Authors - S M Boomika, C M Tulasi, Sharvani V Nagur, Bhagyashri Badakali, Nalini C Iyer, Preeti Pillai, Ujwala Patil Abstract - LiDAR and cameras play a vital role in autonomous vehicles by providing complementary data for object detection and environmental perception. However, achieving seamless data integration from these sensors depends on partial and temporal synchronization. Unlike conventional methods that depend on pre-calibrated datasets, our methodology utilizes a custom-acquired multimodal dataset comprising both image and video data from a monocular camera and point cloud data from a VLP-16 Velodyne LiDAR sensor. In this paper, we proposed a comprehensive framework for LiDAR and camera calibration and temporal synchronization of real time data, synthesized and validated in a controlled lab environment. Calibration of the raw data was performed using a checkerboard as the target to ensure accurate spatial alignment between heterogeneous sensor systems.The collected corpus is further timestamped, synchronized, and validated.The accuracy of the proposed methodology is evaluated by projecting LiDAR points onto image frames, enabling qualitative verification of spatial and temporal consistency. The proposed method integrates target-based calibration with software-level timestamp synchronization to create a reproducible and scalable calibration pipeline. Results demonstrate accurate alignment across modalities, validating the effectiveness of our approach. This 1 work provides a practical contribution to multi-sensor fusion research, especially for applications requiring custom datasets or operating in constrained environments.
Authors - Wendrila Biswas, Arunangshu Giri, Dipanwita Chakrabarty, Dibyendu Rath Abstract - The study has examined the effect of user engagement (UE), perceived benefit (PB), and perceived risk (PR) of wearable sensor-based healthcare devices adoption. User empowerment (UEM) in IOT-enabled healthcare has been explored on the basis of two established theories, Technology Acceptance Model (TAM) and Behavioral Reasoning Theory (BRT). A cross-sectional online survey was conducted from November 2024 to January 2025 involving 361 valid Indian respondents and the collected responses were analyzed through NVivo software for qualitative analysis. SEM (structural equation modeling) was done for quantitative analysis and hypothesis testing. The findings have shown a positive association between UE and PB and between UE and PR. Again, the study has revealed that PB and PR positively influenced UEM. The study contributes both to existing literatures and making managerial decisions by establishing how benefits from wearable sensor-based healthcare devices can be explored by avoiding the perceived risk of the consumers and how they can get empowered with the same.
Authors - Vaishali Langote, Siddhesh Kulkarni, Aaditya Ghorpade, Aditya Songirkar, Aditya Chincholkar Abstract - Identifying customer retention is essential for decreasing lost revenues as well as maintaining an established base of loyal customers. By reviewing historical data that includes customer demographics, purchasing habits and behaviours, businesses will be able to determine which customers are going to discontinue using their services or products. In generating models that can identify customers at risk, this process includes machine learning models such as decision trees, logistic regression and neural networks. It is important that predictive retention can work provided the right algorithms are selected, and reliable data is sourced. Continual updates and improved models will enhance accuracy, giving firms the opportunity to keep up with changes in how consumers behave. The models will also give businesses the ability to produce more targeted retention marketing plans since they will not only identify at-risk customers but also give clear data on what they are doing to create customer churn.
Authors - Sharon Koshy, Padmadas Sundaram Abstract - The intensifying depletion of natural resources, fueled by world population growth and unsustainable consumption, poses severe threats to global sustainability. Specifically, the ICT and smart infrastructure industries make substantial contributions to resource inefficiencies through growing e-waste, inefficient material recovery, and unsustainable construction methods. Forecasts suggest that by 2050, with a projected 9.8 billion world population, resource use will surpass planetary limits, urging rapid interventions in resource management and the transition to circular economies. Despite growing recognition, inefficiencies in recycling infrastructure, defective waste-to-energy technologies, and inadequate water management persist to drive global resource insecurity and further environmental degradation. Solutions must be backed by evidence-based policy design, technological development, and systemic change. In this context, the combination of Artificial Intelligence and biomimicry offers a new way to increase sustainability and resilience in systems. AI-based models improve resource efficiency, reduce environmental footprint, optimize waste management, facilitate predictive maintenance, and enhance material recovery, while biomimicry offers nature-inspired solutions for sustainable design, energy efficiency, and waste reduction. These technologies not only foster resource recovery but also set the stage for the creation of wiser, more sustainable industries and cities. In conclusion, this study high- lights the revolutionary power of ICT that AI and biomimicry make possible to create closed-loop, self-sustaining models that boost urban resilience, sustainability, and efficiency, maximize recovery of resources, minimize waste, and maximize value for a truly circular future.
Authors - Priya Surana, Sushma Vispute, Madhura Kalbhor, Shubhangi Vairagar, Pragati Ugale, Imtiyaz Syeda, Mahek Yakumsha, Ashish Suryawanshi Abstract - This research presents a YouTube Comments Analyzer that leverages machine learning and deep learning algorithms to examine and classify user comments. A large volume of comments is processed by the system, enabling it to detect key patterns, including sentiment classification and emotion detection. Using natural language processing and machine learning techniques, the tool provides meaningful insights to content creators for understanding their audience and to moderators for identifying problematic content. Researchers can also benefit by studying online commentary at scale. Our team collected video comments from various genres to train and develop the models, followed by evaluation using multiple performance metrics. The analysis tool achieves 96% accuracy in sentiment detection and 90% accuracy in emotion detection, successfully identifying complex patterns that manual evaluation often misses. To demonstrate the practical applicability of our models, we further developed a web-based application that integrates the analysis pipeline, providing an accessible platform for real-time comment analysis. This research highlights the effectiveness of automated text analysis in social media environments and demonstrates real-world applications for YouTube content management and audience engagement strategies.
Authors - Omkar Kalantre, Jyoti Joglekar Abstract - Optimized Bounding box fitting around an object is necessary for accurate localization of the Region of Interest (ROI), so that features extracted from the ROI are useful for many computer vision applications. Current methods tend to be inefficient, imprecise, and with high computational complexity. In this work a novel algorithm is presented that is designed for fitting a bounding box around an object that covers maximum part of the object as ROI,. The improvement in inserting bounding box enhances the process of recognizing, tracking, and classifying objects, which is highly valuable for applications such as surveillance, autonomous driving, and security. In this work we are proposing a novel algorithm for fitting a bounding box around an object to maximize the object area covering and for minimizing the background clutter as a part of ROI.
Authors - Mohan Sellappa Gounder, Rohan Mahantesh Kamatgi, Sharath Prabhu T M, Sanya Gupta, Seema Abstract - This research investigates the application of the DINO (Distillation with No Labels) framework, a self-supervised learning approach, for efficient road and pothole segmentation. By integrating a DINO-enhanced ResNet-50 backbone with a U-Net model, this study addresses segmentation challenges in dynamic environments. The framework employs momentum encoders, multi-crop training, and stability mechanisms to facilitate robust feature extraction without requiring labeled datasets. Through strategic fine-tuning, the model achieves precise segmentation of road surfaces and potholes, making it a promising approach for real-world applications in autonomous systems and infrastructure assessment. This study further discusses model evaluation, comparison with state-of-the-art approaches, and its implications for transportation infrastructure.
Authors - Gopal D. Upadhye, Ranjana Jadhav, Aryan Pungale, Ashish Shadija, Nikita Rajput, Pranav Pendse Abstract - A data-informed system is described for generating crop recommendations and crop yield forecast based on a variety of data sources of farmer-level soil characteristics, historical crop yield records, and meteorological variable data. In the proposed system, crop recommendations based on a classification algorithm and crop yield estimates based on a regression algorithm are provided to farmers. The data-driven crop recommendations and crop yield forecasts will improve decision-making by providing the farmer with data-based recommendations providing the productivity isolation. The data-informed system will utilize machine learning algorithms to process the data and analyze the complex interaction of the various farming agri-parameters in the farm operation. Composition of soil nutrient values, weather patterns, and historical productivity variable data will be a key ingredient in the model to provide farmers with singularly specific crop selections. Ability to yield prediction gives farmers anticipate yield of the crops, improve resource planning. The validation tests demonstrate better accuracy than traditional heuristics, improving farmer overall risk reliability and increasing efficiency, sustainability. The results shows us that the transformative role of machine learning in agriculture and the associated movement toward precision farming practices
Authors - Vanshika R Kavi, Sujata Kotabagi Abstract - Semiconductor production demands high-quality control to detect faulty wafers early on in the production process. Manual inspection and rule-based systems are conventional methods that are time consuming and error-prone. This research investigates machine learning (ML) based wafer detection on a dataset of 590 sensor readings per wafer, with wafers being labeled as good (+1) or faulty (-1). Several traditional ML models, such as Logistic Regression (LR), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest, are tested for defect classification effectiveness. The processing of data includes handling missing values by dropping features with high missing data and using median imputation. Feature selection is done through SHAP (Shapely Additive Explanations) analysis and correlation filtering to select only the most important sensor readings. Feature scaling is done to maintain consistency in data distribution. For handling the class imbalance in the dataset, SMOTE (Synthetic Minority Over-sampling Technique) is employed to create synthetic samples for the minority class to enhance model learning. Once trained, the models are evaluated on the basis of accuracy, precision, recall, F1-score, confusion matrix, and SHAP-based explainability analysis. SVM and Random Forest perform better compared to other models with 97-99% accuracy, and KNN does not perform well because of high dimensionality. The research showcases how ML is able to automate defect detection, increase production efficiency, and minimize human inspection errors. Work for the future encompasses ensemble learning optimization, real-time deployment, and semi-supervised learning optimization for enhanced defect classification in the semiconductor industry.
Authors - Piyusha S. Shetgar, Asha V. Thalange, Rohini R. Mergu, Aishwarya Khobare Abstract - Throughout the world, the number of educational institutions has significantly increased in recent decades. But the majority of recently established universities continue to manage their resources, including their hostels, using traditional methods. These conventional methods are frequently hindered by innate restrictions that negatively impact the organization's overall effectiveness. This study suggests an automated hostel lodging management system that is made with Microsoft Access as the underlying database and Visual Basic as the programming language to handle these issues. To stop unwanted access, the system has an integrated authentication algorithm. The system that has been built leverages face recognition technology to address the shortcomings of conventional approaches. It provides a graphical user interface, dependability, efficiency, and improved security by implementing access control mechanisms.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room DGOA, India
