venkatesan ramachandran

Scopus Publications

Guardian-Based Anonymous Password Management with Privacy Preservation Using Threshold Cryptography
Harivignesh K.S., Venkatesan R., Selvarathi M., Jasmine David D.
Journal of Trends in Computer Science and Smart Technology, 2026
Most password managers are designed as a compromise between providing a secure way to handle difficult login credentials and creating a system that keeps user information private. Most password managers store users’ sensitive information in a central location, leaving users vulnerable to hacking attacks. The proposed work creates a better way to manage passwords and provide more protection to users based on their private information through a new concept called Guardian-Anonymous Password Management (GAPM). The idea is to create a unique architecture that stores passwords in decentralized locations using guardian anonymity, creating a hybrid architecture of secret sharing with post-quantum encrypted wraps. Accordingly, GAPM separates the act of recovering user passwords from a person, using a set of anonymous guardians who securely recover users’ passwords without putting any of them at risk of being hacked or located through social engineering techniques like phishing. This is achieved by using a Shamir-style secret sharing scheme combined with verifiably reassured commitments, where none of the guardians know each other, and they are required to reach a certain threshold of agreement to combine their shares into an easily accessible password recovery key. The GAPM system supports multiple guardian sets, allowing participants to be added or removed, and there is no need to reissue all the shares each time users make a change. The user can also change the recovery threshold in real-time. Finally, the shares are further secured through the use of a post-quantum Key Encapsulation Mechanism (KEM) to ensure that, no matter what kind of attack (classical or quantum), the password recovery process will remain strong and secure.
An Elegant Privacy Preservation and De-Duplication Model With Elliptic Revocation Cryptography (PPD-ERC) Framework for Cloud Security
L. Selvam, R. Gomathi, R. Venkatesan
Transactions on Emerging Telecommunications Technologies, 2026
Due to the rapid advancement of communication technology, ensuring cloud data privacy and security is now regarded as one of the most important and difficult tasks. The traditional works are highly concentrated on creating cryptographic models for enhancing cloud system security. However, it encountered issues and problems because of the following factors: increased system overhead, time and storage requirements, complex mathematical operations, and ineffective data handling. In order to guarantee the security, privacy, and access control of cloud data sharing, the proposed work aims to develop a novel framework known as the Privacy Preservation and De‐duplication Model with Elliptic Revocation Cryptography (PPD‐ERC). This framework includes the entities of Cloud User (CU) (i.e., owner or receiver), Cloud Encryption Server (CES), sub‐CES, and Trusted Authority. Here, the lightweight ERC methodology encrypts user data using the private and public key pair. Then, the convergent keys are distributed to the blockchain, and the CU uses the Share algorithm for splitting the convergent keys. The CES validates the user authenticity based on the access controlling mechanism, which allows only the authorized users to obtain the data from server. Moreover, data de‐duplication is performed to avoid redundant encrypted data storage in the cloud system, and it increases the processing speed, minimizes the storage space, and optimizes the key generation process. During performance analysis, various evaluation metrics have been used to validate and compare the results of the proposed PPD‐ERC mechanism.
Federated Transformer-Based Deep Learning Framework for Secure and Early Autism Diagnosis
R. Maddala Kranthi, R. Venkatesan, C. Amuthadevi, C. Priyadharsini
SN Computer Science, 2026
Temporal CAT-Based Data Fusion for IoMT System
R. Venkatesan, A. Saravanan, A. Sathya
Proceedings of the 12th International Conference on Biosignals Images and Instrumentation Icbsii 2026, 2026
IoMT (Internet of Medical Things) is purely real time series data operation where the temporal information plays the major role. Data fusion mechanisms represent a patient's complete information in a unique way that makes the prediction easier. But a patient's diagnosis always depends on their previous investigation and records, which need to be fused, and a decision has to be made based on that. Thus, the proposed work involves the temporal Cross Attention Fusion mechanism for unified representation of patient information using the current and last updated information. The proposed work utilizes the benefit of the Long Short-Term Memory (LSTM) based Variational Autoencoder (VAE) for obtaining meaningful information from the patient multi modal data. These features are then combined and fused along with the previously recorded patient information, that shown 96.2 % fusion effectiveness using the HER, Imaging and Sensor modalities dataset. The contribution for fusion is accounted for maximum by the image modality where the temporal-based fusion provided a balanced contribution by EHR and Sensor data.
TinyML-based intrusion detection systems for sustainable and energy-constrained IoT devices
Amuthadevi C, Venkatesan R, Mythily M, Aroul Canessane R
Results in Engineering, 2025
• TinyML for IoT Security : Explores using Tiny Machine Learning (TinyML) for lightweight, energy-efficient intrusion detection on low-power IoT devices. • GraBoost-AAGT Model : Proposes a novel Gradient Boosting-based Adaptive Artificial Gorilla Troops (GraBoost-AAGT) model, combining AAGTO and XGBoost for efficient on-device threat detection. • Optimized for Energy Efficiency : Designed for ultra-low power consumption, the model maintains high detection accuracy while minimizing CPU usage and energy overhead. • Advanced Preprocessing : Utilizes categorical encoding, Z-score normalization, and Fast Fourier Transform (FFT) to extract relevant features for high-performing intrusion detection. • Real-Time Performance : Achieves real-time detection of threats like denial-of-service and probing attacks, with a detection accuracy of 99.50% and F1-score of 99.45%. • Sustainable IoT Security : Demonstrates a sustainable approach to IoT security, enabling secure, scalable, and energy-conscious deployment in environmentally sensitive and resource-constrained settings. With the exponential growth of the Internet of Things (IoT), ensuring real-time security in energy-constrained and environmentally sensitive environments has become a significant challenge. Traditional Intrusion Detection Systems (IDS), though effective, are often resource-intensive and unsuitable for deployment on low-power edge devices. This research explores the potential of Tiny Machine Learning (TinyML) to provide lightweight, on-device intelligence for threat detection. Research proposes a Gradient Boosting Based Adaptive Artificial Gorilla Troops (GraBoost-AAGT), which combines Adaptive Artificial Gorilla Troops Optimization (AAGTO) and Extreme Gradient Boosting (XGBoost), optimized for ultra-low power consumption and memory efficiency. Preprocessing involves categorical encoding and Z-score normalization, followed by Fast Fourier Transform (FFT) to extract relevant frequency-domain features. Specifically designed for embedded IoT systems, this model, referred to as GraBoost-AAGT, supports real-time detection of cyber threats, such as denial-of-service and probing attacks, while maintaining minimal central processing unit (CPU) and energy overhead. To evaluate performance, network traffic data encompassing both normal and malicious activities is collected using IoT intrusion detection dataset, traffic simulators, and replay tools under controlled attack and non-attack scenarios. Results demonstrate that GraBoost-AAGT deployed via TinyML achieves a high detection accuracy (99.50%), precision (99.8%), recall (99.12%), F1-score (99.45%), specificity (99.81%), and computation time (4.5s), reduced energy consumption, and lower CPU usage compared to traditional edge-based ML models. These findings confirm that TinyML offers a viable and sustainable solution for deploying intelligent threat detection in smart and green IoT ecosystems. This research provides a foundational step toward secure, scalable, and environmentally responsible IoT security frameworks.
Context-Aware IoT Models for Detecting Dangerous Scenarios-A Systematic Review
Kommu Kishore Babu, R. Venkatesan
Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks Icicv 2025, 2025
The integration of Internet of Things (IoT) devices and machine learning (ML) algorithms has significantly advanced the monitoring and protection of vulnerable populations, such as the elderly, individuals with disabilities, and those at risk of unexpected threats like abductions or assaults. This review explores a context-aware threat scenario identification framework that utilizes data from smartwatches and smartphones to assess a user’s geolocation, stress levels, and body posture in real time. Stress and location are interpreted using threshold-based methods, while body posture is evaluated through machine learning classification. The study discusses various IoT-based monitoring systems, data integration approaches, and ML techniques used for accurate anomaly detection and risk prediction. It also examines the privacy, energy efficiency, and environmental adaptability challenges associated with deploying such systems. Future directions highlight the role of edge computing, AI ethics, and healthcare integration in improving the responsiveness and ethical deployment of these technologies. The findings underscore the potential of context-aware IoT-ML systems in enhancing safety, provided critical challenges are addressed effectively.
A deep learning-driven multi-layer digital twin framework with miot for precision oncology in cancer diagnosis
Golden Nancy, E. Bhuvaneswari, Venkatesan R.
Journal of Intelligent Systems and Internet of Things, 2025
A Multi-modal Fusion Approach for Drug Discovery and Efficacy Prediction
Agnes Chella Wanika R, A.M. Anusha Bamini, R Venkatesan
2025 1st International Conference on Advancement in Futuristic Technologies Icaft 2025, 2025
Discovery of drug behavior performed in clinical laboratories are time consuming and costly, with large percentage of drug samples are failing due to safety or efficacy issues. This work aims to propose a Machine Learning-based framework to predict whether a drug sample is likely to succeed or not using preclinical molecular and trail-related parameters. A dataset of 200 biologically safe-range samples are generated following the Lipinski’s rule of five, including the features such as molecular weight, LogP, hydrogen bond donors and acceptors, toxicity score, and trail phase. Three ML algorithms Linear Regression, Random Forest and XGBoost - were implemented and compared using performance evaluation standards such as accuracy, precision, recall, F1-score, and ROC-AUC. The Random Forest pattern achieved the best overall performance with an accuracy of 87.5%, demonstrating strong predictive capability and interpretability. Feature importance analysis showed that toxicity score and trail phase were the most influential factors in determining trail success. The work bring forward, transparent and cost-effective approach for early prediction of drug viability, potentially reducing research costs and accelerating the drug discovery development pipeline unlike complex system
MindMate: AI-Powered Multilingual Mental Health Chatbot with Personalized Voice and Text Support with Rasa and Streamlit
Dharshini S, Samson Arun Raj A, Venkatesan R
Proceedings of the International Conference on Intelligent Computing and Control Systems Iciccs 2025, 2025
An AI motivated mental health chat bot which can enable personalized support through voice as well as text based interaction, it’s dubbed as MindMate. Using the Rasa framework for natural language understanding (NLU) and dialogue management, along with Streamlit to make the interface user friendly and breathable for people, it makes an empathetic environment to allow people to express themselves and get responses from it. It is a multilingual chatbot, so it can be used by a lot of people with different languages. MindMate approaches users with a personalized approach to learn from user interactions to provide personalized responses, suggestions and resources that respond to the individual needs and preferences. This unique system not only makes it easy to share how you feel, but also gives you useful resources for dealing with whatever problem or issue you may be facing, helping to solve those problems. MindMate hopes to change the game of mental health support, incorporating advanced AI technology into an intuitive interface meant to ensure that mental well-being is readily available and effective for people seeking assistance with how to manage that aspect of their life.
Smart Digital Twin Ecosystem Powered by RETFound for Lung Cancer Diagnosis and Monitoring
Golden Nancy, R. Venkatesan, K Ramalakshmi
2025 5th Asian Conference on Innovation in Technology Asiancon 2025, 2025
The advancement of medical foundation models such as RETFound, a repurposed vision transformer pre-trained on large-scale radiological data, opens new avenues for precision oncology. This study proposes a novel Smart Digital Twin Ecosystem (SDTE) that integrates RETFound with a multi-modal patient-specific digital twin to enable personalized diagnosis, real-time monitoring, and therapeutic planning for lung cancer. The architecture combines high-resolution thoracic CT scans, histopathology, genomic variants of EGFR, ALK, KRAS, and longitudinal clinical records to dynamically simulate tumor evolution and patient response. RETFound was fine-tuned on an internal lung cancer dataset comprising 8,420 annotated CT studies from LIDC-IDRI and 2,135 histopathology samples from TCGA-LUAD and TCGA-LUSC. The proposed system achieved an average accuracy of 98.16%, Sensitivity of 97.92%, Specificity of 97.53%, Recall of 90.23% and F1-score 0.95. The model achieved a Dice Similarity Coefficient (DSC) of 0.894 for 3D tumor segmentation, an AUC-ROC of 0.976 for malignancy classification, and a Pearson correlation of 0.832 between predicted tumor volume trajectories and actual longitudinal growth patterns. The Digital Twin Layer incorporates a spatiotemporal attention module fused with RETFound embeddings and genomics-informed Graph Convolution Networks (GCNs) to simulate individualized tumor progression under multiple treatment regimens. The SDTE supports clinical decision-making by generating counterfactual treatment outcomes, allowing oncologists to compare radiotherapy vs. immunotherapy scenarios in silico. The proposed results validate the synergistic potential of RETFound and digital twin technology in reshaping lung cancer care through continuous, predictive, and personalized insights.
Smart Farming of Leafy Herbs: Conceptualizing a Block Diagram for Automated Cultivation
Radhika K, K Ramalakshmi, R Venkatesan
Proceedings of 5th International Conference on Pervasive Computing and Social Networking Icpcsn 2025, 2025
Privacy-Preserving Autism Detection Using Federated CNN-LSTM Networks: A Spatiotemporal Deep Learning Framework for Decentralized Behavioural Diagnosis
Maddala Kranthi, R Venkatesan, K Ramalakshmi, Agnes Chella Wanika R
2025 International Conference on Sensors and Related Networks Sennet 2025 Special Focus on Digital Healthcare 64220, 2025
Sarcasm Aware Sentiment Analysis with BERT Transformer Models
Samuel M, K Ramalakshmi, R Venkatesan, Agnes Chella Wanika R
Proceedings of the 4th International Conference on Intelligent Computing Information and Control Systems Icoiics 2025, 2025
Using federated learning for detecting autism in children
, R. R., , , , Saswati Debnath, Priyadharsini Priyadharsini, R. Venkatesan
Fusion Practice and Applications, 2025
Advancing Network Security: A Comparative Evaluation of Machine Learning and Ensemble Methods in Intrusion Detection Systems
I R Chris Rogen, Darshika Kelin AR, R. Venkatesan, Keirolona Safana Seles J
Proceedings of 5th International Conference on Pervasive Computing and Social Networking Icpcsn 2025, 2025
Classification of AI- Generated vs. Human Voices Using Convolutional Neural Networks(CNNs) and Sprectrogram Analysis
Carolin Joanna Sheryl K, K Ramalakshmi, N Cinthia Jemima, R Venkatesan
2025 Global Conference in Emerging Technology Ginotech 2025, 2025
Alzheimer's Disease Prediction and Detection using Machine Learning and Deep Learning Approaches
Prince Nickson A, K Ramalakshmi, S. Shirly, R Venkatesan
2025 International Conference on Computing and Communications Computingcon 2025, 2025
ML-Based Medical diagnosis for skin disease
Gokul B, R Venkatesan, Swetha Shekarappa G, Senbagavalli M
Icrteect 2025 2nd International Conference on Recent Trends in Electrical Electronics and Computing Technologies, 2025
Demystifying the Industry 5.0 Version
Venkatesan Ramachandran, Feroze Ahamed Zahir Ahamed, Thanga Helina Stalin, Shirley Chellathurai Pon Anna Bai
Edge AI for Industry 5 0 and Healthcare 5 0 Applications, 2025
Enhancing IoT Intrusion Detection with a Hybrid Deep Learning-Evolutionary Algorithm: An Ensemble Strategy Approach
, Basil Basil, , , , , Jaspher Willsie Kathrine, Priyadharsini Priyadharsini, Gladwin Rufus, R. Venkatesan
Fusion Practice and Applications, 2025
An Efficient Approach for Fake Review Detection with Machine and Deep Learning Techniques
Vijay Khanna M A, K Ramalakshmi, D. Ponmary Pushpa Latha, R Venkatesan, Agnes Chella Wanika R
Proceedings of the 7th International Conference on Innovative Data Communication Technologies and Application Icidca 2025, 2025
A Hybrid Stacked Ensemble Approach for Stock Market Volatility Prediction
David Rosario Selvaraj, K Ramalakshmi, D. Ponmary Pushpa Latha, Agnes Chella Wanika R, R Venkatesan
Proceedings of 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2025, 2025
Enhanced Intrusion Detection Using AI-Driven Data Balancing and VQ-VAE-Based Feature Extraction
Shivanthana S., Manicka Raja M., Lalitha Krishnasamy, Karthik R., R. Venkatesan
Journal of Cybersecurity and Information Management, 2025
Enhancing Decentralized Privacy Through Integration of Blockchain with IoT for Healthcare Applications
R. Manjula Devi, M. Sangeetha, R. Venkatesan, Lalitha Balasubramanian, P. Keerthika
Blockchain Technology for the Engineering and Service Sectors, 2025
AI-based Bonsai Species Classification and Care Recommendation System using YOLO
Derry Jeswyn S, K Ramalakshmi, S. Shirly, R Venkatesan, G Naveen Sundar, Saravana Kumar C S
Proceedings of the International Conference on Intelligent Computing and Control Systems Iciccs 2025, 2025
Automated Subjective Answer Evaluation using GenAI and NLP by Enhancing Accuracy, Fairness, and Feedback in Education
Yuvasri J, Senbagavalli M, Rahul R, R Venkatesan, G Naveen Sundar, Saravana Kumar C S
Proceedings of the International Conference on Intelligent Computing and Control Systems Iciccs 2025, 2025
An Intelligent Traffic Monitoring System in Congested Regions with Prioritization for Emergency Vehicle Using UAV Networks
V. D. Ambeth Kumar, Venkatesan Ramachandran, Mamoon Rashid, Abdul Rehman Javed, Shayla Islam, Abdullah Al Hejaili
Tsinghua Science and Technology, 2025
Quantum Transfer Learning via Pennylane and ResNet using Machine Learning
16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
An Internet of Medical Things-Based Mental Disorder Prediction System Using EEG Sensor and Big Data Mining
V. D. Ambeth Kumar, Sowmya Surapaneni, D. Pavitra, R. Venkatesan, Marwan Omar, A. K. Bashir
Journal of Circuits Systems and Computers, 2024
IoT based automatic temperature monitoring and adjustment system for the organic farm
R. Venkatesan, T. Jemima Jebaseeli, K. Ramalakshmi, P. B. Hemanthkumar
Aip Conference Proceedings, 2024
Smart Farming with Improved Security using Ascon Encryption and Authentication
Rahul R, R. Venkatesan, T. Jemima Jebaseeli
2nd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2024, 2024
Role of Digital Twins in Promoting Sustainability in Commerce
Venkatesan R, Brijit Benny, Shirley C P, Berin Jeba Jingle I
Proceedings 2024 4th International Conference on Pervasive Computing and Social Networking Icpcsn 2024, 2024
Fusing Deep Learning Techniques for Intrusion Detection in Smart Grids
R. R., , , , , Sindhu. P., G. Naveen Sundar, R. Venkatesan
Fusion Practice and Applications, 2024
Estimating the Association of Gene Disease using Knowledge Graph Embedding
15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
Artificial Intelligence based Chatbot Therapist to Improve the Mental Health of Trauma Patients
15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
EfficientV2 for Real-Time Skin Type Detection with Enhanced Image Processing
Rishab Haldar, Senbagavalli M, Saswati Debnath, C P Shirle, R Venkatesan
3rd International Conference on Automation Computing and Renewable Systems Icacrs 2024 Proceedings, 2024
Reinforcement Learning based Adaptive Healthcare Decision Support Systems using Time Series Forecasting
Shirley C P, Berin Jeba Jingle I, Abisha M B, Venkadesan R, Absin S J
Proceedings of the 5th International Conference on Data Intelligence and Cognitive Informatics Icdici 2024, 2024
Retraction Notice: Hash Tag-Based Sentiment Analysis Using A Deep Learning Approach (2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS) DOI: 10.1109/ICACCS60874.2024.10717166)
R. Venkatesan, M. Sangeetha, S. Nivedha, M. Selvakumar, K.S. Chandru, P. Parthasarathi
10th International Conference on Advanced Computing and Communication Systems Icaccs 2024, 2024
Deepfake Detection Using Multi-Modal Fusion Combined with Attention Mechanism
Shirley C P, Berin Jeba Jingle I, Abisha M B, Venkatesan R, Yashvanth Ram R V, Elakkiya Elango
4th International Conference on Sustainable Expert Systems Icses 2024 Proceedings, 2024
FocusRec RNN: Enhancing 3D Human Pose Reconstruction and Prediction with Wearable Motion Capture Technology
S.V. Evangelin Sonia, C Beulah Christalin Latha, R. Venkatesan, G.Naveen Sundar, C.P. Shirley, E Harris Stuart
2024 Asian Conference on Intelligent Technologies Acoit 2024, 2024
EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images
Ezekiel Alaric Majaw, G. Naveen Sundar, D. Narmadha, Senthil Kumar Thangavel, Venkatesan, Adeyemi Abel Ajibesin
3rd International Conference on Automation Computing and Renewable Systems Icacrs 2024 Proceedings, 2024
Ocimum Sanctum Linn Plant growth monitoring and irrigation system
Darwin Raj A, K Ramalakshmi, R Venkatesan, G Naveen Sundar, Golden Nancy, S Shirly
Proceedings 2024 4th International Conference on Pervasive Computing and Social Networking Icpcsn 2024, 2024
Mitigating Economic losses in Potato farming: Accurate identification of Potato leaves diseases
Jeffrey Philip Biju, K Ramalakshmi, R Venkatesan, G Naveen Sundar, Golden Nancy, Manoshika Catherine J
2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
ML based Text Summarization for Sentiment Analysis in Information Retrieval using Feature Engineering
Shirley C P, Berin Jeba Jingle I, Venkatesan R, Tamilarasan R, Yashvanth Ram R V, Joyce Beryl Princess P
4th International Conference on Sustainable Expert Systems Icses 2024 Proceedings, 2024
Integrating Cloud-based Data Mining Algorithms for Smart City Infrastructure Management and Decision Support Systems
M. Guru Vimal Kumar, J. Jude Moses Anto Devakanth, D. Selvapandian, Venkatesan R, J. Revathi, R. Aruna
Proceedings 2024 4th International Conference on Soft Computing for Security Applications Icscsa 2024, 2024
Ocimum Sanctum Linn Plant Fertilizer and Insecticide Spraying System with Disease Prediction using Machine learning
K R Aagaash, K Ramalakshmi, R Venkatesan, G Naveen Sundar, Golden Nancy, S Shirly
Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
Precision Cultivation for Bonsai through IoT Implemented Smart Irrigation
Vaibhav Daniel, K Ramalakshmi, R Venkatesan, G Naveen Sundar, Golden Nancy, S Shirly
Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
Deciphering Depression: Linguistic Analysis of Social Media Data
Manoshika Catherine S J, Manicka Raja, Saravana Kumar C S, C P Shirley, R Venkatesan, Sheril Angel J
3rd International Conference on Automation Computing and Renewable Systems Icacrs 2024 Proceedings, 2024
Innovative Cyberattack Detection Mechanism for Hospital Management System Using Hybrid Deep Learning Algorithm
Deniel Sampson J, K Ramalakshmi, R Venkatesan, G Naveen Sundar, Golden Nancy, Prince Nickson
2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
A Comparative Analysis of Machine Learning Algorithms for Crime Rate Prediction
Anagha Dileep, K Ramalakshmi, R Venkatesan, G Naveen Sundar, Golden Nancy, S Shirly
Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
RETRACTION:Model transformation and code generation using a secure business process model
M. Mythily, Beaulah David, R. Venkatesan, Iwin Thanakumar Joseph
Journal of Intelligent and Fuzzy Systems, 2023
A Queueing Model-Based Experimental Analysis of Mobile-Energy Distribution Stations (M-Eds) for Smart City Urbanization
Samson Arun Raj, Venkatesan Ramachandran, G. Naveen Sundar, Subramaniam Nachiyappan
Journal of Advanced Research in Applied Sciences and Engineering Technology, 2023
Opinion on Student’s Educational Performance and Sleeping Patterns Using Data Analytics Technique
International Journal of Intelligent Systems and Applications in Engineering, 2023
Pest Management System for Food Crops using Deep Learning Techniques
14th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2023, 2023
A Methodical Review of Iridology-Based Computer-Aided Organ Status Assessment Techniques
Suja Alphonse, Ramachandran Venkatesan, Theena Jemima Jebaseeli
Engineering Proceedings, 2023
Software Defined Network aided cluster key management system for secure fusion multicast communication in Internet of Vehicles
Antony Taurshia, , , , Jaspher Willsie Kathrine, Venkatesan Venkatesan
Fusion Practice and Applications, 2023
Relevance Mapping based CNN model with OSR-FCA Technique for Multi-label DR Classification
S. .., , , , , V. D. Ambeth .., R. Venkatesan, S. Malathi
Fusion Practice and Applications, 2023
Human Emotion Detection Using DeepFace and Artificial Intelligence †
Ramachandran Venkatesan, Sundarsingh Shirly, Mariappan Selvarathi, Theena Jemima Jebaseeli
Engineering Proceedings, 2023
Automated spinal MRI-based diagnostics of disc bulge and desiccating using LS-RBRP with RF
S. Shirly, R. Venkatesan, D. Jasmine David, T. Jemima Jebaseeli
Journal of Autonomous Intelligence, 2023
De-Noising and Segmentation of Medical Images using Neutrophilic Sets
C. S. Manigandaa, , , , , , V. D. Ambeth Kumar, G. Ragunath, R. Venkatesan, N. Senthil Kumar
Fusion Practice and Applications, 2023
Deep Residual Learning for Lung Cancer Nodules Detection and Classification
M. Sangeetha, R.Manjula Devi, Hemalatha Gunasekaran, R. Venkatesan, K. Ramalakshmi, P. Murugesan
Proceedings 7th International Conference on Computing Methodologies and Communication Iccmc 2023, 2023
Plant Classification based on Grey Wolf Optimizer based Support Vector Machine (GOS) Algorithm
P. Keerthika, R.Manjula Devi, S.J. Suji Prasad, R. Venkatesan, Hemalatha Gunasekaran, K. Sudha
Proceedings 7th International Conference on Computing Methodologies and Communication Iccmc 2023, 2023
An efficient and secure data sharing scheme for cloud data using hash based quadraplet wavelet permuted cryptography approach
Selvam Lakshmanan, Braveen Manimozhi, Venkatesan Ramachandran
Concurrency and Computation Practice and Experience, 2022
Post-quantum confidential transaction protocols
R. Manjula Devi, P. Keerthika, P. Suresh, R. Venkatesan, M. Sangeetha, C. Sagana, K. Devendran
Quantum Blockchain an Emerging Cryptographic Paradigm, 2022
Unsupervised Lumbar IVD Localization and Segmentation using GFMM and Boundary Refined Region Growing Techniques
Shirly S, Golden Nancy R, Venkatesan R, Jemima Jebaseeli T, Ramalakshmi K
International Journal of Engineering Trends and Technology, 2022
Traffic Aware Channel Assignment for Dynamic Wireless Mesh Networks
13th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2022, 2022
Weight Aware Channel Assignment with Node Stability in Wireless Mesh Networks
13th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2022, 2022
Introduction to ARM processors its types and Overview to Cortex M series with deep explanation of each of the processors in this Family
Hemanthkumar P B, Shreekar Reddy Anireddy, Josh F T, Venkatesan R
2022 International Conference on Computer Communication and Informatics Iccci 2022, 2022
A Mathematical Queuing Model Analysis Using Secure Data Authentication Framework for Modern Healthcare Applications
A. Samson Arun Raj, R. Venkatesan, S. Malathi, V. D. Ambeth Kumar, E. Thenmozhi, Anbarasu Dhandapani, M. Ashok Kumar, B. Chitra
Journal of Sensors, 2022
A mathematical model for cost budget optimization in the early stage of house construction budget analysis
S. Malathi, Ankit Kumar, R. Venkatesan, Naveen Sundar, A. S. Athigiri Arulalan, V. D. Ambeth Kumar, R. Priya, Shilpa Sharma, Linesh Raja
Journal of Interdisciplinary Mathematics, 2022
Prediction of COVID'19 through multiple organ analysis using iot devices and machine learning techniques
Jemima Jebaseeli T, Jasmine David D, Venkatesan R
International Journal of Engineering Trends and Technology, 2021
Literature Review of Disease Detection in Tomato Leaf using Deep Learning Techniques
Hepzibah Elizabeth David, K. Ramalakshmi, Hemalatha Gunasekaran, R. Venkatesan
2021 7th International Conference on Advanced Computing and Communication Systems Icaccs 2021, 2021
Fraud detection for credit card transactions using random forest algorithm
T. Jemima Jebaseeli, R. Venkatesan, K. Ramalakshmi
Advances in Intelligent Systems and Computing, 2021
Tomato leaf disease detection using hybrid CNN-RNN model
Hepzibah Elizabeth David, K. Ramalakshmi, R. Venkatesan, G. Hemalatha
Advances in Parallel Computing, 2021
Secure online payment through facial recognition and proxy detection with the help of TripleDES encryption
R. Venkatesan, B. Anni Princy, V. D. Ambeth Kumar, Manish Raghuraman, Mukesh Kumar Gupta, Ankit Kumar, Abhishek Kumar, Ajoy Kumar Khan
Journal of Discrete Mathematical Sciences and Cryptography, 2021
Safe engineering application for analysis the food calorie using deep learning
Journal of Green Engineering, 2020
Fuzzy based trust index calculation system for wireless sensor networks
K. Ramalakshmi, T. Jemima Jebaseeli, R. Venkatesan, C. Anand Deva Durai
Advances in Mathematics Scientific Journal, 2020
Survey in Finding the Best Algorithm for Data Analysis of Privacy Preservation in Healthcare
D. Evangelin, R. Venkatesan, K. Ramalakshmi, S. Cornelia, J. Padmhavathi
Lecture Notes on Data Engineering and Communications Technologies, 2020
A Comparison of Machine Learning Techniques for the Prediction of the Student’s Academic Performance
Jyoti Kumari, R. Venkatesan, T. Jemima Jebaseeli, V. Abisha Felsit, K. Salai Selvanayaki, T. Jeena Sarah
Lecture Notes on Data Engineering and Communications Technologies, 2020
Exploration of an innovative geometric parameter based on performance enhancement for foot print recognition
V.D. Ambeth Kumar, S. Malathi, R. Venkatesan, K. Ramalakshmi, K. Vengatesan, Weiping Ding, Abhishek Kumar
Journal of Intelligent and Fuzzy Systems, 2020
Technical irrigation system to predict soil moisture and water level in agricultural field
International Journal of Innovative Technology and Exploring Engineering, 2019
A survey on task scheduling using intelligent water drops algorithm in cloud computing
Vinay Ahari, R Venkatesan, D Ponmary Pushpa Latha
Proceedings of the International Conference on Trends in Electronics and Informatics Icoei 2019, 2019
Application of decision tree algorithm for prediction of student’s academic performance
International Journal of Innovative Technology and Exploring Engineering, 2019
Energy consumption analysis and load management for smart home
Aswathi Balachandran, Ramalakshmi, Venkatesan, Maha Lakshmi, Kodavati Jahnavi, V Jothi
Proceedings of the International Conference on Trends in Electronics and Informatics Icoei 2019, 2019
Enhancement of accuracy on a medical dataset by the usage of different data preprocessing techniques
International Journal of Innovative Technology and Exploring Engineering, 2019
Two Three Step Authentication in ATM Machine to Transfer Money and for Voting Application
K. Sudharsan, V.D. Ambeth Kumar, R Venkatesan, V. Sathyapreiya, G. Saranya
Procedia Computer Science, 2019
Review on various approaches for resource management and job scheduling in Grid environment
R Venkatesan, K Ramalakshmi, D. Ponmary Pushpa Latha
Journal of Computational and Theoretical Nanoscience, 2018
Internet of things based pest management using natural pesticides for small scale organic gardens
R Venkatesan, G. Jaspher W Kathrine, K Ramalakshmi
Journal of Computational and Theoretical Nanoscience, 2018
Resource management in computational grid with economic based allocation model using particle swarm optimization (PSO)
Journal of Theoretical and Applied Information Technology, 2013
Analysis of load balancing techniques in grid
R. Venkatesan, M. Blessy Rathna Solomi
Communications in Computer and Information Science, 2011
Scheduling framework for queuing and advanced reservation using TBRA in grid computing environments: Trust based resource agent
R. Venkatesan, K. Ramalakshmi, J. Isaac Victor Bhasme, K. Thanushkodi
2011 IEEE 3rd International Conference on Communication Software and Networks Iccsn 2011, 2011
Scheduling framework with resource level load balance using agents in grid computing environments
R. Venkatesan, K. Ramalakshmi, Arati Patro, K. Thanushkodi
2011 IEEE 3rd International Conference on Communication Software and Networks Iccsn 2011, 2011
Trust oriented resource allocation using bidding
Sonu Mariam Paulose, R. Venkatesan, K. Ramalakshmi
Icect 2011 2011 3rd International Conference on Electronics Computer Technology, 2011
Perspective study on resource level load balancing in grid computing environments
P. Lalitha Hima Bindu, R. Venkatesan, K. Ramalakshmi
Icect 2011 2011 3rd International Conference on Electronics Computer Technology, 2011
Correction of distortion for images of printed materials
Lecture Notes in Engineering and Computer Science, 2007