Dr.C. ராஜன்

Scopus Publications

Integrating Blockchain with SDN for Secured and Scalable Network Function Virtualization in 5G+
R Udayakumar, Rajan C, Nidhi Mishra, Ulmas Ibragimov, Nurali Saidov
Icfnds 2025 2025 the 9th International Conference on Future Networks and Distributed Systems, 2026
The newer generation of mobile telecommunications networks (5G and 5G Plus networks) is spawning an increasing need for networks that are flexible, dynamic, and secure. The combination of Software Defined Networking (SDN) and Network Function Virtualization (NFV) approaches almost fully autonomous orchestration of networks. However, and unfortunately, the design of both SDN and NFV presents some degree of security and scalability challenges, notably in the context of centralized control and multi-tenant network virtualization. In this paper, focus on the application of blockchain approaches to integrating SDN to secure, scalable, trestles management of NFV for 5G Plus networks. For the first time, the proposed model connects the SDN network layer and NFV service layer through a blockchain-based DLT to ensure secure authentication, provide decentralized control, and verify the network state in real time. The use of smart contracts that service chaining and resource allocation automate reduction of managerial overhead and latency. When compared to SDN with NFV, the proposed model provides a significant improvement in the overall system resiliency of the network, the speed of fault recovery, and the protection of the system against DDoS and configuration attacks. The proposed integration of blockchain with SDN provides a scalable, robust SDN-NFV infrastructure that allows for the construction and deployment of smart and fully autonomous network systems.
Multi-Agent Reinforcement Learning for Optimizing Cloud Resource Scheduling in Hybrid Cloud Environments
С Rajan, Saravanakumar Veerappan, Moti Ranjan Tandi, P Balamurugan, Sanjar Goyipnazarov
Icfnds 2025 2025 the 9th International Conference on Future Networks and Distributed Systems, 2026
The growing complexity and scale of machine learning (ML) operations in the cloud are among the key problems with traditional resource allocation and parallel processing mechanisms. Traditional cloud systems struggle to meet the increasing demands of dynamic ML workloads, particularly concerning throughput, latency, and energy efficiency. This paper bridges this gap by proposing a Multi-Agent Reinforcement Learning (MARL) framework to optimize Cloud Resource Scheduling in Hybrid Cloud Environments (comprising private, public, and edge resources). It models the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), outlining how decentralized agents can coordinate resource allocation and workload distribution to maximize resource utilization and minimize latency and cost. The MARL approach enables adaptive, distributed decision-making for dynamic workload offloading, significantly enhancing the performance across heterogeneous classical resources. Empirical research, validated via Cloud Sim and Edge Cloud Sim, demonstrates that the proposed MARL framework achieves superior performance, showing marked improvements in average response time, resource utilization, and energy efficiency compared to traditional heuristic methods and single-agent RL models. The framework offers an efficient and scalable solution for accelerating ML tasks and provides a path towards intelligent, dynamic cloud resource orchestration.
Data Contract Virtual Machine (DC-VM): An Intelligent Governance Architecture for Reliable and Privacy-preserving AI Pipelines
International Journal of Intelligent Engineering and Systems, 2026
The problem of ensuring compliance with data integrity and privacy in multi-faceted pipelines of AI has become a pressing issue due to the implementation of heterogeneous machine learning pipelines across distributed systems by organizations.Traditional data governance implementations are based on static schema validation and human audit trails which do not offer runtime validation or cryptographic accountability.The proposed work is the Data Contract Virtual Machine (DC-VM), which is a programmable execution environment that implements data contracts based on policy all through AI dataflows.The DC-VM proposes a stratified validation system that combines deterministic rule programme execution and probabilistic anomaly detection to ensure that compliance and reliability are attained at every pipeline phase.To achieve end-to-end traceability, a lightweight proof-of-enforcement token allows revealing sensitive raw data.The prototype deployment shows that DC-VM is able to minimize incidents of contract-violation by 37 per cent and enhance throughput by 18 per cent over declarative governance frameworks.The findings substantiate the fact that executable contracts may be processed as high-level data structures of smart data infrastructures, which provide formal accountability, adaptive control, ad quantifiable resilience to data drift and privacy leakage.
An Intelligent Energy-Efficient Computing Framework Using Machine Learning-Driven Optimization for Sustainable
A. Surendar, M. Kavitha, Rajan. C
Proceedings of the 5th International Conference on Sentiment Analysis and Deep Learning Icsadl 2026, 2026
The fast emerging smart infrastructures within urban, industrial, and IoT-based settings have greatly contributed to the energy consumption at the computing infrastructures, and therefore, energy efficient operation has become a pressing concern of long-term sustainability. Traditional energy management strategies are usually founded on fixed or policy-based policies and cannot respond well to workload dynamics and workload-nonhomogeneous system properties. This paper will attempt to eliminate these weaknesses by introducing a smart energy-efficient computing system with a reinforcement learning approach to dynamic energy management toward sustainable smart systems. The proposed framework continuously monitors the state of the system, pressure of workload, and the use of resources and automatically adjusts the allocation of computational resources according to an energy saving learning strategy. A clearly spelled out rewards functionality that uses a direct measure of energy via the Joules and Watt-hours metrics appropriately direct the learning process to optimise the reducing of power use without sacrificing the system performance to an unacceptable degree. The strategy proposed can be tested using the effectiveness of various simulations in a realistic smart computing environment. The experimental findings indicate that the suggested reinforcement learning-based model can attain up to 34.4 % total energy saving relative to the traditional static and rule-based energy management systems. The findings verify that reinforcement learning can promote adaptive and sustainable optimization of energy and hence the proposed framework can be effectively applied to future generation applications of IoTenabled and intelligent smart systems.
CypherXpert: A Blockchain-Powered Platform for Enhancing Transparency, Traceability, and Efficiency in Product Supply Chains
Rithika S, Jaya Krishna S, Rajan C
Ccic 2026 Contemporary Computing Innovations Conference 2026, 2026
Deep Residual Learning for Precise Electric Motor Temperature Estimation
Ajay B, Arulselvam D, Balakrishnan D, C Rajan
2026 International Conference on Communication Computing and Emerging Technologies Ic3et 2026, 2026
The article focuses on developing an efficient and accurate approach to detecting faults with rotating machinery bearings through the analysis of non-contact vibration signals. The vibration signals that were collected, were recorded under variable load conditions and were denoised using the Hilbert transform to identify useful features, before running Principal Component Analysis (PCA) to transform the variable space to a lower dimension and using Sequential Floating Forward Selection (SFFS) to select the most useful features. The chosen features are classified by utilizing Support Vector Machines (SVM) and Artificial Neural Networks (ANN) models to classify the fault types of inner race faults, outer race faults and ball faults. This overall method reduces noise interference, supports higher classification accuracies, and improves fault detection time to reduce machinery maintenance time and increase reliability. Based on the results, the proposed method is reliable in providing an accurate diagnosis and has the potential to be incorporated as a standard method for predictive maintenance of industrial systems.
Multimodal Knowledge-Enhanced Misinformation Detection with Adversarial Robustness and Explainable AI
G Krishnamoorthi, M S Surendhar, R P Harshini, C Rajan
2026 2nd International Conference on Advances in Intelligent Computing and Applications Aicaps 2026, 2026
The out-of-control growth of misinformation spreading across social media is creating critical problems for information integrity and public trust. Traditional detection systems often fall short because they look at only one thing at a timejust text or just images-which badly limits their usefulness against clever deceptions that mix both channels. This paper puts forward a complete framework that merges multimodal learning with knowledge-powered verification and strong adversarial defenses to catch misinformation in both text and pictures. Our architecture pairs BERT-based language encoders with ResNet visual extractors, using a novel cross-modal attention method to capture the semantic ties between textual claims and their accompanying images. To provide external factual grounding, we bring in knowledge graph embeddings through structured relationship modeling, making the system much better at identifying inconsistencies. To ensure it is resilient against intentional manipulations, our framework includes defense mechanisms we’ve tested against Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks. What’s more, an explainable AI module produces understandable predictions with SHAP and LIME visualizations, creating transparency in the decision process. Our experimental tests on benchmark datasets like Weibo and GossipCop show it performs much better than unimodal and existing multimodal systems, reaching solid accuracy while staying efficient enough for deployment in realtime. Bringing together adversarial training, knowledge checking, and explainability creates a well-rounded answer for the difficult, multi-layered challenges of finding fake news in today’s digital world.
Automated Solar Panel Fault Classification Using VGG16-Based Transfer Learning
Gokulan S, Lalith Kishore V S, Suthi G, Rajan C
Proceedings of 8th International Conference on Intelligent Sustainable Systems Iciss 2026, 2026
Automated defect identification in solar panel systems plays a vital role in ensuring peak operational efficiency and lowering upkeep expenses. A deep neural network solution employing VGG16-based knowledge reuse is proposed to detect and classify faults in photovoltaic modules. Panel images are sorted into six defect types: avian excrement contamination, defect-free surfaces, particulate accumulation, electrical faults, structural defects, and snow accumulation. By leveraging prelearned features from a VGG16 model with frozen convolutional layers, the system maintains stable pattern extraction. A tailored classification module, combined with image augmentation and dropout techniques, improves robustness and mitigates overlearning. The approach effectively identifies minor visual distinctions across fault types, even in adverse weather conditions. Experimental results confirm superior detection performance for all categories, demonstrating strong adaptability to operational deployments. Accurate multi-category defect recognition enables predictive maintenance planning, curtails operational interruptions, and boosts power generation efficiency. The integration of intelligent fault detection marks a critical step toward optimized monitoring and economical management of large-scale photovoltaic installations.
A Real-Time Deep Learning Solution for Helmetless Rider Detection in Urban Traffic
Archana S, Vishesha R, Janani C, Praveen K, Rajan C
Proceedings of 5th International Conference on Communication Computing and Electronics Systems Iccces 2026, 2026
Road accidents involving two-wheeler riders remain a critical concern worldwide, with a significant number of fatalities caused by the neglect of wearing helmets. Manual surveillance to enforce helmet regulations is inefficient and prone to human oversight. This study presents a smart helmet detection framework that employs deep learning-based real-time object recognition to identify riders not wearing helmets. The proposed approach utilises the YOLOv8 (You Only Look Once, version 8) model to precisely detect riders, helmets, and motorcycles from both live video feeds and static imagery. By analysing the spatial relationship between detected helmets and riders, the system classifies whether a rider is helmeted or helmetless. The experimental results demonstrate high detection accuracy and efficient frame processing, proving the model's capability for real-time traffic monitoring. The developed system can be integrated into smart city infrastructure to assist traffic authorities in improving road safety and ensuring law enforcement compliance.
Hybrid Machine Learning-Based Anomaly Detection Framework for Wireless Sensor Network Security Monitoring
M A Atheswar, K Praveen, A Yukesh Kumaran, C Rajan, R P Harshini
2026 1st International Electronics Packaging Design and Manufacturing Conference Bridging Skills and Innovation for India S Industry Epdmc 2026, 2026
The rapid expansion of Wireless Sensor Networks (WSNs) in large-scale and critical infrastructures has increased the need for efficient and real-time network security solutions. Traditional rule-based and single-model intrusion detection systems often struggle to scale when handling high-dimensional and continuously evolving network traffic. To address these challenges, this paper proposes a hybrid machine learning-based anomaly detection framework for identifying malicious activities in network traffic. The proposed approach integrates multiple supervised classification algorithms, including Decision Tree, Random Forest, k-Nearest Neighbors, Support Vector Machine, and Naïve Bayes, to enable comparative evaluation and robust intrusion detection. To improve scalability and computational efficiency, optimization strategies such as intelligent data sampling and a fast-converging LinearSVC variant are employed. The framework incorporates standardized feature scaling and persistent model storage to support seamless deployment. In addition, an interactive web-based dashboard developed using Streamlit enables real-time visualization and classification of newly uploaded network traffic data. Experimental results demonstrate that the proposed framework achieves high detection accuracy, improved precision, recall, and F1-score, while maintaining practical feasibility for continuous and real-time network security monitoring.
Skin Disease Prediction using SVM and ResNet-50
THEREKA M, PRIYANKA A, INDUMATHI M S, RAJAN C, HARSHINI RP
Proceedings of 8th International Conference on Intelligent Sustainable Systems Iciss 2026, 2026
Intelligent Weed Management in Paddy Fields via Convolutional Neural Networks
Vinayagam M, Vishwahari R, Sivaranjani R S, Surendar K, Rajan C
Proceedings of 8th International Conference on Intelligent Sustainable Systems Iciss 2026, 2026
Physics Informed Deep Learning for Real Time Prediction of Solar Terrestrial Plasma and Global Climate Change
Shokhruh Sultanov Rahmon Ugli, Dr.C. Rajan, M.A. Bruno
International Academic Journal of Science and Engineering, 2025
Autonomous Multi-Agent Reinforcement Learning for Resilient Navigation and Space Weather Prediction in Microgravity
Dr.C. Rajan, Najmitdinov Akhadkhon Khamitdkhanovich, M. Nagarajan, Dr. Anupa Sinha
International Academic Journal of Science and Engineering, 2025
Causal Neuromorphic Graph Learning with Physics-Informed Micro-Twins for Explainable Wind Turbine Fault Diagnosis
Surendar Aravindhan, Shyamalagowri M, Karthika J, Rajan C
Ssrg International Journal of Electrical and Electronics Engineering, 2025
EnsembleNet: a novel deep ensemble learning model with GANBERT and BiLSTM for automated fake news detection
B. M. Brinda, C. Rajan, K. Geetha
Knowledge and Information Systems, 2025
AI-Enabled Sentiment Analysis for Strategic Content Curation
Nigora Bo‘tayeva, Gulnora Mirazimova, C. Rajan, Haider Mohammed Abbas, Akmaljon Abdullaev, Samida Mustafaeva, Ravshan Khomitov
Indian Journal of Information Sources and Services, 2025
An Energy-Aware Sleep Scheduling Coverage Protocol for Wireless Sensor Network
P. Dineshkumar, K. Geetha, C. Rajan
Journal of Circuits Systems and Computers, 2025
Coverage Optimization and Prediction in Wireless Sensor Network Based on Enhanced Decisive Red Fox Black-Winged Kite With Multistrategies
P. Dineshkumar, K. Geetha, C. Rajan
International Journal of Communication Systems, 2025
A hybrid optimization and machine learning based energy-efficient clustering algorithm with self-diagnosis data fault detection and prediction for WSN-IoT application
N. Nathiya, C. Rajan, K. Geetha
Peer to Peer Networking and Applications, 2025
SBERO: Skill Al-Biruni Earth Radius Optimization for Alzheimer's Disease Classification Using Magnetic Resonance Image
P. Gowsikraja, K. Geetha, C. Rajan
NMR in Biomedicine, 2025
Proactive Resilient Adaptive Sensor Node Management with Neuro-Symbolic Systems for Optimized Coverage Efficiency in the Mission-Critical Applications
P. Dineshkumar, K. Geetha, C. Rajan
Ecs Journal of Solid State Science and Technology, 2025
Balanced-IDS: A Deep Hybrid Model to Overcome Class Imbalance in Network Intrusion Detection
Jishnu S, Ruban B, Balakrishnan D, C Rajan
Proceedings of 6th International Conference on Iot Based Control Networks and Intelligent Systems Icicnis 2025, 2025
Adaptive AES-Encrypted Image Steganography with Key-Driven Dynamic Pixel Embedding
Sandhiya K, Saranya S, Insol Rajasekar S, Jothi S, Rajan C
Proceedings of 6th International Conference on Iot Based Control Networks and Intelligent Systems Icicnis 2025, 2025
An Anomaly—Misuse Hybrid System for Efficient Intrusion Detection in Clustered Wireless Sensor Network Using Neural Network
N. Nathiya, C. Rajan, K. Geetha, S. Dinesh, S. Aruna, B. M. Brinda
Communications in Computer and Information Science, 2025
Bone Fracture Detection Based on Deep Learning Techniques
Lingeshwaran Sekar, Mohit Karthikeyan S, Jegan Samynathan, Kalaivani Pachiappan, P. Iyyanar, C. Rajan
Proceedings 3rd International Conference on Artificial Intelligence and Machine Learning Applications Healthcare and Internet of Things Aimla 2025, 2025
Optimal Roadside Unit Deployment with Effective Collision Detection for Smart Vehicular Communication
Mohammed I. Habelalmateen, Dulfikar Jawad Hashim, Rajan C., Karar Aljawaheri, Fatima H. Alsalamy, Israa Badi Khalaf
2025 International Conference on Next Generation Computing Systems Intelligent System for Sustainable Development Icngcs 2025 Conference Proceedings, 2025
RETRACTED ARTICLE: An AdaBoost-modified classifier using stochastic diffusion search model for data optimization in Internet of Things (Soft Computing, (2020), 24, 14, (10455-10465), 10.1007/s00500-019-04554-7)
E. Suganya, C. Rajan
Soft Computing, 2024
Retraction Note: Improvement in Hadoop performance using integrated feature extraction and machine learning algorithms (Soft Computing, (2019), 24, (627-636), 10.1007/s00500-019-04453-x)
C. K. Sarumathiy, K. Geetha, C. Rajan
Soft Computing, 2024
Retraction Note: A modified shuffled frog leaping algorithm for scientific workflow scheduling using clustering techniques (Soft Computing, (2020), 24, 1, (637-646), 10.1007/s00500-019-04484-4)
M. Karpagam, K. Geetha, C. Rajan
Soft Computing, 2024
A constructive Steiner graph matching for Radio frequency identification device tag detection in wireless and internet of things environment
P. Sathishkumar, C. Rajan, K. Geetha
International Journal of Communication Systems, 2024
An energy-efficient cluster routing for internet of things-enabled wireless sensor network using mapdiminution-based training-discovering optimization algorithm
N Nathiya, C Rajan, K Geetha
Sadhana Academy Proceedings in Engineering Sciences, 2024
Detecting Evolving Fake News in Social Media by Leveraging Heterogeneous Deep Learning Model
B.M. Brinda, C. Rajan, K. Geetha
2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
Artificial Intelligence Enabled Hybrid Machine Learning Application for Dyslexia Detection using Optimized Multiclass Support Vector Machine and Personalized Interactive and Assistive tools using Adaptive Reinforcement
C. Rajan, Bala Suruthika A G, Sudharshini B, Gowtham K
4th International Conference on Innovative Practices in Technology and Management 2024 Iciptm 2024, 2024
Anandha Hridaya-Fetal Heart Beat Monitor
Rajan C, Arumugam C, Balabharathi V, Mohanapriya R
2nd International Conference on Artificial Intelligence and Machine Learning Applications Healthcare and Internet of Things Aimla 2024, 2024
A Review on Segmented Flood Attack Detection in Cloud Networks Using Machine Learning Methods
Dineshkumar P, Geetha K, Viswanathan R V, Sujatha S, Rajan C
5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024
Simplex Social Spider Optimization with Decision Tree Consumer Behavior Patterns
Mohammed I. Habelalmateen, S. Sindhuja, C. Rajan, M. Hemamalini, R. Navin Kumar
2nd IEEE International Conference on Integrated Intelligence and Communication Systems Iciics 2024, 2024
Oral Cancer Detection Using Deep Learning
P. Kalaivani, P. Iyyanar, C. Rajan, R. Harshini Priya, P. Janani, A S. Jayasudha
2nd International Conference on Artificial Intelligence and Machine Learning Applications Healthcare and Internet of Things Aimla 2024, 2024
Applying Deep Neural Networks and NLP Techniques for Sentiment Analysis in Social Media Data
B M Brinda, C. Rajan, K. Geetha, N. Nathiya, P.J. Raguraman, K. Srinivasan
2nd International Conference on Artificial Intelligence and Machine Learning Applications Healthcare and Internet of Things Aimla 2024, 2024
Chronic Kidney Disease Diagnosis Using Conditional Variational Generative Adversarial Networks and Squirrel Search Algorithm
B.M. Brinda, Rajan C
Information Technology and Control, 2023
Geomentoy - Promoting Inclusive Education for Kids
C Rajan, T Sneha, Ruthuja Mahesh Dabade, R Surya
2022 Opju International Technology Conference on Emerging Technologies for Sustainable Development Otcon 2022, 2023
The Cocoon of Anthropoid on Trauma Alleviation using Virtual Reality
C. Rajan, M. Bhavatharani, R. Abarna, M. Dhanasri
2022 Opju International Technology Conference on Emerging Technologies for Sustainable Development Otcon 2022, 2023
Deep Learning with Multi-Class Classification for Detection of Covid-19 and Pneumonia
P Kalaivani, A Dhivya, G Dharani, S Bharathi, C. Rajan
6th International Conference on Inventive Computation Technologies Icict 2023 Proceedings, 2023
IoT-Embedded Deep Learning Model for Real-Time Remote Health Monitoring and Early Identification of Diseases
Meenakshi Sharma, B. Latha, Ankur Gupta, Dler Salih Hasan, C. Rajan, Madhu Munagala
Proceedings International Conference on Technological Advancements in Computational Sciences Ictacs 2023, 2023
Energy-Efficient Clustering and Routing Algorithm Using Ant Lion Optimization in WSN
P. Sathish Kumar, C. Rajan, R. Gopinath, A. Gnanabaskaran, R. Vijaisai, P. Manimaran
2023 International Conference on Next Generation Electronics Nelex 2023, 2023
Improved CAPSNET model with modified loss function for medical image classification
J. Deepika, C. Rajan, T. Senthil
Signal Image and Video Processing, 2022
An Efficient Handwritten Digit Recognition Based on Convolutional Neural Networks with Orthogonal Learning Strategies
T. Senthil, C. Rajan, J. Deepika
International Journal of Pattern Recognition and Artificial Intelligence, 2022
An Optimized Data Replication Algorithm in Mobile Edge Computing Systems to Reduce Latency in Internet of Things
N. Saranya, K. Geetha, C. Rajan
Lecture Notes in Networks and Systems, 2022
An adaboost-modified classifier using particle swarm optimization and stochastic diffusion search in wireless IoT networks
E. Suganya, C. Rajan
Wireless Networks, 2021
An efficient cnn model with squirrel optimizer for handwritten digit recognition
T. Senthil, C. Rajan, J. Deepika
International Journal of Advanced Technology and Engineering Exploration, 2021
A reactive search optimization algorithm for scientific workflow scheduling using clustering techniques
M. Karpagam, K. Geetha, C. Rajan
Journal of Ambient Intelligence and Humanized Computing, 2021
An improved optimization technique using Deep Neural Networks for digit recognition
T. Senthil, C. Rajan, J. Deepika
Soft Computing, 2021
Security and Privacy of Cloud- And IoT-Based Medical Image Diagnosis Using Fuzzy Convolutional Neural Network
J. Deepika, C. Rajan, T. Senthil
Computational Intelligence and Neuroscience, 2021
Role of Ag microalloying on glass forming ability and crystallization kinetics of ZrCoAgAlNi amorphous alloy
A Surendar, K Geetha, C Rajan, M Alaazim
Chinese Physics B, 2021
An AdaBoost-modified classifier using stochastic diffusion search model for data optimization in Internet of Things
E. Suganya, C. Rajan
Soft Computing, 2020
Enhancing pathologic staging diagnosis of lung cancer using datamining techniques
International Journal of Pharmaceutical Research, 2020
Data Replication in Mobile Edge Computing Systems to Reduce Latency in Internet of Things
N. Saranya, K. Geetha, C. Rajan
Wireless Personal Communications, 2020
A modified shuffled frog leaping algorithm for scientific workflow scheduling using clustering techniques
M. Karpagam, K. Geetha, C. Rajan
Soft Computing, 2020
Improvement in Hadoop performance using integrated feature extraction and machine learning algorithms
C. K. Sarumathiy, K. Geetha, C. Rajan
Soft Computing, 2020
Hierarchical clustering analysis for specific liver disease-dengue hemorrhagic fever (Dhf)
International Journal of Pharmaceutical Research, 2020
Literature survey of data latency reduction techniques in mobile edge computing-IoT
Journal of Critical Reviews, 2020
Lasso and ridge regression for optimized resource allocation in cloud computing
Journal of Advanced Research in Dynamical and Control Systems, 2020
Automatic classification for preventing duplication of online multimedia data in secure cloud infrastructure
E. Suganya, N. Aravindhraj, S. Sountharrajan, G. Sathish Kumar, C. Rajan
International Journal of Advanced Intelligence Paradigms, 2020
HDFS-based parallel and scalable pattern mining using clouds for incremental data
S. Sountharrajan, E. Suganya, N. Aravindhraj, S. Sankarananth, C. Rajan
International Journal of Computer Aided Engineering and Technology, 2020
A secured software architecture for providing data security in cloud
Journal of Advanced Research in Dynamical and Control Systems, 2019
Automatic glioblastoma multiforme detection using hybrid-SVM with improved particle swarm optimisation
S. Sountharrajan, E. Suganya, M. Karthiga, C. Rajan
International Journal of Biomedical Engineering and Technology, 2018
Machine learning algorithms: A background artifact
International Journal of Engineering and Technology Uae, 2018
Biclustering of gene expression data using biclustering iterative signature algorithm and biclustering coherent column
E. Saravana Kumar, K. Vengatesan, R.P. Singh, C. Rajan
International Journal of Biomedical Engineering and Technology, 2018
Automatic classification on bio medical prognosisof invasive breast cancer
S. Sountharrajan, M. Karthiga, E. Suganya, C. Rajan
Asian Pacific Journal of Cancer Prevention, 2017
Heuristic classifier for observe accuracy of cancer polyp using video capsule endoscopy
K. Geetha, C. Rajan
Asian Pacific Journal of Cancer Prevention, 2017
Metaheuristic optimization technique for feature selection to detect the Alzheimer disease from MRI
Journal of Advanced Research in Dynamical and Control Systems, 2017
Automatic colorectal polyp detection in colonoscopy video frames
K. Geetha, C. Rajan
Asian Pacific Journal of Cancer Prevention, 2016
Genetic based optimization for multicast routing algorithm for MANET
C RAJAN, N SHANTHI
Sadhana Academy Proceedings in Engineering Sciences, 2015
Mobility based key management technique for multicast security in mobile Ad Hoc networks
B. Madhusudhanan, S. Chitra, C. Rajan
Scientific World Journal, 2015
Swarm optimized multicasting for wireless network
Life Science Journal, 2013
Misbehaving attack mitigation technique for multicast security in mobile ad hoc networks (MANET)
Journal of Theoretical and Applied Information Technology, 2013

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