Dr.N.Nathiya
Assistant Professor
About
I am Dr. N. Nathiya, currently working as an Assistant Professor in the School of Computer Science and Engineering at RV University, Bengaluru. I am a dedicated academician in the field of Artificial Intelligence, Machine Learning, Deep Learning, Network Security, With over 10 years of teaching experience and 4 years of focused research experience. I am also pursuing my Postdoctoral Fellowship (PDF) at SR University, Telangana. I hold a Ph.D. in Computer Science and Engineering from Anna University, Chennai. I completed my Master of Engineering (M.E.) in Computer Science and Engineering (Network Engineering) from Anna University, graduating as a University Gold Medalist, and earned my Bachelor of Engineering (B.E.) in Computer Science and Engineering from Anna University.
My core expertise lies in Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things (IoT), Network Security, Optimization Algorithms, and Intelligent Computing, with a focus on energy-efficient and secure AI-driven IoT-enabled WSNs, intrusion detection systems, and predictive analytics for real-world applications. I also work with advanced ML/DL models such as CNN, RNN, LSTM, GAN, and optimization-based techniques.
I have published numerous research papers in reputed Scopus-indexed journals and international conferences, including publications with Springer, IEEE, and CCIS, and hold a UK patent on an IoT-Based Digital Hydroponic Nutrient Prediction Device. I actively contribute as a reviewer and editorial member for reputed journals and continuously engage in high-quality research and scholarly collaborations.
Beyond research, I actively contribute to curriculum development, academic administration, and mentoring undergraduate and postgraduate students in research and innovation. I have organized and participated in faculty development programmes, workshops, seminars, and conferences. My current research focuses on Artificial Intelligence for Sustainable Development, with emphasis on developing AI-driven solutions for smart energy systems, climate-resilient technologies, sustainable smart cities, and environmental monitoring applications. Through interdisciplinary collaborations, I aim to design scalable, energy-efficient, and responsible AI models that address real-world sustainability challenges and contribute to achieving global sustainable development goals for the benefit of society.
Beyond academics, I am passionate about continuous learning, mentoring aspiring researchers, and exploring emerging technologies. I strongly believe that education is a transformative journey that nurtures critical thinking, innovation, ethical values, and lifelong learning, empowering students to become responsible professionals and future leaders.
This publication developed a hybrid optimization and machine learning-based clustering algorithm to enhance energy efficiency in WSN-IoT applications. The proposed framework integrates self-diagnosis, data fault detection, and predictive analytics to improve network reliability, accuracy, and operational lifetime. Journal: Peer-to-Peer Networking and Applications 18, 13 (2025).
This work proposes an energy-efficient cluster routing algorithm for IoT-enabled Wireless Sensor Networks using a map diminution-based training-discovering optimization technique. The approach enhances network lifetime, optimizes energy consumption, and improves reliable data transmission. Journal: Sādhanā 49, 12 (2024).
Developed a hybrid anomaly–misuse intrusion detection system for clustered Wireless Sensor Networks using neural networks. The proposed model accurately detects known and unknown attacks while enhancing network security, detection accuracy, and energy efficiency. Computing Science, Communication and Security. COMS2 2024. Communications in Computer and Information Science, vol 2174. Springer, Cham.
This work develops a deep learning and Natural Language Processing (NLP)-based ramework for sentiment analysis of social media data. The proposed model accurately classifies user sentiments, enabling meaningful insights into public opinion and online behavioral trends. 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Namakkal, India, 2024, pp. 1-9, doi: 10.1109/AIMLA59606.2024.10531507.
This work proposed an enhanced intrusion detection system using an Advanced Deep Scaled Multi-Layer Perceptron Neural Network optimized with an Enhanced Particle Swarm Optimization algorithm. The proposed framework improves attack detection accuracy, minimizes false alarms, and strengthens network security. 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 2024, pp. 534-542, doi: 10.1109/I-SMAC61858.2024.10714797.
This paper developed a deep learning-based intrusion detection system using a Gated Recurrent Neural Network (GRNN) with a sigmoid activation function to identify cyber threats in network environments. The proposed model enhances detection accuracy, reduces false positives, and improves overall network security. 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichirappalli, India, 2024, pp. 1-8, doi: 10.1109/ICEEICT61591.2024.10718479.
This paper proposed an intelligent malware detection framework combining LSTM-based sequential learning with a Hyper Capsule CNN for deep feature engineering. The model improves threat detection, minimizes false classifications, and supports reliable cyber defense. 2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Namakkal, India, 2025, pp. 1-8, doi: 10.1109/AIMLA63829.2025.11040375.
This paper developed an AI-based driver drowsiness detection system using optimal scalar vector feature selection and a Convolutional Neural Network (CNN). The proposed framework accurately identifies signs of driver fatigue, enhancing road safety through real-time monitoring and intelligent decision support. 2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Namakkal, India, 2025, pp. 1-8, doi: 10.1109/AIMLA63829.2025.11040861.
This paper developed a smart intelligence-based heart disease classification framework using deep feature engineering and an Adaptive Fuzzy Integrated Multilayer Perceptron Neural Network. The proposed model enhances diagnostic accuracy, supports early disease prediction, and facilitates intelligent clinical decision-making. 2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Namakkal, India, 2025, pp. 1-9, doi: 10.1109/AIMLA63829.2025.11040876.
This publication developed an enhanced sarcasm detection framework for social media content analysis using support scalar lexical semantic analysis and a Bidirectional Gated Recurrent Neural Network (Bi-GRNN). The proposed model improves sarcasm recognition, contextual understanding, and sentiment classification accuracy. 2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Namakkal, India, 2025, pp. 1-9, doi: 10.1109/AIMLA63829.2025.11040418.
This work developed a smart intelligence-based intrusion detection system using Optimal Elephant Herding feature selection and a Deep Generative Adversarial Neural Network (GAN) classifier. The proposed framework enhances attack detection accuracy, optimizes feature selection, and strengthens network security against evolving cyber threats. Advances in Smart Computing and Applications. ICASCA 2025. Communications in Computer and Information Science, vol 2619. Springer, Cham.
This work developed a smart lane detection system for autonomous driving using regional Canny segmentation and a Lan-Net Convolutional Neural Network (CNN). The proposed framework accurately detects road lane markings in real time, enhancing driving safety, navigation, and intelligent vehicle assistance. Soft Computing: Theories and Applications. SoCTA 2025. Lecture Notes in Networks and Systems, vol 1980. Springer, Cham.
Dr. N. Nathiya specializes in the application of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Cybersecurity to develop intelligent, secure, and energy-efficient solutions for real-world applications.
Research focuses on AI, Deep Learning, IoT, Network Security, and Intelligent Optimization.
Developed energy-efficient routing, clustering, and intrusion detection solutions for IoT-enabled WSNs.
Applied CNN, LSTM, GRNN, GAN, and optimization algorithms for cybersecurity and malware detection.
Worked on AI applications in healthcare, smart transportation, NLP, sentiment, and sarcasm analysis.
Current research integrates AI, IoT, Edge Computing, Explainable AI, and optimization for secure and sustainable intelligent systems.
Research Interests
Dr. N. Nathiya’s research focuses on AI, Deep Learning, IoT, WSNs, and Cybersecurity, with expertise in intelligent optimization, intrusion detection, energy-efficient routing, and AI-driven healthcare and smart systems. Her current work emphasizes secure, scalable, and sustainable AI solutions.
Awarded the University Gold Medal for outstanding academic performance and excellence in M.E. CSE(NE) from Anna University, Chennai.
Achieved Elite + Silver certification in NPTEL Big Data Computing.
Reviewer for reputed journals including Frontiers in AI, IJCS, and Springer Nature journals.
Dr.N.Nathiya
Assistant Professor
M.E., Ph.D., (PDF)School of Computer Science and Engineering
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nathiyanatarajan@rvu.edu.in -
Bengaluru
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