Babitha Ganesh Kulal

@canaraengineering.in

Assistant Professor-Department of CSE
Canara Engineering college

Babitha Ganesh Kulal


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https://researchid.co/babithaganesh
Academician with 10 years of teaching experience in different engineering colleges and 1.8 years of research experience at Manipal Institute of Technology, Manipal. Area of interests include Satellite image processing, remote sensing, machine learning and deep learning techniques.

EDUCATION

M.Tech in CSE

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science, Multidisciplinary, Computer Vision and Pattern Recognition
12

Scopus Publications

112

Scholar Citations

5

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Landslide susceptibility mapping using tree-based machine learning classifiers and remote sensing derived conditioning factors: A case study of Chikmagalur District, Western Ghats, India
    Babitha Ganesh, Shweta Vincent, Sameena Pathan, Ganesh V. Bhat
    Egyptian Journal of Remote Sensing and Space Science, 2026
    Chikmagalur district of Karnataka state, situated within the boundaries of the Western Ghats is highly susceptible to landslides, especially during the monsoon season. Despite the recurring nature of these slope failures, limited research has been conducted to assess and mitigate the risk of landslides in the region. Existing studies often lack a comprehensive analysis of the triggering elements and rely on basic machine learning (ML) techniques, even though there are several advanced techniques that are being adopted across the world. A comprehensive dataset of the study area was prepared by integrating twenty different Landslide Conditioning Factors (LCFs) sourced from different remote sensing techniques and the information of 197 historical landslide events acquired from the Geological Survey of India (GSI). The 5-fold stratified cross validation method was applied to generate training and testing dataset in different iterations. Four different tree-based ML classifiers including Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost) were employed to prepare the models to predict the landslide prone areas of the district. These classifiers were specifically chosen because they have the capability to handle feature importance and do not require separate feature selection methods, which are often subjective and difficult to standardize. These ensemble models were then evaluated using different performance metrics that are generally used to evaluate classification models. CatBoost classifier exhibited superior performance, achieving an accuracy of 87.93%, with a precision of 0.85, recall of 0.913, F1-score of 0.88, and an AUC-ROC value of 0.95. Although the RF model also demonstrated strong and competitive performance across all the evaluation metrics, CatBoost was selected for the final preparation of landslide susceptibility map (LSM) due to its comparatively higher recall and AUC-ROC values, which are critical for reliably identifying landslide-prone areas. Consequently, the final LSM was generated using the CatBoost model. According to the LSM, approximately 20.53% of the total district falls within the range of high susceptibility with prediction probability values ranging from 0.6 to 1.0. A major portion, around 47.04% lies within moderate susceptibility zones, and the remaining percentage corresponds to places that are relatively safe from slope failures. Furthermore, the feature importance scores extracted from CatBoost model revealed that slope, rainfall, soil type and distance to road are the main factors that contribute to triggering slope failures in the study area. The application of reverse geocoding techniques on the final LSM indicated that, southwestern and southern taluks including Mudigere, Sringeri, border regions of Koppa and the southern parts of Chikmagalur exhibit a high concentration of landslide prone areas compared to other places. This map serves as a critical tool for early warning systems and informed decision-making to reduce landslide risks in the district.
  • Optimized machine learning based model for the large-scale spatial prediction of landslides at Western Ghats in the State of Karnataka, India
    Babitha Ganesh, Shweta Vincent, Sameena Pathan, Ganesh V. Bhat, Janardhana Bhat K
    Natural Hazards Research, 2026
    The Western Ghats (WG) of India, as per National Disaster Management Authority (NDMA) statistics, is one of the most landslide-prone regions, with frequent monsoon-triggered landslips. Landslide susceptibility Mapping (LSM) is crucial tool for supporting risk mitigation strategies. This study develops large-scale LSM using an optimized Machine Learning (ML) model for the six districts of Karnataka state including Chikmagalur, Uttara Kannada, Dakshina Kannada, Udupi, Shimoga and Kodagu within WG, all with a history of recurrent landslides. A dataset of 1267 landslides (2014-2020) from the Geological Survey of India (GSI) and twenty landslide conditioning factors (LCF) from diverse sources were used. Feature selection using Pearson correlation, Chi-square identified thirteen key LCFs, with geomorphology, slope, lineament density and rainfall as the most significant. Fourteen ML classifiers including single and ensemble models such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbours (KNN), Gaussian Process (GP), Linear Discriminant Analysis (LDA), Light Gradient Boosting (LGBM), Random Forest (RF), Artificial Neural Networks (ANN), Extreme XGBoost (XGB), ADAboost, Categorical Boosting (CatB), stacking and bagging were trained using LCFs and LIM. XGB, LGBM and CatB classifiers performed well. Further, hyper-parameter tuning techniques including grid search (GS), random search (RS), Bayesian optimization (BO) and nature inspired Particle Swarm Optimization (PSO) were applied. PSO tuned CatB demonstrated superior performance with the values of 0.9854, 0.952, 0.958, 0.937 and 0.948 for AUC-ROC, accuracy, precision, recall and F1-score respectively, making it as the most effective approach. Final LSM maps were classified into five susceptibility levels: very high, high, moderate, low and very low. Results show that Chikmaglur, Kodagu and Uttara Kannada districts have 5.43% , 4.10% and 1.21% of the highly vulnerable areas. Dakshina Kannada, Shimoga and Udupi districts have less portion of vulnerable areas. These maps provide crucial input for the policymakers to differentiate between vulnerable and safe regions, enabling informed development planning and minimizing landslide risk. • The research mainly focuses on creating the large-scale landslide susceptibility map for the six landslide prone districts of Karnataka state that fall within the boundaries of Western Ghats covering the total geographic area of 38513 km 2 . • Out of twenty initially selected landslide triggering factors, thirteen key factors were identified by applying different feature selection methods and used in creating the dataset. • Fourteen ML models were applied including both the simple and the advanced techniques like CatBoost, XGBoost, LightGBM to predict the landslide susceptibility. • CatBoost integrated with PSO achieved the highest performance with an AUC-ROC of 0.9854 and high accuracy, recall, precision and F1-score. • Separate LSMs of 30 m resolution have been prepared to for each six districts considered in the study area. The LSMs have shown that Chickmaglur, Uttara Kannada and Kodagu districts have the highest landslide susceptibility.
  • Generative AI Powered Modern Education Tool to Enhance Teaching Learning Experience
    Babitha Ganesh, Nagaraja Thamankar, B K Pratheek Nayak, B H Skanda, V Suraksha Pai, A S Shivani
    Annual International Conference on Data Science Machine Learning and Blockchain Technology Aicdmb 2025, 2025
    This article proposes an innovative educational tool that combines virtual canvas with cutting-edge technologies like generative AI, natural language processing (NLP), and gesture detection to improve the teaching and learning experience. The tool’s core component is an air canvas system that converts hand gestures into digital text, providing instructors with an intuitive and interactive medium for teaching. A predictive text module, trained on a dataset specifically designed for teaching contexts, simplifies content delivery by providing accurate, context-aware suggestions. This reduces cognitive strain on educators and ensures that instructional content aligns with learning objectives. One of the most notable features of the tool is its integration with Gemini AI, a state-of-the-art generative AI system. Gemini AI enables the tool to instantly respond to educators’ queries, providing contextually relevant answers that enhance class discussions and help address student concerns. Additionally, the tool’s voice recognition functionality allows teachers to control applications, play multimedia, and navigate slides hands-free, further improving ease of use. By integrating these AI-powered elements, the tool creates a dynamic and responsive learning environment that adapts to the varied needs of both teachers and students. While the auto correction module of the system is currently optimized for mathematics alone, it holds potential for expansion to diverse educational domains. Overall, this tool bridges the gap between pedagogy and technology, promoting inclusion and participation, and supporting Sustainable Development Goal 4 (SDG 4) by fostering a more engaging, customized, and effective learning experience.
  • Smart AI for Landslide Risk Reduction: An XGBoost Bagging Model for a Safer Tomorrow
    Babitha Ganesh, Shweta Vincent, Sameena Pathan, Ganesh V. Bhat, Om Prakash Kumar, Janardhana Bhat K
    Proceedings of the 16th Student Research Conference on Applied Computing AI Innovations for A Better Tomorrow Src 2025, 2025
    This study presents an AI-based model for predicting the rainfall-induced landslide prone areas in Uttara Kannada district. A landslide inventory map (LIM) was generated by 432 historical landslide occurrences and 478 randomly generated non-landslide points. Thirteen landslide conditioning factors (LCFs) including elevation, slope, aspect, and others were analyzed. Feature selection techniques identified slope, profile curvature, distance to roads, elevation and lineament density as the most influential factors. The dataset, prepared by combining LIM and LCF maps within a GIS environment, was used to train various ensemble classification models including stacking, XGboost, LightGBM, Catboost, and bagging+XGboost. The bagging+XGboost model outperformed with the highest AUC-ROC value of 0.9234. The resulting landslide susceptibility map (LSM) provides valuable insights into the spatial distribution of slope failure prone areas of the study area, supporting risk assessment and disaster management efforts to build a safer and more resilient future.
  • Avatar Closet: An Augmented Reality Based Multi-Modal Virtual Try-On System for Fashion Retail
    Sushma Vittal, Babitha Ganesh, Guruprasad Bhat, Sneha Shanbhag, Swati Shet, Vasudeva, G K Aruna Kumari
    3rd International Conference on Recent Advances in Information Technology for Sustainable Development Icrais 2025 Proceedings, 2025
    The rapid advancement of immersive technologies has paved the way for innovative solutions in the fashion retails industry, particularly in enhancing user engagement and personalization in online shopping. This article proposes an innovative virtual try on system that can revolutionize the online shopping experience by integrating Augmented Reality (AR), Artificial Intelligence (AI) and modern web technologies. This system is architected using ReactJS for a dynamic frontend interface, supporting features such as user registration, authentication, and a wish list mechanism for storing preferred apparel selections. A distinguishing aspect of the platform lies in its multi-modal virtual try-on capabilities, designed to accommodate valid user preferences and technological accessibility. The avatar mode enables the users to create a customizable 3D avatar, offering a simulated preview of garments in virtual environment. The AR mode, built on Snapchat's Lens studio, facilitates real time outfit projection onto the user through the device's camera, enhancing realism and engagement. Additionally, the Image dressing mode allows users to upload personal photographs and digitally overlay selected outfits, offering a simplified but effective try on experience. To support accurate and responsive interaction, the system incorporates the Haar Cascade classifiers for real time body and facial recognition. Furthermore, the integration of the Gemini conversational assistant introduces a layer of intelligent interaction, providing outfit recommendations based on user preferences and guiding them through the application workflow. The fusion of these technologies results in scalable, user-centric and robust system aimed at transforming the virtual shopping landscape.
  • Hyperautomation in precision agriculture using different unmanned aerial vehicles
    Kanmani Jayaprakash, Babitha Ganesh, Aripekatta Mohan Kavya
    Hyperautomation in Precision Agriculture Advancements and Opportunities for Sustainable Farming, 2024
  • Integration of GIS and Machine Learning Techniques for Mapping the Landslide-Prone Areas in the State of Goa, India
    Babitha Ganesh, Shweta Vincent, Sameena Pathan, Silvia Raquel Garcia Benitez
    Journal of the Indian Society of Remote Sensing, 2023
    A landslide susceptibility map (LSM) assists in reducing the danger of landslides by locating the landslide-prone locations within the designated area. One of the locations that are prone to landslides in India's Western Ghats of which Goa is a part. This article presents the LSMs prepared for the state of Goa using four standard machine learning algorithms, namely Logistic Regression (LR ), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Random Forest (RF). In order to create LSMs, a 78-point landslide inventory, as well as 14 landslide conditioning factors, has been used, including slope, elevation, aspect, total curvature, plan curvature, profile curvature, yearly rainfall, Stream Power Index, Topographic Wetness Index, distance to road, depth to bedrock/soil depth, soil type, lithology, and land use land cover. The most pertinent features for the models' construction have been chosen using the Pearson correlation coefficient test and the Random Forest method. The presence of landslides is shown to be strongly influenced by the distance to road, slope of the terrain, and the annual rainfall. The LSMs generated were classified into five levels ranging from very low susceptibility level to very high susceptible. The prediction accuracy, precision, recall, F1-score, area under the ROC (AUC-ROC), and True Skill Statistics (TSS) have been used to analyse and compare the LSMs created using various methodologies. All of these algorithms perform pretty well, as evidenced by the overall accuracy scores of 81.90% for LR, 83.33% for SVM, 81.94% for KNN, and 86.11% for RF. SVM and RF are the better approaches for forecasting landslide vulnerability in the research area, according to TSS data. The maximum AUC-ROC of 86% was achieved by the RF algorithm. The results of performance metrics lead to the conclusion that the tree-based RF approach is most appropriate for producing LSM for the state of Goa. The results of this study indicate that more landslide-prone areas can be found in the Sattari, Dharbandora, Sanguem, and Canacona regions of Goa.
  • Utilizing LANDSAT data and the Maximum Likelihood Classifier for Analysing Land Use Patterns in Shimoga, Karnataka
    Babitha Ganesh, Shweta Vincent, Sameena Pathan, Silvia Raquel Garcia Benitez
    Journal of Physics Conference Series, 2023
    The loss of natural resources has been linked to rapid and invasive urbanization, which in turn worsens the local environment’s scenery and conditions. Preparation of a land use land cover(LULC) map is one of the methods to observe the changes in the geological structure of the study area. The LULU map gives an idea of changes that are occurring during the specified period which will in turn helps in suggesting the measures to be taken to prevent the chances of natural disasters that might occur because of these changes. This study uses a collection of LANDSAT images to evaluate changes in LULC in the Shimoga district for the years 2010, 2015, and 2020. For the classification and creation of LULC maps for the chosen periods, a supervised technique using a Maximum Likelihood Classifier(MLC) has been used. Waterbodies, urban areas, forest areas, and agricultural land have been recognized as the main classes of LULC. The overall accuracy of these maps has been evaluated while taking into account ground facts from Google Earth Pro. The overall accuracy for classification obtained is 85.03% for 2010, 85.27% for 2015, and 85.61% for 2020. The classifier created using LANDSAT scenes and the MLC approach performs well for the research area, as seen by the Kappa index values of 0.8, 0.8, and 0.81 for the years 2010, 2015, and 2020, respectively. The study’s findings indicate that over ten years, the proportion of built-up areas has expanded from 2.8% to 5.4%. When a 2.49% increase occurs in just 10 years, it is necessary to be concerned given the rise of only 1.6% over the previous 40 years. It can also be observed that the proportion of agricultural land has expanded while the fraction of forests has diminished in the study area. The findings of this study are useful in determining that LULC changes are one of the causes of natural disasters including landslides, floods, and forest fires.
  • Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution
    Babitha Ganesh, S. Vincent, Sameena Pathan, Silvia Raquel Garcia Benitez
    Remote Sensing Applications Society and Environment, 2023
  • A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India
    Shweta Vincent, Babitha Ganesh, Sameena Pathan, Vishwajeet Kulkarni, Parth Sirohi, Tushar Agarwal, Silvia Raquel Garcia Benitez
    2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology Icares 2023, 2023
    This article presents a light-weight ANN model for the creation of a landslide susceptibility map (LSM) for the district of Idukki in the South Indian state of Kerala. The landslide conditioning factors (LCF) considered for the creation, training, validation and testing of the LSM are elevation, slope, aspect, curvature, topographic wetness index (TWI), stream power index (SPI), rainfall, topographic ruggedness index (TRI), geology, soil type and land use and land cover. The Frequency Ratio (FR) analysis has been carried out on the LCFs and those having the highest Predictive Rate (PR) have been determined as aspect, slope, rainfall and soil type. Once the LSM is created, it is tested using landslide and non-landslide points using the proposed ANN model which yields an accuracy of 83.5%. Future scope in this work is to improve the accuracy of the model by using metaheuristic algorithms for optimization of weights of the ANN model.
  • Landslides in Goa: A Weight of Evidence(WoE) Approach for Mapping
    Babitha, Shweta Vincent, Sameena Pathan, Silvia Raquel Garcia Benitez
    Proceedings 2022 International Conference on Recent Trends in Microelectronics Automation Computing and Communications Systems Icmacc 2022, 2022
  • Smart Farming with IoT: A Case Study
    Roopashree, Kanmani, Babitha, Pavanalaxmi
    Studies in Big Data, 2021

RECENT SCHOLAR PUBLICATIONS

  • Landslide susceptibility mapping using tree-based machine learning classifiers and remote sensing derived conditioning factors: A case study of Chikmagalur District, Western …
    B Ganesh, S Vincent, S Pathan, GV Bhat
    The Egyptian Journal of Remote Sensing and Space Sciences 29 (1), 209-222 , 2026
    2026.0
    Citations: 1
  • CNN based Multi-class Vehicle Detection, Tracking and Counting on Road scenes
    GKA Kumari, S Shetty, M Saritha, S Suvarna
    2025 IEEE North Karnataka Subsection Flagship International Conference … , 2025
    2025.0
  • Smart AI for Landslide Risk Reduction: An XGBoost Bagging Model for a Safer Tomorrow
    B Ganesh, S Vincent, S Pathan, GV Bhat, OP Kumar
    2025 16th Student Research Conference on Applied Computing (SRC), 1-6 , 2025
    2025.0
  • Avatar Closet: An Augmented Reality Based Multi-Modal Virtual Try-On System for Fashion Retail
    S Vittal, B Ganesh, G Bhat, S Shanbhag, S Shet, GKA Kumari
    2025 3rd International Conference on Recent Advances in Information … , 2025
    2025.0
  • Optimized Machine Learning based Model for the Large-Scale Spatial Prediction of Landslides at Western Ghats in the state of Karnataka, India
    B Ganesh, S Vincent, S Pathan, GV Bhat
    Natural Hazards Research , 2025
    2025.0
    Citations: 3
  • Generative AI Powered Modern Education Tool to Enhance Teaching Learning Experience
    B Ganesh, N Thamankar, BKP Nayak, BH Skanda, VS Pai, AS Shivani
    2025 Annual International Conference on Data Science, Machine Learning and … , 2025
    2025.0
  • Rainfall-Triggered Landslide Susceptibility Mapping Using GIS and LightGBM: A Case Study of Uttara Kannada
    GVB Babitha Ganesh, Shweta Vincent, Janardhana Bhat K, Sameena Pathan
    First International Conference on Computer, Computation and Communication … , 2025
    2025.0
  • Remote Sensing Analysis of Urbanization and Agricultural Land Conversion in Bantwal Taluk, Karnataka
    BBK Janardhana Bhat K, Babitha Ganesh, Om Prakash Kumar, Shweta Vincent
    First International Conference on Computer, Computation and Communication … , 2025
    2025.0
  • Hyperautomation in precision agriculture using different unmanned aerial vehicles
    K Jayaprakash, B Ganesh, AM Kavya
    Hyperautomation in Precision Agriculture, 323-330 , 2025
    2025.0
  • A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India
    S Vincent, B Ganesh, S Pathan, V Kulkarni, P Sirohi, T Agarwal, ...
    2023 IEEE International Conference on Aerospace Electronics and Remote … , 2023
    2023.0
    Citations: 2
  • Utilizing LANDSAT data and the maximum likelihood classifier for analysing land use patterns in Shimoga, Karnataka
    B Ganesh, S Vincent, S Pathan, SRG Benitez
    Journal of Physics: Conference Series 2571 (1), 012001 , 2023
    2023.0
    Citations: 9
  • Integration of GIS and machine learning techniques for mapping the landslide-prone areas in the state of Goa, India
    B Ganesh, S Vincent, S Pathan, SRG Benitez
    Journal of the Indian Society of Remote Sensing 51 (7), 1479-1491 , 2023
    2023.0
    Citations: 26
  • A Review of the COVID-19 Pandemic Impact on Higher Education Globally
    B K.Ganaraj, C. S. Ashwini, Rajatha,Sumiksha Shetty
    Global Higher Education and the COVID-19 Pandemic 1, 13 , 2023
    2023.0
  • Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution
    B Ganesh, S Vincent, S Pathan, SRG Benitez
    Remote Sensing Applications: Society and Environment 29, 100905 , 2023
    2023.0
    Citations: 56
  • Landslides in goa: A weight of evidence (WoE) approach for mapping
    S Vincent, S Pathan, SRG Benitez
    2022 International Conference on Recent Trends in Microelectronics … , 2022
    2022.0
    Citations: 2
  • Smart Farming with IoT: A Case Study
    Roopashree, Kanmani, Babitha, Pavanalaxmi
    Internet of Things and Analytics for Agriculture, Volume 3, 273-286 , 2021
    2021.0
    Citations: 7
  • A Survey on the Machine Learning Techniques used in IVF Treatment to Improve the Success Rate
    M Babitha
    Int J Eng Res Technol (IJERT) 7 (08) , 2019
    2019.0
    Citations: 6
  • Remote Sensing Analysis of Urbanization and Agricultural Land Conversion in Bantwal Taluk, Karnataka
    K Janardhana Bhat, B Ganesh, OP Kumar, S Vincent, B Basappa
  • Remote Sensing Applications: Society and Environment
    B Ganesh, S Vincent, S Pathan, SRG Benitez

MOST CITED SCHOLAR PUBLICATIONS

  • Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution
    B Ganesh, S Vincent, S Pathan, SRG Benitez
    Remote Sensing Applications: Society and Environment 29, 100905 , 2023
    2023.0
    Citations: 56
  • Integration of GIS and machine learning techniques for mapping the landslide-prone areas in the state of Goa, India
    B Ganesh, S Vincent, S Pathan, SRG Benitez
    Journal of the Indian Society of Remote Sensing 51 (7), 1479-1491 , 2023
    2023.0
    Citations: 26
  • Utilizing LANDSAT data and the maximum likelihood classifier for analysing land use patterns in Shimoga, Karnataka
    B Ganesh, S Vincent, S Pathan, SRG Benitez
    Journal of Physics: Conference Series 2571 (1), 012001 , 2023
    2023.0
    Citations: 9
  • Smart Farming with IoT: A Case Study
    Roopashree, Kanmani, Babitha, Pavanalaxmi
    Internet of Things and Analytics for Agriculture, Volume 3, 273-286 , 2021
    2021.0
    Citations: 7
  • A Survey on the Machine Learning Techniques used in IVF Treatment to Improve the Success Rate
    M Babitha
    Int J Eng Res Technol (IJERT) 7 (08) , 2019
    2019.0
    Citations: 6
  • Optimized Machine Learning based Model for the Large-Scale Spatial Prediction of Landslides at Western Ghats in the state of Karnataka, India
    B Ganesh, S Vincent, S Pathan, GV Bhat
    Natural Hazards Research , 2025
    2025.0
    Citations: 3
  • A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India
    S Vincent, B Ganesh, S Pathan, V Kulkarni, P Sirohi, T Agarwal, ...
    2023 IEEE International Conference on Aerospace Electronics and Remote … , 2023
    2023.0
    Citations: 2
  • Landslides in goa: A weight of evidence (WoE) approach for mapping
    S Vincent, S Pathan, SRG Benitez
    2022 International Conference on Recent Trends in Microelectronics … , 2022
    2022.0
    Citations: 2
  • Landslide susceptibility mapping using tree-based machine learning classifiers and remote sensing derived conditioning factors: A case study of Chikmagalur District, Western …
    B Ganesh, S Vincent, S Pathan, GV Bhat
    The Egyptian Journal of Remote Sensing and Space Sciences 29 (1), 209-222 , 2026
    2026.0
    Citations: 1
  • CNN based Multi-class Vehicle Detection, Tracking and Counting on Road scenes
    GKA Kumari, S Shetty, M Saritha, S Suvarna
    2025 IEEE North Karnataka Subsection Flagship International Conference … , 2025
    2025.0
  • Smart AI for Landslide Risk Reduction: An XGBoost Bagging Model for a Safer Tomorrow
    B Ganesh, S Vincent, S Pathan, GV Bhat, OP Kumar
    2025 16th Student Research Conference on Applied Computing (SRC), 1-6 , 2025
    2025.0
  • Avatar Closet: An Augmented Reality Based Multi-Modal Virtual Try-On System for Fashion Retail
    S Vittal, B Ganesh, G Bhat, S Shanbhag, S Shet, GKA Kumari
    2025 3rd International Conference on Recent Advances in Information … , 2025
    2025.0
  • Generative AI Powered Modern Education Tool to Enhance Teaching Learning Experience
    B Ganesh, N Thamankar, BKP Nayak, BH Skanda, VS Pai, AS Shivani
    2025 Annual International Conference on Data Science, Machine Learning and … , 2025
    2025.0
  • Rainfall-Triggered Landslide Susceptibility Mapping Using GIS and LightGBM: A Case Study of Uttara Kannada
    GVB Babitha Ganesh, Shweta Vincent, Janardhana Bhat K, Sameena Pathan
    First International Conference on Computer, Computation and Communication … , 2025
    2025.0
  • Remote Sensing Analysis of Urbanization and Agricultural Land Conversion in Bantwal Taluk, Karnataka
    BBK Janardhana Bhat K, Babitha Ganesh, Om Prakash Kumar, Shweta Vincent
    First International Conference on Computer, Computation and Communication … , 2025
    2025.0
  • Hyperautomation in precision agriculture using different unmanned aerial vehicles
    K Jayaprakash, B Ganesh, AM Kavya
    Hyperautomation in Precision Agriculture, 323-330 , 2025
    2025.0
  • A Review of the COVID-19 Pandemic Impact on Higher Education Globally
    B K.Ganaraj, C. S. Ashwini, Rajatha,Sumiksha Shetty
    Global Higher Education and the COVID-19 Pandemic 1, 13 , 2023
    2023.0
  • Remote Sensing Analysis of Urbanization and Agricultural Land Conversion in Bantwal Taluk, Karnataka
    K Janardhana Bhat, B Ganesh, OP Kumar, S Vincent, B Basappa
  • Remote Sensing Applications: Society and Environment
    B Ganesh, S Vincent, S Pathan, SRG Benitez