Computer Science, Artificial Intelligence, Computer Engineering, Information Systems
17
Scopus Publications
140
Scholar Citations
6
Scholar h-index
6
Scholar i10-index
Scopus Publications
Federated Learning and Blockchain-Based Collaborative Framework for Real-Time Wild Life Monitoring Preetha Jagannathan, Kalaivanan Saravanan, Subramaniyam Deepajothi, Sharmila Vadivel Cybernetics and Information Technologies, 2025 Effective wildlife monitoring in hilly and rural areas can protect communities and diminish human-wildlife conflicts. A collaborative framework may overcome challenges like inadequate data integrity and security, declining detection accuracy over time, and delays in critical decision-making. The proposed study aims to develop a real-time wildlife monitoring framework using Federated Learning and blockchain to improve conservation strategies. Min-max normalization enhances training data and Elastic Weight Consolidation (EWC) for real-time adaptation. The improvised YOLOv8+EWC enables real-time classification and continual learning and prevents catastrophic forgetting. It also automates actions based on detection results using smart contracts and ensures secure, transparent data management with blockchain. Compared to existing classifiers such as Deep Neural Network, Dense-YOLO4, and WilDect: YOLO, YOLOv8+EWC performs exceptionally well across several metrics, accomplishing an accuracy of 98.91%. Thus, the proposed model enables reliable decision-making by providing accurate, real-time information about wildlife.
Practical Application of Quantum-Enhanced Machine Learning: Prediction of Atomic Mass and Properties S. Deepajothi, U. V. Anbazhagu, K. Priyadharshini, Sibi Amaran, G. Kalanandhini, S. Boopathi AI Frameworks and Tools for Software Development, 2025 This chapter discusses the application of techniques from quantum machine learning at unprecedented levels of accuracy that are used in predicting atomic mass and properties. These would mean it will explain advanced machine algorithms, tailored specifically for systems involving quantum, which lend deeper knowledge and better understanding of atomic nature such as electronic structure and chemical bonding. The integration of QML techniques results in a process of which it will be efficient to process large datasets of quantum, implying that meaningful insights may come out of complex quantum phenomena. Some focal areas are, for example, neural networks and their utilization in quantum support vector machines and generative models that can predict atomic properties. The chapter explores the practical applications of Quantum Machine Learning in scientific research, highlighting the transformative potential of AI in advancing quantum science.
Quantum computing in healthcare using AI-driven medical technologies U. V. Anbazhagu, K. Priyadharshini, S. Deepajothi, Haripriya M. P., E. Afreen Banu Modern Superhypersoft Computing Trends in Science and Technology, 2024 Quantum computing with AI-driven medical technologies represents an absolute frontier of transformation in precision medicine, diagnostics, and drug discovery. This chapter explores the intersection of quantum computing and artificial intelligence in transforming complex medical challenges, namely in big data analysis, acceleration in molecular simulations, and personalizing treatment plans. Quantum algorithms can scale exponentially beyond AI's capabilities in the breakthrough of genomic analysis, radiological interpretation, and predictive modeling for patient outcomes. The focus of the chapter will be on state-of-the-art achievements in quantum machine learning, possible applications in healthcare areas, and their future prospects for the revolutionizing of medical imaging, optimization of clinical trials, and real-time data processing on wearable devices. They confront the computational limits of classical systems and offer new ground for significant progress in both medical research and health care delivery by solving problems that have until now been intractable.
Quantum computing and machine learning for advanced cybersecurity solutions S. Deepajothi, C. Senthil Selvi, P. Shanmugaraja, B. Karthikeyan, A. R. Aravind, A. Rajendra Prasad Modern Superhypersoft Computing Trends in Science and Technology, 2024 Quantum computing and machine learning provide an entirely new paradigm for advanced solutions in cybersecurity. The supercomputational power that quantum computing has will immediately be capable of solving tough cryptographic problems that classical computers find very hard to solve, thus providing extra-strength encryption and decryption techniques. In parallel, machine learning algorithms detect changing cyber threats; they identify complex patterns in very large datasets and provide real-time predictions of future vulnerabilities. This chapter will describe the synergies between quantum computing and ML, which might potentially lead to the development of mighty cybersecurity frames able to thwart attacks by sophisticated breaches such as those based on quantum principles. It takes up discussions regarding how QML models will change the entire mechanism of threat detection and response in a world like never before of unprecedented speed and accuracy.
Exploring Gene Expression Biclustering with Integrated PSO-SA-Fuzzy Logic Methodology and its Application to Air Pollution Analysis Global Nest Journal, 2024 <p class="AbstractText"><span lang="EN-US">Gene expression data analysis is crucial for understanding complex biological mechanisms, yet current biclustering techniques struggle with noise, unpredictability, and intrinsic uncertainty. This study proposes an innovative biclustering method combining particle swarm optimization (PSO), simulated annealing (SA), and fuzzy logic to improve gene expression analysis. By integrating natural language processing (NLP) semantic similarity into the PSO framework, the method enhances the capture of intricate gene interactions. Additionally, environmental factors, particularly air pollution, are incorporated to explore their impact on gene expression patterns. The approach leverages the complementary strengths of PSO's exploration capabilities, SA's exploitation efficiency, and fuzzy logic's ability to handle data ambiguity. Comparative assessments on benchmark datasets reveal that this integrated strategy significantly outperforms individual methods in accuracy and resilience. The results demonstrate the PSO-SA-Fuzzy Logic method's superior capacity to detect nuanced and context-dependent gene expression patterns, offering a robust solution to the limitations of existing biclustering techniques. The method not only improves precision and computational efficiency but also enhances the detection of significant gene expression patterns under varying conditions, including environmental stressors. This advancement represents a notable contribution to computational biology, providing a more effective tool for gene expression data analysis.</span></p>
Predicting Software Energy Consumption Using Time Series-Based Recurrent Neural Network with Natural Language Processing on Stack Overflow Data S Deepajothi, Kalyankumar Dasari, N Krishnaveni, R Juliana, Neeraj Shrivastava, Kireet Muppavaram 2024 Asian Conference on Communication and Networks Asiancomnet 2024, 2024 In recent years, there has been an increasing number of software solutions presented to tackle the issue of energy usage at the application level. Nevertheless, there is little knowledge about the level of concern among software developers over energy use, the specific areas of energy consumption that they deem significant, and the potential solutions they propose for enhancing energy efficiency. Especially, the increasing amount of data and IoT devices require more storage space and computational power, which results in higher energy consumption. In order to address this problem, academics and professionals have been investigating several strategies to enhance energy efficiency in computer systems. It may be an interesting project to use deep learning algorithms, especially those that make use of natural language processing (NLP) methods, to estimate software energy usage based on Stack Overflow data. This NLP techniques can analyze the text of questions and answers. This involves tokenization, lemmatization, and named entity recognition to identify terms and phrases related to energy consumption. This study examines the concerns of practitioners about energy consumption on Stack Overflow via the utilization of lexicon-based sentiment analysis, a concept in NLP, combined with RNNs. The objective is to improve energy efficiency by forecasting time series data. The results of this study indicate that the practitioners’ desire to start conversations in the field of energy is closely linked to the utilization of ideas. This analysis of software energy consumption issues may assist academics in identifying the most significant concerns for software developers and end users.
A Novel Optimized Artificial Intelligence Based Deep Learning for Predicting the Infectious Disease Using Computed Tomography International Journal of Intelligent Systems and Applications in Engineering, 2023
Hereditary factor-based multi-featured algorithm for early diabetes detection using machine learning S. Deepajothi, R. Juliana, S.K. Aruna, R. Thiagarajan Artificial Intelligent Techniques for Wireless Communication and Networking, 2022 Today's advent in the medical industry have given numerous chances to improve the quality of detection and reporting the diseases at the early stages for a better diagnosis. Modern day datasets generate fruitful information for timely and periodic monitoring of patients' health conditions. Such information is hidden to a naked eye or hidden in multiple track records of highly affected population. Diabetes mellitus is one such disease which is predominant among a global population which ultimately leads to blindness and death in some cases. The model proposed in this system attempts to design and deliver an intelligent solution for predicting diabetes in the early stages and address the problem of late detection and diagnosis. Intensive research is carried out in many tropical countries for automating this process through a machine learning model. The accuracy of machine learning algorithms is more than satisfactory in the detection of Type 2 diabetes from the dataset of PIMA Indians Diabetes Dataset. An additional feature of hereditary factor is implemented to the existing multiple objective fuzzy classifiers. The proposed model has improved the accuracy to 83% in the training and tested datasets when compared to NGSA model of prediction.
Artifcial Intelligence in Agriculture: A Review Harshitha Sirineni, Thakur Santosh, S. Deepajothi Machine Learning for Business Analytics Real Time Data Analysis for Decision Making, 2022 According to sources, the world population in 2050 might increase by another 2 billion and the land cover for the production or the area under cultivation will be around 4%. When coming to India, there are many challenges for India not only in terms of technology and cost but also in terms of resources like water. Out of the total cultivation area, only 39% is irrigated and 61% is totally dependent on rainwater. If rain fails at any particular period of time, then there is damage to the crops. So this is the time when we increase our efficient farming practices with the help of AI and try to meet the demand. By introducing AI, old tradition farming practices will completely shift—what we are witnessing today. So AI gives many solutions, like a strategy for crops to enhance the quality and reduce the cost.
Predicting software energy consumption using time series-based recurrent neural network with natural language processing on stack overflow data S Deepajothi, K Dasari, N Krishnaveni, R Juliana, N Shrivastava, ... 2024 Asian Conference on Communication and Networks (ASIANComNet), 1-6 , 2024 2024 Citations: 11
Exploring Gene Expression Biclustering with Integrated PSO-SAFuzzy Logic Methodology and its Application to Air Pollution Analysis S Deepajothi, M Umamaheswari, A Viswanathan, R Juliana GLOBAL NEST JOURNAL 26 (9) , 2024 2024
Reliable and efficient lane changing behaviour for connected autonomous vehicle through deep reinforcement learning S Alagumuthukrishnan, S Deepajothi, R Vani, S Velliangiri Procedia Computer Science 218, 1112-1121 , 2023 2023 Citations: 17
Detection and stage classification of UNet segmented lung nodules using CNN S Deepajothi, R Balaji, T Madhuvanthi, P Priakanth, T Daniya, ... 2022 5th International Conference on Multimedia, Signal Processing and … , 2022 2022 Citations: 4
Intelligence in H Sirineni, T Santosh, S Deepajothi Machine Learning for Business Analytics: Real-Time Data Analysis for … , 2022 2022
Artificial Intelligence in Agriculture: A Review H Sirineni, T Santosh, S Deepajothi Machine Learning for Business Analytics, 87-95 , 2022 2022 Citations: 1
Infection detection in older person using artificial intelligence S Saidhbi, AA Endris, S Deepajothi, R Juliana, PR Kshirsagar, A Mohan AIP Conference Proceedings 2393 (1), 020084 , 2022 2022
Survey on intrusions detection system using deep learning in iot environment R Balaji, S Deepajothi, G Prabaharan, T Daniya, P Karthikeyan, ... 2022 International Conference on Sustainable Computing and Data … , 2022 2022 Citations: 21
Hereditary Factor‐Based Multi‐Featured Algorithm for Early DiabetesDetection Using Machine Learning S Deepajothi, R Juliana, SK Aruna, R Thiagarajan Artificial Intelligent Techniques for Wireless Communication and Networking … , 2022 2022 Citations: 1
Utility‐Based Joint Power Control and Resource Allocation Algorithm for Heterogeneous Cloud Radio Access Network (H‐CRAN) H Shaheen, MS Bhuvaneswari, N Balaganesh, BK Rani, PJ Paul, ... Wireless Communications and Mobile Computing 2022 (1), 8594449 , 2022 2022 Citations: 1
Intelligent traffic management for emergency vehicles using convolutional neural network S Deepajothi, DP Rajan, P Karthikeyan, S Velliangiri 2021 7th International Conference on Advanced Computing and Communication … , 2021 2021 Citations: 28
An Empirical Data Analysis Of Digital Resources Using Access Level Response Model P Rane, S Deepajothi, KV Khanna, K Siva Prasad, D Rani Roy, M Shelke Int. J. of Aquatic Science 12 (1), 237-241 , 2021 2021
A Cloud based Virtual Brain Connectivity with EEG Sensor using Internet of Things (IoT) M Vasuki, R Ramakrishnan, TA Victoire, B Preethi 2019
Electrocorticography Based Brain Computer Interface with a Novel Binary BAT Algorithm S Deepajothi, S Selvarajan Journal of Computational and Theoretical Nanoscience 13 (8), 4964-4970 , 2016 2016 Citations: 1
Classification of motor imagery ecog signals using support vector machine for brain computer interface N Rathipriya, S Deepajothi, T Rajendran 2013 Fifth International Conference on Advanced Computing (ICoAC), 63-66 , 2013 2013 Citations: 15
Performance evaluation of SVM–RBF kernel for classifying ECoG motor imagery S Deepajothi, S Selvarajan International Journal of Computer Science and Telecommunications 4 (5), 44-48 , 2013 2013 Citations: 6
Survey of Clustering Algorithm in Wireless Sensor Networks R Juliana, S Deepajothi April , 2013 2013 Citations: 2
Privacy preservation of data sets in data mining AA John, S Deepajothi International Journal of Engineering Research & Technology (IJERT) 2, 2278-0181 , 2013 2013 Citations: 2
A comparative study of classification techniques on adult data set S Deepajothi, S Selvarajan International Journal of Engineering Research & Technology (IJERT) 1 (8) , 2012 2012 Citations: 30
MOST CITED SCHOLAR PUBLICATIONS
A comparative study of classification techniques on adult data set S Deepajothi, S Selvarajan International Journal of Engineering Research & Technology (IJERT) 1 (8) , 2012 2012 Citations: 30
Intelligent traffic management for emergency vehicles using convolutional neural network S Deepajothi, DP Rajan, P Karthikeyan, S Velliangiri 2021 7th International Conference on Advanced Computing and Communication … , 2021 2021 Citations: 28
Survey on intrusions detection system using deep learning in iot environment R Balaji, S Deepajothi, G Prabaharan, T Daniya, P Karthikeyan, ... 2022 International Conference on Sustainable Computing and Data … , 2022 2022 Citations: 21
Reliable and efficient lane changing behaviour for connected autonomous vehicle through deep reinforcement learning S Alagumuthukrishnan, S Deepajothi, R Vani, S Velliangiri Procedia Computer Science 218, 1112-1121 , 2023 2023 Citations: 17
Classification of motor imagery ecog signals using support vector machine for brain computer interface N Rathipriya, S Deepajothi, T Rajendran 2013 Fifth International Conference on Advanced Computing (ICoAC), 63-66 , 2013 2013 Citations: 15
Predicting software energy consumption using time series-based recurrent neural network with natural language processing on stack overflow data S Deepajothi, K Dasari, N Krishnaveni, R Juliana, N Shrivastava, ... 2024 Asian Conference on Communication and Networks (ASIANComNet), 1-6 , 2024 2024 Citations: 11
Performance evaluation of SVM–RBF kernel for classifying ECoG motor imagery S Deepajothi, S Selvarajan International Journal of Computer Science and Telecommunications 4 (5), 44-48 , 2013 2013 Citations: 6
Detection and stage classification of UNet segmented lung nodules using CNN S Deepajothi, R Balaji, T Madhuvanthi, P Priakanth, T Daniya, ... 2022 5th International Conference on Multimedia, Signal Processing and … , 2022 2022 Citations: 4
Survey of Clustering Algorithm in Wireless Sensor Networks R Juliana, S Deepajothi April , 2013 2013 Citations: 2
Privacy preservation of data sets in data mining AA John, S Deepajothi International Journal of Engineering Research & Technology (IJERT) 2, 2278-0181 , 2013 2013 Citations: 2
Artificial Intelligence in Agriculture: A Review H Sirineni, T Santosh, S Deepajothi Machine Learning for Business Analytics, 87-95 , 2022 2022 Citations: 1
Hereditary Factor‐Based Multi‐Featured Algorithm for Early DiabetesDetection Using Machine Learning S Deepajothi, R Juliana, SK Aruna, R Thiagarajan Artificial Intelligent Techniques for Wireless Communication and Networking … , 2022 2022 Citations: 1
Utility‐Based Joint Power Control and Resource Allocation Algorithm for Heterogeneous Cloud Radio Access Network (H‐CRAN) H Shaheen, MS Bhuvaneswari, N Balaganesh, BK Rani, PJ Paul, ... Wireless Communications and Mobile Computing 2022 (1), 8594449 , 2022 2022 Citations: 1
Electrocorticography Based Brain Computer Interface with a Novel Binary BAT Algorithm S Deepajothi, S Selvarajan Journal of Computational and Theoretical Nanoscience 13 (8), 4964-4970 , 2016 2016 Citations: 1
Exploring Gene Expression Biclustering with Integrated PSO-SAFuzzy Logic Methodology and its Application to Air Pollution Analysis S Deepajothi, M Umamaheswari, A Viswanathan, R Juliana GLOBAL NEST JOURNAL 26 (9) , 2024 2024
Intelligence in H Sirineni, T Santosh, S Deepajothi Machine Learning for Business Analytics: Real-Time Data Analysis for … , 2022 2022
Infection detection in older person using artificial intelligence S Saidhbi, AA Endris, S Deepajothi, R Juliana, PR Kshirsagar, A Mohan AIP Conference Proceedings 2393 (1), 020084 , 2022 2022
An Empirical Data Analysis Of Digital Resources Using Access Level Response Model P Rane, S Deepajothi, KV Khanna, K Siva Prasad, D Rani Roy, M Shelke Int. J. of Aquatic Science 12 (1), 237-241 , 2021 2021
A Cloud based Virtual Brain Connectivity with EEG Sensor using Internet of Things (IoT) M Vasuki, R Ramakrishnan, TA Victoire, B Preethi 2019
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
PATENTS
• International Patent (Australian Innovation Patent” titled “Machine Learning and Artificial Intelligence Based Intelligent Betting System for Sports Prediction” Application Number : 2021104722, Filed Date : 29/July/2021.
• Patent title “System And Method For Evaluating Packet Detection Attack (Pda) For ImprovingA Delivery Ratio ” Application Number: 202041050708 Date of Filing: 21/11/2020 and Publication Date:04/12/2020.
• Patent title “Determination 0f Soil Nutrients and PH Level Using Deep Learning and IOT” Application Number: 202141010993 Date of Filing: 15/03/2021 and Publication Date:19/03/2021.
• Patent title “Ensuring the Packaging of Quality Fruits Through Appropriate Selection By Implementing the Image Processing Techniques” Application Number : 202141056006 Date of Filing: 02/12/2021 and Publication Date:10/12/2021.
• Patent title “Novel Application on Energy Efficiency in Smart Homes and Smart Grids” Application Number
: 202141061115 Date of Filing: 28/12/2021 and Publication Date:04/02/2022.
• Patent title” Intelligent Secure and Safety System for the Welfare of the Women Society”Application Number- 201941022431,CBR-18096,Docket Dated: 07th June 2019.
• Patent title “Design And Analysis of Self-Protection Framework System Integrate With Fog Computing And IOT” Application Number- 202241007575 DATED, Filed Date: 12/02/2022 ,Publis
