Dr. Deepak Kumar

@ucumberlands.edu

Information Technology
University of the Cumberlands

Dr. Deepak Kumar


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https://researchid.co/deepak_kumar
Dr. Deepak Kumar received a Ph.D degree in Information Technology from University of the Cumberlands, KY, USA in 2022, an M.S in Computer Science from San Francisco Bay University, CA, USA, in 2016, and a B.E degree in Electronics & Communication Engineering from Visvesvaraya Technological University in 2008, Karnataka, India. His area of interest are the Internet of Things, Machine Learning, Big Data, Artificial Intelligence, Cyber Security, Blockchain, Large Language Model, Data Analytics etc. He has been working as Software Developer from last 12 years, where he has experience on web development, big data projects and machine learning implementation. He has been actively involved in research over the last 7 years. He is currently working at clinet Meta Inc., focusing on distributed computing, large language models (LLMs), privacy implementations, big data, and optimizing hardware resources to efficiently compute petabytes of data.

EDUCATION

1. Ph.D. in Information Technology (2022), University of the Cumberlands, Kentucky, USA.
2. Master’s in Computer Science (2016), San Francisco Bay University, California, USA.
3. Bachelor’s in Electronics & Communication Engineering: VTU (2008), India.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Software, Artificial Intelligence, Information Systems and Management
34

Scopus Publications

700

Scholar Citations

17

Scholar h-index

29

Scholar i10-index

Scopus Publications

  • AI-Driven Hybrid Deep Learning and Swarm Intelligence for Predictive Maintenance of Smart Manufacturing Robots in Industry 4.0
    Deepak Kumar, Santosh Reddy Addula, Mary Lind, Steven Brown, Segun Odion
    Electronics Switzerland, 2026
    Advancements in Industry 4.0 technologies, which combine big data analytics, robotics, and intelligent decision systems to enable new ways to increase automation in the industrial sector, have undergone significant transformations. In this research, a Hybrid Attention-Gated Recurrent Unit (At-GRU) model, combined with Sand Cat Optimization (SCO), is proposed to enhance fault identification and predictive maintenance capabilities. The model utilized multivariate sensor data from cyber-physical and IoT-enabled robotic platforms to learn operational patterns and predict failures with enhanced reliability. The At-GRU provides deeper temporal feature extraction, thereby improving classification performance. The robustness of the proposed model is validated through analysis of a benchmark dataset for industrial robots, and the results demonstrate that the proposed model exhibits impressive predictive capacity, surpassing other prediction methods and predictive maintenance approaches. Additionally, the performance evaluation indicates a lower computational cost due to the lightweight gating architecture of GRU, combined with attention. The robotic motion is further optimized by the SCO algorithm, which reduces energy usage, execution delay, and trajectory deviations while ensuring smooth operation. Overall, the proposed work offers an intelligent and scalable solution for next-generation industrial automation systems. Furthermore, the proposed model demonstrates the real-world applicability and significant benefits of incorporating hybrid artificial intelligence models into real-time robot control applications for smart manufacturing environments.
  • A Smart Optimization Model for Reliable Signal Detection in Financial Markets Using ELM and Blockchain Technology
    Deepak Kumar, Priyanka Pramod Pawar, Santosh Reddy Addula, Mohan Kumar Meesala, Oludotun Oni, et al.
    Fintech, 2025
    This study proposes a novel approach to improve the reliability of trading signals for gold market prediction by integrating technical analysis indicators, Moving Averages (MAs), MACD, and Ichimoku Cloud, with a Particle Swarm-Optimized Extreme Learning Machine (PSO-ELM). Traditional time-series models often fail to capture the complex, non-linear dynamics of financial markets, whereas technical indicators combined with machine learning enhance predictive accuracy. Using daily gold prices from January–October 2020, the PSO-ELM model demonstrated superior performance in filtering false signals, achieving high precision, recall, and overall accuracy. The results highlight the effectiveness of combining technical analysis with machine learning for robust signal validation, providing a practical framework for traders and investors. While focused on gold, this methodology can be extended to other financial assets and market conditions. The integration of machine learning and blockchain enhances both predictive reliability and operational trust, offering traders, investors, and institutions a robust framework for decision support in dynamic financial environments.
  • AI-Powered Security for IoT Ecosystems: A Hybrid Deep Learning Approach to Anomaly Detection
    Deepak Kumar, Priyanka Pramod Pawar, Santosh Reddy Addula, Mohan Kumar Meesala, Oludotun Oni, et al.
    Journal of Cybersecurity and Privacy, 2025
    The rapid expansion of the Internet of Things (IoT) has introduced new vulnerabilities that traditional security mechanisms often fail to address effectively. Signature-based intrusion detection systems cannot adapt to zero-day attacks, while rule-based solutions lack scalability for the diverse and high-volume traffic in IoT environments. To strengthen the security framework for IoT, this paper proposes a deep learning-based anomaly detection approach that integrates Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRUs). The model is further optimized using the Moth–Flame Optimization (MFO) algorithm for automated hyperparameter tuning. To mitigate class imbalance in benchmark datasets, we employ Generative Adversarial Networks (GANs) for synthetic sample generation alongside Z-score normalization. The proposed CNN–BiGRU + MFO framework is evaluated on two widely used datasets, UNSW-NB15 and UCI SECOM. Experimental results demonstrate superior performance compared to several baseline deep learning models, achieving improvements across accuracy, precision, recall, F1-score, and ROC–AUC. These findings highlight the potential of combining hybrid deep learning architectures with evolutionary optimization for effective and generalizable intrusion detection in IoT systems.
  • Security and Privacy in AI: IoT- Enabled Banking and Finance Services
    R. Seranmadevi, Santosh Reddy Addula, Deepak Kumar, Amit Kumar Tyagi
    Monetary Dynamics and Socio Economic Development in Emerging Economies, 2025
    The integration of Artificial Intelligence (AI) and the IoThas led to significant advancements in the banking and finance sector, providing personalized, efficient, and data-driven services. However, these AI-IoT enabled systems also introduce complex security and privacy challenges that need to be addressed to protect sensitive financial data and maintain customer trust. This paper surveys the key security and privacy issues in AI-IoT enabled banking, including data breaches, cyber-attacks, unauthorized access, and data misuse. We examine current methodologies for securing AI-IoT systems, such as encryption, blockchain, alongside AI-driven threat detection and response techniques.The survey explores regulatory considerations and compliance requirements that shape security protocols in financial services. By identifying gaps in existing security measures and highlighting advanced privacy-preserving technologies, this study aims to provide a comprehensive understanding of the challenges and future directions in securing AI-IoT applications within banking and finance.
  • Enhancing manufacturing efficiency: leveraging CRM data with Lean-based DL approach for early failure detection
    Venkata Saiteja Kalluri, Sai Chakravarthy Malineni, Manjula Seenivasan, Jeevitha Sakkarai, Deepak Kumar, et al.
    Bulletin of Electrical Engineering and Informatics, 2025
    In the pursuit of enhancing manufacturing competitiveness in India, companies are exploring innovative strategies to streamline operations and ensure product quality. Embracing Lean principles has become a focal point for many, aiming to optimize profitability while minimizing waste. As part of this endeavour, researchers have introduced various methodologies grounded in Lean principles to track and mitigate operational inefficiencies. This paper introduces a novel approach leveraging deep learning (DL) techniques to detect early failures in manufacturing systems. Initially, realtime data is collected and subjected to a normalization process, employing the weighted adaptive min-max normalization (WAdapt-MMN) technique to enhance data relevance and facilitate the training process. Subsequently, the paper proposes the utilization of a triple streamed attentive recalling recurrent neural network (TSAtt-RRNN) model to effectively identify Leanbased manufacturing failures. Through empirical evaluation, the proposed approach achieves promising results, with an accuracy of 99.23%, precision of 98.79%, recall of 98.92%, and F-measure of 99.2% in detecting early failures. This research underscores the potential of integrating DL methodologies with customer relationship management (CRM) data to bolster early failure detection capabilities in manufacturing, thereby fostering operational efficiency and competitive advantage.
  • Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics
    Snehal Satish, Hari Gonaygunta, Akhila Reddy Yadulla, Deepak Kumar, Mohan Harish Maturi, et al.
    Computers, 2025
    This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. The primary goal is to develop a predictive framework that integrates a wide range of data sources, including seismic, geospatial, and ecological data, toward improving the accuracy and lead times of tsunami occurrence predictions. The study employs machine learning methods, including Random Forest and Logistic Regression, for binary classification of tsunami events. Data collection is performed using a Kaggle dataset spanning 1995–2023, with preprocessing and exploratory analysis to identify critical patterns. The Random Forest model achieved superior performance with an accuracy of 0.90 and precision of 0.88 compared to Logistic Regression (accuracy: 0.89, precision: 0.87). These results underscore Random Forest’s effectiveness in handling imbalanced data. Challenges such as improving data quality and model interpretability are discussed, with recommendations for future improvements in real-time warning systems.
  • Role of emerging technologies with natural language processing for the next decade
    Shabnam Kumari, Amit Kumar Tyagi, Deepak Kumar, Avinash Kumar Sharma
    Establishing AI Specific Cloud Computing Infrastructure, 2025
    The role of emerging technologies like big data, cloud computing, and virtual reality in Natural Language Processing (NLP) is transformative, opening up new frontiers and enhancing existing capabilities. Here's how each technology contributes like Big Data, Cloud Computing, Virtual Reality (VR) and Other Emerging Technologies like Augmented Reality (AR), Blockchain and Edge Computing. In summary, big data, cloud computing, virtual reality, and other emerging technologies play important roles in advancing the capabilities of NLP, enabling more robust language understanding, generation, and interaction in various domains and applications.
  • Natural Language Processing (NLP)-based intelligence for pattern mining using artificial intelligence, robotics, and cloud computing
    Amit Kumar Tyagi, G. Balamurugan, Deepak Kumar, Shabnam Kumari
    Establishing AI Specific Cloud Computing Infrastructure, 2025
    In the recent decade, Natural Language Processing (NLP) has emerged as a important tool for extracting valuable insights from vast amounts of textual data. This work discusses the integration of NLP techniques with AI and robotics to enhance pattern mining capabilities. Using AI algorithms such as machine learning and deep learning, coupled with robotics for physical data gathering, enables the creation of sophisticated systems capable of understanding and interpreting human language in diverse contexts. In this study, we present a comprehensive framework that combines NLP intelligence with AI and robotics to extract meaningful patterns from textual data sources. We discuss the utilization of techniques such as sentiment analysis, named entity recognition, and topic modeling to analyze text data. We discuss about the integration of these NLP capabilities with AI algorithms for pattern identification and prediction. Moreover, the incorporation of robotics adds a tangible dimension to the pattern mining process, allowing for real-time data collection in various environments.
  • Smart sensors for Hospital 4.0/5.0: an introduction
    Deepak Kumar, V. Hemamalini, Amit Kumar Tyagi, Richa
    Human Centric Integration of Next Generation Data Science and Blockchain Technology Advancing Society 5 0 Paradigms, 2025
  • A walrus optimization-enhanced long short-term memory model for credit fraud detection in banking
    Sanjaikanth E. Vadakkethil Somanath Pillai, Geeta Sandeep Nadella, Karthik Meduri, Naveena A. Priyadharsini, A. Bhuvanesh, et al.
    International Journal of Information Technology Singapore, 2025
  • Hierarchical Blockchain Framework for Node Authentication in IoT Networks: A Comprehensive Analysis
    Deepak Kumar, Akhila Reddy Yadulla, A. Bhuvanesh, Priyanka Pawar, Vinay Kumar Kasula, et al.
    2025 International Conference in Advances in Power Signal and Information Technology Apsit 2025, 2025
  • Exploring Blockchain-Enabled Secure Storage and Trusted Data Sharing Mechanisms in IoT Systems
    Priyanka Pawar, Vinay Kumar Kasula, A. Bhuvanesh, Deepak Kumar, Akhila Reddy Yadulla, et al.
    2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2025, 2025
  • A Blockchain-Based IoT Framework for Smart Homes: Enhancing Energy Prediction and Security with LSTM and Equilibrium Optimization
    Icoicc 2025 3rd International Conference on Intelligent and Cloud Computing, 2025
  • Blockchain-enabled cybersecurity for IoT using elliptic curve cryptography and black winged kite model
    Priyanka Pramod Pawar, F. Fanax Femy, N. Rajkumar, S. Jeevitha, A. Bhuvanesh, et al.
    International Journal of Information Technology Singapore, 2025
  • Enhanced Blockchain-Based Big Data Authentication for Distributed Environments: An Analytical Study
    Priyanka Pawar, Akhila Reddy Yadulla, A. Bhuvanesh, Deepak Kumar, Vinay Kumar Kasula, et al.
    2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2025, 2025
  • Enhanced affinity propagation clustering with a modified extreme learning machine for segmentation and classification of hyperspectral imaging
    V. Antony Asir Daniel, K. Vijayalakshmi, Priyanka Pramod Pawar, Deepak Kumar, A. Bhuvanesh, et al.
    E Prime Advances in Electrical Engineering Electronics and Energy, 2024
  • Study on Empowering Cyber Security by Using Adaptive Machine Learning Methods
    Hari Gonaygunta, Geeta Sandeep Nadella, Priyanka Pramod Pawar, Deepak Kumar
    2024 Systems and Information Engineering Design Symposium Sieds 2024, 2024
  • Enhancing Cybersecurity: The Development of a Flexible Deep Learning Model for Enhanced Anomaly Detection
    Hari Gonaygunta, Geeta Sandeep Nadella, Priyanka Pramod Pawar, Deepak Kumar
    2024 Systems and Information Engineering Design Symposium Sieds 2024, 2024
  • An Advanced Wasserstein-Enabled Generative Adversarial Network Enabled Attack Detection for Blockchain-Assisted Intelligent Transportation System
    Priyanka Pramod Pawar, Deepak Kumar, Bhuvanesh Ananthan, Sj.Ben Christopher, R. Surya
    2024 3rd International Conference on Artificial Intelligence for Internet of Things Aiiot 2024, 2024
  • Mayfly Optimization Algorithm with Bidirectional Long-Short Term Memory for Intrusion Detection System in Internet of Things
    Sanjaikanth E Vadakkethil, Kiran Polimetla, Zaid Alsalami, Piyush Kumar Pareek, Deepak Kumar
    3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics Icdcece 2024, 2024
  • ChOs_LSTM: Chebyshev Osprey Optimization-Based Model for Detecting Attacks
    Deepak Kumar, Priyanka Pramod Pawar, Bhuvanesh Ananthan, S. Indhumathi, M.Senthil Murugan
    2024 3rd International Conference on Artificial Intelligence for Internet of Things Aiiot 2024, 2024
  • An Efficient DDoS Attack Detection using Attention based Hybrid Model in Blockchain based SDN-IoT
    Priyanka Pramod Pawar, Deepak Kumar, Bhuvanesh Ananthan, A.Shiny Pradeepa, A.Sugirtha Selvi
    2024 3rd International Conference on Artificial Intelligence for Internet of Things Aiiot 2024, 2024
  • Optimized Support Vector Machine Based Fused IoT Network Security Management
    Deepak Kumar, Priyanka Pramod Pawar, Bhuvanesh Ananthan, S. Rajasekaran, T.Velu Prabhakaran
    2024 3rd International Conference on Artificial Intelligence for Internet of Things Aiiot 2024, 2024
  • Investigation on Digital Forensic Using Graph Based Neural Network with Blockchain Technology
    Priyanka Pramod Pawar, Deepak Kumar, Raghavi K Bhujang, Piyush Kumar Pareek, H M Manoj, et al.
    2nd IEEE International Conference on Data Science and Network Security Icdsns 2024, 2024
  • PFCM Based Segmentation and TFA Based DCNN Model for Skin Cancer Classification Using Dermoscopic Images
    Manoj H M, Priyanka Pramod Pawar, Krupa R, Piyush Kumar Pareek, Deepak Kumar, et al.
    2nd IEEE International Conference on Data Science and Network Security Icdsns 2024, 2024
  • Innovative Horizons in Agricultural Technology with TSA Based StrawberrySqueezeNet Classification Model
    H M Manoj, Priyanka Pramod Pawar, R Krupa, Piyush Kumar Pareek, Deepak Kumar, et al.
    2nd IEEE International Conference on Data Science and Network Security Icdsns 2024, 2024
  • Smart Agriculture in the Era of Big Data: IoTAssisted Pest Forecasting and Resource Optimization for Sustainable Farming
    Deepak Kumar, Priyanka Pramod Pawar, Mohan Kumar Meesala, Piyush Kumar Pareek, Santosh Reddy Addula, et al.
    2nd IEEE International Conference on Integrated Intelligence and Communication Systems Iciics 2024, 2024
  • Securing Digital Governance: A Deep Learning and Blockchain Framework for Malware Detection in IoT Networks
    Priyanka Pramod Pawar, Deepak Kumar, Mohan Kumar Meesala, Piyush Kumar Pareek, Santosh Reddy Addula, et al.
    2nd IEEE International Conference on Integrated Intelligence and Communication Systems Iciics 2024, 2024
  • SINN Based Federated Learning Model for Intrusion Detection with Blockchain Technology in Digital Forensic
    Priyanka Pramod Pawar, Deepak Kumar, R Krupa, Piyush Kumar Pareek, H M Manoj, et al.
    2nd IEEE International Conference on Data Science and Network Security Icdsns 2024, 2024
  • Trustworthy IoT Infrastructures: Privacy-Preserving Federated Learning with Efficient Secure Aggregation for Cybersecurity
    Deepak Kumar, Priyanka Pramod Pawar, Mohan Kumar Meesala, Piyush Kumar Pareek, Santosh Reddy Addula, et al.
    2nd IEEE International Conference on Integrated Intelligence and Communication Systems Iciics 2024, 2024
  • A Patient-Centric Blockchain Framework for Transparent and Secure Medical Data Sharing using Modified AES
    Priyanka Pramod Pawar, Deepak Kumar, Mohan Kumar Meesala, Piyush Kumar Pareek, Santosh Reddy Addula, et al.
    2nd IEEE International Conference on Integrated Intelligence and Communication Systems Iciics 2024, 2024
  • Dynamic Load Balancing in Cloud Computing using Hybrid Kookaburra-Pelican Optimization Algorithms
    Santosh Reddy Addula, Prathusha Perugu.P, Mungara Kiran Kumar, Deepak Kumar, Bhuvanesh Ananthan, et al.
    2024 International Conference on Augmented Reality Intelligent Systems and Industrial Automation Ariia 2024, 2024
  • Human-centered AI for personalized workload management: A multimodal approach to preventing employee burnout
    Karthik Meduri, Geeta Sandeep Nadella, Hari Gonaygunta, Deepak Kumar, Santosh Reddy Addula, et al.
    Journal of Infrastructure Policy and Development, 2024

RECENT SCHOLAR PUBLICATIONS

  • Evolving IoT Botnet Threats and Practical Honeypot Observation: A Summary Review and Experimental Study
    R Banoth, SR Addula, AK Godishala, R Sannapu, GS Sajja, D Kumar, ...
    Journal of Cybersecurity and Privacy , 2026
    2026
  • AI-Driven Hybrid Deep Learning and Swarm Intelligence for Predictive Maintenance of Smart Manufacturing Robots in Industry 4.0
    D Kumar, SR Addula, M Lind, S Brown, S Odion
    Electronics 15 (3), 715 , 2026
    2026
    Citations: 8
  • Security and Privacy in AI: IoT-Enabled Banking and Finance Services
    R Seranmadevi, SR Addula, D Kumar, AK Tyagi
    Monetary Dynamics and Socio-Economic Development in Emerging Economies, 163 , 2026
    2026
    Citations: 13
  • AI-Powered security for IoT ecosystems: a hybrid deep learning approach to anomaly detection
    D Kumar, PP Pawar, SR Addula, MK Meesala, O Oni, QN Cheema, ...
    Journal of Cybersecurity and Privacy 5 (4), 90 , 2025
    2025
    Citations: 28
  • A Smart Optimization Model for Reliable Signal Detection in Financial Markets Using ELM and Blockchain Technology
    D Kumar, PP Pawar, SR Addula, MK Meesala, O Oni, QN Cheema, ...
    FinTech 4 (4), 56 , 2025
    2025
    Citations: 6
  • Banking fraud detection using optimized enhanced stacked autoencoder approach
    D Kumar, P Anitha, J Murugachandravel, S Jeevitha, A Bhuvanesh, ...
    Security and Privacy 8 (4), e70054 , 2025
    2025
    Citations: 6
  • Enhancing manufacturing efficiency: leveraging CRM data with Lean-based DL approach for early failure detection
    VS Kalluri, SC Malineni, M Seenivasan, J Sakkarai, D Kumar, B Ananthan
    Bulletin of Electrical Engineering and Informatics 14 (3), 2319-2329 , 2025
    2025
    Citations: 11
  • Hierarchical blockchain framework for node authentication in IoT networks: a comprehensive analysis
    D Kumar, AR Yadulla, A Bhuvanesh, P Pawar, VK Kasula, ...
    2025 International Conference in Advances in Power, Signal, and Information … , 2025
    2025
    Citations: 4
  • Blockchain-enabled cybersecurity for IoT using elliptic curve cryptography and black winged kite model
    PP Pawar, FF Femy, N Rajkumar, S Jeevitha, A Bhuvanesh, D Kumar
    International Journal of Information Technology, 1-11 , 2025
    2025
    Citations: 8
  • A Walrus optimization-enhanced long short-term memory model for credit fraud detection in banking
    SEVS Pillai, GS Nadella, K Meduri, NA Priyadharsini, A Bhuvanesh, ...
    International Journal of Information Technology, 1-17 , 2025
    2025
    Citations: 6
  • Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics
    S Satish, H Gonaygunta, AR Yadulla, D Kumar, MH Maturi, K Meduri, ...
    Computers 14 (5), 175 , 2025
    2025
    Citations: 9
  • A Blockchain-Based IoT Framework for Smart Homes: Enhancing Energy Prediction and Security with LSTM and Equilibrium Optimization
    PP Pawar, D Kumar, SR Addula, QN Cheema, AU Haq, MK Meesala
    2025 International Conference on Intelligent and Cloud Computing (ICoICC), 1-8 , 2025
    2025
    Citations: 4
  • Role of Emerging Technologies With Natural Language Processing for the Next Decade
    S Kumari, AK Tyagi, D Kumar, A Sharma
    Establishing AI-Specific Cloud Computing Infrastructure, 479-502 , 2025
    2025
    Citations: 2
  • Natural Language Processing (NLP)-Based Intelligence for Pattern Mining Using Artificial Intelligence, Robotics, and Cloud Computing
    AK Tyagi, G Balamurugan, D Kumar, S Kumar
    Establishing AI-Specific Cloud Computing Infrastructure, 269-284 , 2025
    2025
    Citations: 3
  • Enhanced Blockchain-Based Big Data Authentication for Distributed Environments: An Analytical Study
    P Pawar, AR Yadulla, A Bhuvanesh, D Kumar, VK Kasula, ...
    2025 IEEE International Conference on Interdisciplinary Approaches in … , 2025
    2025
  • Exploring blockchain-enabled secure storage and trusted data sharing mechanisms in iot systems
    P Pawar, VK Kasula, A Bhuvanesh, D Kumar, AR Yadulla, ...
    2025 IEEE International Conference on Interdisciplinary Approaches in … , 2025
    2025
    Citations: 22
  • Smart sensors for Hospital 4.0/5.0: an introduction
    D Kumar, V Hemamalini, AK Tyagi, Richa
    Human-Centric Integration of Next-Generation Data Science and Blockchain … , 2025
    2025
    Citations: 2
  • Dynamic load balancing in cloud computing using hybrid kookaburra-pelican optimization algorithms
    SR Addula, MK Kumar, D Kumar, B Ananthan
    2024 International Conference on Augmented Reality, Intelligent Systems, and … , 2024
    2024
    Citations: 12
  • Enhanced Stock Market Trend Prediction on the Indonesia Stock Exchange Using Improved Bacterial Foraging Optimization and Elitist Whale Optimization Algorithms
    D Kumar, PP Pawar, MK Meesala, PK Pareek, SR Addula, KS Shwetha
    2024 International Conference on Integrated Intelligence and Communication … , 2024
    2024
    Citations: 5
  • A patient-centric blockchain framework for transparent and secure medical data sharing using modified AES
    PP Pawar, D Kumar, MK Meesala, PK Pareek, SR Addula, KS Shwetha
    2024 International Conference on Integrated Intelligence and Communication … , 2024
    2024
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Enhancing cybersecurity: The development of a flexible deep learning model for enhanced anomaly detection
    H Gonaygunta, GS Nadella, PP Pawar, D Kumar
    2024 systems and information engineering design symposium (SIEDS), 79-84 , 2024
    2024
    Citations: 68
  • Enhancing Cyber Security Using Quantum Computing and Artificial Intelligence: A Review
    S Singh, D Kumar
    International Journal of Advanced Research in Science, Communication and … , 2024
    2024
    Citations: 50
  • Exploring the impact of AI-driven solutions on cybersecurity adoption in small and medium enterprises
    GS Nadella, H Gonaygunta, D Kumar, PP Pawar
    World Journal of Advanced Research and Reviews 22 (1), 1190-1197 , 2024
    2024
    Citations: 36
  • Study on empowering cyber security by using adaptive machine learning methods
    H Gonaygunta, GS Nadella, PP Pawar, D Kumar
    2024 systems and information engineering design symposium (SIEDS), 166-171 , 2024
    2024
    Citations: 35
  • Machine learning’s role in personalized medicine & treatment optimization
    D Kumar, PP Pawar, H Gonaygunta, GS Nadella, K Meduri, S Singh
    World Journal of Advanced Research and Reviews 21 (2), 1675-1686 , 2024
    2024
    Citations: 30
  • AI-Powered security for IoT ecosystems: a hybrid deep learning approach to anomaly detection
    D Kumar, PP Pawar, SR Addula, MK Meesala, O Oni, QN Cheema, ...
    Journal of Cybersecurity and Privacy 5 (4), 90 , 2025
    2025
    Citations: 28
  • Adaptive Intelligence: GPT-Powered Language Models for Dynamic Responses to Emerging Healthcare Challenges
    K Meduri, H Gonaygunta, GS Nadella, PP Pawar, D Kumar
    2024
    Citations: 23
  • Exploring blockchain-enabled secure storage and trusted data sharing mechanisms in iot systems
    P Pawar, VK Kasula, A Bhuvanesh, D Kumar, AR Yadulla, ...
    2025 IEEE International Conference on Interdisciplinary Approaches in … , 2025
    2025
    Citations: 22
  • An efficient ddos attack detection using attention based hybrid model in blockchain based SDN-IOT
    PP Pawar, D Kumar, B Ananthan, AS Pradeepa, AS Selvi
    2024 3rd International Conference on Artificial Intelligence For Internet of … , 2024
    2024
    Citations: 22
  • CHOS_LSTM: Chebyshev Osprey optimization-based model for detecting attacks
    D Kumar, PP Pawar, B Ananthan, S Indhumathi, MS Murugan
    2024 3rd International conference on artificial intelligence for internet of … , 2024
    2024
    Citations: 22
  • Analyzing the Impact of Machine Learning Algorithms on Risk Management and Fraud Detection in Financial Institution
    D Kumar, S Singh
    International Journal of Research Publication and Reviews 5 (5), 1797-1804 , 2024
    2024
    Citations: 22
  • Investigation on digital forensic using graph based neural network with blockchain technology
    PP Pawar, D Kumar, RK Bhujang, PK Pareek, HM Manoj, KS Deepika
    2024 International Conference on Data Science and Network Security (ICDSNS), 1-7 , 2024
    2024
    Citations: 20
  • Optimized support vector machine based fused IOT network security management
    D Kumar, PP Pawar, B Ananthan, S Rajasekaran, TV Prabhakaran
    2024 3rd International Conference on Artificial Intelligence For Internet of … , 2024
    2024
    Citations: 20
  • Securing Digital Governance: A Deep Learning and Blockchain Framework for Malware Detection in IoT Networks
    PP Pawar, D Kumar, MK Meesala, PK Pareek, SR Addula, S KS
    2024 International Conference on Integrated Intelligence and Communication … , 2024
    2024
    Citations: 19
  • Human-centered AI for personalized workload management: A multimodal approach to preventing employee burnout
    K Meduri, GS Nadella, H Gonaygunta, D Kumar, SR Addula, S Satish, ...
    Journal of Infrastructure, Policy and Development 8 (9), 6918 , 2024
    2024
    Citations: 18
  • Enhanced affinity propagation clustering with a modified extreme learning machine for segmentation and classification of hyperspectral imaging
    VAA Daniel, K Vijayalakshmi, PP Pawar, D Kumar, A Bhuvanesh, ...
    E-Prime-Advances in Electrical Engineering, Electronics and Energy 9, 100704 , 2024
    2024
    Citations: 17
  • The Detection and Prevention of Cloud Computing Attacks Using Artificial Intelligence Technologies
    H Gonaygunta, GS Nadella, K Meduri, PP Pawar, D Kumar
    International Journal of Multidisciplinary Research and Publications 6 (8 … , 2024
    2024
    Citations: 17
  • Factors Relating to the Adoption of IoT for Home
    D Kumar
    University of the Cumberlands , 2022
    2022
    Citations: 17
  • How can we make IOT applications better with federated learning-A Review
    H Gonaygunta, D Kumar, S Maddini, SF Rahman
    nternational journal of advanced research in computer and communication … , 2023
    2023
    Citations: 16
  • Mayfly Optimization Algorithm with Bidirectional Long-Short Term Memory for Intrusion Detection System in Internet of Things
    SE Vadakkethil, K Polimetla, Z Alsalami, P Pareek, D Kumar
    2024 Third International Conference on Distributed Computing and Electrical … , 2024
    2024
    Citations: 14