Aswath, ...
    2026 6th International Conference on Trends in Material Science and … , 2026
    2026
  • Evolutionary reinforcement learning framework for energy-efficient fault resilience and topological stability in WSNs
    S Lakshmi, S Aswath, A Swaminathan, A Haldorai
    Scientific Reports , 2026
    2026
  • Hybrid net powered large scale audit of dataset licensing and attribution practices for enhanced transparency and compliance
    V Ayyamperumal, S Aswath, S Vignesh, T Thamaraimanalan
    Scientific Reports 16 (1), 2854 , 2026
    2026
  • Advanced Beamforming and Multi‐Access Edge Computing: Empowering Ultra‐Reliable and Low‐Latency Applications in 6G Networks
    K Gunasekaran, S Dhanasekaran, RV Kumar, S Aswath
    International Journal of Communication Systems 38 (5), e6027 , 2025
    2025
    Citations: 8
  • Enhanced Early Brain Tumor Detection Crossing Blood− Brain Barrier through MRI Images Using Berkeley Wavelet-Transformation-Based Segmentation
    D Santhakumar, P Ravichandran, M Sivakumar, S Aswath, PS Arthy, ...
    Critical Reviews™ in Therapeutic Drug Carrier Systems 42 (4) , 2025
    2025
  • Optimizing federated learning approaches with hybrid Convolutional Neural Networks‐Bidirectional Encoder Representations from Transformers for precise estimation of average …
    R Lakshminarayanan, S Dhanasekaran, R Vinod Kumar, A Selvaraj
    International Journal of Communication Systems 37 (13), e5822 , 2024
    2024
    Citations: 12
  • Deep learning-enhanced image segmentation for medical diagnostics
    V Malathy, N Maiti, N Kumar, D Lavanya, S Aswath, SB Banu
    2024 International Conference on Advances in Computing, Communication and … , 2024
    2024
    Citations: 6
  • Artificial Bee-Optimized CNN for Osteoporosis Detection using Leg X-ray Images
    A Bakiya, V Vetrivel, K Kamalanand, A Anitha, S Aswath, M Valliammai, ...
    2024 Tenth International Conference on Bio Signals, Images, and … , 2024
    2024
    Citations: 3
  • EFFECTIVE BIOMEDICAL SYSTEM FOR DETECTING, TRACKING, AND PREVENTING ASYMPTOMATIC COVID-19 PATIENTS NON-INVASIVELY USING IoT AND MIXED REALITY
    R Prasanna, T Ragupathi, NG Kumar, BP Prathaban, S Aswath, ...
    International Journal for Multiscale Computational Engineering 22 (6) , 2024
    2024
    Citations: 1
  • An adaptive sleep apnea detection model using multi cascaded atrous-based deep learning schemes with hybrid artificial humming bird pity beetle algorithm
    S Aswath, VRS Sundaram, M Mahdal
    IEEe Access 11, 113114-113133 , 2023
    2023
    Citations: 13
  • A review on effective open-source web-based tools to teach signals and systems course in online mode
    S Aswath, P Anandan, RS Valarmathi, CHMS Kumar, DB Dhasan
    2023 5th international conference on inventive research in computing … , 2023
    2023
    Citations: 3
  • Modified Box Filter Design and Noise Analysis on Two-Dimensional Images
    R Nanmaran, S Aswath, K Vishalatchi, S Srimathi, P Banu Priya
    International Conference on Recent Trends in Computing, 205-216 , 2023
    2023
  • An Enhanced Analysis of Blood Cancer Prediction Using ANN Sensor-Based Model
    K Hemalatha, NM Priya, S Aswath, S Jaiswal
    Engineering Proceedings 59 (1), 65 , 2023
    2023
    Citations: 7
  • Obstructive Sleep Apnea Severity Prediction Model GUI using Anthropometrics
    S Aswath, S Valarmathi
    International Journal of Electrical and Electronics Engineering 9 (12) , 2022
    2022
    Citations: 3
  • DNA sequence classification with improved performance of supervised classifiers using nature inspired algorithms
    S Aswath, CHMS Kumar, VH Deepthi, SI Javeed, SVN Rupesh
    2022 2nd International Conference on Intelligent Technologies (CONIT), 1-7 , 2022
    2022
    Citations: 6
  • Modified Spotted Hyena Optimizer Based Leukemia Microscopic Images
    S Aswath, N Bharanidharan, RS Valarmathi, H Rajaguru
    6th Kuala Lumpur International Conference on Biomedical Engineering 2021 … , 2022
    2022
  • Enhancing the performance of classifiers in detecting abnormalities in medical data using nature inspired optimization techniques
    S Aswath, CMS Kumar, K Reethi, B Deepthi, K Chikitha, S Rupesh
    2022 International Conference for Advancement in Technology (ICONAT), 1-7 , 2022
    2022
    Citations: 3
  • Modified Spotted Hyena Optimizer Based Leukemia Microscopic Images Classification
    S Aswath, N Bharanidharan, RS Valarmathi, H Rajaguru
    Kuala Lumpur International Conference on Biomedical Engineering, 123-131 , 2022
    2022
    Citations: 1
  • Highly Secured Steganography Method for Image Communication using Random Byte Hiding and Confused & Diffused Encryption
    S Aswath, RS Valarmathi, CH Mohan Sai Kumar, M Pandiyarajan
    Computer Networks and Inventive Communication Technologies, 867-884 , 2022
    2022
  • An empirical review on image dehazing techniques for change detection of land cover
    CHMS Kumar, RS Valarmathi, S Aswath
    2021 Asian Conference on Innovation in Technology (ASIANCON), 1-9 , 2021
    2021
    Citations: 5

MOST CITED SCHOLAR PUBLICATIONS

  • An adaptive sleep apnea detection model using multi cascaded atrous-based deep learning schemes with hybrid artificial humming bird pity beetle algorithm
    S Aswath, VRS Sundaram, M Mahdal
    IEEe Access 11, 113114-113133 , 2023
    2023
    Citations: 13
  • Optimizing federated learning approaches with hybrid Convolutional Neural Networks‐Bidirectional Encoder Representations from Transformers for precise estimation of average …
    R Lakshminarayanan, S Dhanasekaran, R Vinod Kumar, A Selvaraj
    International Journal of Communication Systems 37 (13), e5822 , 2024
    2024
    Citations: 12
  • Advanced Beamforming and Multi‐Access Edge Computing: Empowering Ultra‐Reliable and Low‐Latency Applications in 6G Networks
    K Gunasekaran, S Dhanasekaran, RV Kumar, S Aswath
    International Journal of Communication Systems 38 (5), e6027 , 2025
    2025
    Citations: 8
  • An Enhanced Analysis of Blood Cancer Prediction Using ANN Sensor-Based Model
    K Hemalatha, NM Priya, S Aswath, S Jaiswal
    Engineering Proceedings 59 (1), 65 , 2023
    2023
    Citations: 7
  • Deep learning-enhanced image segmentation for medical diagnostics
    V Malathy, N Maiti, N Kumar, D Lavanya, S Aswath, SB Banu
    2024 International Conference on Advances in Computing, Communication and … , 2024
    2024
    Citations: 6
  • DNA sequence classification with improved performance of supervised classifiers using nature inspired algorithms
    S Aswath, CHMS Kumar, VH Deepthi, SI Javeed, SVN Rupesh
    2022 2nd International Conference on Intelligent Technologies (CONIT), 1-7 , 2022
    2022
    Citations: 6
  • An empirical review on image dehazing techniques for change detection of land cover
    CHMS Kumar, RS Valarmathi, S Aswath
    2021 Asian Conference on Innovation in Technology (ASIANCON), 1-9 , 2021
    2021
    Citations: 5
  • Artificial Bee-Optimized CNN for Osteoporosis Detection using Leg X-ray Images
    A Bakiya, V Vetrivel, K Kamalanand, A Anitha, S Aswath, M Valliammai, ...
    2024 Tenth International Conference on Bio Signals, Images, and … , 2024
    2024
    Citations: 3
  • A review on effective open-source web-based tools to teach signals and systems course in online mode
    S Aswath, P Anandan, RS Valarmathi, CHMS Kumar, DB Dhasan
    2023 5th international conference on inventive research in computing … , 2023
    2023
    Citations: 3
  • Obstructive Sleep Apnea Severity Prediction Model GUI using Anthropometrics
    S Aswath, S Valarmathi
    International Journal of Electrical and Electronics Engineering 9 (12) , 2022
    2022
    Citations: 3
  • Enhancing the performance of classifiers in detecting abnormalities in medical data using nature inspired optimization techniques
    S Aswath, CMS Kumar, K Reethi, B Deepthi, K Chikitha, S Rupesh
    2022 International Conference for Advancement in Technology (ICONAT), 1-7 , 2022
    2022
    Citations: 3
  • Implementation of random byte hiding algorithm in video steganography
    S Aswath, K Akshara, P Pavithra, DS Abinaya
    Int. J. Eng. Res. Technol 5 (13), 1-5 , 2017
    2017
    Citations: 2
  • EFFECTIVE BIOMEDICAL SYSTEM FOR DETECTING, TRACKING, AND PREVENTING ASYMPTOMATIC COVID-19 PATIENTS NON-INVASIVELY USING IoT AND MIXED REALITY
    R Prasanna, T Ragupathi, NG Kumar, BP Prathaban, S Aswath, ...
    International Journal for Multiscale Computational Engineering 22 (6) , 2024
    2024
    Citations: 1
  • Modified Spotted Hyena Optimizer Based Leukemia Microscopic Images Classification
    S Aswath, N Bharanidharan, RS Valarmathi, H Rajaguru
    Kuala Lumpur International Conference on Biomedical Engineering, 123-131 , 2022
    2022
    Citations: 1
  • Novel Isolation Enhanced Broadband MIMO Antenna Design for 5G Communications: Extended Mid-Microwave Band of 6 GHz to 13 GHz
    IJ Settu, D Muthukumaran, R Dileepan, M Pandiyarajan, S Aswath, ...
    2026 6th International Conference on Trends in Material Science and … , 2026
    2026
  • Evolutionary reinforcement learning framework for energy-efficient fault resilience and topological stability in WSNs
    S Lakshmi, S Aswath, A Swaminathan, A Haldorai
    Scientific Reports , 2026
    2026
  • Hybrid net powered large scale audit of dataset licensing and attribution practices for enhanced transparency and compliance
    V Ayyamperumal, S Aswath, S Vignesh, T Thamaraimanalan
    Scientific Reports 16 (1), 2854 , 2026
    2026
  • Enhanced Early Brain Tumor Detection Crossing Blood− Brain Barrier through MRI Images Using Berkeley Wavelet-Transformation-Based Segmentation
    D Santhakumar, P Ravichandran, M Sivakumar, S Aswath, PS Arthy, ...
    Critical Reviews™ in Therapeutic Drug Carrier Systems 42 (4) , 2025
    2025
  • Modified Box Filter Design and Noise Analysis on Two-Dimensional Images
    R Nanmaran, S Aswath, K Vishalatchi, S Srimathi, P Banu Priya
    International Conference on Recent Trends in Computing, 205-216 , 2023
    2023
  • Modified Spotted Hyena Optimizer Based Leukemia Microscopic Images
    S Aswath, N Bharanidharan, RS Valarmathi, H Rajaguru
    6th Kuala Lumpur International Conference on Biomedical Engineering 2021 … , 2022
    2022