Sahil Kailas Shah

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

Scopus Publications

Enhancing Medical Image Quality Using Deep Learning Techniques
Nikita Patil, Sahil K. Shah, Vidya Kumbhar, and T. P. Singh
IEEE
With the advent of advanced imaging technologies and the growth of digitization, the use of medical images for the precise diagnosis of patients’ health conditions is increasing daily. However, medical images generated using traditional X-ray machines produce blurred or low-resolution images owing to poor lighting or machines that require maintenance. Medical practitioners face challenges while diagnosing diseases/injuries in certain body parts due to such low-resolution images. This can lead to flawed diagnoses and misleading treatment. To address these challenges, this study uses state-of-the-art deep learning techniques to convert low-resolution images to super-resolution (SR) images. This study focuses on the training and evaluation of two deep learning models, superresolution convolutional neural network (SRCNN) and very deep super-resolution (VDSR), on a benchmark dataset of low-resolution X-ray images. Through rigorous experimentation, the hyperparameters pertaining to these two models were carefully optimized to achieve maximum performance. The performance of both models was assessed using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The experimental results show that the VDSR model outperformed SRCNN, achieving a PSNR of 98.3302 and SSIM of 1.0000, compared with SRCNN's PSNR of 43.5399 and SSIM of 0.9964. The higher value of PSNR in VDSR indicates the model's capability to effectively reduce noise and enhance the image quality. Compared with traditional image enhancement techniques, these deep learning models offer more precise and reliable results, enabling faster and more accurate diagnoses. This study provides insights into the strengths and weaknesses of each model, and aims to contribute to the development of better technologies for quicker and more accurate patient diagnosis.


Neural Network-Based Hybrid Recommendation System
Bhuvan Singh Mangat, Sahil K. Shah, Vidya Kumbhar, and T.P. Singh
IEEE
The main goal of this study is to improve the user experience by providing personalized product recommendations based on user preferences and behaviors. The study examines the various methods and frameworks commonly used in e-commerce recommendation processes, including collaborative filtering, content filtering, and using these methods in neural networks system. Additionally, the proposed algorithm solves the “cold start problem” by newly visited users. Results obtained by proposed approach that use popularity-based algorithm to create the initial set of recommendations highlights how effective the system is at providing accurate and relevant recommendations to users, ultimately increasing customer satisfaction and sales conversion rates of supply. These findings and insights contribute to the development of e-commerce recommendation systems, and present possible avenues for future research and development.


Deep Learning-Driven Approaches for Early Detection of Lung Cancer
Sahil Kumar Shah, Richa Jain, Vineet Yadav, Aditya Kumar, Pankaj Singh, and Pulkit Tikmani
IEEE
Detecting lung cancer sooner is very much essential for improving the survival rates and reducing the mortality rate as well. This study offers an extensive approach for detecting stages of lung cancer as early as possible from CT scan images using Deep learning. Data set used in this research consists of CT scans of healthy individuals as well as of those diagnosed with various stages of lung cancer. Elastic transformation is used as preprocessing to address class imbalance. Data augmentation is applied to improve model generalization after splitting the data into training, testing and validation. Three pre-trained convolutional neural network architecture (DenseNet201, VGG16, EfficientNetB7) are employed, with transfer learning utilized to fine-tune the models for lung cancer multi-class classification. In the end, weighted CNN ensemble is implemented to combine the predictions of individual models. Popular deep learning metrics such as accuracy, recall, f1-score and precision is used to access the models performance. Final result showcases effectiveness of proposed deep learning model, with the weighted ensemble method achieving an accuracy of 98.19% on the test data. Future work includes exploring different hyper parameter tuning, incorporating additional CNN architectures, and further preprocessing techniques to enhance model accuracy.


Optimizing Fertilizer Usage using Machine Learning Techniques
Niyaj M. Attar, Sahil K. Shah, Vijayatai Hukare, Vidya Kumbhar, and T. P. Singh
IEEE
India is a leading force in agriculture, responsible for more than 80% of the global crop production. However, it experiences challenges like as droughts and floods that damage the crops. Many farmers cultivate a single crop continuously and apply fertilizers without a clear understanding of its composition. This study explores the application of advanced ML techniques to determine the optimal fertilizers for agricultural use. We evaluated three commonly used techniques: Decision Trees, Random Forest, and Multilinear Regression, to forecast the most appropriate fertilizers: Nitrogen, Phosphorus, and Potassium that should be applied. ML techniques were evaluated for individual fertilizer recommendations (N, P, and K) as well as a combined recommendation approach. It is observed that RF achieved an accuracy of 93.68%, followed by DT at 89%, but MLR underperformed with an accuracy below 50%. This study attempts to assist farmers in increasing their crop yields by providing tailored guidance on fertilizer use. As evident from the obtained results, this study shows the potential to significantly enhance the sustainability of farming and guarantee sufficient food supply on a global scale.


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, and T. P. Singh
International Digital Organization for Scientific Information (IDOSI)



Deep Learning-Based Liver Tumor Segmentation: A Comparative Study of U-Net Variants for Medical Imaging Analysis
Reetam Ghosh, Sahil K. Shah, and Vidya Kumbhar
IEEE
Liver tumor segmentation(LiTS) is a critical task in medical imaging analysis, playing a key role in the early detection, diagnosis, and treatment planning of liver diseases. Accurate segmentation of liver tumors from medical images is vital for quantifying tumor size, monitoring disease progression, and assessing treatment outcomes. However, manual segmentation is time-consuming and subjective, highlighting the need for automated and reliable deep learning-based methods. This study aims to evaluate and compare the performance of deep learning architectures: U-Net and its variants (U-Net, U-Net++, and newly introduced U-Net Square, and U-Net Cube) for segmenting liver tumors using medical imaging data. Each variant’s unique architectural modifications are examined, emphasizing their potential benefits for liver tumor segmentation tasks. The efficacy and performance of these models for detecting liver tumors is analyzed using metrics like Dice Coefficient. It is observed that basic U-Net outperforms other architectures in liver tumor segmentation. This study further provides potential implications for enhanced diagnostic accuracy and treatment planning in clinical practice. The results contribute valuable insights into the strengths and limitations of U-Net variants, assisting researchers in selecting optimal deep-learning models for liver tumor segmentation.


Enhanced Convolutional Neural Network Model for Crop Disease Identification using UAV Imagery
Mihir Singh, Sahil K. Shah, Vidya Kumbhar, T. P. Singh, and Deepali Tomar
IEEE
With the rising growth in population, the need for food production is increasing day by day. This fosters the need for sustainable food production. However, agricultural products are majorly hampered by diseases on crops. To handle such diseases, automated crop disease identification systems can play a pivotal role. This study attempts to provide a comprehensive exploration of the application of geo-intelligent techniques for automated disease identification. The study presents Northern Leaf blight (NLB) disease recognition in Maize crops using advanced deep-learning techniques applied on high-resolution maize crop leaf imagery obtained through UAVs. Deep learning techniques involving sequential model and transfer learning approaches were explored on high-resolution imagery to classify maize leaves into diseased and healthy categories. It is observed that transfer learning models like Xception performed well in comparison with other models for precise disease identification. Additionally, the study has also explored the potential of YOLOv8 which demonstrates minimal loss with comparable performance. An accuracy of 81.1%, 80.7%, 80.3%, 78.9%, 78.8%, and 75.9% is observed using Xception, CNN, ResNet50, NASNet Mobile, VGG16, and YOLOv8, respectively. This study contributes valuable insights into automated NLB recognition and also holds implications for revolutionizing disease detection across the broader landscape of Indian agriculture. The utilization of advanced deep-learning methodologies offers a promising avenue for safeguarding crop health and enhancing food security in the nation.


A Systematic Review on Crop Leaf Disease Identification Using Machine Learning and Deep Learning Techniques
Sahil K. Shah, Vidya Kumbhar, and T. P. Singh
IEEE
World Population is increasing drastically and is expected to be around 12.3 billion in 2100. With the increase in population, it becomes important to foster the basic needs of mankind suitably. To withstand larger food requirements, getting higher crop yields is important. However, challenges like crop diseases, environmental factors like soil type, soil moisture, nutrient contents in soil, climate change, etc., affect various crop yields severely. The modern technologies can help in minimizing the adverse effects of these factors on crop yields. The advent of advanced automation techniques in Artificial Intelligence (AI) & Machine Learning (ML) can help in automated detection of crop diseases more accurately. However, use of such automated techniques face several challenges. Current study attempts to present a systematic literature review (SLR) of the existing body of knowledge in crop disease recognition & classification using automated techniques. The review followed PRISMA guidelines & searched four major databases with a comprehensive search query. 60 relevant research studies were identified after filtering through systematic inclusion & exclusion criteria for further analysis. Each research article is examined for the crop species used, the dataset used, & the methodology used. In each of the studied research papers, we looked at the performance measures utilized to assess the overall acquired findings for crop disease diagnosis & classification. It is observed that such systems have great potential to improve food security & agricultural productivity. However, more research is needed to overcome the challenges & limitations of the current methods and to ensure their validity & reliability in different settings and platforms. The readers of this study could acquire challenges faced by researchers in automated crop disease identification systems & their potential solutions to improve the performance of advanced algorithms.


Enhancing E-Commerce Insights: Sentiment Analysis Using Machine Learning and Ensemble Techniques
Shubham Shedekar, Sahil K. Shah, and Vidya Kumbhar
IEEE
This research aims to explore the impact of customer reviews on consumer decisions in the e-commerce era. Machine learning techniques, including Naive Bayes, Random Forest, Decision Tree, Extra Trees, and Logistic Regression, are utilized for sentiment analysis of customer reviews. A comprehensive dataset is collected through web scraping and subjected to exploratory data analysis (EDA) to gain a deeper understanding of its characteristics. Feature extraction techniques are applied to convert raw text data into meaningful numerical representations. The ensemble learning approach, specifically the voting ensemble method, is employed to combine individual model predictions, enhancing overall performance and robustness. The findings contribute valuable insights into customer sentiment, empowering businesses to understand preferences and enhance their offerings.


Analysis and Forecasting of Industrial Production of Non-Durable Food Items
Harsh K. Thakur, Sahil K. Shah, and Vidya Kumbhar
IEEE
With the increasing growth in population, it is important to foster food requirements with ever-growing demand. Food items are perishable and can cause severe issues to mankind. In view of this, such food items need to be analyzed and insights from this analysis can be very important for food production industries. This study attempts to analyze and forecast the non-durable food items in the food industry using advanced machine learning techniques in order to provide valuable insights and practical implications for businesses in various sectors. For analysis and forecasting, a larger dataset comprising the samples from the years 1972 to 2020 is utilized which enabled a comprehensive analysis of long-term trends. Various forecasting models, including simple exponential smoothing, double exponential smoothing, triple exponential smoothing, ARIMA, SARIMA, MLP, LSTM, and CNN, were developed and evaluated using established evaluation parameters. Ensemble learning techniques were also employed to enhance forecast accuracy. The results obtained using the ensemble technique are comparable with existing techniques and contribute to the tailored analysis and development of a forecasting framework for non-durable food item industries. This study can help industries in production planning, inventory management, and meeting consumer demand in order to optimize operations and make informed decisions.


Predicting Credit Risk in European P2P Lending: A Case Study of "Bondora" Using Supervised Machine Learning Techniques
Sampurna Mondal, Sahil K. Shah, and Vidya Kumbhar
IEEE
Peer-to-Peer (P2P) lending has emerged as an attractive investment option for higher returns, contrasting with conventional bank deposits. However, the inherent risk of loan and interest non-repayment necessitates a thorough understanding of credit risk factors. This research delves into the credit risk analysis of one of Europe's prominent P2P lending platforms, “Bondora.” Leveraging publicly available loan application data, supervised machine learning techniques are employed to investigate and identify pivotal factors affecting credit risk. The study evaluates the performance of cutting-edge machine learning models, including Random Forest, Logistic Regression, XGBoost, Regularized Logistic Regression, Decision Tree, and Naïve Bayes, in predicting credit risk within the “Bondora” platform. Notably, XGBoost emerges as the most effective model, boasting the highest accuracy and AUC in loan default prediction. Additionally, Random Forest showcases exceptional recall in detecting default instances. Through this comprehensive analysis, the research facilitates the selection of the most suitable credit risk prediction method for P2P lending networks like “Bondora.” The study's insights empower investors to make informed decisions when considering future investments in such platforms, aiding in the management of potential risks and optimizing investment strategies.


Machine Learning Methods for Crop Yield Prediction
Vijayatai Hukare, Vidya Kumbhar, and Sahil K. Shah
Springer Nature Switzerland



Agricultural Field Boundary Delineation Using Deep Learning Techniques
Arbaz Sayed, Vidya Kumbhar, Swapnil Jadhav, and Sahil K. Shah
IEEE
Agriculture Stakeholders need to have a precise understanding of the field boundaries. This is a required resource for farm owners to onboard farms on modern farming software programs, it enhances multiple cropping categorization accuracies and it is used by government agencies to track incentives and food production, along with many other uses. The awareness of the location of fields on the farms is of great importance to a farmer. Even though this can be done for small farm holders by manually surveying the field but it is a very human resource-intensive and time-consuming task for huge farms. Knowing the partition of the fields by their serial number or name can significantly improve the management of fields especially when it is reaping season. Also, the boundary shapefiles can be integrated with a precision farming platform through which farmers can keep a track of all the activities taking place in all their fields, which was not possible before. The current study uses modern state-of-the-art techniques integrated with geospatial technology which helps to reduce the time required for the same process by 95%. The deep learning approach helped to achieve 93% accuracy over the manually drawn fields.


An approach towards text detection from complex images using morphological techniques
Vyankatesh V. Rampurkar, Sahil K. Shah, Gyankamal J. Chhajed, and Sanjay Kumar Biswash
IEEE
Text information in natural scene images serves as important clues for many image-based applications such as scene understanding, content-based image retrieval, assistive navigation, and automatic geocoding. However, locating text from a complex background with multiple colors is a challenging task. Two different techniques using morphological operations are proposed to find text strings from any natural scene images. Proposed techniques are based on siblings' method i.e. adjacent character grouping method and text line grouping method. Text line grouping method can locate text strings situated at arbitrary orientations. Proposed techniques can find text strings by using structure-based partition and grouping methods using morphological operations. Proposed system strives toward Morphological methodologies that aid automatic detection, segmentation and recognition of visual text entities in complex several images and thus resulting in optimal performance as compared with existing techniques.

RECENT SCHOLAR PUBLICATIONS

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



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



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



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



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



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



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



Machine Learning Methods for Crop Yield Prediction
V Hukare, V Kumbhar, SK Shah
Agriculture-Centric Computation: First International Conference, ICA 2023 2023



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



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



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



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



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



Design and Analysis of Algorithms (310250) Study Material _Video Lecture
SK Shah
2020



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



An Approach towards Text Detection from Complex Images Using Morphological Techniques
SKB Vyankatesh V. Rampurkar,Sahil K. Shah,Gyankamal J. Chhajed
2nd IEEE International Conference on Inventive Systems and Control (ICISC 2018



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



iASSIST:An Intelligent Assistance System
SK Shah, SA Takale
c-PGCON-2013,Second Post graduate symposium for Computer Engineering 2013



Search Engine Based Intelligent Help Desk System: iAssist
SK Shah, SA Takale
Proceedings of the IEEE International Conference on Advanced Research in 2013



Review on text string detection from natural scenes
RV Vijaykumar, GJ Chhajed, SK Shah
Certified International Journal of Engineering and Innovative Technology 2012

MOST CITED SCHOLAR PUBLICATIONS

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
Citations: 10



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
Citations: 7



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
Citations: 6



Machine Learning Methods for Crop Yield Prediction
V Hukare, V Kumbhar, SK Shah
Agriculture-Centric Computation: First International Conference, ICA 2023 2023
Citations: 3



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
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
Citations: 2



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
Citations: 1



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
Citations: 1



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
Citations: 1



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
Citations: 1



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
Citations: 1



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
Citations: 1



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
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
Citations: 1