RAMESH SEKARAN

Scopus Publications

Bio inspired Coati optimization and sparse convolutional encoding framework for intelligent epileptic seizure detection
J. Viswanath, S. Annamalai, S. Ramesh
Discover Applied Sciences, 2026
Epileptic seizure detection is crucial for clinical diagnosis and ongoing patient monitoring, especially for those with chronic epilepsy. EEG-based seizure detection leverages the brain’s electrical signals to capture temporal and spatial variations that characterize seizure events. However, seizure patterns are often non-linear, patient-specific, and temporally dynamic, making their detection complex and error-prone. Current methods that use machine learning and deep learning models are constrained by challenges such as irrelevant feature inclusion, overfitting, poor generalization, and ineffective management of high-dimensional EEG data. To address these challenges, a novel model is proposed in this research work by incorporating Coati Optimization Algorithm-Based Feature Selection with Convolutional Sparse Autoencoder for accurate and efficient epileptic seizure detection. The proposed model aims to reduce feature redundancy through biologically inspired optimization and improve learning through sparse, high-level feature representations. The methodology was evaluated on the benchmark UCI Epileptic Seizure Recognition Dataset, and experimental analysis ensures the model robust performance with an accuracy of 96.5%, sensitivity of 95.2%, specificity of 97.3%, precision of 96.0%, F1-score of 95.6%, and an AUC of 97.8%. These results of proposed model outperform traditional classifiers such as CNN, SVM with PCA, Random Forest, k-NN, and Deep Autoencoder across all metrics. The integration of optimized feature selection with sparse convolutional encoding contributed significantly to the model’s ability to generalize and minimize false detections, offering a promising tool for automated and reliable seizure detection in clinical and real-time environments.
Blockchain and Smart Contracts with Barzilai-Borwein Intelligence for Industrial Cyber-Physical System
Gowrishankar Jayaraman, Ashok Kumar Munnangi, Ramesh Sekaran, Arunkumar Gopu, Manikandan Ramachandran
Computers Materials and Continua, 2026
Industrial Cyber-Physical Systems (ICPSs) play a vital role in modern industries by providing an intellectual foundation for automated operations. With the increasing integration of information-driven processes, ensuring the security of Industrial Control Production Systems (ICPSs) has become a critical challenge. These systems are highly vulnerable to attacks such as denial-of-service (DoS), eclipse, and Sybil attacks, which can significantly disrupt industrial operations. This work proposes an effective protection strategy using an Artificial Intelligence (AI)-enabled Smart Contract (SC) framework combined with the Heterogeneous Barzilai–Borwein Support Vector (HBBSV) method for industrial-based CPS environments. The approach reduces run time and minimizes the probability of attacks. Initially, secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain (BC) integrated within a BC network. The SC executes the HBBSV strategy to verify the security consensus. The Barzilai–Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions. When a component remains within these conditions, no security breach occurs. Conversely, if a component does not satisfy the condition boundaries, a security lapse is detected, and those components are isolated. The HBBSV method thus strengthens protection against DoS, eclipse, and Sybil attacks. Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.
Cayley–Purser secured communication and jackknife correlative classification for COVID patient data analysis
Ramesh Sekaran, Ashok Kumar Munnangi, Manikandan Ramachandran, Mohammad Khishe
Scientific Reports, 2025
Internet of Medical Things (IoMT) is a group of medical devices that connect the healthcare information technology to minimize the redundant hospital visit and healthcare system troubles. IoMT connect the patients to the doctor and transmit the medical data over the network. The spread of corona virus has put the people at high risk. Due to increasing number of cases and its stress on health professionals, IoMT technology is used in many healthcare centers. But, the security level and data classification accuracy was not improved by existing methods during the data communication. In order to solve these issues, Cayley-Purser Cryptographic Secured Communication based Jackknife Correlative Data Classification (CPCSC-JCDC) method is designed. The key objective of CPCSC-JCDC method is to collect the patient information through IoMT devices and send to the doctor in more secured manner. Initially in CPCSC-JCDC method, the patient data is collected. After the data collection process, the data gets encrypted with help of public key of the patient by using cayley-purser cryptosystem. After the encryption process, the data is sent to the doctor. The doctor receives and decrypts the patient data by using their private key. After decryption process, the doctor analyses the patient data and classifies the data as emergency case or normal case by using jackknife correlation function. This helps to minimize the patient readmission rate and increase the patient satisfaction level. Experimental evaluation is carried out by Novel Corona Virus 2019 dataset using different metrics like data classification accuracy, data classification time and security level. The evaluation result shows that CPCSC-JCDC method improves the security level as well as accuracy and minimizes the time consumption during data communication than existing works.
Hybrid Metaheuristic-Driven Intrusion Detection System Using Opto-Romar Swarm Bee Genesis Optimization on IoT Network Data
Swetha A, Ramesh Sekaran, Annamalai S
Ssrg International Journal of Electronics and Communication Engineering, 2025
The Internet of Things (IoT) devices have rapidly grown in numbers, posing critical threats in the process of securing networks against emerging cyber-attacks. Traditional Intrusion Detection Systems (IDS) suffer from low accuracy, are not flexible and are inefficient in handling massive multidimensional IoT data. To mitigate these shortcomings, the study presents a new hybrid metaheuristic-based IDS system that combines Cat-Scale normalization technique, the feature selection algorithm: Mutualk-Best, the optimization technique Opto-Romar Swarm Bee Genesis and the hyperparameter optimization algorithm: Evalmax Hyper Net. The framework is developed to provide a balanced representation of features, minimize redundancy, enhance convergence, and learn dynamic parameters to provide robust intrusion detection. Experimental results on the IoT-IDS dataset show the effectiveness of the proposed work. The framework recorded 95% accuracy, 97% precision, 98.6% recall and 98.4% F1-score with an AUC of 0.9999, utilizing better results compared to other techniques of IDS like BESO-HDL and Modified Isolation Forest. These findings support that the combination of swarm intelligence and genetic algorithms, along with adaptive tuning, can present a better detection performance with the ability to scale in highly complex IoT environments. The results indicate the framework as a promising future-proof IDS solution. Future work will consider the deployment of real-world IoT to resource-limited devices, robustness against adversarial attack, and variants to edge and mobile computing tasks.
Message digest and blockchain based chaotic ordered cyber secured cloud of things for smart health care
Ashok Kumar Munnangi, Kumaresan Maruthapillai, Sivaram Rajeyyagari, Ramesh Sekaran, Vinayakumar Ravi, Alanoud Al Mazroa
Peer to Peer Networking and Applications, 2025
A Graded Node-Dependent Allocation Method for Energy Scavenging in Wireless Sensor Networks
M. Vasim Babu, Ramesh Sekaran, Suthendran Kannan, Vinayakumar Ravi
Wireless Personal Communications, 2025
Wireless 5G Network in Edge Computing Based On MIMO with Federated Learning and Clustering Integrated Reinforcement Learning
Manikandan Parasuraman, Sivaram Rajeyyagari, Ramesh Sekaran, Suthendran Kannan, Vinayakumar Ravi
Eai Endorsed Transactions on Internet of Things, 2025
Edge Computing (EC) is a revolutionary architecture that brings Cloud Computing (CC) services closer to data sources than ever before. This research proposed novel technique in edge computing network based on wireless 5G technology using MIMO_federated learning integrated with Reinforcement neural network. Here the aim is to enhance the resource allocation by Decentralized Federated learning in multiple user based MIMO (De_Fed_L- MIMO) networks. Then the energy efficiency and channel optimization of the network is carried out using K-means clustering integrated with Reinforcement learning (K-means_RL). Here the experimental analysis is carried out in terms of number of users of network as well as number of edge server by DoF of 92%, Spectral efficiency of 92%, Energy efficiency of 96%, Signal to noise ratio (SNR) of 85%, Coverage area of 92%, RL training accuracy of 95%, FL training accuracy of 98%.
A Novel Approach for Mobility-aware Energy-efficient Clustering-based Routing Using EANN With GA Algorithm on WSNs
Vasim Babu M, Ramesh Sekaran, Suthendran Kannan, Vinayakumar Ravi, Tahani Jaser Alahmadi
International Journal of Sensors Wireless Communications and Control, 2025
Aim: This research aims to explore and evaluate various strategies for improving energy efficiency within wireless sensor networks (WSNs). Specifically, the study focuses on the critical challenge of extending network lifespan through energy conservation by establishing balanced clusters within the WSN architecture. Background: In wireless sensor networks (WSNs), ensuring prolonged network operation while conserving energy resources is a significant concern. One promising approach to address this challenge is the implementation of equalized clusters, which requires an effective selection of cluster heads (CHs). However, this task presents considerable complexity and demands innovative solutions to overcome. Objective: The primary objective of this study is to develop and assess a novel methodology for selecting precise cluster heads (CHs) within WSNs. This methodology is based on the utilization of Bluetooth low energy (BLE) sensors deployed in a randomly distributed manner across the study area. By employing an enhanced artificial neural network and greedy approach (EANN-GA), the proposed technique seeks to identify CHs with optimal proximity to the cluster center and substantial remaining energy reserves. Methods: The proposed methodology involves the deployment of BLE sensors distributed randomly throughout the study region, which are then organized into clusters. Using the enhanced artificial neural network and greedy approach (EANN-GA), the sensor node nearest to the cluster center with the highest remaining energy is selected as the cluster head (CH). Additionally, a mobile sink (MS) is introduced to harness the power of CHs, and the number of paths utilized by the MS is estimated through a genetic approach. Based on this path information, the MS enters each cluster to initiate the data-gathering process. Results: Performance analysis of the presented methodology demonstrates significant improvements in energy efficiency and the extension of network lifetime. By employing the proposed EANN-GA technique for CH selection and optimizing MS path utilization, the study showcases enhanced operational effectiveness within WSNs. Conclusion: The findings of this research underscore the effectiveness of the proposed methodology in enhancing energy efficiency and prolonging the lifespan of wireless sensor networks. Through the innovative integration of BLE sensors, EANN-GA CH selection, and genetic-based MS path estimation, the study contributes valuable insights toward addressing the critical challenges of energy conservation in WSNs.
Context is King: Rethinking Query Expansion in Modern Search Engines
Krishnan Batri, S Lakshmi, S Ramesh, N Sangeetha, S. Annamalai
2025 International Conference on Artificial Intelligence and Data Engineering Aide 2025 Proceedings, 2025
In the era of digital information overload, search engines have evolved beyond simple keyword matching to embrace context-aware methodologies. However, the task of accurately interpreting user intent remains a critical challenge, with traditional query expansion techniques often falling short of capturing semantic and contextual nuances. This paper critically examines the evolution of query expansion strategies, highlighting their limitations and exploring state-of-the-art techniques such as machine learning models, knowledge graphs, and transformer-based systems like BERT and GPT. Experimental results compare traditional methods like synonym expansion with modern approaches, demonstrating the superior relevance and diversity of results from context-aware systems. The study underscores the importance of integrating real-time contextual signals and dynamic user preferences to enhance search engine performance, paving the way for more personalized and accurate retrieval systems.
An Optimized Transmission Mechanism for Mitigating Jamming Attacks in Multi-Hop Wireless Networks
Manikandan Parasuraman, Ramesh Sekaran, Suthendran Kannan, Vinayakumar Ravi, Tahani Jaser Alahmadi
International Journal of Sensors Wireless Communications and Control, 2025
Aim: To address the vulnerability of Multi-Hop Wireless Network Systems (MHWNs) to jamming attacks and propose an effective solution to maintain communication integrity and Quality of Service (QoS). Background: In MHWNs, the open-access nature makes them susceptible to jamming attacks, which disrupt communication by interfering with authenticated nodes in the wireless medium. Existing methods primarily focus on tracking and countering jammers but lack effectiveness in preventing communication disruptions. Objective: The objective of this study is to introduce a novel algorithm, Optimized Transmission Mechanism (OTM), to mitigate the impact of jamming attacks on MHWNs. OTM aims to optimize node handover and packet routing to bypass jammed areas, ensuring uninterrupted packet transmission and preserving QoS. Methods: The proposed OTM algorithm determines the optimal transmission route based on radio transmitter location and connection quality. It prioritizes routes with the highest connection quality to maintain QoS even in jammed conditions. Additionally, it incorporates mechanisms for packet redirection away from jammed areas to ensure successful transmission. Results: Evaluation of the Extended Optimized Transmission Mechanism (EOTM) demonstrates significant improvements in packet delivery performance compared to existing algorithms. The enhanced algorithm effectively mitigates the impact of jamming attacks, ensuring reliable communication and preserving QoS in MHWNs. Conclusion: The proposed OTM algorithm presents a promising approach to counter-jamming attacks in MHWNs by dynamically routing packets to avoid jammed areas and maintain communication integrity. The results highlight the effectiveness of EOTM in improving packet delivery performance and ensuring uninterrupted communication in the face of jamming threats.
Enhancing IoT Healthcare with Federated Learning and Edge Computing
S. Ramesh, Ankitha Prasanth, Raj Jain, Bhattu Meenakshi, Simran Kumari, M. John Basha
Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2025, 2025
An Effective and Deep Neural Network for Brain Tumor Segmentation and Detection
S. Saravanakumar, M. Senbagavalli, Saswati Debnath, Ramesh Sekaran, Manikandan Parasuraman, et al.
Lecture Notes in Networks and Systems, 2025
UAV communication for metaverse intelligent systems with blockchain technology
M. Vasim Babu, S. Ramprabhu, Ramesh Sekaran
Ubiquitous Computing and Technological Innovation for Universal Healthcare, 2024
Ubiquitous computing and technological innovation for universal healthcare
Ganesan, Geetha 1970-, Krishnan, Batri 1980-, Sekaran, Ramesh 1987-, Suresh Kumar, Arumugam 1979-, Veerappan, Kousik 1982-
Ubiquitous Computing and Technological Innovation for Universal Healthcare, 2024
Preface
Ubiquitous Computing and Technological Innovation for Universal Healthcare, 2024
Epileptic Seizure Prediction through ML And DL Models: A Survey
J. Viswanath, S. Annamalai, S. Ramesh
8th International Conference on Electronics Communication and Aerospace Technology Iceca 2024 Proceedings, 2024
A Comprehensive Survey of AI-Driven Biomedical Image Processing for Intracerebral Hemorrhage Detection and Classification: Current Trends, Challenges, and Future Directions
P. Raguraman, M. Kumaresan, S. Ramesh
8th International Conference on Electronics Communication and Aerospace Technology Iceca 2024 Proceedings, 2024
Optimizing Oral Cancer Diagnosis with Advanced Deep Learning Approaches
Gowrishankar, Ramesh Sekaran, Manikandan Parasuraman, Wael Suliman, Vinayakumar Ravi
2024 6th International Symposium on Advanced Electrical and Communication Technologies Isaect 2024, 2024
Handwriting Analysis for Bank Cheque Verification Using EfficientNet
Jaydeep Ranpariya, Roshan Saravanan, Alvin James, Pranav Abraham, S. Ramesh
Communications in Computer and Information Science, 2024
Enhancing IoT Security and Efficiency: Advanced Public Key Cryptographic Solutions for the Modern Era
Ramesh Sekaran, Dhanalakshmi Ramasamy, John Basha Mohamed Basha, Kumaresan Maruthapillai, S. Annamalai, Manikandan Parasuraman
Proceedings 4th International Conference on Technological Advancements in Computational Sciences Ictacs 2024, 2024
Exploring the Potential of Secure Shell (SSH) as a Wireless Network Security Tool
Abhinav Mishra, Girija Shankar Sahoo, Ramesh Sekaran, B Umamaheswari, Pravin R. Futane, Sanjay Choudhary
2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024, 2024
Generic Sentimental Analysis in Web Data Recommendation Based on Social Media Scalable Data Analytics Using Machine Learning Architecture
Ramesh Sekaran, Sivaram Rajeyyagari, Ashok Kumar Munnangi, Manikandan Parasuraman, Manikandan Ramachandran, Anil Kumar
Lecture Notes in Networks and Systems, 2024
Privacy and security assurance in order to adopt a substantiation protocol in ultra-lightweight RFID
G. Sathya, M. Sathiya, P. Sumitra, S. Sabitha, S. Bhuvaneswari, Ramesh Sekaran
Ubiquitous and Transparent Security Challenges and Applications, 2024
Ransomware Classification and Detection: A Supervised Machine Learning Approach
T. Subburaj, K. Suthendran, Srinivasarao Dharmireddi, S. V. Suji Aparna, K. Anish Pon Yamini, S. Ramesh, C. Abhinav
Lecture Notes in Networks and Systems, 2024
The AI-based COVID-19 personal protective equipment is smarty and secure
Mohd Asif Shah, Ramesh Sekaran
Dynamics of Swarm Intelligence Health Analysis for the Next Generation, 2023
CE2RV: Commissioned energy-efficient virtualization for large-scale heterogeneous wireless sensor networks
M. Vasim Babu, Ramesh Sekaran, Suthendran Kannan, Vinayakumar Ravi
International Journal of Communication Systems, 2023
Retraction Note to: Advanced resilient data consigning mechanism for mobile adhoc networks (Journal of Ambient Intelligence and Humanized Computing, (2021), 12, 3, (4255-4268), 10.1007/s12652-020-01820-7)
K. Karthikeyan, S. Ramesh, Namaskaram Kirubakaran, P. Uma Maheswari, R. Arun Prasath, M. Muthukumar
Journal of Ambient Intelligence and Humanized Computing, 2023
Survival study on deep learning techniques for IoT enabled smart healthcare system
Ashok Kumar Munnangi, Satheeshwaran UdhayaKumar, Vinayakumar Ravi, Ramesh Sekaran, Suthendran Kannan
Health and Technology, 2023
Tripartite Transmitting Methodology for Intermittently Connected Mobile Network (ICMN)
Ramesh Sekaran, Fadi Al-Turjman, Rizwan Patan, Velmani Ramasamy
ACM Transactions on Internet Technology, 2023
Wearable Sensor Based Cloud Data Analytics Using Federated Learning Integrated with Classification by Deep Learning Technique
Ashok Kumar Munnangi, Sivaram Rajeyyagari, Ramesh Sekaran, Nashreen Begum Jikkiriya, Manikandan Ramachandran
Lecture Notes in Networks and Systems, 2023
5G Wireless Network-Based Cybersecurity Analysis Using Software Defined Phy_HetNets and Boltzmann Encoder Convolutional Basis Neural Network
Manikandan Parasuraman, Ashok Kumar Munnangi, Sivaram Rajeyyagari, Ramesh Sekaran, Manikandan Ramachandran
Lecture Notes in Networks and Systems, 2023
Enabling Autonomous Deep Learning Strategies for Real-Time Data Analysis in Machine Learning
Girija Shankar Sahoo, Ramesh Sekaran, G. Renuka, Dharmesh Dhabliya, Pramod Kumar Faujdar, Deepak Minhas
2023 3rd International Conference on Smart Generation Computing Communication and Networking Smart Gencon 2023, 2023
Evaluation of soft computing in methodology for calculating information protection from parameters of its distribution in social networks
Pooja Sapra, Divya Paikaray, Nutan Gusain, Monika Abrol, S. Ramesh, Shambhu Bhardwaj
Soft Computing, 2023
Stock Price Prediction Model Using LSTM: A Comparative Study
M Kumaresan, M John Basha, P Manikandan, S Annamalai, Ramesh Sekaran, A. Suresh Kumar
2023 3rd Asian Conference on Innovation in Technology Asiancon 2023, 2023
Detection of Objects in High-Definition Videos for Disaster Management
Gowri Shankar J, A Suresh Kumar, Ramesh Sekaran, Manikandan Parasuraman, S Annamalai, Narmadha T
2023 International Conference on Computer Science and Emerging Technologies Cset 2023, 2023
3D Based CNN for Improved Segmentation from the Hyperspectral Images
Narmadha T, Arockia Raj A, P Saravanan, A Suresh Kumar, Ramesh Sekaran, Gowri Shankar J
2023 International Conference on Computer Science and Emerging Technologies Cset 2023, 2023
A Novel Enhanced Krill Herd Optimization Based Quality Prediction for Health Care Services
Sivaram Rajeyyagari, R. Gopal, P Saravanan, M John Basha, Kempanna M, Ramesh Sekaran
2023 International Conference on Computer Science and Emerging Technologies Cset 2023, 2023
CNN-Based Analysis of Ultrasound Images for PCOS Diagnosis
A. Suresh Kumar, S. Annamalai, M. Kumaresan, P. Manikandan, Ramesh Sekaran, H. Aditya Pai
Proceedings International Conference on Technological Advancements in Computational Sciences Ictacs 2023, 2023
Ant colony resource optimization for Industrial IoT and CPS
Ramesh Sekaran, Ashok Kumar Munnangi, Sivaram Rajeyyagari, Manikandan Ramachandran, Fadi Al‐Turjman
International Journal of Intelligent Systems, 2022
Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier
Malathy Jawahar, J. Prassanna, Vinayakumar Ravi, L. Jani Anbarasi, S. Graceline Jasmine, R. Manikandan, Ramesh Sekaran, Suthendran Kannan
Multimedia Tools and Applications, 2022
3D brain slice classification and feature extraction using Deformable Hierarchical Heuristic Model
Ramesh Sekaran, Ashok Kumar Munnangi, Manikandan Ramachandran, Amir H. Gandomi
Computers in Biology and Medicine, 2022
CovMnet–Deep Learning Model for classifying Coronavirus (COVID-19)
Malathy Jawahar, Jani Anbarasi L, Vinayakumar Ravi, J. Prassanna, S. Graceline Jasmine, R. Manikandan, Rames Sekaran, Suthendran Kannan
Health and Technology, 2022
A high performance scalable fuzzy based modified Asymmetric Heterogene Multiprocessor System on Chip (AHt-MPSOC) reconfigurable architecture
Arun Prasath Raveendran, Jafar A. Alzubi, Ramesh Sekaran, Manikandan Ramachandran
Journal of Intelligent and Fuzzy Systems, 2022
Computational Intelligence Using Big Data for Fight Against Covid-19 Pandemic in Healthcare Environment
Ashok Kumar Munnangi, Ramesh Sekaran, Arun Prasath Raveendran, Manikandan Ramachandran
How Covid 19 is Accelerating the Digital Revolution Challenges and Opportunities, 2022
Nonlinear Cosine Neighborhood Time Series-Based Deep Learning for the Prediction and Analysis of COVID-19 in India
Ashok Kumar Munnangi, Ramesh Sekaran, Sivaram Rajeyyagari, Manikandan Ramachandran, Suthendran Kannan, Subrato Bharati
Wireless Communications and Mobile Computing, 2022
A Novel Approach for Efficient Packet Transmission in Volunteered Computing MANET
Ramesh Sekaran, Rizwan Patan, Fadi Al-Turjman
ACM Transactions on Internet Technology, 2021
A novel handover mechanism of PmIpv6 for the support of multi-homing based on virtual interface
Indumathi Lakshmi Krishnan, Fadi Al-Turjman, Ramesh Sekaran, Rizwan Patan, Ching-Hsien Hsu
Sustainability Switzerland, 2021
Diabetic Retinopathy Detection using Deep Learning
Koganti Nishitha Sai Sree, Dasi Veda Sree, Garikipati Hema Lakshmi, S. Ramesh
Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems Icesc 2021, 2021
Multivariate regressive deep stochastic artificial learning for energy and cost efficient 6G communication
Ramesh Sekaran, Manikandan Ramachandran, Rizwan Patan, Fadi Al-Turjman
Sustainable Computing Informatics and Systems, 2021
An Improved IDAF-FIT Clustering Based ASLPP-RR Routing with Secure Data Aggregation in Wireless Sensor Network
M. Vasim Babu, Jafar A. Alzubi, Ramesh Sekaran, Rizwan Patan, Manikandan Ramachandran, Deepak Gupta
Mobile Networks and Applications, 2021
Advanced resilient data consigning mechanism for mobile adhoc networks
K. Karthikeyan, S. Ramesh, Namaskaram Kirubakaran, P. Uma Maheswari, R. Arun Prasath, M. Muthukumar
Journal of Ambient Intelligence and Humanized Computing, 2021
5G Integrated Spectrum Selection and Spectrum Access using AI-based Frame work for IoT based Sensor Networks
Ramesh Sekaran, Surya Narayana Goddumarri, Suresh Kallam, Manikandan Ramachandran, Rizwan Patan, Deepak Gupta
Computer Networks, 2021
Wearable Smart Devices for Remote Healthcare Monitoring to Detect Cardiac Diseases
Ashok Kumar Munnangi, Ramesh Sekaran, Geetha Velliyangiri, Manikandan Ramachandran, Ambeshwar Kumar
Internet of Things, 2021
Medical Data Compression for Lossless Data Transmission and Archival
Ramesh Sekaran, Vimal Kumar Maaanuguru Nagaraju, Vijayalakshmi Jagadeesan, Manikandan Ramachandran, Ambeshwar Kumar
Internet of Things, 2021
Image analysis and data processing for COVID-19
Ambeshwar Kumar, R. Manikandan, S. Magesh, Rizwan Patan, S. Ramesh, Deepak Gupta
Data Science for Covid 19 Volume 1 Computational Perspectives, 2021
Smart healthcare and quality of service in IoT using grey filter convolutional based cyber physical system
Rizwan Patan, G S Pradeep Ghantasala, Ramesh Sekaran, Deepak Gupta, Manikandan Ramachandran
Sustainable Cities and Society, 2020
A brief history on intermittently connected networks
Horizons in Computer Science Research Volume 18, 2020
De-noising of images from salt and pepper noise using hybrid filter, fuzzy logic noise detector and genetic optimization algorithm (HFGOA)
A. Senthil Selvi, K. Pradeep Mohan Kumar, S. Dhanasekeran, P. Uma Maheswari, S. Ramesh, S. Senthil Pandi
Multimedia Tools and Applications, 2020
An efficient detection and segmentation of brain tumor using robust active shape model
Journal of Critical Reviews, 2020
Survival Study on Blockchain Based 6G-Enabled Mobile Edge Computation for IoT Automation
Ramesh Sekaran, Rizwan Patan, Arunprasath Raveendran, Fadi Al-Turjman, Manikandan Ramachandran, Leonardo Mostarda
IEEE Access, 2020
Efficient way to detect bone cancer using image segmentation
Professor, Department of Computer science, Engineering, Mallareddy Engineering College for Women, Dr.S.P. Anandaraj, Dr.N. Kirubakaran, Professor, Department of Electronics, Professor,Department of Computer science, Engineering, Mallareddy Engineering College for Women, Dr.S. Ramesh, Professor, Department of Electronics, Professor,Department of Computer science, et al.
International Journal of Engineering and Advanced Technology, 2019
An illustrative disquisition on trust based routing techniques in MANET
G. Abirami, S. Ramesh
Proceedings of 2017 International Conference on Innovations in Information Embedded and Communication Systems Iciiecs 2017, 2017
An Efficient Secure Routing for Intermittently Connected Mobile Networks
S. Ramesh
Wireless Personal Communications, 2017
Artificial bee colony algorithm to find optimum path for mobile agents in wireless sensor networks
R. Selva Bharathi, R. Priyadharshni, S. Ramesh
Proceedings of 2014 IEEE International Conference on Advanced Communication Control and Computing Technologies Icaccct 2014, 2015
B-LoDiS routing for intermittently connected MANET with agent AES approach
Internetworking Indonesia Journal, 2015
Implementing One Time Password based security mechanism for securing personal health records in cloud
K Ramesh, S Ramesh
2014 International Conference on Control Instrumentation Communication and Computational Technologies Iccicct 2014, 2014
Ant routing protocol for intermittently connected mobile networks
S. Ramesh, R. Praveen, R. Indira, P. Ganesh Kumar
2013 5th International Conference on Advanced Computing Icoac 2013, 2014
BCR routing for intermittently connected mobile Ad hoc networks
International Journal of Engineering and Technology, 2014
A secure 3-way routing protocols for intermittently connected mobile ad hoc networks
Ramesh Sekaran, Ganesh Kumar Parasuraman
Scientific World Journal, 2014
A comparative analysis of routing techniques in intermittently connected MANETs
S. Ramesh, P. Ganesh Kumar
Asian Journal of Scientific Research, 2013
Spray and wait routing with agents in intermittently connected MANETs
S. Ramesh, P. Ganesh Kumar
Journal of Artificial Intelligence, 2013
A-Routing for intermittently connected mobile ad hoc networks
S. Ramesh, R. Indira, R. Praveen, P. Ganesh Kumar
2013 International Conference on Recent Trends in Information Technology Icrtit 2013, 2013
Bee routing protocol for intermittently connected mobile networks
S. Ramesh, R. Praveen, R. Indira, P. Ganesh Kumar
2013 International Conference on Recent Trends in Information Technology Icrtit 2013, 2013
A survey on routing methodologies for ICMANET
S. Ramesh, R. Praveen, R. Indira, P. Ganesh Kumar
4th International Conference on Advanced Computing Icoac 2012, 2012

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