Sahil Kailas Shah

@siu.edu.in

Assistant Professor in Computer Engineering
Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune

Sahil Kailas Shah


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https://researchid.co/sahil89
Sahil completed his Bachelor of Engineering in Information Technology in 2010 from Savitribai Phule Pune University with distinction. He completed his Masters in Computer Engineering in 2012 from Savitribai Phule Pune University with distinction. He has been an academic topper throughout his career. Sahil has around 12+ years of teaching experience at undergraduate level. He has also taught various courses at postgraduate level. He likes to learn and share recent technologies in the IT world. He has participated and received certificates of participation in various STTPs, ISTE workshops, Conferences, Seminars and Symposia’s. He has 08 International & 03 National Publications. He is currently associated with Symbiosis International(Deemed University), Pune as an Assistant Professor in Computer Engineering.

EDUCATION

3X Microsoft Certified
B.E.(Information Technology)
M.E. (Computer Engg.)
GATE(CSE) Qualified,
Fellow, IASc Bangalore

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems
24

Scopus Publications

79

Scholar Citations

6

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Sugarcane Crop Yield Prediction Using Supervised Machine Learning Techniques
    Vijayatai Hukare, Vidya Kumbhar, Sahil K. Shah
    SN Computer Science, 2026
  • Machine Learning-Based Approach for Sugarcane Crop Classification Using Spectral Signature from Sentinel-2 Data in Maharashtra, India
    Varad Purandare, Vidya Kumbhar, Sahil K. Shah, T. P. Singh, Shilpa Gite, Biswajeet Pradhan
    Smart Sensors Measurement and Instrumentation, 2026
  • Classification and Phenological Stage Monitoring of Grape Crop using Sentinel-1 and Sentinel-2 Time Series and Deep Learning Techniques
    Shweta Mukherjee, Sahil K. Shah, T. P. Singh, Vidya Kumbhar
    ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2025
    The demand for food production is increasing rapidly with a surge in the population. To cope with this increasing food demand, precise agricultural management is essential. The existing techniques involve extensive field surveys for agricultural land discrimination. To minimize the man-hour efforts and time required by these techniques, automated techniques for precise crop type mapping and monitoring have been used. These techniques utilize satellite imagery and advanced machine learning techniques for crop type mapping and monitoring. However, the performance of such techniques is affected by factors such as fragmented land parcels, seasonal variability, and inconsistent field-level observations. To overcome these issues, this study attempts to classify grape and non-grape crops and monitor their phenological stages in the study area in Pune district, India, using Sentinel-2 satellite imagery and deep learning (DL) segmentation techniques: U-Net and DeepLabV3. Further, Sentinel- 1C SAR imagery (VV and VH polarization) for the years 2016 to 2024 was utilized to train and evaluate a long short-term memory network (LSTM) model with an aim to analyze the temporal behavior of the grape crop from pruning to harvesting stage with emphasis on growth stages like leaf set, fruit set, and ripening. The experimental results demonstrate that U-Net outperforms DeepLabV3 (F1-score: 0.96; mAP: 0.95) in grape crop classification. The LSTM model showed performance (F1-score 0.82) for phenological stage identification. This study can help agricultural stakeholders in effective and large-scale crop discrimination with minimum human intervention. It has the potential to reveal grape distribution and development stages in a faster time.
  • Landfill Suitability Mapping using Satellite Imagery and Machine Learning Techniques: A Case Study for Delhi-NCR, India
    Kunal Tanwar, Palak Attri, Sahil K. Shah, T. P. Singh
    ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2025
    The improper disposal of municipal waste is a worldwide problem that is dangerous to the environment. They are responsible for soil, water, and air pollution, resulting in ecological imbalance. Thus, effective landfill site selection reduces these adverse effects and supports sustainable urban development. However, it is difficult to identify suitable landfill sites, especially in fast-growing urban areas, because of the complexity of adjusting various environmental, infrastructural, and demographic factors. Moreover, the unavailability of comprehensive datasets and reliable predictive models worsens the problem, leading to frequent suboptimal site selection. This study overcomes these difficulties by combining satellite imagery and machine learning (ML) techniques to develop an integrated web-based framework for assessing landfill site suitability in the Delhi-NCR region. A comprehensive dataset comprising features such as spectral features captured using Landsat-9 satellite imagery, Digital Elevation Model (DEM), Land Use/Land Cover (LULC), proximity to roads, railways, rivers, industries, restricted zones, and settlements, along with Land Surface Temperature (LST), population density, Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) was utilized for model development. The potential of ML techniques, such as random forest (RF) and extreme gradient boost (XGB), was assessed for landfill site classification. The experimental results show that RF outperforms XGB (F1-score:0.91, AUC:0.98). This study can help policymakers in sustainable waste management and provide a means for improved environmental sustainability with optimal landfill site selection in urbanizing areas.
  • Quantification of Sugarcane Crop Water Footprint Using Remote Sensing and Machine Learning Techniques: Case Study of Kolhapur District, Maharashtra, India
    Shrinivas Jadhav, Sahil K. Shah, Vidya Kumbhar, T. P. Singh
    ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2025
    Water footprint (WF) analysis assists in measuring the freshwater utilized by the crops, which provides information regarding sustainable water utilization. Growing water scarcity, irrigation requirements, and climatic variability emphasize the need to effectively monitor and regulate water resources. Considering the limited availability of water resources, estimating the water usage of crops such as sugarcane with high water requirements is necessary. This study aims to calculate the blue water footprint (BWF) and green water footprint (GWF) requirements of sugarcane crops using empirical methods and machine learning techniques in the Kolhapur district of India. By employing robust ground truth data and spectral signatures of Sentinel-2 satellite images, the sugarcane crop masks were identified using advanced machine learning techniques: random forest (RF), support vector machines (SVM), and logistic regression (LR). Furthermore, BWF and GWF were quantified for the identified sugarcane crop using empirical methods that utilized precipitation, evapotranspiration (ET), minimum temperature, and maximum temperature data for the years 2018 to 2023. Following these initial estimations, the potential of machine learning techniques was assessed for predicting WF. The efficacy of RF, support vector regression (SVR), gradient boosting regression (GBR), and artificial neural networks (ANN) was assessed by training and validating them based on the identified features. The RF model (R²:92) outperformed the other models in the precise prediction of sugarcane crop WF. The results show a lower WF in the northern and eastern talukas and a higher WF in the southern talukas of the district. This study can aid in the identification of water-stress areas and sustainable water resource management for sugarcane crops.
  • Analyzing and predicting PM₁₀ using remote sensing and machine learning: A case study of Delhi, India
    Masud Karim, Saakshi Tomar, Sahil K. Shah, T. P. Singh
    Discover Applied Sciences, 2025
    Airborne particulate matter (PM 10 ) has emerged as a critical indicator of deteriorating air quality, with significant implications for human health, urban sustainability, and climate across the globe. Given its strong association with respiratory and cardiovascular diseases, predicting PM 10 concentrations becomes vital for safeguarding both ecological balance and human well-being. In this context, this study attempts to predict and analyze the PM₁₀ concentrations in Delhi, India, for the year 2023 with the aid of remote sensing and supervised and unsupervised machine learning techniques. The dataset utilized for the analysis comprises meteorological parameters, land use data, environmental factors, and daily station-specific datasets, incorporating PM 10 concentrations and land surface temperature (LST). The selected parameters include ground-based measurements collected from the 39 monitoring stations of the Central Pollution Control Board (CPCB) across Delhi, aerosol optical depth (AOD), fine aerosol optical depth (FAOD), aerosol index (AI), and air humidity (AH), along with gaseous pollutants (O₃, NO₂, SO₂, CO), and wind speed. Moreover, the spectral signatures derived in the form of normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), built-up index (BUI), and urban thermal field variance index (UTFVI) were used for the predictive model development. The efficacy of six regressors, multiple linear regression (MLR), support vector regressor (SVR), random forest (RFR), stochastic gradient boosting regressor (SGB), extreme gradient boosting regressor (XGB), and categorical boosting regressor (CBR) was assessed for predicting PM₁₀ concentrations. The CBR model demonstrated an impressive performance (R² = 0.94; RMSE = 4.77 µg/m 3 ), surpassing the other regressors. The CBR model shows prominence in ordered boosting and capturing complex nonlinear relationships more effectively than conventional regression models. Further, the K‑Means clustering technique was used to delineate pollution hotspots across the city. The resulting maps reveal persistently elevated PM₁₀ in dense residential and industrial zones, offering actionable inputs for local air‑quality management and urban planning in Delhi. This study reinforces the importance of evidence-based approaches that align public health priorities with sustainable environmental policies.
  • Leveraging UAV Imagery and Deep Learning for Automated Object Detection
    Sohan Kanse, Vara Prasad Lingam, Sahil K. Shah, Vidya Kumbahr, T. P. Singh, Kumar Karunendra
    Journal Europeen Des Systemes Automatises, 2025
    India is one of the leading countries in rapid global infrastructure development.Road infrastructure is one of the major contributors to the same.This raises a need for the realtime maintenance of the developed infrastructure.In maintenance, precise identification and management of potholes are important, considering the safety of citizens.The current study presents a geo-intelligent framework for real-time detection of potholes.It uses advanced deep learning techniques such as PSP-Net and U-Net for pothole detection.It employs high-resolution unmanned aerial vehicle (UAV) imagery, digital surface model (DSM), along with training samples identified through annotations for model training and evaluation.Experimental results show that U-Net outperforms PSP-Net with an F1-score of 0.78, demonstrating high precision in pothole determination.This novel framework is further deployed in the form of a toolkit in the ESRI ArcGIS ecosystem.The two tools developed using the Python API were deployed for the determination of pothole volume and fill quantity estimation, respectively.The American Concrete Institute (ACI) approach was used to estimate the amount of repair materials needed for the identified potholes.The study helps in the reduction of man-hour efforts needed for lengthy field surveys for pothole identification.The Geo-Image Analytics toolbox offers a scalable solution for evolving urban infrastructure needs, marking a significant step forward in modernizing pothole management practices and the sustainability of the road infrastructure.
  • Generative AI-Driven Spatial Data Extraction in OpenStreetMap using Natural Language
    M.L. Vohra, T.P. Singh, K. Illayaraja, S.K. Shah
    International Journal of Geoinformatics, 2025
    With the rising availability and support of geospatial data and tools, geospatial data analysis is increasing rapidly. However, geospatial data is challenging to extract and understand by individuals with limited or no prior knowledge of handling such data. This study presents a novel platform that integrates generative artificial intelligence (Gen. AI) and prompt engineering techniques for geospatial data retrieval and analysis. This is achieved by firing natural language queries and integrating a generative pre-trained transformer GPT-3.5 for data retrieval and analysis. The platform translates unstructured natural language inputs into structured Overpass API queries, retrieving detailed geospatial data from OpenStreetMap (OSM). The system streamlines the process, from query to visualization, enabling users without technical geospatial expertise to access spatial information seamlessly. It supports geospatial data retrieval tasks such as Point of Interest (PoI) extraction, proximity queries, and attribute-based retrieval. The experimental results show that the proposed approach outperforms existing tools such as Google Earth Engine (GEE), GeoGPT, GeoInsight, MapQA, OSM-GPT with an average query-execution time of 17.3 seconds and an average accuracy of 95%. It shows a significant improvement in usability over manual Overpass query construction. The proposed framework achieves higher performance while maintaining a lightweight design that does not require model fine-tuning or external training data. Unlike existing tools that heavily rely on fine-tuned transformers with tightly coupled components, the proposed framework is modular, prompt-driven, and API-based, which enables its rapid deployment and minimal resource usage. This lightweight architecture helps to improve system maintainability, scalability, and makes it easily accessible for real-time applications and end-users with limited technical infrastructure. Overall, the framework offers a scalable, accessible, and extensible solution for spatial data querying in open-source GIS workflows. This study can transform conventional geospatial data analysis practices into a more inclusive and user-friendly approach that features a geointelligent environment.
  • DQN Trader: Reinforcement Learning for Automated Trading
    Avesh Kumar Bhati, Vidya Kumbhar, T. P. Singh, Sahil K. Shah
    Lecture Notes in Networks and Systems, 2025
  • Multimodal Intelligence Framework for Video Content Analysis
    Praveen Choudhary, Sahil K. Shah, Prashant Mane, Vidya Kumbhar, T. P. Singh
    2025 IEEE 6th Global Conference for Advancement in Technology Gcat 2025, 2025
    The exponential growth of enterprise meeting recordings has created a pressing need for intelligent systems that can extract, structure, and present key insights from video content in a timely manner. This study presents a multimodal intelligence framework that integrates audio (via Whisper ASR and speaker diarization) and visual streams (via OCR-enhanced keyframe extraction) with dynamic language model selection guided by the benchmarking tools for the generation of meeting summaries on the fly. The Proposed system automatically generates structured meeting summaries, supports Retrieval-Augmented Generation (Video-RAG) for natural language querying, and enables timestamped playback of relevant video segments. The developed model was validated against multiple Gemini large language models (LLMs) using a curated set of multiple-choice questions. The results demonstrate optimal model choice, ensuring higher accuracy in both documentation and interactive Q&A. Evaluations on 30 diverse meeting videos demonstrate over 90% reduction in redundant frame extraction and up to 54% precision in question answering, highlighting the framework's efficacy for enterprise knowledge management.
  • Grape (Vitis Vinifera) Leaf Disease Detection and Classification Using Deep Learning Techniques: A Study on Real-Time Grape Leaf Image Dataset in India
    S. K. Shah, V. Kumbhar, T. P. Singh
    International Journal of Engineering Transactions B Applications, 2024
  • Neural Network-Based Hybrid Recommendation System
    Bhuvan Singh Mangat, Sahil K. Shah, Vidya Kumbhar, T.P. Singh
    2024 5th International Conference for Emerging Technology Incet 2024, 2024
  • Enhancing Medical Image Quality Using Deep Learning Techniques
    Nikita Patil, Sahil K. Shah, Vidya Kumbhar, T. P. Singh
    2024 1st International Conference for Women in Computing Incowoco 2024 Proceedings, 2024
  • Optimizing Fertilizer Usage using Machine Learning Techniques
    Niyaj M. Attar, Sahil K. Shah, Vijayatai Hukare, Vidya Kumbhar, T. P. Singh
    2024 MIT Art Design and Technology School of Computing International Conference Mitadtsocicon 2024, 2024
  • Deep Learning-Driven Approaches for Early Detection of Lung Cancer
    Sahil Kumar Shah, Richa Jain, Vineet Yadav, Aditya Kumar, Pankaj Singh, Pulkit Tikmani
    Proceedings of International Conference on Communication Computer Sciences and Engineering Ic3se 2024, 2024
  • Analysis and Forecasting of Industrial Production of Non-Durable Food Items
    Harsh K. Thakur, Sahil K. Shah, Vidya Kumbhar
    2023 International Conference on Integration of Computational Intelligent System Icicis 2023, 2023
  • Machine Learning Methods for Crop Yield Prediction
    Vijayatai Hukare, Vidya Kumbhar, Sahil K. Shah
    Communications in Computer and Information Science, 2023
  • A Systematic Review on Crop Leaf Disease Identification Using Machine Learning and Deep Learning Techniques
    Sahil K. Shah, Vidya Kumbhar, T. P. Singh
    2023 7th International Conference on Computing Communication Control and Automation Iccubea 2023, 2023
  • Predicting Credit Risk in European P2P Lending: A Case Study of "Bondora" Using Supervised Machine Learning Techniques
    Sampurna Mondal, Sahil K. Shah, Vidya Kumbhar
    2023 4th IEEE Global Conference for Advancement in Technology Gcat 2023, 2023
  • Enhancing E-Commerce Insights: Sentiment Analysis Using Machine Learning and Ensemble Techniques
    Shubham Shedekar, Sahil K. Shah, Vidya Kumbhar
    2023 International Conference on Integration of Computational Intelligent System Icicis 2023, 2023
  • Deep Learning-Based Liver Tumor Segmentation: A Comparative Study of U-Net Variants for Medical Imaging Analysis
    Reetam Ghosh, Sahil K. Shah, Vidya Kumbhar
    2023 Global Conference on Information Technologies and Communications Gcitc 2023, 2023
  • Agricultural Field Boundary Delineation Using Deep Learning Techniques
    Arbaz Sayed, Vidya Kumbhar, Swapnil Jadhav, Sahil K. Shah
    2023 International Conference on Emerging Smart Computing and Informatics Esci 2023, 2023
  • Enhanced Convolutional Neural Network Model for Crop Disease Identification using UAV Imagery
    Mihir Singh, Sahil K. Shah, Vidya Kumbhar, T. P. Singh, Deepali Tomar
    2023 IEEE Pune Section International Conference Punecon 2023, 2023
  • An approach towards text detection from complex images using morphological techniques
    Vyankatesh V. Rampurkar, Sahil K. Shah, Gyankamal J. Chhajed, Sanjay Kumar Biswash
    Proceedings of the 2nd International Conference on Inventive Systems and Control Icisc 2018, 2018

RECENT SCHOLAR PUBLICATIONS

  • Machine Learning-Based Approach for Sugarcane Crop Classification Using Spectral Signature from Sentinel-2 Data in Maharashtra, India
    V Purandare, V Kumbhar, SK Shah, TP Singh, S Gite, B Pradhan
    Advancements in IoT Sensors and Security: Harnessing AI/ML for Secured IoT … , 2026
    2026
  • Sugarcane Crop Yield Prediction Using Supervised Machine Learning Techniques
    V Hukare, V Kumbhar, SK Shah
    SN Computer Science 7 (1), 67 , 2026
    2026
  • Database Management Systems
    SA Takale, SK Shah
    2026
  • Landfill Suitability Mapping using Satellite Imagery and Machine Learning Techniques: A Case Study for Delhi-NCR, India
    K Tanwar, P Attri, SK Shah, TP Singh
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2025
    2025
  • Quantification of Sugarcane Crop Water Footprint Using Remote Sensing and Machine Learning Techniques: Case Study of Kolhapur District, Maharashtra, India
    S Jadhav, SK Shah, V Kumbhar, TP Singh
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2025
    2025
  • Classification and Phenological Stage Monitoring of Grape Crop using Sentinel-1 and Sentinel-2 Time Series and Deep Learning Techniques
    S Mukherjee, SK Shah, TP Singh, V Kumbhar
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2025
    2025
    Citations: 1
  • Analyzing and predicting PM₁₀ using remote sensing and machine learning: A case study of Delhi, India
    M Karim, S Tomar, SK Shah, TP Singh
    Discover Applied Sciences 7 (11), 1291 , 2025
    2025
  • Multimodal Intelligence Framework for Video Content Analysis
    P Choudhary, SK Shah, P Mane, V Kumbhar, TP Singh
    2025 IEEE 6th Global Conference for Advancement in Technology (GCAT), 1-6 , 2025
    2025
  • Generative AI-Driven Spatial Data Extraction in OpenStreetMap using Natural Language
    MA Vohra, TP Singh, K Illayaraja, SK Shah
    International Journal of Geoinformatics 21 (6), 47-61 , 2025
    2025
    Citations: 3
  • Leveraging UAV Imagery and Deep Learning for Automated Object Detection
    S Kanse, VP Lingam, SK Shah, V Kumbahr, TP Singh, K Karunendra
    Journal Européen des Systèmes Automatisés 58 (7), 1417 , 2025
    2025
  • Enhancing Medical Image Quality Using Deep Learning Techniques
    N Patil, SK Shah, V Kumbhar, TP Singh
    2024 First International Conference for Women in Computing (InCoWoCo), 1-7 , 2024
    2024
    Citations: 1
  • Neural Network-Based Hybrid Recommendation System
    BS Mangat, SK Shah, V Kumbhar, TP Singh
    2024 5th International Conference for Emerging Technology (INCET), 1-8 , 2024
    2024
  • Optimizing fertilizer usage using machine learning techniques
    NM Attar, SK Shah, V Hukare, V Kumbhar, TP Singh
    2024 MIT Art, Design and Technology School of Computing International … , 2024
    2024
    Citations: 6
  • DQN Trader: Reinforcement Learning for Automated Trading
    AK Bhati, V Kumbhar, TP Singh, SK Shah
    International Conference on Computational Intelligence in Pattern … , 2024
    2024
  • Grape (Vitis vinifera) leaf disease detection and classification using deep learning techniques: a study on real-time grape leaf image dataset in India
    SK Shah, V Kumbhar, TP Singh
    International Journal of Engineering 37 (8), 1522-1533 , 2024
    2024
    Citations: 12
  • Enhanced Convolutional Neural Network Model for Crop Disease Identification using UAV Imagery
    M Singh, SK Shah, V Kumbhar, TP Singh, D Tomar
    2023 IEEE Pune Section International Conference (PuneCon), 1-7 , 2023
    2023
    Citations: 2
  • Deep learning-based liver tumor segmentation: A comparative study of U-Net variants for medical imaging analysis
    R Ghosh, SK Shah, V Kumbhar
    2023 Global Conference on Information Technologies and Communications (GCITC … , 2023
    2023
    Citations: 2
  • Predicting credit risk in european p2p lending: A case study of “bondora” using supervised machine learning techniques
    S Mondal, SK Shah, V Kumbhar
    2023 4th IEEE Global Conference for Advancement in Technology (GCAT), 1-6 , 2023
    2023
    Citations: 4
  • Machine Learning Methods for Crop Yield Prediction
    V Hukare, V Kumbhar, SK Shah
    Agriculture-Centric Computation: First International Conference, ICA 2023 … , 2023
    2023
    Citations: 8
  • A systematic review on crop leaf disease identification using machine learning and deep learning techniques
    SK Shah, V Kumbhar, TP Singh
    2023 7th International Conference On Computing, Communication, Control And … , 2023
    2023
    Citations: 4

MOST CITED SCHOLAR PUBLICATIONS

  • Grape (Vitis vinifera) leaf disease detection and classification using deep learning techniques: a study on real-time grape leaf image dataset in India
    SK Shah, V Kumbhar, TP Singh
    International Journal of Engineering 37 (8), 1522-1533 , 2024
    2024
    Citations: 12
  • An approach towards text detection from complex images using morphological techniques
    VV Rampurkar, SK Shah, GJ Chhajed, SK Biswash
    2018 2nd International Conference on Inventive Systems and Control (ICISC … , 2018
    2018
    Citations: 11
  • Different crop leaf disease detection using convolutional neural network
    A Pawar, M Singh, S Jadhav, V Kumbhar, TP Singh, SK Shah
    International Conference on Applications of Machine Intelligence and Data … , 2023
    2023
    Citations: 10
  • Machine Learning Methods for Crop Yield Prediction
    V Hukare, V Kumbhar, SK Shah
    Agriculture-Centric Computation: First International Conference, ICA 2023 … , 2023
    2023
    Citations: 8
  • Optimizing fertilizer usage using machine learning techniques
    NM Attar, SK Shah, V Hukare, V Kumbhar, TP Singh
    2024 MIT Art, Design and Technology School of Computing International … , 2024
    2024
    Citations: 6
  • An Intelligent Web Search Using Multi-Document Summarization
    SK Sheetal A. Takale, Prakash J. Kulkarni, Shah
    International Journal of Information Retrieval Research 2 (6), Pages 41-65 , 2016
    2016
    Citations: 6
  • Predicting credit risk in european p2p lending: A case study of “bondora” using supervised machine learning techniques
    S Mondal, SK Shah, V Kumbhar
    2023 4th IEEE Global Conference for Advancement in Technology (GCAT), 1-6 , 2023
    2023
    Citations: 4
  • A systematic review on crop leaf disease identification using machine learning and deep learning techniques
    SK Shah, V Kumbhar, TP Singh
    2023 7th International Conference On Computing, Communication, Control And … , 2023
    2023
    Citations: 4
  • Generative AI-Driven Spatial Data Extraction in OpenStreetMap using Natural Language
    MA Vohra, TP Singh, K Illayaraja, SK Shah
    International Journal of Geoinformatics 21 (6), 47-61 , 2025
    2025
    Citations: 3
  • Enhanced Convolutional Neural Network Model for Crop Disease Identification using UAV Imagery
    M Singh, SK Shah, V Kumbhar, TP Singh, D Tomar
    2023 IEEE Pune Section International Conference (PuneCon), 1-7 , 2023
    2023
    Citations: 2
  • Deep learning-based liver tumor segmentation: A comparative study of U-Net variants for medical imaging analysis
    R Ghosh, SK Shah, V Kumbhar
    2023 Global Conference on Information Technologies and Communications (GCITC … , 2023
    2023
    Citations: 2
  • Agricultural Field Boundary Delineation Using Deep Learning Techniques
    A Sayed, V Kumbhar, S Jadhav, SK Shah
    2023 International Conference on Emerging Smart Computing and Informatics … , 2023
    2023
    Citations: 2
  • Enhancing E-Commerce Insights: Sentiment Analysis Using Machine Learning and Ensemble Techniques
    S Shedekar, SK Shah, V Kumbhar
    2023 International Conference on Integration of Computational Intelligent … , 2023
    2023
    Citations: 2
  • Analysis and Forecasting of Industrial Production of Non-Durable Food Items
    HK Thakur, SK Shah, V Kumbhar
    2023 International Conference on Integration of Computational Intelligent … , 2023
    2023
    Citations: 2
  • Search Engine Based Intelligent Help Desk System: iAssist
    SK Shah, SA Takale
    Proceedings of the IEEE International Conference on Advanced Research in … , 2013
    2013
    Citations: 2
  • Classification and Phenological Stage Monitoring of Grape Crop using Sentinel-1 and Sentinel-2 Time Series and Deep Learning Techniques
    S Mukherjee, SK Shah, TP Singh, V Kumbhar
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2025
    2025
    Citations: 1
  • Enhancing Medical Image Quality Using Deep Learning Techniques
    N Patil, SK Shah, V Kumbhar, TP Singh
    2024 First International Conference for Women in Computing (InCoWoCo), 1-7 , 2024
    2024
    Citations: 1
  • Review on text string detection from natural scenes
    RV Vijaykumar, GJ Chhajed, SK Shah
    Certified International Journal of Engineering and Innovative Technology … , 2012
    2012
    Citations: 1
  • Machine Learning-Based Approach for Sugarcane Crop Classification Using Spectral Signature from Sentinel-2 Data in Maharashtra, India
    V Purandare, V Kumbhar, SK Shah, TP Singh, S Gite, B Pradhan
    Advancements in IoT Sensors and Security: Harnessing AI/ML for Secured IoT … , 2026
    2026
  • Sugarcane Crop Yield Prediction Using Supervised Machine Learning Techniques
    V Hukare, V Kumbhar, SK Shah
    SN Computer Science 7 (1), 67 , 2026
    2026