Dr. Santosh Rao Borde

@jspmrscoe.edu.in

Assistant Director - SP&IR
JSPM's Rajarshi Shahu College Of Engineering, Tathewadi

Dr. Santosh Rao Borde


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https://researchid.co/santoshborde

RESEARCH, TEACHING, or OTHER INTERESTS

Human-Computer Interaction, Multidisciplinary, Plant Science, Human Factors and Ergonomics
16

Scopus Publications

115

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • An Edge-Intelligent Predictive Maintenance System for Underwater Data Centers Using Multi-Domain Sensor Fusion
    Alka Suryawanshi, Santosh Borde, Sonali Rangdale, Navnath Kale
    International Journal of Drug Delivery Technology, 2026
    Underwater data centers are increasingly considered for deploying computing infrastructure in constrained environments; however, maintaining such systems after deployment remains challenging due to limited physical access and harsh operating conditions. In many cases, faults are detected only after noticeable degradation or service disruption has already occurred. Most existing monitoring approaches remain reactive in nature or focus mainly on cooling and protection, which provides limited support for identifying early-stage degradation. In this work, an edge-intelligent predictive maintenance system for underwater data centers is presented based on multi-domain sensor fusion. The system integrates pressure, temperature, conductivity, moisture, vibration, acoustic, turbidity, and electrical health sensors, with data processing performed directly on an embedded edge platform. A Raspberry Pi is used to acquire sensor data and carry out preprocessing, feature extraction, and time-series analysis locally. Machine learning methods are employed to learn normal operating behavior and to identify abnormal patterns across correlated sensor signals. Instead of relying on fixed threshold limits, the proposed approach evaluates combined sensor trends to support condition-based maintenance decisions. Experimental evaluation under simulated underwater conditions indicates that multi-sensor analysis can provide earlier and more reliable fault indication when compared with single-sensor monitoring approaches. Overall, the proposed system demonstrates a practical and scalable predictive maintenance framework for underwater data center environments and supports improved long-term reliability through embedded intelligence and data-driven condition monitoring.
  • Time Series Analysis of Inter-Beat Intervals: Arima Forecasting and Hybrid CNN-KNN Approach
    Aryani Gangadhara, Santosh Borde
    2025 2nd International Conference on Integration of Computational Intelligent System Icicis 2025, 2025
    The heart rate is a basic indicator of human body; it is significant to measure the heart rate uninterruptedly, consistently, and precisely. However, due to limitations in sensors measurement, it is difficult to measure the heart rate in diverse circumstances. To address this, we forecast inter beat intervals (IBI) to measure heart rate using various models. stochastic univariate time series models are used to forecast future times stamps on the learning from preceding time lags, assuming equally spaced intervals. Time series analysis is an important but challenging task in machine learning. In this study, we compared two methodologies applied to IBI data: stochastic processes (ARIMA models) and deep learning models. These approaches hold great assurance in time series analysis. The proposed model's performance was compared with stochastic models namely Naïve Bayes and ARIMA, as well as machine learning models such as CNN and KNN. Experimental results demonstrate that the ARIMA model achieved a Mean Absolute Error (MAE) 27 outperforming classic models in prediction accuracy. K-fold validation is used for all the models. Among deep learning models, proposed model given the lowest MAE of 0.054, surpassing advanced models like CNN and KNN. These results highlight that while stochastic models outperform the traditional models, the proposed model offers superior performance among deep learning approaches, achieving highest accuracy in IBI prediction
  • A Comprehensive Review of Underwater Data Centers: Challenges, Technologies, and Future Directions
    Alka B Suryawanshi, Santosh Borde, Navnath Kale, Sonali Rangdale
    2025 2nd International Conference on Integration of Computational Intelligent System Icicis 2025, 2025
    With the rapid expansion of digital data and the increasing need for sustainable, energy-efficient computing solutions, underwater data centers (UDCs) have emerged as a promising innovation. These submerged facilities offer significant benefits, such as efficient natural cooling from ocean waters, minimal land use, and close proximity to coastal populations, which helps reduce data transmission latency. This paper provides an in-depth review of UDCs, focusing on their architectural design, deployment methods, and technological operations. It highlights major challenges including deep-sea pressure, corrosion, limited maintenance access, energy provisioning, and network connectivity. Additionally, the study examines how Internet of Things (IoT) technologies contribute to improved real-time monitoring, automation, and predictive maintenance in underwater environments. Current developments such as modular structures, the integration of renewable energy, and the adoption of edge computing are also explored. The paper concludes by discussing future research directions and the role of UDCs in advancing eco-friendly and resilient data infrastructure [2].
  • Integrated Agricultural Decision Support System Leveraging Random Forest for Crop Prediction and EfficientNet B0 for Disease Prediction
    Dipmala Salunke, Rutwik Shinde, Tejas Chechar, Ajay Biradar, Kiran Patil, Santosh Borde, Sonali Rangadale, Pallavi Tekade
    Lecture Notes in Networks and Systems, 2025
  • Pothole detection model for road safety using computer vision and machine learning
    Vijaykumar S Bidve, Kiran S Kakakde, Rahul H Bhole, Pakiriswamy Sarasu, Ashfaq Shaikh, Pradnya Mehta, Santosh Borde, Shailesh Kediya
    Iaes International Journal of Artificial Intelligence, 2024
    <div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Potholes pose significant threats to vehicular movement, causing damage to vehicles and risking the safety of drivers and pedestrians. The escalating issue of potholes has led to substantial financial losses for vehicle owners and drivers. Traditional methods of pothole detection are impractical, necessitating an innovative approach. The study focuses on implementing a detection system capable of accurately identifying potholes, empowering vehicles to adapt their speed or halt to prevent damage. The transformative solution presented in this research leverages cutting-edge technologies, specifically computer vision and machine learning, aiming to enhance road safety and streamline maintenance efforts. By addressing the interdependence of modern civilization on road networks, the Pothole Detection Model promises improved road safety, efficient maintenance practices, and the emergence of an era in intelligent transportation systems. The integration of technology into transportation infrastructure highlights the proactive measures needed to combat road imperfections, ensuring a safer and more efficient road network for the benefit of society.</p></td></tr></tbody></table></div>
  • Transfer learning based leaf disease detection model using convolution neural network
    Rahul Raut, Vijaykumar Bidve, Pakiriswamy Sarasu, Kiran Shrimant Kakade, Ashfaq Shaikh, Shailesh Kediya, Santosh Borde, Ganesh Pakle
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
    The plants are attacked from various micro-organisms, bacterial illnesses, and pests. The signs are normally identified via leaves, stem, or fruit inspection. Illnesses that generally appeared on vegetation are from leaves and causes big harm if not managed in the early ranges. To stop this huge harm and manipulate the unfold of disorder this work implements a software system. This research work customs deep neural network to gain knowledge of probable illnesses on leaves within the early phases so it can be stopped early. Deep neural network (DNN) used for image classification. This work mainly focuses a neural network model of leaves ailment detection. The commonly available plant leaves dataset is undertaken with a dataset having special training of disease detection. In this work VGG16, ResNet50, Inception V3 and Inception ResNetV2 architectural techniques are implemented to generate and compare the results. Results are compared on the factors like precision, accuracy, recall and F1-Score. The results lead to the conclusion, that the convolution neural network (CNN) is more impactful technique to perceive and predict plant diseases.
  • Use of explainable AI to interpret the results of NLP models for sentimental analysis
    Vijaykumar Bidve, Pathan Mohd Shafi, Pakiriswamy Sarasu, Aruna Pavate, Ashfaq Shaikh, Santosh Borde, Veer Bhadra Pratap Singh, Rahul Raut
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
    The use of artificial intelligence (AI) systems is significantly increased in the past few years. AI system is expected to provide accurate predictions and it is also crucial that the decisions made by the AI systems are humanly interpretable i.e. anyone must be able to understand and comprehend the results produced by the AI system. AI systems are being implemented even for simple decision support and are easily accessible to the common man on the tip of their fingers. The increase in usage of AI has come with its own limitation, i.e. its interpretability. This work contributes towards the use of explainability methods such as local interpretable model-agnostic explanations (LIME) to interpret the results of various black box models. The conclusion is that, the bidirectional long short-term memory (LSTM) model is superior for sentiment analysis. The operations of a random forest classifier, a black box model, using explainable artificial intelligence (XAI) techniques like LIME is used in this work. The features used by the random forest model for classification are not entirely correct. The use of LIME made this possible. The proposed model can be used to enhance performance, which raises the trustworthiness and legitimacy of AI systems.
  • Drone View Segmentation: Deep Learning and Transfer Insights
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Pioneering the Future of Crowdfunding with Blockchain
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • Blockchain Powered Automated Crowdfunding via Smart Contracts
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • A Comparative Analysis of ARIMA and VARAlgorithms for Performance Analysis of High-Speed Diesel Pumps
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Personalized Stress Mitigation Through EEG Based Stress Classification and Music Recommendation
    Janhavi Patil, Nihar M. Ranjan, Prajakta Dange, Arpita Patil, Dipmala Salunke, Santosh Borde
    Lecture Notes in Networks and Systems, 2024
  • A Diabetic Retinopathy Detection Using Customized Convolutional Neural Network
    Deepak Mane, Sunil Sangve, Prashant Kumbharkar, Snehal Ratnaparkhi, Gopal Upadhye, Santosh Borde
    International Journal of Electrical and Electronics Research, 2023
  • Zero Trust Security Paradigm: A Comprehensive Survey and Research Analysis
    Et al. Shaikh Ashfaq
    Journal of Electrical Systems, 2023
  • A research of adaptation for E-learning system by learning preferences
    Yogesh Kumar, Professors Sharma
    International Journal of Recent Technology and Engineering, 2019
  • ICT Gadget: Design of e-learning system for rural community
    International Journal of Applied Environmental Sciences, 2009

RECENT SCHOLAR PUBLICATIONS

  • A Comprehensive Review of Underwater Data Centers: Challenges, Technologies, and Future Directions
    AB Suryawanshi, S Borde, N Kale, S Rangdale
    2025 2nd International Conference on Integration of Computational … , 2025
    2025
  • Time Series Analysis of Inter-Beat Intervals: Arima Forecasting and Hybrid CNN-KNN Approach
    A Gangadhara, S Borde
    2025 2nd International Conference on Integration of Computational … , 2025
    2025
  • Transfer learning based leaf disease detection model using convolution neural network
    R Raut, V Bidve, P Sarasu, KS Kakade, A Shaikh, S Kediya, G Pakle
    Indonesian Journal of Electrical Engineering and Computer Science 36 (3 … , 2024
    2024
    Citations: 21
  • Use of explainable AI to interpret the results of NLP models for sentimental analysis
    V Bidve, PM Shafi, P Sarasu, A Pavate, A Shaikh, S Borde, VBP Singh, ...
    Indonesian Journal of Electrical Engineering and Computer Science 35 (1 … , 2024
    2024
    Citations: 21
  • A comprehensive survey On Real Time Crowd Detection And Management Using Vineland Social Maturity Scale: Deep Learning Study
    S Rangdale, N Raykar, S Borde, P Kumbharkar
    International Journal of Digital Technologies 3 (I) , 2024
    2024
  • Improved CNN Based Sign Langiage Recognition For Specially Disabled People
    S Rangdale, N Raykar, P Kumbharkar, S Borde
    International Journal of Digital Technologies 3 (I) , 2024
    2024
    Citations: 1
  • Online Voting Platform Using Blockchain Technologies
    S Rangdale, N Raykar, S Borde, P Kumbharkar
    International Journal of Digital Technologies 3 (I) , 2024
    2024
    Citations: 1
  • Intelligent Retail Shopping System Based on RFID
    S Rangdale, N Raykar, S Borde, P Kumbharkar
    International Journal of Digital Technologies 3 (I) , 2024
    2024
    Citations: 1
  • Blockchain Powered Automated Crowdfunding via Smart Contracts.
    PC Kamble, S Borde, AC Kamble
    Grenze International Journal of Engineering & Technology (GIJET) 10 , 2024
    2024
  • Integrated Agricultural Decision Support System Leveraging Random Forest for Crop Prediction and EfficientNet B0 for Disease Prediction
    D Salunke, R Shinde, T Chechar, A Biradar, K Patil, S Borde, ...
    International Conference on Intelligent Communication, Control and Devices … , 2024
    2024
    Citations: 2
  • Personalized stress mitigation through eeg based stress classification and music recommendation
    J Patil, NM Ranjan, P Dange, A Patil, D Salunke, S Borde
    International Conference on Computing and Machine Learning, 181-191 , 2024
    2024
    Citations: 2
  • Pothole detection model for road safety using computer vision and machine learning
    VS Bidve, KS Kakade, RH Bhole, P Sarasu, A Shaikh, PS Mehta, ...
    Int J Artif Intell ISSN 2252 (8938), 4481 , 2024
    2024
    Citations: 12
  • A Comparative Analysis of ARIMA and VAR Algorithms for Performance Analysis of High-Speed Diesel Pumps
    SB Smita Mahajan1*, Shivali Amit Wagle2, Nihar Ranjan3
    International Journal ofINTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING … , 2024
    2024
  • Pioneering the Future of Crowdfunding with Blockchain
    SBACK Prajwal Chandrakant Kamble
    GRENZE International Journal of Engineering and Technology 10 (4), 6 , 2024
    2024
  • Blockchain Powered Automated Crowdfunding via Smart Contracts
    S Borde
    GRENZE International Journal of Engineer 10 (2), 848-855 , 2024
    2024
  • A diabetic retinopathy detection using customized convolutional neural network
    D Mane, S Sangve, P Kumbharkar, S Ratnaparkhi, G Upadhye, S Borde
    International Journal of Electrical and Electronics Research 11 (2), 609-615 , 2023
    2023
    Citations: 7
  • Diabetic Retinopathy Detection using Deep Learning.
    D Mane, R Ashtagi, R Jotrao, P Bhise, P Shinde, P Kadam
    Journal of Electrical Systems 19 (2) , 2023
    2023
    Citations: 3
  • Zero Trust Security Paradigm: A Comprehensive Survey and Research Analysis.
    S Ashfaq, SA Patil, S Borde, P Chandre, PM Shafi, A Jadhav
    Journal of Electrical Systems 19 (2) , 2023
    2023
    Citations: 31
  • Drone View Segmentation: Deep Learning and Transfer Insights
    International Journal of Intelligent Systems and Applications in Engineering … , 2023
    2023
  • Smart Event Management Platform UsingMachine Learning.
    A Singh, SU Kadam, S Borde, N Dixit
    NeuroQuantology 20 (12), 1548-1552 , 2022
    2022
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Zero Trust Security Paradigm: A Comprehensive Survey and Research Analysis.
    S Ashfaq, SA Patil, S Borde, P Chandre, PM Shafi, A Jadhav
    Journal of Electrical Systems 19 (2) , 2023
    2023
    Citations: 31
  • Transfer learning based leaf disease detection model using convolution neural network
    R Raut, V Bidve, P Sarasu, KS Kakade, A Shaikh, S Kediya, G Pakle
    Indonesian Journal of Electrical Engineering and Computer Science 36 (3 … , 2024
    2024
    Citations: 21
  • Use of explainable AI to interpret the results of NLP models for sentimental analysis
    V Bidve, PM Shafi, P Sarasu, A Pavate, A Shaikh, S Borde, VBP Singh, ...
    Indonesian Journal of Electrical Engineering and Computer Science 35 (1 … , 2024
    2024
    Citations: 21
  • Pothole detection model for road safety using computer vision and machine learning
    VS Bidve, KS Kakade, RH Bhole, P Sarasu, A Shaikh, PS Mehta, ...
    Int J Artif Intell ISSN 2252 (8938), 4481 , 2024
    2024
    Citations: 12
  • Vehicle-type classification using customized fuzzy convolutional neural network
    DT Mane, PB Kumbharkar, PS Dhotre, S Borde
    Data Engineering and Intelligent Computing: Proceedings of ICICC 2020, 419-429 , 2021
    2021
    Citations: 9
  • A diabetic retinopathy detection using customized convolutional neural network
    D Mane, S Sangve, P Kumbharkar, S Ratnaparkhi, G Upadhye, S Borde
    International Journal of Electrical and Electronics Research 11 (2), 609-615 , 2023
    2023
    Citations: 7
  • Diabetic Retinopathy Detection using Deep Learning.
    D Mane, R Ashtagi, R Jotrao, P Bhise, P Shinde, P Kadam
    Journal of Electrical Systems 19 (2) , 2023
    2023
    Citations: 3
  • A research of adaptation for E-learning system by learning preferences
    YK Sharma, SP Borde
    Int. J. Recent Technol. Eng.(IJRTE) 8 (2S11), 919-923 , 2019
    2019
    Citations: 3
  • Integrated Agricultural Decision Support System Leveraging Random Forest for Crop Prediction and EfficientNet B0 for Disease Prediction
    D Salunke, R Shinde, T Chechar, A Biradar, K Patil, S Borde, ...
    International Conference on Intelligent Communication, Control and Devices … , 2024
    2024
    Citations: 2
  • Personalized stress mitigation through eeg based stress classification and music recommendation
    J Patil, NM Ranjan, P Dange, A Patil, D Salunke, S Borde
    International Conference on Computing and Machine Learning, 181-191 , 2024
    2024
    Citations: 2
  • Improved CNN Based Sign Langiage Recognition For Specially Disabled People
    S Rangdale, N Raykar, P Kumbharkar, S Borde
    International Journal of Digital Technologies 3 (I) , 2024
    2024
    Citations: 1
  • Online Voting Platform Using Blockchain Technologies
    S Rangdale, N Raykar, S Borde, P Kumbharkar
    International Journal of Digital Technologies 3 (I) , 2024
    2024
    Citations: 1
  • Intelligent Retail Shopping System Based on RFID
    S Rangdale, N Raykar, S Borde, P Kumbharkar
    International Journal of Digital Technologies 3 (I) , 2024
    2024
    Citations: 1
  • Smart Event Management Platform UsingMachine Learning.
    A Singh, SU Kadam, S Borde, N Dixit
    NeuroQuantology 20 (12), 1548-1552 , 2022
    2022
    Citations: 1
  • A Comprehensive Review of Underwater Data Centers: Challenges, Technologies, and Future Directions
    AB Suryawanshi, S Borde, N Kale, S Rangdale
    2025 2nd International Conference on Integration of Computational … , 2025
    2025
  • Time Series Analysis of Inter-Beat Intervals: Arima Forecasting and Hybrid CNN-KNN Approach
    A Gangadhara, S Borde
    2025 2nd International Conference on Integration of Computational … , 2025
    2025
  • A comprehensive survey On Real Time Crowd Detection And Management Using Vineland Social Maturity Scale: Deep Learning Study
    S Rangdale, N Raykar, S Borde, P Kumbharkar
    International Journal of Digital Technologies 3 (I) , 2024
    2024
  • Blockchain Powered Automated Crowdfunding via Smart Contracts.
    PC Kamble, S Borde, AC Kamble
    Grenze International Journal of Engineering & Technology (GIJET) 10 , 2024
    2024
  • A Comparative Analysis of ARIMA and VAR Algorithms for Performance Analysis of High-Speed Diesel Pumps
    SB Smita Mahajan1*, Shivali Amit Wagle2, Nihar Ranjan3
    International Journal ofINTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING … , 2024
    2024
  • Pioneering the Future of Crowdfunding with Blockchain
    SBACK Prajwal Chandrakant Kamble
    GRENZE International Journal of Engineering and Technology 10 (4), 6 , 2024
    2024