Muthukumar Arunachalam

Scopus Publications

Enhanced fingerprint authentication: a deep learning and error correction-based biometric cryptosystem
Netha Merin Mathew, A. Muthukumar
International Journal of System Assurance Engineering and Management, 2026
Robust Medical Image Reconstruction Using a Self-Evolving Encoder–Decoder and Adaptive Convolutional Power Scaling
Dhanusha P B, J. Bennilo Fernandes, A. Muthukumar, A. Lakshmi
International Journal of Advanced Computer Science and Applications, 2026
Robust medical image reconstruction is a critical requirement for accurate diagnosis and clinical decision-making, particularly when images are affected by degradation, noise, or low resolution. Conventional encoder–decoder-based reconstruction methods compress input images into low-dimensional representations and subsequently decode them into high-resolution outputs; however, such approaches often suffer from artifacts and loss of fine anatomical details under severe degradation. To address these limitations, this work proposes a robust medical image reconstruction framework using a self-evolving encoder–decoder and adaptive convolutional power scaling. The proposed super-resolution model incorporates a dynamic encoder and decoder that adaptively evolve during training to capture color contrast, structural similarity, and high-frequency details from medical images. An MLP enhanced with an adaptive power flex layer is embedded within the reconstruction pipeline, enabling learnable power-based feature scaling through weight-wise modulation and initialization. This mechanism improves feature discrimination and stabilizes the reconstruction of subtle anatomical structures. The DRIVE and CHASE_DB1 retinal image datasets are employed for experimental validation, with appropriate preprocessing applied before training and testing. The selected images are processed through the proposed super-resolution model, and performance is quantitatively evaluated using PSNR, SSIM, sensitivity, and specificity metrics. Experimental results demonstrate that the proposed method achieves significant improvements in reconstruction quality and robustness compared to existing approaches, yielding enhanced perceptual quality and structural fidelity in reconstructed medical images. These findings indicate that the proposed self-evolving encoder–decoder with adaptive convolutional power scaling is well-suited for reliable medical image reconstruction applications.
A comprehensive approach to enhance emotion recognition through advanced feature extraction and Attention
A. Vidhyasekar, J. Jaya, B. Paulchamy, A. Muthukumar
Biomedical Signal Processing and Control, 2025
Medicinal plants of South India: A comprehensive dataset for species identification
Muthukumar Arunachalam, T. Gopu, K. Uma, Sabari Nathan
Data in Brief, 2025
The identification and classification of medicinal plants are crucial for botanical research, traditional medicine, and AI-driven applications. However, the absence of a standardized, high-quality dataset limits advancements in automated species recognition. This study introduces SIMPD Version 1 (South Indian Medicinal Plants Dataset), a curated dataset comprising high-resolution images of diverse medicinal plant species native to South India. The dataset integrates detailed taxonomic classifications and metadata to facilitate precise species identification and biodiversity analysis. Images were acquired under real-world conditions, considering variations in illumination, pose, and environmental factors to enhance dataset robustness. SIMPD is designed to support machine learning applications, particularly in image-based plant classification, object detection, and segmentation tasks. By providing an extensive dataset for AI-driven research, this work aims to bridge the gap between traditional ethnobotanical knowledge and modern computational methodologies, fostering advancements in medicinal plant classification, conservation, and ecological research.
DABiG: Breath pattern classification using the hybrid deep learning with optimal feature selection
P Kaleeswari, R Ramalakshmi, T Arun Prasath, A Muthukumar, R Kottaimalai, M Thanga Raj
Technology and Health Care, 2025
Background A person's breathing pattern can be a reflection of their emotional and physical well-being because it shows the frequency, intensity, and rhythm of their breathing. Objective This research article presents a comprehensive approach to breathe pattern classification utilizing gyroscope and accelerometer readings obtained from individuals using two distinct sensors. The study encompasses the acquisition of six diverse breathing patterns, with a focus on data pre-processing through Min-Max normalization. Methods To select essential features from the normalized data, an innovative optimization algorithm, Adaptive Chimp Optimization (AdCO), is introduced. AdCO integrates an adaptive weighting strategy into the conventional Chimp optimization algorithm, enhancing convergence rates and enabling global optimal feature selection. Furthermore, the article introduces the application of the selected features in breath pattern classification using a hybrid deep learning mechanism, DABiG. DABiG leverages the Bidirectional Gated Recurrent Unit (BiGRU), a neural network architecture capable of processing sequential data bi-directionally. Results Spatial and temporal attention mechanisms are incorporated into DABiG to enhance its ability to focus on relevant spatial regions and time steps within the breath pattern data. Conclusion Spatial attention assigns weights to spatial regions, while temporal attention assigns weights to time steps, improving feature extraction and classification accuracy.
Deep Feature Blend Attention: A New Frontier in Super Resolution Image Generation
P.B. Dhanusha, A. Muthukumar, A. Lakshmi
Neurocomputing, 2025
Predictive analytics for renal health: Machine learning in chronic kidney disease prediction
M. Radha, A. Muthukumar
A Study on Next Generation Materials and Devicesv, 2025
Chronic kidney disease (CKD) is a serious and paralyzing state with profound implications for patient morbidity and mortality. Despite its widespread impact, early detection and risk stratification remain challenging. In this study, we present a comprehensive framework utilizing predictive analytics and machine learning algorithms to advance CKD prediction. By integrating diverse datasets encompassing patient demographics, clinical variables, and biomarker profiles, our model exhibits superior performance in identifying individuals at various stages of CKD progression. Furthermore, we elucidate the accountability of our model, offering many perspectives on the critical elements pertaining to predictive accuracy. Through the deployment of this predictive tool in clinical settings, healthcare providers can proactively identify high-risk individuals, facilitate timely interventions, and ultimately improve patient outcomes in the realm of renal health.
An explorative analysis of T cell activating drugs
S. S. Salins, A. Muthukumar, Raj M. Thanga, P. Kaleeswari
A Study on Next Generation Materials and Devicesv, 2025
Protein-based drug design has become a viable alternative for reorganizing the drug research and development process in recent years. This paper looks on incorporating information in genes into the medication development process and offers an account of the various trials used in protein-based drug design. We examine the approaches, difficulties, and developments in this area, emphasizing its potential to provide targeted and customized care for a variety of ailnments. Monoclonal antibodies (mAbs) are lab-made proteins mimicking the immune system’s antibodies. These targeted drugs bind to specific molecules involved in diseases, offering a precise attack with minimal side effects. mAbs can work by neutralizing harmful interactions, triggering the immune system to destroy targeted cells, or even delivering therapeutic payloads. With proven effectiveness in cancer, autoimmune diseases, and more, mAbs represent a rapidly growing class of drugs with vast potential for future medical advancements. By analyzing the field as it is today, we hope to provide academics and practitioners with an overview of the latest developments and trends in the production of monoclonal antibody -based drugs.
Deep learning approaches for classification of abnormal respiratory signals
P. Kaleeswari, R. Ramalakshmi, Arunachalam Muthukumar, Raj M. Thanga
A Study on Next Generation Materials and Devicesv, 2025
Correct classification of abnormal respiratory signals is very important for early diagnosis and treatment of pulmonary conditions like asthma, COPD, pneumonia, sleep apnea, and COVID-19. The conventional diagnostic methods depend on clinical examination and spirometry, which are frequently time-consuming and require expertize. In this paper, we introduce a sophisticated deep learning model using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for machine learning-based automated respiratory sound classification. We train on the ICBHI 2017 Respiratory Sound Database, consisting of 5.5 hours of labeled respiratory sound recordings from 920 audio segments from 126 patients, as well as on another dataset drawn from PhysioNet’s COVID-19 Respiratory Sounds containing 1,192 recordings of 908 subjects. The suggested framework uses a hybrid CNN-BiLSTM model accompanied by an attention mechanism to observe spatial and temporal dependencies in respiratory signals. Feature extraction methods through spectrograms like Mel-frequency cepstral coefficients (MFCCs) and short-time Fourier transform (STFT) are used for richer representation. Training is done and evaluated on an 80:20 stratified divisions, yielding overall accuracy of 94.32%, sensitivity of 92.85%, and specificity of 95.68% for multi-class classification experiments. Comparative study with existing top-performing architectures like ResNet, GRU, and Transformer models reveals 3.5–5.2% gain in F1-score and AUC-ROC measures. Additionally, a federated learning setup is investigated for privacy-enhancing collaborative training over decentralized healthcare organizations. The new system is benchmarked on embedded edge AI boards with a 5.7× latency reduction in inference time, rendering real-time deployment over clinical and mobile health settings practical. The results highlight the effectiveness of deep learning for reliable respiratory disease classification and open doors to smart, scalable diagnostic technologies.
Adaptive contextual emotion-infused transfer learning network for respiratory surveillance
P. Kaleeswari, R. Ramalakshmi, A. Muthukumar, Raj M. Thanga
A Study on Next Generation Materials and Devicesv, 2025
Precise tracking of respiratory patterns in servicemen is vital for health optimization, especially during high-stress situations, but current approaches are generally lacking in real-time two accuracy and responsiveness. This work presents Soldier Breath Wave Net, a reliable brain-computer interface (BCI) algorithm designed to track respiratory patterns from real-time EEG signals. Soldier Breath Wave Net combines the power of deep learning methods and unites multilayer CNNs with RNNs for efficient extraction and analysis of breathing features from neuro signals. On a large dataset of EEG consisting of more than 1,000 hours of recordings, Soldier Breath Wave Net outperforms other traditional approaches such as sole CNN, RNN, and SVM models at 95.7% accuracy, 94.8% precision, 96.2% recall, and a 95.5% F1 score with a latency of less than 50 milliseconds, the system provides real-time adaptability with 10 timely and accurate respiratory evaluations for military personnel. The results highlight Soldier 11 Breath Wave Net’s ability to set new standards for health monitoring with improved precision and 12 timeliness in respiratory pattern analysis required to maintain performance and well-being under 13 operational stress.
Data Security Enhancements in IoT Communication Networks Using Homomorphic Encryption and Location Based Data Access
A. Muthukumar, Avulapati Giridhar, Ganjikunta Raghavendra, Ummadisetty Venkata Teja, Gummagattu Sathya Deepak, Guttikonda Ajay Kumar
Proceedings of the International Conference on Multi Agent Systems for Collaborative Intelligence Icmsci 2025, 2025
Advances in artificial pancreas technology: A comprehensive review and future prospects
M. Radha, A. Muthukumar, J. Friska, M. Uma, G. Krishnaveni, M. Saranya
A Study on Next Generation Materials and Devicesv, 2025
Correction to: Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121 (Journal of Ambient Intelligence and Humanized Computing, (2024), 15, 9, (3519-3531), 10.1007/s12652-024-04829-4)
A. Muthukumar, M. Thanga Raj, R. Ramalakshmi, A. Meena, P. Kaleeswari
Journal of Ambient Intelligence and Humanized Computing, 2024
Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121
A. Muthukumar, M. Thanga Raj, R. Ramalakshmi, A. Meena, P. Kaleeswari
Journal of Ambient Intelligence and Humanized Computing, 2024
FUZZY-PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLER WITH INTERACTIVE DECISION TREE
RAJENDRAN SEKAR, MUTHUKUMAR ARUNACHALAM, KANNAN SUBRAMANIAN
Revue Roumaine Des Sciences Techniques Serie Electrotechnique Et Energetique, 2024
VSC-STATCOM Performance Under Different Fault Sensing using PSO Tuned Hybrid SMC
S. Rajendran, A. Muthukumar, K. Vijayakumar, K. Rajesh
International Journal of Electrical and Electronics Research, 2024
Towards Secure Authentication: Fingerprint-Driven Key Generation with Deep Convolutional Networks
Netha Merin Mathew, A Muthukumar
Proceedings 2024 4th International Conference on Soft Computing for Security Applications Icscsa 2024, 2024
SIMPD Net: A Novel Dataset for South Indian Medicinal Plants in Natural Conditions
Muthukumar Arunachalam, T. Gopu, Sabari Nathan, K. Uma, V. Srivatsav, M. Thangaraj
2024 IEEE Silchar Subsection Conference Silcon 2024, 2024
Détection of phishing websites
Lakshmipathi Gejjala, Muthukumar Arunachalam, Bala Manikanta Eswar Duggisetty, Jaswanth Kumar Reddy Vardireddy
AI Tools for Protecting and Preventing Sophisticated Cyber Attacks, 2023
Real-time object détection in video for traffic monitoring
Sai Deepak Alapati, Muthukumar Arunachalam, Chandana Chennamsetty, Pujitha Dantam, Anusha Dabbara
AI Tools for Protecting and Preventing Sophisticated Cyber Attacks, 2023
A Comprehensive Methodology to Design and Develop Suspension System Bolted Joints using Vehicle Test Loads and CAE Simulation
Vikraman Vellandi, Prasad Namani, Sharad Nair, Bhargav A. Nayak, Varun Chaudhari, Avinash Patnala, T. Senthil Raja, M Arunachalam
SAE Technical Papers, 2023
Electronic Voting Machine Using Fingerprint Scanner with Image Enhancement Technique
Sai Deepak Alapati, Chandana Chennamsetty, Pujitha Dantam, Anusha Dabbara, Muthukumar Arunachalam
2023 International Conference on Computer Communication and Informatics Iccci 2023, 2023
Design and Development of UAV for Challange faced for Data Muling problem in Path
Muthukumar Arunachalam, Mani Kanta Animisetty, Bhanu Prakash Nammi, Suman Jajala, Siva Raju Dokku
2023 International Conference on Computer Communication and Informatics Iccci 2023, 2023
Custom and Design of Agri Drone
A. Muthukumar, M V Muthukumar, S Tamil Varshini, N Prem Mathavan, K Vishnu
2023 International Conference on Computer Communication and Informatics Iccci 2023, 2023
A Review on Machine Learning Models for Breathing Pattern Analysis of Soldiers
Kaleeswari P, Ramalakshmi R, Arunprasath Thiyagarajan, Muthukumar Arunachalam, Kottaimalai Ramaraj, Thanga Raj M
2023 International Conference on Energy Materials and Communication Engineering Icemce 2023, 2023
A Review on the Detection of Deep Fake and Propaganda Videos and Images-based Voice and Facial Manipulation using AI Techniques
M Thanga Raj, Muthukumar Arunachalam, R Ramalakshmi, Kottaimalai Ramaraj, Meena Arunachalam, P Kaleeswari
2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023
GWO-PI Controlled Re-Lift LUO Converter for Standalone Load System
Suguna R, M. Muthalakshmi, Pradeep Katta, Mohammed Ovaiz A, Tamilarasan G K D, Arunachalam M
Proceedings of the International Conference on Circuit Power and Computing Technologies Iccpct 2023, 2023
An approach to data redundancy reduction and secured data delivery using spatial-temporal correlation factors in heterogeneous Mobile Wireless Sensor Network
Preethiya Thandapani, Muthukumar Arunachalam, Durairaj Sundarraj
International Journal of Communication Systems, 2022
Particle Swarm Optimization tuned Hybrid Sliding Mode Controller based static synchronous compensator with LCL filter for Power Quality improvement
S. Rajendran, A. Muthu Kumar, V. Agnes Idhaya Selvi
Sustainable Energy Technologies and Assessments, 2022
Power efficient Space Division Multiplexing–Wavelength Division Multiplexing system using multimode EDFA with elevated refractive index profile
Muthukumar Arunachalam, Sumathy Raju
International Journal of Communication Systems, 2022
A GoogleNet architecture based Facial emotions recognition using EEG data for future applications
Talliboina Ramu, A. Muthukumar
2022 International Conference on Computer Communication and Informatics Iccci 2022, 2022
A Novel Approach for Early Intervention of Retinal Disorders Using Machine Learning Techniques
P. B. Dhanusha, A. Muthukumar, A. Lakshmi
Lecture Notes in Networks and Systems, 2022
Super-resolution of Retinal Fundus Images Using Generative Adversarial Networks
Dhanusha P B, A Muthukumar, A Lakshmi
Proceedings 2nd International Conference on Next Generation Intelligent Systems Icngis 2022, 2022
Wavelength division multiplexing transmission using multimode erbium doped fiber amplifier with elevated refractive index profile
R. Sumathy, A. Muthukumar
Optical and Quantum Electronics, 2021
Innovative approach for multimodal fusion recognition based feature extraction using band-limited phase-only correlation and discrete orthonormal stockwell transfor
A. Kavipriya, A. Muthukumar
Iet Image Processing, 2020
Bio-PUF-MAC authenticated encryption for iris biometrics
Sivasankari Narasimhan, Muthukumar Arunachalam
Computational Intelligence, 2020
Finger knuckle print recognition using MMDA with fuzzy vault
MuthuKumar Arunachalamand, Kavipriya Amuthan
International Arab Journal of Information Technology, 2020
An energy-efficient clustering and multipath routing for mobile wireless sensor network using game theory
Preethiya Thandapani, Muthukumar Arunachalam, Durairaj Sundarraj
International Journal of Communication Systems, 2020
Automotive Wheel Metamodeling Using Response Surface Methodology (RSM) Technique
Gurumoorthy S, Muthukumar Arunachalam, Thomas Oery, Sundaravadivelu Mohan
SAE Technical Papers, 2020
Double Cluster Head Heterogeneous Clustering for Optimization in Hybrid Wireless Sensor Network
T. Preethiya, A. Muthukumar, S. Durairaj
Wireless Personal Communications, 2020
Human Age Estimation based on Ear Biometrics using KNN
A. Kavipriya, A. Muthukumar
2019 International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development Incces 2019, 2019
Mobility Handling in Cluster based Mobile Wireless Sensor Network
T. Preethiya, A. Muthukumar, S. Durairaj
2019 International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development Incces 2019, 2019
Reconfigurable unique key generation using variable duty cycle PUF
Sivasankari Narasimhan, Muthukumar Arunachalam, Rampriya Kaniram
Cluster Computing, 2019
A biometric system based on Gabor feature extraction with SVM classifier for Finger-Knuckle-Print
A. Muthukumar, A. Kavipriya
Pattern Recognition Letters, 2019
Performance of Fiber with Elevated Refractive Index at Core Axis
Sumathy Raju, Muthukumar Arunachalam
IEEE International Conference on Intelligent Techniques in Control Optimization and Signal Processing Incos 2019, 2019
New Insights on Finger Knuckle Print Identification with Steerable Filter for Faster and Accurate Matching
A. Kavipriya, A. Muthukumar
IEEE International Conference on Intelligent Techniques in Control Optimization and Signal Processing Incos 2019, 2019
Smart Water Leak Controller in Metro Water Supply Lines
M. Saravanan, A. Muthukumar, R. Ramya, K.K. Rashika, S. Saravanan
2019 5th International Conference on Advanced Computing and Communication Systems Icaccs 2019, 2019
Anti aging controllable true random number generator for secured AES-based crypto system
Muthukumar Arunachalam, Sivasankari Narasimhan, Rampriya Kaniram
International Journal of Systems Control and Communications, 2019
Performance analysis of various soft computing controller-based dynamic voltage restorer
J. Kohila, A. Muthu Kumar, S. Kannan
International Journal of Systems Control and Communications, 2019
Evaluation of Human Age with FKP Using K-NN
A. KaviPriya, A. Muthukumar
Icsns 2018 Proceedings of IEEE International Conference on Soft Computing and Network Security, 2018
Providing Secured Data Aggregation in Mobile Wireless Sensor Network
T. Preethiya, A. Muthukumar, S. Durairaj
2018 IEEE 4th International Symposium in Robotics and Manufacturing Automation Roma 2018, 2018
Simulation Driven Optimization of Automotive Floor Console Mounting Brackets - An Overview
Raghuraman Taruvai Sankaran, Arunkumar S, Muthukumar Arunachalam, harinadh Gudla
SAE Technical Papers, 2018
Anti-aging true random number generator for secured database storage
A. Muthukumar, N. Sivasankari, K. Rampriya
2017 4th International Conference on Advanced Computing and Communication Systems Icaccs 2017, 2017
An efficient ear recognition system using DWT & BLPOC
Muthukumar Arunachalam, Santham Bharathy Alagarsamy
Proceedings of the International Conference on Inventive Communication and Computational Technologies Icicct 2017, 2017
Fusion of dimensionality reduction techniques for Fkp recognition with Gabor filter
A. Muthukumar, A. Kavipriya
Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing Iccsp 2017, 2017
Static Loading Analysis of Third Row Floor Duct System Using Finite Element Method
B. Vasanth, Muthukumar Arunachalam, Sathya Narayana, S. Sathish Kumar, Murali govindarajalu
SAE Technical Papers, 2017
A Sensitivity Study on Inertance Frequency Response Function through Non-Parametric Variability Approach
Muthukumar Arunachalam, Arunkumar S, PraveenKumar Sampath, Abdul Haiyum, Yash Khakhar
SAE Technical Papers, 2017
A Novel Virtual Approach of Air Intake System Acoustic Sensitivity Study for Passenger Vehicle
Muthukumar Arunachalam, Sankarasubramanian Thirukkotti, S Arunkumar, Abdul Haiyum
SAE Technical Papers, 2017
A review on recent techniques in multimodal biometrics
N. Sivasankari, A. Muthukumar
2016 International Conference on Computer Communication and Informatics Iccci 2016, 2016
An Investigation of Body Inertance Response for Occupant Safety Control Module Attachment Regions
Muthukumar Arunachalam, S Arunkumar, PraveenKumar Sampath, Abdul Haiyum, Beverly Katz
SAE Technical Papers, 2016
Finger knuckle print authentication using AES and K-means algorithm
International Arab Journal of Information Technology, 2015
AES based multimodal biometric authentication using cryptographic level fusion with Fingerprint and Finger Knuckle Print
International Arab Journal of Information Technology, 2015
Iris authentication using Gray Level Co-occurrence Matrix and Hausdorff Dimension
P. Steffi Vanthana, A. Muthukumar
2015 International Conference on Computer Communication and Informatics Iccci 2015, 2015
Multimodal biometrics authentication using iris and palmprint with SVM classifier
International Journal of Applied Engineering Research, 2015
Multibiometric based authentication using feature level fusion
IEEE International Conference on Advances in Engineering Science and Management Icaesm 2012, 2012

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