A Machine Learning-Driven Framework for Accurate Brain Image Registration in Multimodal and Noisy Environments M. S. Minu, Mutharasu M, S. Hemamalini, Sunitha T, Mohanaprakash T A, Justindhas Y International Journal of Advanced Computer Science and Applications, 2026 Brain image registration is fundamental for medical imaging to allow the matching of images from multiple modalities, temporal sequences, and people to offer spatial correlation. This is crucial for activities such as cohort studies, intervention recommendations, and treatment monitoring, where exact alignment assures consistent analysis. Notwithstanding their importance, modern brain image registration techniques have many shortcomings, including limited resistance to noise, misalignment in multi-modality images, and costly computational expenses. These limits may impede real-time clinical environment practical implementation and provide less than optimal registration accuracy. This study addresses these issues by means of an Improved Brain Image Registration Technique Using Machine Learning Algorithms (BIRT-MLA). The proposed architecture detects significant image properties by means of convolutional neural networks (CNNs), therefore enabling feature extraction. By applying a supervised learning method, it guarantees precise alignment even in noisy and demanding imaging situations by forecasting transformation parameters. Lowering the registration error by modern optimization techniques helps to save processing time and maintain remarkable accuracy even in this respect. Using CNNs, the proposed method helps to effectively classify brain images, thereby improving diagnostic support and the usefulness of registered images for downstream operations. Improving clinical judgment and simplifying processes rely on grouping registration and categorization into a logical sequence. By means of enhanced alignment precision, resistance to picture faults, and shortened computing time compared to current approaches, experimental findings expose the advantages of the suggested technology. This development may be very useful in clinical and experimental settings, thereby supporting the accuracy and efficiency of brain picture analysis.
Development of Hybrid Explainable Artificial Intelligence With Swin Vision Transformer Intrusion Detection for Securing VANETs From Attacks Bharathiraja N, M. S. Minu, Richa Vijay, M. Rajalakshmi, Pellakuri Vidyullatha, K. Balamurugan Transactions on Emerging Telecommunications Technologies, 2025 Vehicular Ad‐hoc Networks (VANETs) are a cornerstone of Intelligent Transportation Systems (ITS), enabling efficient vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication. However, their open and dynamic nature makes them highly susceptible to security threats such as Distributed Denial of Service (DDoS) attacks and the injection of false data by malicious nodes. Existing security mechanisms often fall short in addressing these challenges due to the real‐time and mobile characteristics of VANETs. This paper proposes a Hybrid Explainable Artificial Intelligence (XAI) framework integrated with a Swin Vision Transformer for robust intrusion detection in VANET environments. The proposed model leverages the Swin Transformer's hierarchical feature extraction capabilities and the interpretability of XAI to accurately classify network nodes based on behavioral and transmission characteristics. Key features such as packet transmission duration, communication regularity, and node status are analyzed to detect anomalies and differentiate between benign and malicious nodes. The inclusion of explainability allows for transparent decision‐making, facilitating trust and understanding in critical automotive applications. Simulation results validate the model's effectiveness in detecting a wide range of attack vectors while maintaining high accuracy and low false‐positive rates. This study contributes to the development of adaptive, intelligent, and trustworthy security solutions for next‐generation vehicular networks operating in complex urban traffic scenarios.
A smart review on imputation techniques for handling missing data R. Sivakani, J. Rahila, P. Sudha, S. Silvia Priscila, T. Shynu, M. S. Minu, V. Pradeep Machine Learning Predictive Analytics and Optimization in Complex Systems, 2025 Missing data is an important problem in the digital world. Missing data means no value will be present in the dataset. Missing occurs for many reasons, including data loss, typing errors, system problems, less interest of the respondent, etc. This is a major issue for researchers, industry people, academicians, doctors, etc. The incomplete data will not support any solution. In this paper, a review of various research articles from 2009 to 2020 is analyzed. The techniques and algorithms used for the imputation have been analyzed. Imputation is the process involved in replacing the missing value. In addition, various algorithms, techniques, and methods are discussed in this paper to solve the above-said issue, and significant discussion is also done on the types of missing mechanisms. The main focus has been on the traditional and novel imputation methods.
Android-based Action Recognition with 3D CNN and UCF 101 dataset Abhijith Dasan, R. Darshan, Ashvyn Kumar, M. S. Minu, J. Arthy EPJ Web of Conferences, 2025 A real-time video classification capability for mobile devices operates through an Android-based system supported by 3D CNN architecture and UCF-101 dataset processing. A new method uses MoViNet models integrated with TensorFlow Lite to execute video analysis directly on devices, so users get immediate feedback together with security of personal data. The system provides extensive practical benefits as it helps paralysis patients while also spotting dangerous training techniques and detecting strange movements during surveillance activities. The implemented model reaches 77.2% accuracy on UCF-101 while operating at a 45ms latency and requiring 6.0 GFLOPs which surpasses X3D-XL. Efficient resource management of this lightweight design enables mid-range mobile devices to work with the system which advances video analysis methods for edge computing systems. Future efforts will concentrate on raising power efficiency by using hardware-aware techniques while adding basic-processing technology support as well.
Sentinal.AI on emergency call operator using artificial intelligence and blockchain M. S. Minu, Prayasu Satapathy, Ishaan Dwivedi, S. S. Subashka Ramesh, M. B. Sudhan, G. Agalya Optimizing Patient Outcomes Through Multi Source Data Analysis in Healthcare, 2025 Emergency call centers in India face critical challenges that can significantly impact response times and, ultimately, the lives of needy individuals. Factors such as call center overload and understaffing can lead to delays in attending to emergency calls, resulting in severe consequences. The Importance of Prompt Response in Life-Threatening Situations Prompt response is crucial in life-threatening situations, as it can significantly save lives and minimize property damage. However, overloaded or understaffed emergency call centers can hinder the promptness of response. One such innovative solution that addresses these challenges is Sentinel.AI, which leverages the power of artificial intelligence to prioritize emergency calls and efficiently relay vital information to emergency handlers. Sentinel.AI is an innovative solution designed specifically for emergency call centers in India to address these critical challenges.
Blockchain-enabled evoting on reinventing electoral processes for university elections M. S. Minu, K. S. Dattatreya, J. Mitesh, G. Raghunath, B. Vaidianathan, R. Regin Multidisciplinary Approaches to AI Data and Innovation for A Smarter World, 2025 Elections in university settings frequently have difficulties with transparency, security, and accessibility. Traditional voting systems are prone to various issues, such as manipulation and logistical complexities. Over the past few years, blockchain technology has arisen as a hopeful remedy to tackle these obstacles, offering a transparent, secure, and decentralized avenue for conducting electronic voting (e-voting). This project presents a conceptual architecture for implementing a blockchain-based e-voting system tailored specifically for university-level elections. The suggested framework utilizes the intrinsic characteristics of blockchain to ensure the honesty and equity of the voting procedure. Key components of the architecture include a distributed ledger for recording votes, smart contracts for enforcing voting rules and conducting tallying, and a user-friendly interface for voters to cast their ballots securely.
A NeRF-Transformer Hybrid Framework for High-Quality 3D Scene Reconstruction and Contextual Interpretation M.S. Minu, Pujari Keerthika, Padmalakshmi S, Moksha R 2025 1st International Conference on Smart and Intelligent Systems Siscon 2025, 2025 In this project, we would be exploring the integration of N eRFs and Transformers, creating a hybrid pipeline for 3D Scene Understanding. NeRFs is a novice approach to reconstructing 3D scenes from 2D sparse image inputs. However, there are limitations in spatial understanding and complex scene understanding. Transformers offer a global attention mechanism and feature extraction abilities, and hence leveraging them would improve the spatial representation and coherence of reconstructed scenes. Performance is evaluated on both synthetic and real-world datasets, and bench marked against standard metrics like PSNR and SSIM. This project holds the capability to significantly impact applications in virtual reality, autonomous systems, and augmented reality by advancing the scalability and robustness of 3D scene reconstruction techniques.
Internet of Things Assisted Sleep Quality Recognition using Hunger Games Search Optimization with Deep Learning on Smart Healthcare Systems M. B. Sudhan, .A DeepakKumar, Mathan Kumar Mounagurusamy, S. Navaneethan, B. Venkataramanaiah Journal of Intelligent Systems and Internet of Things, 2025 Rapid urbanization needs major cities that change into smart cities to increase our lifestyle with respect to transportation, people, government, environmental sustainability, and more. In recent times, Internet of Things (IoT) and healthcare wearables have played a vital play in the progress of smart cities by providing enhanced healthcare services and an entire standard of living. Wearables offer
Scalable Summarization of Long-Form Video Transcripts using NLP Minu M S, Feroza D Mirajkar, T. A. Mohanaprakash, Kaviya D, Jasmine Fathima K, Maha S. 6th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2025 Proceedings, 2025 This research designs a system that, with the help of NLP, would sum up the YouTube video transcripts properly without losing any important information. Day in day out, time keeps on recording increasing numbers of videos that are housed in internet-based platforms. Educating is the most prominent explanation for all the global content being available and accessible. One might also train on the different networks such as Google, Facebook, Instagram, YouTube, and once again Google. The underlying truth that the viewer has to watch the entire film to really understand and appreciate the broader perspective shows one key element-is comparable with images where so much information and data can be extracted and understood from just one frame; it indicates that data extraction from videos is a both an important and challenging question. Thus, the aim of this research project is to comfortably shorten and summarize the length of the video transcript so that it can easily be understood. The proposed approach is to get transcripts by using the link given from the video provided by the user and then use TD-IDF combined with the BART transformer in order to text summarize. The model developed here successfully satisfies the consideration of video URLs shared by the user and the exact summary duration needed. At the end, this results in the detailed summary of the transcript for the videos. Comparing with the other proposed methods put forward, the final text translation has been found to be present much earlier than what the results initially suggested. Moreover, a significant point has to be added that the final text absolutely depicts the theme of the movie without any kind of change or alteration.
Greensight: Advanced Carbon Emission Analytics 15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
Enhanced image capturing using CNN International Journal of Engineering and Advanced Technology, 2019
Cervical cancer prediction using naïve bayes classification International Journal of Engineering and Advanced Technology, 2019
Data analyzer using the concept of machine learning International Journal of Engineering and Advanced Technology, 2019
Prevention of hacking in vanets using network security International Journal of Recent Technology and Engineering, 2019
Real time college bus monitoring and notification system International Journal of Recent Technology and Engineering, 2018
RECENT SCHOLAR PUBLICATIONS
A NeRF-Transformer Hybrid Framework for High-Quality 3D Scene Reconstruction and Contextual Interpretation MS Minu, P Keerthika, P S, M R 2025 1st International Conference on Smart and Intelligent Systems (SISCON) , 2026 2026
Implementing Cybersecurity Policies to Minimize the Impacts of Deepfakes on Universities S Manikandan, MS Minu, SSS Ramesh, K Sivasankari, S Saranya, ... Safeguarding Educational Integrity Through Deepfake Face Detection, 361-384 , 2026 2026
A Smart Review on Imputation Techniques for Handling Missing Data R Sivakani, J Rahila, P Sudha, SS Priscila, T Shynu, MS Minu, V Pradeep Machine Learning, Predictive Analytics, and Optimization in Complex Systems … , 2026 2026
Development of Hybrid Explainable Artificial Intelligence With Swin Vision Transformer Intrusion Detection for Securing VANETs From Attacks N, Bharathiraja, MS Minu, R Vijay, M Rajalakshmi, P Vidyullatha, ... TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 36 (10) , 2025 2025
CommunityDetectioninFinancialNetworksforAMLUsingGNNs TA M.S. Minu1,*, S. Eswaran2 , Janisa Ria D2 , V.B. Shreesha2 2025
EnhancedBrainTumorDelineationUsingt-SNEandMachine learningalgorithms VK M. S. Minu1,*, N M Vedhinee1 , Prakratee Singh1 Proceedings of the 4th International Conference on Information Technology … , 2025 2025
Privacy-Preserving Real-Time Action Recognition on Mobile Devices Using Edge Computing and CNNs MS Minu, K Selvi, A Dasan 2025 International Conference on Machine Learning and Autonomous Systems … , 2025 2025 Citations: 2
Navigating the Complexities of Municipal Waste Management: Enhancing Cost Prediction for Sustainable Urban Solutions A Minu, M.S., Samal, T., Bisani, K., ...Tewari Communications in Computer and Information Science 2361 CCIS, pp. 204-220 … , 2025 2025
Scalable Summarization of Long-Form Video Transcripts Using NLP MS Minu, FD Mirajkar, TA Mohanaprakash, D Kaviya, S Maha 2025 6th International Conference on Mobile Computing and Sustainable … , 2025 2025 Citations: 2
Android-based Action Recognition with 3D CNN and UCF 101 dataset A Dasan, R Darshan, A Kumar, MS Minu, J Arthy EPJ Web of Conferences 328, 01001 , 2025 2025 Citations: 3
Sentinal. AI on Emergency Call Operator Using Artificial Intelligence and Blockchain MS Minu, P Satapathy, I Dwivedi, SSS Ramesh, MB Sudhan, G Agalya Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare … , 2025 2025 Citations: 1
Blockchain-Enabled e-voting on reinventing electoral processes for university elections MS Minu, KS Dattatreya, J Mitesh, G Raghunath, B Vaidianathan, R Regin Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World … , 2025 2025 Citations: 1
Internet of Things Assisted Sleep Quality Recognition using Hunger Games Search Optimization with Deep Learning on Smart Healthcare Systems. MB Sudhan, MS Minu, MK Mounagurusamy, S Navaneethan, ... Journal of Intelligent Systems & Internet of Things 14 (1) , 2025 2025 Citations: 27
Secure Routing E-voting Protocol based on Wireless Sensor Network Platform with Block chain TA Mohanaprakash, R VC, MS Minu, K Cinthuja International Journal of Electrical and Electronics Research 12 (4), 1381-1390 , 2024 2024 Citations: 1
Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning MS Minu 2024 2nd International Conference on Advances in Computation, Communication … , 2024 2024
Customized Movie Recommendation for Enhanced Entertainment MS Minu, S Talapalli, S Chevuri, M TS 2024 International Conference on Cybernation and Computation (CYBERCOM), 151-156 , 2024 2024
Enhanced Weld Defect Categorization via Nature-Inspired Optimization-Driven Neural Networks MS Antony Vigil, K Maheswari, MS Minu, GL Kulkarni, ... SN Computer Science 5 (8), 1035 , 2024 2024 Citations: 3
An Improved Deep Network Model to Isolate Lung Nodules from Histopathological Images Using an Orchestrated and Shifted Window Vision Transformer MSAV Ponnan Sabitha1 , Ramalingam Aroul Canessane2 , Manickarasi Sivathanu ... Traitement du Signal 41 (4), 2081-2091 , 2024 2024 Citations: 2
Empowering Small-Scale Farmers with Decentralized Finance (DeFi) via Ethereum Smart Contracts MS Minu, SR Suryaa, JJ Titus, S Sharan 2024 2nd International Conference on Sustainable Computing and Smart Systems … , 2024 2024 Citations: 2
An innovative privacy preservation and security framework with fog nodes in enabled vanet system using hybrid encryption techniques MS Minu, PJI Rani, VK Sonthi, G Shankar, E Mohan, A Rajesh Peer-to-Peer Networking and Applications 17 (4), 2065-2089 , 2024 2024 Citations: 7
MOST CITED SCHOLAR PUBLICATIONS
Optimal Squeeze Net with Deep Neural Network-Based Arial Image Classification Model in Unmanned Aerial Vehicles. MS Minu, SSS Ramesh Traitement du Signal 39 (1) , 2022 2022.0 Citations: 70
Face recognition system based on haar cascade classifier MS Minu, K Arun, A Tiwari, Rampuria International Journal of Advanced Science and Technology 29 (5), 3799 - 3805 , 2020 2020.0 Citations: 36
Deep learning-based aerial image classification model using inception with residual network and multilayer perceptron MS Minu, RA Canessane Microprocessors and Microsystems 95, 104652 , 2022 2022.0 Citations: 35
Experimental Analysis of UAV Networks Using Oppositional Glowworm Swarm Optimization and Deep Learning Clustering and Classification MS Minu, SSS Ramesh, RA Canessane, M Al-Amin, RB Sulaiman FMDB Transactions on Sustainable Computing Systems 1 (03 2023), 124 -134 , 2023 2023.0 Citations: 33
Internet of Things Assisted Sleep Quality Recognition using Hunger Games Search Optimization with Deep Learning on Smart Healthcare Systems. MB Sudhan, MS Minu, MK Mounagurusamy, S Navaneethan, ... Journal of Intelligent Systems & Internet of Things 14 (1) , 2025 2025.0 Citations: 27
QMLFD Based RSA Cryptosystem for Enhancing Data Security in Public Cloud Storage System ACR P Kaliyamoorthy Wireless Personal Communications 122 (1), 755-782 , 0 Citations: 20
Domain Generalization and Multidimensional Approach for Brain MRI Segmentation Using Contrastive Representation Transfer Learning Algorithm SSS Ramesh, A Jose, PR Samraysh, H Mulabagala, MS Minu, ... Advancements in Clinical Medicine, 17-33 , 2024 2024.0 Citations: 18
An innovative privacy preservation and security framework with fog nodes in enabled vanet system using hybrid encryption techniques MS Minu, PJI Rani, VK Sonthi, G Shankar, E Mohan, A Rajesh Peer-to-Peer Networking and Applications 17 (4), 2065-2089 , 2024 2024.0 Citations: 7
Secure image transmission scheme in unmanned aerial vehicles using multiple share creation with optimal elliptic curve cryptography MS Minu, RA Canessane Indian Journal of Computer Science and Engineering 12 (1), 129-134 , 2021 2021.0 Citations: 7
Augmented Analytics: The Future of Business Intelligence MS Minu, Z Ahmed Recent Trends in Computer Science and Software Technology - Mantech … , 2020 2020.0 Citations: 7
An efficient squirrel search algorithm based vector quantization for image compression in unmanned aerial vehicles MS Minu, RA Canessane 2021 International Conference on Artificial Intelligence and Smart Systems … , 2021 2021.0 Citations: 6
Efficient maintenance of hospital records by entrusted proof of work algorithm in block chain technology MS Minu, SSS Ramesh, SKR Peruru, NM Roshan Computational Intelligence for Clinical Diagnosis, 337-351 , 2023 2023.0 Citations: 5
Crop Yield Prediction Using Machine Learning MS Minu, V Dharrsan, C Immanuel ADALYA JOURNAL (ISSN NO: 1301-2746) 9 (4), 98-102 , 2020 2020.0 Citations: 5
Real time college bus monitoring and notification system MS Minu, KND Adithya International Journal of Advance Research in Science and Engineering (IJARSE … , 2018 2018.0 Citations: 5
Arduino Controlled Multipurpose War Field Spy Robot for Military Surveillance MS Minu, M Alekya, M Supriya, P Malvika International Journal of Advanced Science and Technology 29 (03), 5485 - 5494 , 2020 2020.0 Citations: 4
Flight Delay Prediction using Binary Classification R Musaddi, A Jaiswal, J Pooja, M Girdonia, MS Minu International Journal of Emerging Technologies in Engineering Research … , 2018 2018.0 Citations: 4
Automated EEG based Emotion Detection using Bonobo Optimizer with Deep Learning on Human Computer Interaction VS Siva Satya Sreedhar P. 1 , M. S. Minu2,*, P. Vidyasri3 , Habeeb Omotunde4 ... Journal of Intelligent Systems and Internet of Things 12 (1), 70-83 , 0 Citations: 4
Android-based Action Recognition with 3D CNN and UCF 101 dataset A Dasan, R Darshan, A Kumar, MS Minu, J Arthy EPJ Web of Conferences 328, 01001 , 2025 2025.0 Citations: 3
Enhanced Weld Defect Categorization via Nature-Inspired Optimization-Driven Neural Networks MS Antony Vigil, K Maheswari, MS Minu, GL Kulkarni, ... SN Computer Science 5 (8), 1035 , 2024 2024.0 Citations: 3
A Hybrid Deep IoT Network-Driven Anomaly Detection using Multi-Scale Deep Representation Learning MS Minu, KM Reddy 2023 2nd International Conference on Edge Computing and Applications (ICECAA … , 2023 2023.0 Citations: 3