Machine Intelligence Research Lab (MIRL)
Advancing AI for Social and Healthcare Innovation
The Machine Intelligence Research Lab (MIRL), situated in the Department of Information Technology at Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, India, is a center of excellence dedicated to research in fundamental and applied Machine Intelligence. Our mission is to translate cutting-edge AI methodologies into robust solutions for critical societal challenges.
Team
The MIRL team is comprised of dedicated faculty, research scholars, and associates committed to scientific discovery and innovation.
Faculty
The lab is headed by Dr. A. Shahina, Professor and Head, Department of Information Technology, with a teaching and research experience of over 25 years. , and three faculty with commendable research in specialized research domains.
Research Scholars
Past and present research Scholars of Machine Intelligence Research Lab (MIRL)
| S. No | Name | Thesis title | Period | |
|---|---|---|---|---|
| Past PhD Scholars | ||||
| 1 | Mohamed Hashim C Chalmers University, Sweden | Dependent Speech Recognition Under Constrained Conditions for Under-Resourced Languages Tamil and Urdu | 2023 completed |  |
| 2 | Umamaheswari S, SSN College of Enginnering | Lombard Effect Compensation for Speech and Speaker Recognition Systems Using Deep Learning Neural Networks | 2021 completed |  |
| 3 | Radha N SSN College of engineering | A Multimodal Approach to Develop Speech Recognition Systems | 2020 completed |  |
| Current PhD Scholars | ||||
| 4 | Ms. Botwe Renuka Kakasaheb | 2025 – till date |  |
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| 5 | Ms. S. Ishwarya | 2022 – till date |  |
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| 6 | Ms. P. Sasirekha | 2022 – till date |  |
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| 7 | Ms. P. Dhivya | 2022 – till date |  |
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| 8 | Ms. D. Sharmila | 2022 – till date |  |
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| 9 | Rini P.l | 2022 – till date |  |
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| 10 | Ms. Jeyanthi S | 2023– till date |  |
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| 11 | Ms. Meera R | 2023– till date |  |
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| 12 | Ms. Mary Sawbaghya P | 2024– till date |  |
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| 13 | Ms. Saroja V | 2024 – till date |  |
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| 14 | Mr. Ramesh kumar | 2024– till date |  |
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| 15 | Ms. S.Manimegalai | 2022 – till date |  |
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| 16 | Ms. Bushra K M | 2025 – till date |  |
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| 17 | Ms. Preethi S T | 2025 – till date |  |
Project Associates
- Mr. K. Manideep (May 2024 onwards)
- Ms. Botwe Renuka Kakasaheb (June 2024 onwards)
Research Thrusts
MIRL’s research is driven primary focus on Computational Healthcare and the integration of AI across key sectors. Our core research thrusts include:
Neuro-Cognitive Systems & BCIs
Brain-Computer Interface (BCI) based Neurorehabilitation: Developing BCI based systems to (1) Convert thoughts to speech for neurodegenerative and speech-impaired patients, and (2) to rehabilitate neurologically affected patients
Neurological Disease Detection: Epilepsy detection and localization, sleep pattern analysis, and cognitive decline assessment.
Computational Pathology & Medical Imaging
Precision Diagnosis: Leveraging Deep Learning for lung disease detection, cancer diagnosis, and brain tumor radiogenomics.
Digital Health: Developing AI models for cardiac disease prediction and enhanced medical image analysis.
Core Machine Intelligence
Speech Processing: Speech recognition, speaker identification, and compensation for noisy environments (e.g., Lombard effect).
Sustainable Systems: AI for automatic fault detection in PV power plants and solar irradiance prediction.
Funded Projects
Government and Industry Funded Projects
| Type | Funding Source | Project Focus | Grant Amount |
|---|---|---|---|
| Government | ANRF (formerly SERB) | Brain-Computer Interface (BCI) for speech-impaired. | ₹. 1966660 |
| International Industry | ST-Microelectronics | Deep learning models for automatic classification of multiple sleep disorders using wearable sensors. | ₹2700000 |
| International Industry | Nvidia | NutriApp: Multi-Agent System for Complex Decision Making using LLM | ₹3100000 |
| Government | DST-NCSTC (National Council for S&T Communication) | Outreach and awareness programs on AI, ML, IoT, and Cybersecurity. | ₹2205000 |
| Government | MeitY | Innerspeech Classification using EEG signals | ₹. 100000 |
| Government | MeitY | Analysing Tremors and Gait Patterns for Early Detection of Motor Disorder Symptoms in Neurodegenerative Disease | ₹. 100000 |
| Government | MeitY | Voice Craft AI | ₹. 100000 |
| Government | MeitY | Innerspeech Classification using EEG signals | ₹. 100000 |
| Government | TNSCST | ₹. 7000 |
Seed Grant Projects
| S. No | Faculty and Students involved | Project Focus | Grant Amount |
|---|---|---|---|
| 1 | Dr. A. Shahina, Siddarth Manthala Srikumar V Sudarshan O Vishal S | Brain Waves for Communication | Rs.70000 |
| 2 | Dr. A. Shahina, Chiranjeev Prasannaa V V Dharanikaran S Sowmya C | An AI based Stress Detection System using Brain Signals and Yoga recommender system | Rs. 79000 |
| 3 | Dr. A. Shahina Francis Rohith Karanam Hema Sudha Poornima Maghizhvanban E S Sandhya Sridar | Converting Hand Signs into Speech using EEG – Using EEG to aid Communication for Deaf and Mute people | Rs.70000 |
| 4 | Dr. A. Saravanan, Dr. V. Durgadevi | Decoding pulmonary health : Enhancing rapid patient lung assessment via computer aided analysis of interlobular septum thickness | Rs.300000 |
| 5 | Dr. S. Anitha, Dr. A. Saravanan, Dr. E.M. Malathy | Analysis of lung computed tomography images for the detection of nodules using deep learning techniques | Rs.300000 |
| 6 | Dr. V. Durgadevi IT, Dr. R. Ramaprabha EEE | Development of effective AI models for automatic fault detection in PV power plants | Rs.380000 |
| 7 | Dr. K. S. Gayathri, Dr. R. Vinob Chander | Ambient assisted dementia care through smart home with activity recognition, abnormality detection and decision support system using artificial intelligence and machine learning technique | Rs.200000 |
| 8 | Dr. V. Durgadevi, Pranav Krishna P Venkatachalam S | Data Encryption with Hashing to secure Mobile User Information for Cloud-based Backup Solution | Rs.17000 |
| 9 | Dr. A. Saravanan, Anirudh Navadeep Nishanth Kathirvelan | Smart Bus Seat Management and Location Tracking System | Rs.35000 |
Completed Projects
| S. No. | Project Details | Agency | Sanctioned amount | Duration | Status |
|---|---|---|---|---|---|
| 1 | Development of TEXT-to-SPEECH SYSTEM in Indian Languages: High- quality text-to-speech synthesis and small footprint TTS integrated with disability aids. (A consortium project with IIT Madras as Team Leader) Co-P.I – Dr. A. Shahina, Dr. P. Vijayalakshmi, P.I – Dr. T. Nagarajan | Ministry of Communication & Information Technology (MCIT), Government of India, New Delhi | Rs.77,00,000 | 2012 – 2017 | Completed |
| 2 | An IoT and Deep Learning Based Cardiac Disease Prediction System as a Diagnostic Aid Using Multiple Physiological System Co-P.I – Dr. A. Shahina, Dr. Priyadarshini, Dr. C. Jackson, Dr. A. Khader, P.I – Dr. S. Urooj | Ministry of Education, KSA | Rs.5,54,094 | 2023 | On-going |
| 3 | Person Authentication Using Transient Evoked Otoacoustic Emission (TEOAE) Signals With Deep Learning Neural Network Architectures P.I – Dr. A. Shahina Co P.I – Dr. Mirnalinee | SSN Trust | Rs. 1,20,000 | 2020-2022 | Completed |
| 4 | Building a robust speaker recognition system using deep neural networks Co-P.I – Dr. A. Shahina P.I – Dr. S. Umamaheswari | SSN Trust | Rs. 3,00,000 | 2018-2019 | Completed |
| 5 | Audio-Visual Speech Recognition System for Tamil CV Units Co-P.I – Dr. A. Shahina P.I – Dr. N. Radha | SSN Trust | Rs. 2,50,000 | 2018-2021 | Completed |
| 6 | Anatomical Vibration Sensor Speech Corpus for Speech Applications in Noisy Environments P.I – Dr. A. Shahina Co P.I – Dr. P. Vijayalakshmi, Dr. T. Nagarajan | SSN Trust | Rs. 1,23,000 | 2010-2012 | Completed |
| 7 | Design of lab model of an improved speech processor for cochlear implants P.I – Dr. P. Vijayalakshmi Co P.I – Dr. A. Shahina, Dr. T. Nagarajan | SSN Trust | Rs 80,000 | 2010-2012 | Completed |
Recent Publications
| S. No. | Title of the paper | Quartile | Impact Factor |
|---|---|---|---|
| 1 | A. Tamotia, D. S. Karmokar, R. Komal, K. K. Nawas, A. Shahina and A. Nayeemulla Khan, “Fusion of Multimodal Audio Data for Enhanced Speaker Identification Using Kolmogorov-Arnold Networks,” in IEEE Access, vol. 13, pp. 87640-87653, 2025 | Q1 | 3.6 |
| 2 | Nirranjana, R., Aishwarya, R., Tejshree, S, K.S. Gayathri, Surendar Natarajan and P. Thirunavukkarasu. Rainfall Forecasting Model for Amaravathi Basin Using Machine Learning Approach. J. Inst. Eng. India Ser. A, 2025 | Q2 | 1.222 |
| 3 | Yuvaraj Mariappan, Karthikeyan Ramasamy and Durgadevi Velusamy, An optimized deep learning based hybrid model for prediction of daily average global solar irradiance using CNN SLSTM architecture. Sci Reports, vol.15, 10761, 2025. | Q1 | 3.9 |
| 4 | S. Alagarsamy, V. Govindaraj, A. Shahina and D. Nagarajan, “Intelligent Multigrade Brain Tumor Identification in MRI: A Metaheuristic-Based Uncertain Set Framework,” in IEEE Transactions on Artificial Intelligence, vol. 5, no. 11, pp. 5381-5391, Nov. 2024 | Q1 | 0.9 |
| 5 | Khadar Nawas K, A. Shahina, Keshav Balachandar, Maadeshwaran P, Devanathan N. G, Navein Kumar, A. Nayeemulla Khan: Scientific reports 14, 12513 (2024). | Q1 | 4.6 |
| 6 | N. Ilakiyaselvan, A. Nayeemulla Khan, A. Shahina,” Reconstructed phase space portraits for detecting brain diseases using deep learning “, Biomedical Signal Processing and Control, Vol. 71 (2022) 103278. | Q1 | 5.076 |
| 7 | Sriya Ravi, Shreenidhi S, Shahina A, Ilakiselvan N, Nayeemulla Khan A, “Seizure Detection using Deep Learning on Recurrence Plots of EEG Signals”, Multimedia Tools and Applications, 81, 6585–6598 (2022). | Q1 | 3.6 |
| 8 | Harish Garg, Saravanan Alagarsamy, D Nagarajan, A Senthilkumar, Smart system for identifying the various pathologies in MR brain image using Monkey Search based Interval Type-II Fuzzy C-Means technique, Multimedia Tools and Applications (Springer), 2024. | Q1 | 3.6 |
| 9 | SV Hemanth, Saravanan Alagarsamy, T. Dhiliphan Rajkumar, A novel deep learning model for diabetic retinopathy detection in retinal fundus images using pre-trained CNN and HWBLSTM, Journal of Biomolecular Structure and Dynamics, 2024. | Q1 | 4.4 |
| 10 | Abraham, J.V.T., Khan, A.N. & Shahina, A, “A deep learning approach for robust speaker identification using chroma energy normalized statistics and mel frequency cepstral coefficients”, Int J Speech Technol 26, 579–587 (2023). | Q1 | 3.4 (citescore) |
| 11 | Varugeese A, Shahina A, Nawaz K, Nayeemulla Khan A, “EarNet: Biometric Embeddings for End-to-End Person Authentication System Using Transient Evoked Otoacoustic Emission Signals”, Neural Processing Letters 54, 21–41 (2022). | Q2 | 2.908 |
| 12 | Mohamed Hashim C, Shahina A, Badri Narayanan M, and Nayeemulla Khan A, “End to end speech recognition of Tamil language”, Intelligent Automation and Soft Computing, Vol.32, No.2, pp. 1309-1323, 2022. | Q3 | 1.647 |
| 13 | S. Uma Maheswari, A. Shahina, A. Nayeemulla Khan, “Understanding Lombard Speech: A Review of compensation techniques towards improving Speech Based Recognition Systems”, Artificial Intelligence Review, 54, pages 2495–2523 (2021) Sep. 2020 | Q1 | 12 |
| 14 | R. John Wesley, A. Nayeemulla Khan, and A. Shahina, “Phoneme classification in reconstructed phase space with convolutional neural networks”, Pattern Recognition Letters, Volume 135, July 2020, pp. 299-306. | Q1 | 5.1 |
| 15 | S. Uma Maheswari, Rishickesh Ramesh, A. Shahina, A. Nayeemulla Khan, “A Study on the Impact of Lombard Effect on Recognition of Hindi Syllabic Units Using CNN Based Multimodal ASR Systems”, Archives of Acoustics, Vol. 45, No. 3, pp. 419–431 (April 2020). | Q3 | 1.34 |
| 16 | Ilakiyaselvan, A. Nayeemulla Khan, A. Shahina, “Deep learning approach to detect seizure using reconstructed phase space images”, Journal of Biomedical Research (Scopus indexed), 2020 34(3): 238–248. DOI: | Q2 | 2.2 |
| 17 | N. Radha, A. Shahina, P. Prabha, B.T. Preethi Sri, A. Nayeemulla Khan, “An Analysis of The Effect of Combining Standard and Alternate Sensor Signals on Recognition of Syllabic Units for Multimodal Speech Recognition”, Pattern Recognition Letters, Volume 115 (2018), 2018, Pages 39-49. | Q1 | 5.1 |
| 18 | Chockalingam Alagappan, Karthikeyan Ramasamy, and Durgadevi Velusamy, Bagging ensemble of artificial neural networks with weighted averaging and grey wolf optimization for solar photovoltaic power generation prediction, International Journal of Green Energy, Taylor and Francis, 22(8), 1587–1601 | Q2 | 3.1 |
| 19 | Somasundaram Palaniappan, Sundararaju Karuppannan, Durgadevi Velusamy, Categorization of Indian residential consumers electrical energy consumption pattern using clustering and classification techniques, Energy, vol. 289, pp.1 -14, 2024. (Thomson Reuters Impact Factor: 9.0) | Q1 | 9 |
| 20 | Karthikeyan Ramasamy, Kiruthika Balakrishnan, Durgadevi Velusamy, “Classification of inter-patient’s cardiac arrhythmias in ECG signals with enhanced Jaya optimized TQWT parameters and stacked ensemble algorithm”, Soft Computing, vol. 27, pp. 11341 -11356, 2023. | Q2 | 4.1 |
| 21 | Karthikeyan Ramasamy, Arivoli Sundaramurthy, and Durgadevi Velusamy, Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier, Automatika, vol. 64, no.4, pp. 783-797, 2023. | Q2 | 1.9 |
| 22 | Visalini K. Saravanan Alagarsamy, Nagarajan D. Event-Based Epileptic Seizure Detection with Stacked Convolutional Restricted Boltzmann Machine, IETE Journal of Research, https://doi.org/10.1080/03772063.2023.2234854,2023. | Q2 | 1.5 |
| 23 | Rini Pl, Gayathri Ks, Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture, International Journal of Language & Communication Disorders, https://doi.org/10.1111/1460-6984.12973 | Q1 | 2.4 |
| 24 | Rini PL and Gayathri KS, (2024),” Revolutionizing dementia detection: Leveraging vision and Swin transformers for early diagnosis”, American Journal of Medical Genetics Part B: Neuropsychiatric Genetics (AJMGB), Wiley, Published online, ISSN:1552-484 | Q2 | 1.5 |
| 25 | Narayanan, M Badri & Ramesh, Arun & K.s, Gayathri & A., Shahina. (2023). Fake news detection using a deep learning transformer based encoder-decoder architecture. Journal of Intelligent & Fuzzy Systems. 1-13. 10.3233/JIFS-223980 | Q2 | 1 |
Engage With MIRL
We are actively seeking global collaborations with academic and industrial research groups. We believe that partnership is essential to accelerate discovery and deliver impactful solutions in the complex field of Machine Intelligence.
We invite researchers and prospective students to connect with Dr. A. Shahina, Head of MIRL, to explore research opportunities.
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