Dr. Sinu Nambiar

I am an accomplished academician with over 18 years of experience in teaching and research in the fields of Computer Science and Engineering. I have held and do hold a position as an Assistant Professor at various esteemed institutions, where I have contributed significantly to curriculum development and student mentorship. My expertise spans a wide range of subjects including Artificial Intelligence, Operating Systems and Software Engineering. I've published numerous research papers in reputed journals and has authored several books on AI and Machine Learning. My commitment to education is evident through my active participation in faculty development programs and my role in administrative responsibilities within the department. I am dedicated to fostering a learning environment that encourages innovation and critical thinking among students. I am also a lifetime member of professional organizations such as ISTE and QCFI.

EDUCATION

PhD ( Computer Science and Engineering), MTech ( Information Technology), B.E. ( Information Technology)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Science Applications, Computer Science

Scopus Publications

Design and implementation of quantum-resistant cryptographic algorithms in blockchain networks
Shyam Deshmukh, Sinu Nambiar, Pallavi Sachin Patil, Madhura Phatak, Bhavana Tiple, Aniket Prakashrao Munshi
Journal of Discrete Mathematical Sciences and Cryptography, 2025
Traditional cryptographic methods are in big danger from quantum computing, which could make blockchain networks less safe. This essay looks at how to make and use quantum-resistant encryption methods that work in blockchain settings. First, we look at how vulnerable current cryptographic methods are to quantum attacks. This appears how vital it is to discover alternatives that are not influenced by quantum assaults as before long as conceivable. At that point, we see at distinctive post-quantum cryptographic strategies, such as hash-based, code-based, and lattice-based cryptography, and judge how well they can be utilized in blockchain frameworks. The most center is on the issues that come up in genuine life and the trade-offs in execution that come with these quantum-resistant calculations. These incorporate key estimate, handling speed, and the capacity of the organize to develop. The strategies we’ve come up with are implied to create blockchain more secure whereas still working well. We put a few strategies to use in a test blockchain and test them within the genuine world to see how they influence exchange speed and delay. Agreeing to the comes about, quantum-resistant calculations can be utilized in blockchain systems, indeed in spite of the fact that they include a few additional work.
Machine Learning Approaches for Fault Detection and Diagnosis in Electrical Machines: A Comparative Study of Deep Learning and Classical Methods
Ranjit M. Gawande
Panamerican Mathematical Journal, 2024
Fault detection and diagnosis in electrical machines are crucial for ensuring their safe and reliable operation. In recent years, machine learning techniques have emerged as powerful tools for addressing this challenge, offering the potential for more accurate and efficient fault detection and diagnosis compared to traditional methods. Among these techniques, deep learning has gained significant attention due to its ability to automatically learn relevant features from raw data. However, the performance of deep learning models in this domain has not been extensively compared to classical methods. This paper presents a comparative study of deep learning and classical methods for fault detection and diagnosis in electrical machines. The study evaluates the performance of various machine learning algorithms, including deep neural networks, support vector machines, decision trees, and ensemble methods, in detecting and diagnosing faults such as stator winding faults, rotor faults, and bearing faults. The experimental evaluation is conducted using real-world datasets obtained from electrical machines in industrial settings. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the effectiveness of each approach in detecting and diagnosing faults accurately and efficiently. The results of the study indicate that deep learning approaches, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), outperform classical methods in terms of fault detection and diagnosis accuracy. These deep learning models demonstrate the ability to automatically extract informative features from raw sensor data, enabling them to effectively identify subtle patterns indicative of faults. The study investigates the interpretability of deep learning models compared to classical methods, examining the extent to which the models can provide insights into the underlying causes of faults. While deep learning models typically operate as black boxes, techniques such as layer-wise relevance propagation (LRP) are employed to enhance their interpretability and facilitate the identification of relevant features contributing to fault detection and diagnosis. This comparative study provides valuable insights into the strengths and limitations of deep learning and classical methods for fault detection and diagnosis in electrical machines, offering guidance for practitioners and researchers in selecting appropriate approaches for their specific applications.
Image Classification by Transfer Learning using Pre-Trained CNN Models
Jaya H. Dewan, Rik Das, Sudeep D. Thepade, Harsh Jadhav, Nishant Narsale, Ajinkya Mhasawade, Sinu Nambiar
2023 International Conference on Recent Advances in Electrical Electronics Ubiquitous Communication and Computational Intelligence Raeeucci 2023, 2023
In the realm of computer vision, image classification is a critical issue with many applications, including multimedia content analysis, security and surveillance, and medical imaging. The accuracy of image classification algorithms has considerably increased with the development of deep learning. The discipline of image classification has dramatically benefited from the usage of pre-trained Convolutional Neural Network (CNN) models. In this study, we perform image classification on the Wang dataset composed of 1000 images separated into ten categories, using six different pre-trained CNN models. The research made use of pre-trained versions of VGG16, Densenet, Mobilenet, Inception V3, Resnet50, and Xception models. We assessed the model performances in terms of training and testing accuracy. Testing accuracies for ten unique batches of data were calculated and averaged out. Densenet outperformed state-of-the-art models like Xception by a small margin, mainly due to low data availability. The findings of this study show how pre-trained models have advanced the field of image classification and shed light on how well these models perform in diverse image classification tasks. This study can serve as a valuable reference for researchers and practitioners in the field of computer vision and deep learning and help inform their choices when selecting pre-trained models for image classification, depending on their needs, while also considering data availability and computational constraints.
Revolutionizing Agriculture: Mathematical Modelling for Plant Disease Diagnosis viaImage Processing and CNNs
Panamerican Mathematical Journal, 2023

RECENT SCHOLAR PUBLICATIONS

Design and implementation of quantum-resistant cryptographic algorithms in blockchain networks
S Deshmukh, S Nambiar, PS Patil, M Phatak
Journal of Discrete Mathematical Sciences & Cryptography 28 (5-A) , 2025
2025.0
Data-Driven Approach for OEE Enhancement in the Manufacturing Industry
RA Shravani Shalgar, Tanmay Landage, Prerana Jagtap, Sinu Nambiar, Sonali Potdar
International Journal for Research Trends and Innovation 10 (1), 311-316 , 2025
2025.0
‘Enhancing solar PV plant performance with digital twins: Leveraging data science and AI for real-time analysis
T Mane, A Kulkarni, O Shrotri, S Nambiar, M Potadar, A Kulkarni
Int. J. Res. Appl. Sci. Eng. Technol 11, 132-2454 , 2025
2025.0
Citations: 1
Revolutionizing Agriculture : Mathematical Modelling for Plant Disease Diagnosis via Image Processing and CNNs
M AjayVisave, MB Sahu, DS Nambiar, DS Solanki, M DigambarJadhav, ...
Panamerican Mathematical Journal 33 (4), 73-85 , 2024
2024.0
VIRTUAL TRY-ON SYSTEM USING MACHINE LEARNING
MS Nambiar, MH Limbani, MV Choudhari, MK Kadam, MS Tripathi
International Research Journal of Modernization in Engineering Technology … , 2024
2024.0
Blockchain Security Protocols: Enhancing the Resilience of Distributed Networks
DSN Dr. Padmavati Shrivastava, Dr. Mayur Jakhete, Dr.Leena Indise, Shaiqua ...
Computer Fraud and Security 2024 (8), 21-26 , 2024
2024.0
Machine Learning Approaches for Fault Detection and Diagnosis in Electrical Machines: A Comparative Study of Deep Learning and Classical Methods
DRM Gawande, DS Nambiar, S Shinde, DSS Banait, AV Sonawane, ...
Panamerican Mathematical Journal 34 (2), 121-137 , 2024
2024.0
Citations: 11
Future prospects and developments in human intelligence identification using artificial intelligence
MSJ Nambiar, PR Patil
计算机集成制造系统 29 (6) , 2023
2023.0
Citations: 3
Artificial Intelligence Algorithms For Future Prospects And Developments In Human Intelligence Identification
MSJ Nambiar, PR Patil
2023.0
Image classification by transfer learning using pre-trained CNN models
JH Dewan, R Das, SD Thepade, H Jadhav, N Narsale, A Mhasawade, ...
2023 International Conference on Recent Advances in Electrical, Electronics … , 2023
2023.0
Citations: 20
Mobile Botnet Detection
P Jadhav, A Mulla, G Bhoi, S Raj, S Nambiar
International Journal for Research in Applied Science & Engineering … , 2023
2023.0
Real Time Speech To Braille Converter
S Supnekar, M Narkhede, S Pawar, S Nambiar
Applied GIS 10 (3) , 2022
2022.0
Citations: 1
HUMAN INTELLIGENCE MAPPING USING ARTIFICIAL INTELLIGENCE
MSJ Nambiar, PR Patil
NeuroQuantology 20 (16), 440 , 2022
2022.0
Security Enhancing Using Motion Detection
A Barse, A Bhavnani, K Patil, P Patil, S Nambiar
2021.0
A Survey on IOT Powered Wearable Health Band
PSN Kunal Kasture, Siddharth Hatkar, Laxmi Sonikar, Priyanka Suryawanshi
Journal of Emerging Technologies and Innovative Research 6 (6), 59-62 , 2019
2019.0
IOT Based bridge safety monitoring system
G Agrawal, Y Jadhav, S Nair, A Kumar, S Nambiar
Int J Res Appl Sci Eng Technol 7, 2326-2331 , 2019
2019.0
Citations: 4
Converting Spoken Words into Braille in Real Time
S Supnekar, M Narkhede, S Pawar
Applied GIS 6 (4) , 2018
2018.0
Robust Approach for Product Label Reading
SV Chalikwar, S Nambiar
International Journal of Science and Research (IJSR) 4 (12), 1132-1135 , 2015
2015.0
Analysis Of Fault Tolerance Approach For Data Replication In Data Intensive Scientific Applications
S Nambiar, R Pandit, S Patel
International Journal of Advanced Research in Computer and Communication … , 2013
2013.0
Pre-Trained CNN Models
JH Dewan, H Jadhav, R Das, N Narsale, S Nambiar, SD Thepade, ...

MOST CITED SCHOLAR PUBLICATIONS

Image classification by transfer learning using pre-trained CNN models
JH Dewan, R Das, SD Thepade, H Jadhav, N Narsale, A Mhasawade, ...
2023 International Conference on Recent Advances in Electrical, Electronics … , 2023
2023.0
Citations: 20
Machine Learning Approaches for Fault Detection and Diagnosis in Electrical Machines: A Comparative Study of Deep Learning and Classical Methods
DRM Gawande, DS Nambiar, S Shinde, DSS Banait, AV Sonawane, ...
Panamerican Mathematical Journal 34 (2), 121-137 , 2024
2024.0
Citations: 11
IOT Based bridge safety monitoring system
G Agrawal, Y Jadhav, S Nair, A Kumar, S Nambiar
Int J Res Appl Sci Eng Technol 7, 2326-2331 , 2019
2019.0
Citations: 4
Future prospects and developments in human intelligence identification using artificial intelligence
MSJ Nambiar, PR Patil
计算机集成制造系统 29 (6) , 2023
2023.0
Citations: 3
‘Enhancing solar PV plant performance with digital twins: Leveraging data science and AI for real-time analysis
T Mane, A Kulkarni, O Shrotri, S Nambiar, M Potadar, A Kulkarni
Int. J. Res. Appl. Sci. Eng. Technol 11, 132-2454 , 2025
2025.0
Citations: 1
Real Time Speech To Braille Converter
S Supnekar, M Narkhede, S Pawar, S Nambiar
Applied GIS 10 (3) , 2022
2022.0
Citations: 1
Design and implementation of quantum-resistant cryptographic algorithms in blockchain networks
S Deshmukh, S Nambiar, PS Patil, M Phatak
Journal of Discrete Mathematical Sciences & Cryptography 28 (5-A) , 2025
2025.0
Data-Driven Approach for OEE Enhancement in the Manufacturing Industry
RA Shravani Shalgar, Tanmay Landage, Prerana Jagtap, Sinu Nambiar, Sonali Potdar
International Journal for Research Trends and Innovation 10 (1), 311-316 , 2025
2025.0
Revolutionizing Agriculture : Mathematical Modelling for Plant Disease Diagnosis via Image Processing and CNNs
M AjayVisave, MB Sahu, DS Nambiar, DS Solanki, M DigambarJadhav, ...
Panamerican Mathematical Journal 33 (4), 73-85 , 2024
2024.0
VIRTUAL TRY-ON SYSTEM USING MACHINE LEARNING
MS Nambiar, MH Limbani, MV Choudhari, MK Kadam, MS Tripathi
International Research Journal of Modernization in Engineering Technology … , 2024
2024.0
Blockchain Security Protocols: Enhancing the Resilience of Distributed Networks
DSN Dr. Padmavati Shrivastava, Dr. Mayur Jakhete, Dr.Leena Indise, Shaiqua ...
Computer Fraud and Security 2024 (8), 21-26 , 2024
2024.0
Artificial Intelligence Algorithms For Future Prospects And Developments In Human Intelligence Identification
MSJ Nambiar, PR Patil
2023.0
Mobile Botnet Detection
P Jadhav, A Mulla, G Bhoi, S Raj, S Nambiar
International Journal for Research in Applied Science & Engineering … , 2023
2023.0
HUMAN INTELLIGENCE MAPPING USING ARTIFICIAL INTELLIGENCE
MSJ Nambiar, PR Patil
NeuroQuantology 20 (16), 440 , 2022
2022.0
Security Enhancing Using Motion Detection
A Barse, A Bhavnani, K Patil, P Patil, S Nambiar
2021.0
A Survey on IOT Powered Wearable Health Band
PSN Kunal Kasture, Siddharth Hatkar, Laxmi Sonikar, Priyanka Suryawanshi
Journal of Emerging Technologies and Innovative Research 6 (6), 59-62 , 2019
2019.0
Converting Spoken Words into Braille in Real Time
S Supnekar, M Narkhede, S Pawar
Applied GIS 6 (4) , 2018
2018.0
Robust Approach for Product Label Reading
SV Chalikwar, S Nambiar
International Journal of Science and Research (IJSR) 4 (12), 1132-1135 , 2015
2015.0
Analysis Of Fault Tolerance Approach For Data Replication In Data Intensive Scientific Applications
S Nambiar, R Pandit, S Patel
International Journal of Advanced Research in Computer and Communication … , 2013
2013.0
Pre-Trained CNN Models
JH Dewan, H Jadhav, R Das, N Narsale, S Nambiar, SD Thepade, ...