Recently Post Doctoral Research fellowship earned from UniFacvest University centre, Brazil. Received Ph.D in the year 2015, at CEG CAMPUS, ANNA UNIVERSITY, CHENNAI
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering, Computer Networks and Communications, Hardware and Architecture, Signal Processing
27
Scopus Publications
296
Scholar Citations
9
Scholar h-index
8
Scholar i10-index
Scopus Publications
IoT Activity Symphony: Harmonizing DBSCAN Clustering and t-SNE Visualization for Enhanced Recognition. Visweswara Rao Vempali, Omesh Wadhwani, Sonal Malhotra, T S Karthik, Shobhit Garg, Venkata Ramana K Proceedings of International Conference on Circuit Power and Computing Technologies Iccpct 2024, 2024 This study introduces the IoT Activity Symphony Framework, a new algorithm that combines t-SNE with DBSCAN to cluster data from the Internet of Things. Using a variety of datasets obtained from Kaggle, the system outperforms previous research by a wide margin, earning a Silhouette Score of 0.72 and a Davies-Bouldin Index of 1.08. This model is a huge step forward for IoT analytics thanks to its flexibility, optimised hyperparameters, and strong clustering abilities. In particular, the results of our comparison demonstrate the framework's competence in dealing with real-world situations, yielding useful information for anomaly identification and predictive maintenance. The study backs up the suggested technique and presents the framework as a useful resource for academics and professionals working with data from the Internet of Things. With the introduction of a novel clustering technique, this study adds to the developing area and demonstrates the promising future of practical applications in many IoT fields. Additional clustering techniques, real-time capabilities, and assessments in a variety of IoT application scenarios are also potential directions for future study. An encouraging development is the IoT Activity Symphony Framework, which opens the door to better comprehension and use of complicated IoT statistics.
Experimental Methodology to Optimize Power Flow in Utility Grid with Integrated Renewable Energy and Storage Devices Using Hidden Markov Model T. S. Karthik, D. Kamalakkannan, S. Murugesan, Jyoti Prasad Patra, Md. Abul Ala Walid, Kalagotla Chenchireddy, Syed Musthafa A, B. Jagadish Kumar Electric Power Components and Systems, 2024 A continuous energy supply to the load side is required by modern power systems. This calls for a sound understanding of how to forecast load demand in the present and the future with the least degree of inaccuracy. Typically, a sequential method with two steps—forecasting and optimization—is used to derive judgments from data. For achieving this goal, optimized power flow is focused in this paper through load forecasting, mode selection, and optimization of power forecasting. Firstly, load forecasting is implemented using time series, and economic and weather-related information for the different consumer’s load. Then mode selection is implemented using Hidden Markov Model that determines the requested load for grid-connected or RES mode. When composite RES is developed, the percentage of serviced load rises as more renewable energy sources are added. Following the implementation of the consumer load and mode selection, optimization is used to improve the power flow. The empirical findings show enhanced prescriptive performance when compared to answers found in single- and multi-household contexts. Also, we offer insightful information on how explaining performance is described.
INDUSTRY 5.0: AN OVERALL ASSESSMENT OF USING ARTIFICIAL INTELLIGENCE IN INDUSTRIES Journal of Theoretical and Applied Information Technology, 2023
NeuroAI-Driven Advanced Deep Brain Stimulation for Precision Management of Movement Disorders V Balaji, T S Karthik, N Akiladevi, S. Sathya, V Mahalakshmi, D Anup Kumar 2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023 The quality of life of a person can be severely diminished by movement disorders such as Parkinson’s disease or essential tremor. Although deep brain stimulation (DBS) has emerged as a promising therapeutic strategy, there are still gaps in our ability to properly optimize therapy with the tools at our disposal. This study employs state-of-the-art NeuroAI technology to completely modify the way in which movement disorders are treated. The inability to make real-time adjustments to DBS settings in response to changes in the patient’s health is at the heart of the problems that plague current approaches. Traditional approaches typically employ fixed parameters that do not take into account individual differences in how they feel. This rigidity might cause unwanted consequences and subpar performance. NeuroAI, a complex AI system designed to interpret brain signals and individual patient data, lies at the heart of our approach. It permits continuous modifications to stimulation settings based on real-time study of patient reactions and symptom variations. Our method does this by continuously adapting to the patient’s changing requirements. Patients have reported dramatic improvements in symptom management, decreased side effects, and enhanced quality of life, as shown by the study’s promising early results. With the help of NeuroAI, DBS may be administered with unparalleled accuracy, giving patients new hope for a better, symptom-free future. This study is a major step forward in the direction of making deep brain stimulation a regular treatment for movement disorders that is both individualized and extremely effective.
Hybrid Evolutionary Algorithm with Energy Efficient Cluster Head to Improve Performance Metrics on the IoT Jaya Dipti Lal, T. Balachander, T.S. Karthik, Sandy Ariawan, Pratap M S, Mohit Tiwari Proceedings 7th International Conference on Computing Methodologies and Communication Iccmc 2023, 2023 In recent times, the internet of Things (IoT) is an alternative model that is quickly getting ground in the scenario of current wireless telecommunication. Wireless sensor network (WSN) is a significant part of IoT, and it is primarily accountable for reporting and acquiring information. As coverage area and lifetime of WSN directly define the performance of IoT, how to design a technique for conserving node energy and decreasing node death rate becomes crucial problem. Sensor network clustering is an efficient technique to overcome this problem. It splits nodes into clusters and chooses one to be cluster head (CH). The data communication and transmission within single cluster are accomplished by its CH. This study develops a hybrid evolutionary algorithm-based energy efficient cluster head selection (HEA-EECHS) technique in the IoT environment. The presented HEA-EECHS technique concentrates on the effectual choice of CHs in the IoT environment. To do so, the HEA-EECHS technique derives an improved artificial jellyfish search algorithm (IAJSA) by the incorporation of oppositional based learning (OBL) approach into the traditional AJSA. Along with that, the HEA-EECHS technique designs a fitness function incorporating four parameters namely energy, cluster node density, average neighboring distance, and average distance to BS. The experimental assessment of the HEA-EECHS technique is investigated under several IoT nodes and the final results gives the value of 500 WMNs, the HEA-EECHS method has attained decreased CMO of 0.0015. The simulation output highlighted the improvised efficacy of the HEA-EECHS technique.
Cauchy Grasshopper Optimization Algorithm with Deep Learning Model for Cloud Enabled Cyber Threat Detection System C.N. Ravi, T.S. Karthik, K. Manikandan, Pcd Kalaivaani, Priyanka Nandkishor Chopkar, Aviral Srivastava Proceedings of the 7th International Conference on Intelligent Computing and Control Systems Iciccs 2023, 2023 The tremendous growth of internet technology has drastically improved the large amount of connected devices. To secure network infrastructure from the damage that cyberattacks might cause, this has made an enormous attack surface that needs the deployment of practical and effective counter measures. In the contemporary era of active network transmission and throughput, Intrusion Detection System (IDS) plays a vital role in ensuring secure network resource and data from outside invasion. In recent times, IDS becomes an essential tool to enhance the efficiency and flexibility for unpredictable and unexpected invasions of the network. Deep learning (DL) is a well-known and essential method to resolve challenges and could learn rich features of massive information. Therefore, the study focuses on the design and development of the Cauchy Grasshopper Optimization Algorithm with Deep Learning for Cloud Enabled IDS (CGOA-DLCIDS) technique. The presented CGOA-DLCIDS method aims to recognize the presence of intrusions in the cloud platform. To achieve this, the CGOA-DLCIDS technique performs feature subset election by CGOA which reduces the feature subset and enhances the intrusion detection rate. Next, the CGOA-DLCIDS technique employs attention based long short-term memory (ALSTM) module for automated and accurate intrusion detection and classification. The simulations analysis of the CGOA-DLCIDS method on benchmark dataset highlighted the increasing results compared to recent IDS approaches.
Automated Intracranial Haemorrhage Detection and Classification using Rider Optimization with Deep Learning Model T. S. Karthik, Naziya Hussain, N K Anushkannan, Rajasekhar Pinnamaneni, Vijayakrishna Rapaka E, Shyamali Das International Conference on Automation Computing and Renewable Systems Icacrs 2022 Proceedings, 2022 Intracranial haemorrhage (ICH) refers to a pathological disorder that requires quick decision-making and diagnosis. Computed tomography (CT) can be accurate and dependable diagnosis method for identifying haemorrhages. Automated recognition of ICH through CT scans with a computer-aided diagnosis (CAD) method will be useful to classify and detect the distinct grades of ICH. Due to the latest development of deep learning (DL) techniques in image processing applications, numerous medical imaging methods use it. Thus, this article develops an automated ICH detection and classification using Rider Optimization with Deep Learning (ICHDC-RODL) model. The presented ICHDC-RODL technique mainly determines the presence of ICH using DL concepts. In the presented ICHDCRODL technique, the features are generated by the use of Xtended Central Symmetric Local Binary Pattern (XCS-LBP) model. Moreover, the bidirectional long short-term memory (BiLSTM) method is employed for ICH diagnosis. At last, the rider optimization algorithm (ROA) is exploited for the hyperparameter tuning procedure of the BiLSTM method. To demonstrate the enhanced outcomes of the ICHDC-RODL technique, a series of simulations were performed and the results are examined under various aspects. The simulation outcomes indicate the enhancements of the ICHDC-RODL technique over recent approaches.
Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification A. Praveena, N. Senthamilarasi, T. S. Karthik, Abirami S.K, Vijayakrishna Rapaka E, Shyamali Das International Conference on Automation Computing and Renewable Systems Icacrs 2022 Proceedings, 2022 Autism Spectrum Disorder (ASD) is a developing disorder if the symptoms develop obvious in the initial years of age but it could be present in some age groups. ASD is mental health problem that affects communicational, social, and non-verbal performances. It could not be cured entirely but is decreased when identified initially. The primary analysis was hampered by the difference and severity of ASD symptoms and containing symptoms usually realized in other mental health problems as well. With the application of machine learning (ML) for the predictive and recognition of several diseases with optimum accuracy, a ray of hope to initial recognition of ASD dependent upon many physiological and physical parameters is projected. This article designs an Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification (EODL-ASDC) technique. The presented EODL-ASDC technique mainly focuses on the identification and classification of ASD. To attain this, the presented EODL-ASDC technique exploits the deep belief network (DBN) system to perform the classification procedure. In addition, the EO algorithm is employed for the optimal hyperparameter tuning of the DBN approach. To demonstrate the enhanced ASD classification result of the EODL-ASDC approach, an extensive range of experimental evaluates was executed. The experimental results demonstrate the improvements of the EODL-ASDC technique over other approaches.
Evolutionary Optimization Algorithm on Content based Image Retrieval System using Handcrafted features with Squeeze Networks T. S. Karthik, R.V. V. Krishna, T. K. Ramakrishna Rao, V. Manoranjithem, S. Kalaiarasi, B. Jegajothi Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy Icais 2022, 2022 Content based image retrieval (CBIR) is commonly utilized in several application areas due to the rising significance of images in day to day lives. On comparing with textual data, images require high storage area and processing complexity. The latest advances in machine (ML) and deep learning (DL) models can be utilized for the design of effective CBIR In this view, this paper presents an evolutionary optimization algorithm on CBIR system using handcrafted features with squeeze networks (EOCBIR-HFSN) technique. The goal of the EOCBIR-HFSN technique is to proficiently retrieve the related images based on the query image (Q1). The proposed EOCBIR-HFSN technique involves the feature extraction process by the use of local binary patterns (LBP) based handcrafted features and SqueezeNet based deep features. Besides, the hyper-parameter tuning of the SqueezeNet model is performed by the grasshopper optimization algorithm (GOA), shows the novelty of the work. Finally, Euclidean distance metric is used to determine the highly similar images from the database. The comprehensive result analysis of the EOCBIR-HFSN technique take place on benchmark database reported the enhanced outcomes over the other techniques.
Implementing an effective and secure resource architecture for vlsi block encryption Journal of Nuclear Energy Science and Power Generation Technology, 2021
Microstrip patch antenna for pcs and wireless networks Journal of Nuclear Energy Science and Power Generation Technology, 2021
Evaluate and design the mini-hexagon-shaped monopole antenna controller to minimize losses in the unit Journal of Nuclear Energy Science and Power Generation Technology, 2021
The transmission of narrowband and wideb and data communications via low-voltage power lines optimization Journal of Nuclear Energy Science and Power Generation Technology, 2021
Sustainable energy harvesting for self-powered micro/ nanosystems enabled by nanotechnology Journal of Nuclear Energy Science and Power Generation Technology, 2021
Improvements in the performance of the base station antenna due to the use of mimo in a mobile communication system Journal of Nuclear Energy Science and Power Generation Technology, 2021
A REVIEW ON MOISTURIZING HERBAL CREAM KV Kumar, GN Raju, B Pooja, T Kavya, MY Sri, G Navya, ... 2025
5G Resource Allocation Enhancement Via Resnet-InceptionV2 With Non-Linear Analysis. TS Karthik, M Elangovan, AR Prasath, KJ Kumar, AK Shrivastav Journal of Computational Analysis & Applications 33 (2) , 2024 2024
Experimental Methodology to Optimize Power Flow in Utility Grid with Integrated Renewable Energy and Storage Devices Using Hidden Markov Model TS Karthik, D Kamalakkannan, S Murugesan, JP Patra, MAA Walid, ... Electric Power Components and Systems 52 (11), 2047-2064 , 2024 2024 Citations: 7
Pervasive sensing infrastructure for agriculture and wildlife preservation with backward compatibility. KP Kumar, RS Sawant, YS Deshmukh, TS Karthik, R Umanesan, ... 2024
Cauchy Grasshopper Optimization Algorithm with Deep Learning Model for Cloud Enabled Cyber Threat Detection System K T S 2023 7th International Conference on Intelligent Computing and Control … , 2023 2023 Citations: 2
Hybrid Evolutionary Algorithm with Energy Efficient Cluster Head to Improve Performance Metrics on the IoT JD Lal, T Balachander, TS Karthik, S Ariawan, P MS, M Tiwari 2023 7th International Conference on Computing Methodologies and … , 2023 2023 Citations: 9
Traffic congestion detection using Active RFID under Thingspeak in Smart cities AD I S S B Tejaswini, karthik T S, B Rajesh Babu, Vismitha A International Journal of Scientific Research in Engineering and Management 7 … , 2023 2023
Automated intracranial haemorrhage detection and classification using rider optimization with deep learning model TS Karthik, N Hussain, NK Anushkannan, R Pinnamaneni, S Das 2022 International conference on automation, computing and renewable systems … , 2022 2022 Citations: 4
Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification A Praveena, N Senthamilarasi, TS Karthik, A SK, S Das 2022 International Conference on Automation, Computing and Renewable Systems … , 2022 2022 Citations: 1
Equilibrium Optimizer with Deep learning model for Autism spectral Disorder classification A. Praveena, Karthik, T.S International Conference on Automation, Computing and Renewable systems … , 2022 2022
Automated Intracranial Haemorrhage Detection and classification using Rider optimization with deep learning model Karthik, T.S, Anush kananan N K International Conference on Automation, Computing and Renewable systems … , 2022 2022
Study on Emission and Performance of Diesel Engines by adding Nano Particles to the Blended Fuels T Karthik, P Kesavan, P Sanjay, AM Imran International Journal of Vehicle Structures & Systems 14 (7), 876-879 , 2022 2022
Design and fabrication of muffler using natural fiber T Karthik, K Murugesan AIP Conference Proceedings 2446 (1), 180046 , 2022 2022
Effective Routing Attack Detection Analysis in MANET/WSN using a Deep Learning Framework Along with False AES as a Secure Layer TS Srinivas Jhade, Premalatha V, Karthik International Journal on Neuroquantology 20 (12), 306-320 , 2022 2022
METHOD AND SYSTEM FOR MONITORING USAGE OF A USER - DEVICE BY A USER K T S IN Patent The Patent Office Journal No.48/2020 Dated 27/11/2,020 , 2022 2022
The Internet of Things - Case Study & its applications K T S Scientific Intl. Publishing House (SIPH) TN , 2022 2022
Big Data processing using AI K T S Applications of Artificial Intelligence in Real World, 208-215 , 2022 2022
DESIGN AND ANALYSIS OF SELF-PROTECTION FRAMEWORK SYSTEM INTEGRATED WITH FOG COMPUTING AND IOT K T S IN Patent The Patent Office Journal No. 08/2,022 , 2022 2022
Evolutionary Optimization Algorithm on Content based Image Retrieval System using Handcrafted features with Squeeze Networks TS Karthik, RVV Krishna, TKR Rao, V Manoranjithem, S Kalaiarasi, ... 2022 Second International Conference on Artificial Intelligence and Smart … , 2022 2022 Citations: 6
Effect of injection timing on the performance of dual fuel engine fueled with algae nano-biodiesel blends and biogas T Karthik, NR Banapurmath, DN Basavarajappa, SV Ganachari, ... Materials Today: Proceedings 59, 289-296 , 2022 2022 Citations: 17
MOST CITED SCHOLAR PUBLICATIONS
An integrated framework for mechatronics based product development in a fuzzy environment R Parameshwaran, C Baskar, T Karthik Applied Soft Computing 27, 376-390 , 2015 2015.0 Citations: 63
MHD flow of thermally radiative Maxwell fluid past a heated stretching sheet with Cattaneo–Christov dual diffusion K Loganathan, N Alessa, N Namgyel, TS Karthik Journal of Mathematics 2021 (1), 5562667 , 2021 2021.0 Citations: 41
Zero and nonzero mass flux effects of bioconvective viscoelastic nanofluid over a 3D Riga surface with the swimming of gyrotactic microorganisms TS Karthik, K Loganathan, AN Shankar, MJ Carmichael, A Mohan, ... Advances in Mathematical Physics 2021 (1), 9914134 , 2021 2021.0 Citations: 25
Effect of hydrogen flow rates on the performance of two biodiesels fuelled dual fuel engine DM Muralidhara, NR Banapurmath, M Udayaravi, CP Reddy, PA Harari, ... Materials Today: Proceedings 49, 2189-2196 , 2022 2022.0 Citations: 20
A hybrid genetic artificial neural network (G-ANN) algorithm for optimization of energy component in a wireless mesh network toward green computing: B. Prakash et al. B Prakash, S Jayashri, TS Karthik Soft Computing 23 (8), 2789-2798 , 2019 2019.0 Citations: 20
Effect of injection timing on the performance of dual fuel engine fueled with algae nano-biodiesel blends and biogas T Karthik, NR Banapurmath, DN Basavarajappa, SV Ganachari, ... Materials Today: Proceedings 59, 289-296 , 2022 2022.0 Citations: 17
WITHDRAWN: Sentiment analysis of product feedback using natural language processing P Chitra, TS Karthik, S Nithya, JJ Poornima, JS Rao, M Upadhyaya, ... Materials Today: Proceedings , 2021 2021.0 Citations: 17
WITHDRAWN: A brief overview of maximum power point tracking algorithm for solar PV system C Pavithra, P Singh, VP Sundramurthy, TS Karthik, PR Karthikeyan, ... Materials Today: Proceedings , 2021 2021.0 Citations: 16
Hybrid Evolutionary Algorithm with Energy Efficient Cluster Head to Improve Performance Metrics on the IoT JD Lal, T Balachander, TS Karthik, S Ariawan, P MS, M Tiwari 2023 7th International Conference on Computing Methodologies and … , 2023 2023.0 Citations: 9
Orthogonal Stability and Nonstability of a Generalized Quartic Functional Equation in Quasi‐ β ‐Normed Spaces N Alessa, K Tamilvanan, K Loganathan, TS Karthik, JM Rassias Journal of Function Spaces 2021 (1), 5577833 , 2021 2021.0 Citations: 9
Experimental Methodology to Optimize Power Flow in Utility Grid with Integrated Renewable Energy and Storage Devices Using Hidden Markov Model TS Karthik, D Kamalakkannan, S Murugesan, JP Patra, MAA Walid, ... Electric Power Components and Systems 52 (11), 2047-2064 , 2024 2024.0 Citations: 7
WITHDRAWN: Implementation of wireless home-based automation and safety arrangement using power electronic switches R Geethamani, TS Karthik, M Deivakani, V Jain, A Mohan, M Chopra, ... Materials Today: Proceedings , 2021 2021.0 Citations: 7
Empirical wavelet transform based single phase power quality indices T Karthik, AC Umarikar, T Jain 2014 Eighteenth National Power Systems Conference (NPSC), 1-6 , 2014 2014.0 Citations: 7
Evolutionary Optimization Algorithm on Content based Image Retrieval System using Handcrafted features with Squeeze Networks TS Karthik, RVV Krishna, TKR Rao, V Manoranjithem, S Kalaiarasi, ... 2022 Second International Conference on Artificial Intelligence and Smart … , 2022 2022.0 Citations: 6
Investigation on Dielectric Properties of Press Board Coated with Epoxy Resin, Quartz, and Rice Husk Ash S Banumathi, TS Karthik, M Sasireka, K Ramaswamy, J Vishnu, ... Advances in Materials Science and Engineering 2021 , 2021 2021.0 Citations: 5
Automated intracranial haemorrhage detection and classification using rider optimization with deep learning model TS Karthik, N Hussain, NK Anushkannan, R Pinnamaneni, S Das 2022 International conference on automation, computing and renewable systems … , 2022 2022.0 Citations: 4
Certain Class of Analytic Functions Connected with q ‐Analogue of the Bessel Function N Alessa, B Venkateswarlu, K Loganathan, TS Karthik, PT Reddy, ... Journal of Mathematics 2021 (1), 5587886 , 2021 2021.0 Citations: 4
Sustainable energy harvesting for self-powered micro/nanosystems enabled by nanotechnology VS Chandrika, TS Karthik, J Bhaskaran, B Hemanth, MK Loganathan J Nucl Ene Sci Power Generat Technol 10 (9) , 2021 2021.0 Citations: 3
Emission Characteristics of Diesel Engines Fuelled with Blended Biodiesels. T Karthik International Journal of Vehicle Structures & Systems (IJVSS) 12 (3) , 2020 2020.0 Citations: 3
Anantha Christu Raj P, Karthik TS, Kapila D, Mathiazhagan V, et al.,(2021) Implementing an Effective and Secure Resource Architecture for vlsi Block Encryption Y Sucharitha J Nucl Ene Sci Power Generat Techno 10 (9), 2 , 0 Citations: 3
