Gastric cancer classification in saliva data samples using Levy search updated rainfall hybrid deep dual-stage BILSTM M. Kalimuthu, M. Ramya, S. Sreethar, N. Nandhagopal Journal of Experimental and Theoretical Artificial Intelligence, 2025 An innovative approach is needed for the early identification of GC (Gastric cancer) to improve the prediction of GC patients. This work presents a GC prediction system to identify GC depending on saliva data samples. The diagnosis of GC at an early stage will improve the survival rate. At first, Raman data of saliva samples are collected and pre-processed. Afterwards, efficient Raman spectral features are extracted from the pre-processed data. Then, the feature selection process is performed with a Levy search updated rainfall (LURF) optimisation approach. This optimisation scheme decreases the dimensionality of the features by integrating Levy’s flight and rainfall optimisation. Finally, the hybrid deep dual-stage bidirectional long short-term memory (Hybrid LURF) framework effectively classifies the data as normal or abnormal. This model efficiently addresses the issues of insufficient long-term dependency in GC prediction and also enhances the classification performance. The validation of the proposed approach is examined with various existing schemes and achieved better accuracy (98.5%), specificity (97%), sensitivity (96.5%), F1-score (93%), detection rate (98.4%) and ROC curve. Further, the accuracy is 0.06% better than multi-layer ANN and 10% better than SVM-polynomial and KNN models.
Intelligent Medical Diagnosis Using Rule-Based Expert System and Deep Belief Networks G.G. Gokilam, Syed Fakruddin Albeez, D. Sugumaran, R. Padmavathy, N. Nandhagopal, Maheswari S 2025 IEEE 2nd International Conference on Information Technology Electronics and Intelligent Communication Systems Iciteics 2025, 2025 Getting a precise diagnosis is vital for decent healthcare, yet most clinical expert systems and solo deep learning models encounter problems with either complex patient information or transparency. A solution is proposed here, where a system uses expert logic and deep belief networks to help with medical decisions. It uses any rigidly designed inference standards alongside deep neural layers to better explain and predict results. A set of medical data from different diseases was included in the system's training and evaluation. This model proved accurate, with 95.2% results, a sensitivity of 93.1%, a specificity of 94.4%, and an F1-score of 92.6%. It outperformed standalone models and last year's baselines by 4-7% in all measured areas. Comparative results show that inference time drops and the model's reliability improves. The presented approach is flexible and easy to understand, improving clinical judgment, and is useful in different medical branches.
AI-Based Detection of Skin Conditions from Smartphone Images Using Lightweight CNN Architectures Jayant Shekhar, P. Krishnamoorthy, N. Nandhagopal, Sasikala P.S., P. Chitra, Bharathi Ramesh Kumar 2025 International Conference on Computing and Communications Computingcon 2025, 2025 Skin diseases have a high prevalence rate in most parts of the world, and in most cases, timely diagnosis is needed to avoid complications. Nevertheless, there is still poor accessibility of dermatologic treatment in distant and underserved areas. This is a weightless, AI-driven skin condition diagnostic system to identify various skin diseases using smartphone images. Using optimized CNN architecture, MobileNetV3, EfficientNet-Lite, and a specially trained hybrid model on ISIC and DermNet data, the system used optimized CNN architecture. Before inference, image preprocessing with normalization and lesion segmentation is done, whereas realtime inference is based on mobile platforms (TensorFlow Lite support). The suggested model demonstrated 94.6 percent of classification accuracy, 3.6 MB in size, and had an average inference time of 121 ms. The F1-scores of every large type of skin condition (melanoma, eczema) were higher than 93% on a per-class basis. The framework is faster, more accurate, and edge-compatible than other mobile-compatible systems used earlier. Such a direction has proven that it is possible to have an operational and extensible system of skin condition tracking offline on smartphones themselves, offering better avenues for medical diagnostic procedures.
Dual-Band Antennas for AI-Enabled Autonomous Vehicle-to-Infrastructure Communication Koppisetti Giridhar, Dheepika G B, Anusha Preetham, Monica G K, Arun Kumar N, N. Nandhagopal Proceedings Iceconf 2025 2025 2nd International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, 2025 The complexity of the modern systems also needs more effective optimization techniques which must scale, be accurate and robust in a single product. Traditional techniques have discouraged the ability to deal with large sizes in the search space and reaching. the best solutions. To eliminate these shortcomings, topresent an innovative hybrid structure of optimization between quasi -oppositional learning and genetic search to improve performance in GUI test case generation. The validation of the proposed technique was applied to the benchmark datasets and experimental simulations. The findings have indicated that the performance indicators have been improved greatly based on the current approaches: 97.8% accuracy, 96.9% precision, 97.4% recall, and an F1-score of 97.2%. The increase in system efficiency was 15%, the reduction of the computational overhead by 12%, and the adaptability to varied testing environments by 18%. The results justify the effectiveness of the proposed solution and present a feasible and smart model that can be used to perform dependable software testing.
AN EFFICIENT ONLINE BIG DATA STREAM CLUSTERING USING HYBRID GRID-BASED SOFT CLUSTERING APPROACHES Journal of Environmental Protection and Ecology, 2024
Performance Analysis of Optimized MIMO-MFSK based Energy Detection with Interference Cancellation Approach in Fading Channel S Jayapoorani, M Ramya, M Dharmalingam, N Nandhagopal International Journal of Information Technology & Decision Making , 2026 2026
Hybrid machine learning for optimized metasurface SPR biosensor in real-time cervical cancer biomarker detection MB Sudhan, V Mathiyazhagan, D Chandrakala, S Gomathi, V Selvaraj, ... Journal of Optics, 1-13 , 2026 2026
Highly sensitive dual-mode D-shaped photonic crystal fiber SPR sensor with gold–graphene hybrid interface N Nandhagopal, A Vasantharaj, M Dharmalingam, B Balasubramanian, ... Applied Physics A 132 (4), 295 , 2026 2026
Plasmonic Gold Nano disk Array Design for Enhanced Surface-Enhanced Raman Spectroscopy (SERS) Sensing Applications N Nandhagopal, A Vasantharaj, SD Vijayakumar, B Balasubramanian, ... Journal of Circuits, Systems and Computers , 2026 2026
Tailoring plasmonic effects in organic solar cells through dielectric core–shell nanoengineering N Nandhagopal, A Vasantharaj, M Dharmalingam, B Balasubramanian, ... Molecular Crystals and Liquid Crystals 770 (1), 85-104 , 2026 2026
Diverse attack detection in IoT using hybrid deep convolutional with capsule auto encoder for intrusion detection model M Dharmalingam, K Subramaniam, N Nandhagopal Journal of Parallel and Distributed Computing, 105190 , 2025 2025
Gastric cancer classification in saliva data samples using Levy search updated rainfall hybrid deep dual-stage BILSTM M Kalimuthu, M Ramya, S Sreethar, N Nandhagopal Journal of Experimental & Theoretical Artificial Intelligence 37 (6), 897-913 , 2025 2025 Citations: 1
A battery-less hybrid in-tire pressure monitoring SOC for road vehicles using adaptive bayesian system and optimized wireless communication model A Vasantharaj, N Nandhagopal, R Murugesan, OC Mathew Analog Integrated Circuits and Signal Processing 124 (2), 34 , 2025 2025 Citations: 1
An efficient energy supply policy and optimized self-adaptive data aggregation with deep learning in heterogeneous wireless sensor network R Tharmalingam, N Nachimuthu, G Prakash Peer-to-Peer Networking and Applications 17 (6), 3991-4012 , 2024 2024 Citations: 10
Efficient Online Big Data Stream Clustering Using Dual Interactive Wasserstein Generative Adversarial Network S Matheswaran, N Nachimuthu, G Prakash International Journal on Artificial Intelligence Tools 33 (05), 2450009 , 2024 2024
Multi-class facial emotion recognition using hybrid dense squeeze network M Kalimuthu, S Sreethar, R Murugesan, N Nandhagopal International Journal of Pattern Recognition and Artificial Intelligence 37 … , 2023 2023 Citations: 4
A low-cost in-tire-pressure monitoring SoC using integer/floating-point type convolutional neural network inference engine A Vasantharaj, SA Karuppusamy, N Nandhagopal, APV Pillai Microprocessors and Microsystems 98, 104771 , 2023 2023 Citations: 10
Retraction Note to: Improving QoS and efficient multi-hop and relay based communication frame work against attacker in MANET V Nivedita, N Nandhagopal Journal of Ambient Intelligence and Humanized Computing 14 (Suppl 1), 435-435 , 2023 2023
Attention based deep convolutional U-Net with CSA optimization for hyperspectral image denoising R Murugesan, N Nachimuthu, G Prakash Infrared Physics & Technology 129, 104531 , 2023 2023 Citations: 10
Automated Skin Lesion Diagnosis and Classification Using Learning Algorithms. A Soujanya, N Nandhagopal Intelligent Automation & Soft Computing 35 (1) , 2023 2023 Citations: 7
An in-tire-pressure monitoring SoC using FBAR resonator-based ZigBee transceiver and deep learning models A Vasantharaj, N Nandhagopal, SA Karuppusamy, K Subramaniam Microprocessors and Microsystems 95, 104709 , 2022 2022 Citations: 10
A group teaching optimization algorithm for priority-based resource allocation in wireless networks S Sreethar, N Nandhagopal, SA Karuppusamy, M Dharmalingam Wireless Personal Communications 123 (3), 2449-2472 , 2022 2022 Citations: 14
An Enhanced Memetic Algorithm for Feature Selection in Big Data Analytics with MapReduce. U Ramakrishnan, N Nachimuthu Intelligent Automation & Soft Computing 31 (3) , 2022 2022 Citations: 9
Classification Similarity Network Model for Image Fusion Using Resnet50 and GoogLeNet. PS Satya Sreedhar, N Nandhagopal Intelligent Automation & Soft Computing 31 (3) , 2022 2022 Citations: 32
Trust Management-Based Service Recovery and Attack Prevention in MANET. V Nivedita, N Nandhagopal Intelligent Automation & Soft Computing 29 (3) , 2021 2021 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques NN Gopal, M Karnan 2010 IEEE international conference on computational intelligence and … , 2010 2010 Citations: 247
RE-PUPIL: resource efficient pupil detection system using the technique of average black pixel density S Navaneethan, N Nandhagopal Sādhanā 46 (3), 114 , 2021 2021 Citations: 36
Classification Similarity Network Model for Image Fusion Using Resnet50 and GoogLeNet. PS Satya Sreedhar, N Nandhagopal Intelligent Automation & Soft Computing 31 (3) , 2022 2022 Citations: 32
Canny Edge Detection Model in MRI Image Segmentation Using Optimized Parameter Tuning Method. M Radhakrishnan, A Panneerselvam, N Nachimuthu Intelligent Automation & Soft Computing 26 (6) , 2020 2020 Citations: 24
Human Eye Pupil Detection System for Different IRIS Database Images N Nandhagopal, S Navaneethan, V Nivedita, A Parimala, D Valluru Journal of Computational and Theoretical Nanoscience 18 (4), 1239-1242 , 2021 2021 Citations: 21
Automatic Detection Of Brain Tumor Through Magnetic Resonance Image DN NandhaGopal International Journal of Advanced Research in Computer and Communication … , 2013 2013 Citations: 21
Hybrid Markov Random Field with Parallel Ant Colony Optimization and Fuzzy C Means for MRI Brain Image segmentation M Karnan, NN Gopal IEEE International Conference on Computational Intelligence and Computing … , 2010 2010 Citations: 20
RETRACTED ARTICLE: Improving QoS and efficient multi-hop and relay based communication frame work against attacker in MANET V Nivedita, N Nandhagopal Journal of Ambient Intelligence and Humanized Computing 12 (3), 4081-4091 , 2021 2021 Citations: 17
An FPGA-based real-time human eye pupil detection system using E2V smart camera S Navaneethan, N Nandhagopal, V Nivedita Journal of Computational and Theoretical Nanoscience 16 (2), 649-654 , 2019 2019 Citations: 17
A group teaching optimization algorithm for priority-based resource allocation in wireless networks S Sreethar, N Nandhagopal, SA Karuppusamy, M Dharmalingam Wireless Personal Communications 123 (3), 2449-2472 , 2022 2022 Citations: 14
Enhancing the robustness and security against various attacks in a scale: Free network G Keerthana, P Anandan, N Nandhagopal Wireless Personal Communications 117 (4), 3029-3050 , 2021 2021 Citations: 13
Enhancement Techniques and Methods for MRI A Review DNN V.Velusamy 1, Dr.M.Karnan 2, Dr.R.Sivakumar 3 International Journal of Computer Science and Information Technologies 5 (1 … , 2014 2014 Citations: 13
Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks. G Keerthana, P Anandan, N Nachimuthu Computers, Materials & Continua 66 (1) , 2021 2021 Citations: 12
An efficient energy supply policy and optimized self-adaptive data aggregation with deep learning in heterogeneous wireless sensor network R Tharmalingam, N Nachimuthu, G Prakash Peer-to-Peer Networking and Applications 17 (6), 3991-4012 , 2024 2024 Citations: 10
A low-cost in-tire-pressure monitoring SoC using integer/floating-point type convolutional neural network inference engine A Vasantharaj, SA Karuppusamy, N Nandhagopal, APV Pillai Microprocessors and Microsystems 98, 104771 , 2023 2023 Citations: 10
Attention based deep convolutional U-Net with CSA optimization for hyperspectral image denoising R Murugesan, N Nachimuthu, G Prakash Infrared Physics & Technology 129, 104531 , 2023 2023 Citations: 10
An in-tire-pressure monitoring SoC using FBAR resonator-based ZigBee transceiver and deep learning models A Vasantharaj, N Nandhagopal, SA Karuppusamy, K Subramaniam Microprocessors and Microsystems 95, 104709 , 2022 2022 Citations: 10
An Enhanced Memetic Algorithm for Feature Selection in Big Data Analytics with MapReduce. U Ramakrishnan, N Nachimuthu Intelligent Automation & Soft Computing 31 (3) , 2022 2022 Citations: 9
Probabilistic Neural Network Based Brain Tumor Detection and Classification System KRGRS N. Nandhagopal Research Journal of Applied Sciences, Engineering and Technology 10 (12 … , 2015 2015 Citations: 9
Automated Skin Lesion Diagnosis and Classification Using Learning Algorithms. A Soujanya, N Nandhagopal Intelligent Automation & Soft Computing 35 (1) , 2023 2023 Citations: 7