Associate Professor & Head, Department of Computer Science & Engineering, CSPIT, Charotar University of Science & Technology (CHARUSAT), Gujarat, India
Thursday August 27, 2026 9:28am - 9:30am IST Virtual Room DGOA, India
Authors - Ajit Patil, Amol Potgantwar Abstract - In the era of Industry 4.0, accurate time series prediction is crucial for extracting valuable insights from high-frequency sensor data in Industrial Internet of Things (IIoT) applications. This paper presents EERA: A Hybrid Ensemble Regression Model designed to improve predictive accuracy for time series data in IIoT environments. EERA combines the strengths of multiple base models, including REPTree, SMOreg, and Multi-Layer Perceptron (MLP), through a weighted ensemble approach to achieve better overall performance. The model was tested using a real-world dataset that captures heat index data (temperature and humidity), which has diverse applications in areas such as agriculture, weather forecasting, and enterprise maintenance. Comparative analysis shows that EERA outperforms individual models, achieving a Mean Squared Error (MSE) of 4.150960 & R-squared value of 0.872540, demonstrating high predictive accuracy. These findings suggest that EERA is a dependable &1 effective solution for time series prediction in fast-paced IIoT data environments.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room DGOA, India
Authors - Manikrao Dhore, Parth Mahajan, Pratik Meshram, Ashish Nikam, Samarth Otari Abstract - Deforestation and improper plantation of trees are the key issues in realizing sustainable environmental management. The AutoForest PlantBot, an autonomous robot system, is introduced in this paper, which makes use of advanced image processing, path optimization, and real-time navigation for efficient tree plantation. The system employs the Deep Forest Package for 92% accurate tree detection and uses Dijkstra's algorithm to find optimal routes, cutting tree removal by 40% as compared to traditional straight-path approaches. The hardware system consists of an Arduino-controlled rover with BO motors, a GPS module, ultrasonic sensors, and an automated drill mechanism, providing accurate plantation with an accuracy of ±2 cm. The outcomes validate the system's potential for large-scale reforestation applications. Future developments will emphasize integrating reinforcement learning for adaptive path optimization and using renewable energy sources for sustainable operation.
Authors - S. Rahul, Anusha Preetham, Aniketh Patil, Abhishek Nimbal, Sahana Meti Abstract - This paper introduces Dr. BOT, a comprehensive web-based healthcare application designed to overcome language barriers in medical communication across diverse linguistic environments. While initially trained to predict several diseases including diabetes, heart disease, kidney disease, liver disease, and breast cancer, the system's architecture enables expansion to detect and interpret a wide range of medical conditions. Dr. BOT employs robust machine learning algorithms (Random Forest, Support Vector Machine, and Logistic Regression) trained on validated datasets, with special emphasis on multilingual functionality through a hybrid approach combining NLP with neural machine translation models specifically finetuned for medical terminology. The platform operates effectively in low-connectivity environments through innovative offline capabilities, offering preventive healthcare guidance and localized medical resource information in users' native languages, thereby supporting both individuals and healthcare providers in improving health outcomes globally.
Authors - Prasad Chaudhari, Ritesh V. Patil, Parikshit N. Mahalle Abstract - The Agricultural Productivity Enhancement System leverages data-driven pattern classification and machine learning-based fertility detection to improve farming efficiency. The architecture integrates IoT sensors, satellite imagery, and soil analysis to collect crucial agricultural data. A preprocessing module ensures data cleaning and feature extraction, storing refined data in an agricultural repository for further analysis. Machine learning models, including pattern classification and fertility detection, process this data to assess crop health and soil fertility. A decision support system then provides real-time recommendations to farmers, enhancing precision agriculture. Researchers and data analysts contribute to model refinement, ensuring scalability and adaptability. This system optimizes resource allocation, reduces wastage, and increases crop yield by enabling real-time, AI-driven decision-making.
Authors - Shiva Jyoti, Samriddhi Ganguly, B Sri Soumya, Nachiyappan S Abstract - This paper presents a comprehensive study on the Clinical Readiness Score (CRS), a structured evaluation metric for assessing AI models used in lung cancer diagnosis. The CRS incorporates multiple criteria such as interpretability, efficiency, clinical validation, and accuracy, employ- ing the Analytic Hierarchy Process (AHP) for weight assignments. This study discusses the methodology behind CRS, validates its consistency, and explores its practical implications. Additionally, graphical represen- tations of AHP weight distribution, sensitivity analysis, and CRS factor contributions are provided for better comprehension.
Authors - Harjas Singh Bajwa, Lokesh Jayakar, Abhiyanshu Singh, Prafulla Bafna, Mukta Deshpande Abstract - Builders often open sales of their property in India as soon as they purchase out land, they do this in order to secure funds to carry out their construction operations, for this they often make rendered photos and videos of concept property. Traditional property marketing techniques like images and videos lack interactivity and fail to provide a 360-degree view of properties. This research work explores the role of metaverse-driven, gamified, interactive property tours in enhancing pre-construction sales. Unlike previous studies focusing on metaverse real estate as an investment platform, this research emphasizes its ability to engage buyers, boost confidence, and aid decision-making. By combining insights from virtual real estate, Augmented Reality (AR) , virtual Reality VR, gamification, and Artificial intelligence (AI) customization, a metaverse-based property visualization framework is proposed. The study highlights how interactive walkthroughs, real-time customization, and immersive storytelling increase trust and engagement. Gamification elements, such as virtual staging, achievement systems, and AI-led personalization, deepen buyers’ connection with properties.
Authors - Ajay V, Sharon P S, Philomina Simon, Ambily George, Mehanas Shahul Abstract - This work delineates an inquiry into how artificial intelligence identifies multiple emotions in texts. Unlike mere sentiment analysis, which is a simple positive, negative, or neutral classification of text, multilabel emotion classification requires a more intense understanding of the text. The paper examines various challenges in multi-label emotion classification, where emotions often overlap (e.g., joy and surprise) and have varying frequencies in datasets. Traditional machine learning and deep learning based models such as BERT and other transformer-based models, show sufficiently strong performance in capturing nuances of emotional expression in text. Moreover, it addresses the issue of how this task can be distorted by linguistic and contextual diversity and diversity and therefore how such systems should be evaluated with respect to these variables.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room DGOA, India
Authors - Gowri Shaju, Lekha S Nair Abstract - Histopathology refers to the study of a disease at a cellular level which stands as a golden method of predicting breast cancer. In this paper, a comparative study of the performance of machine learning models trained using optimized feature sets is done. The experiments are conducted using two datasets. The first is the Wisconsin Breast Cancer dataset, which contains 30 extracted features of cell nuclei. The second is the MITOS-ATYPIA 14 dataset, consisting of histopathology images, from which hand-crafted features have been extracted. Population based metaheuristic optimization algorithms are used to optimize and choose the key features from the available feature set to increase the efficacy of the model. Support vector machines, logistic regression model and other classification models are tested using this optimized feature set. To evaluate the impact of feature optimization, accuracy, precision, recall, and F1 score are assessed using both the full feature set and the optimized subset from two datasets. The results demonstrate how model performance varies with different feature sets, underscoring the significance of optimization techniques in enhancing machine learning-based breast cancer diagnosis in medical imaging.
Authors - Kalyanasundaram V, Keerthi AJ, Krishnaa RK, Thirumurugan A, Joshua Sunder David Reddipogu Abstract - The volatility of the stock market offers a big challenge to traders depending on timely and correct information for informed decision-making. Security risks, including fraudulent practices and theft of identity, threaten online trading platforms. The present paper presents an AI-based stock trading app that tackles these issues by using predictive analytics with robust security features. The system also employs Azure AutoML to work through historical stock data, identify market trends, and generate livestock predictions to enable traders to respond proactively to fluctuations. For security reasons, the app employs Azure Document Intelligence for live Know Your Customer (KYC) verification to ensure that only valid users have access. Additionally, the platform automates document processing using AI-powered text extraction, minimizing errors from manual input and increasing efficiency. Developed with Flutter for smooth cross-platform use and backed by Azure cloud infrastructure for scalability and dependability, this software solution offers an intelligent, secure, and user-friendly trading experience. Through the integration of AI-based forecasting with robust security measures, this work helps develop more efficient, reliable, and technologically sophisticated stock trading platforms.
Authors - George Sebastian, K. Ananda Krishnan Menon, Migheal Newton, Sonal Shaju, Haneesh K. M Abstract - Deep Vein Thrombosis (DVT) is the formation of blood clots in the lower limb because of prolonged immobility. Such critical medical conditions can be avoided by regularly using compression cuffs on the limbs; however, traditional compression devices lack adaptability, cause patient discomfort, and have inconsistent pressure application. This study presents a smart wearable and portable compression system integrated with sensors to receive real-time feedback. A DC motor, controlled by an H-bridge converter, inflates the system’s inflatable sleeves. The DC motor speed controls the pumping pressure, and a solenoid valve controls the inflation rate. Pressure, temperature, and moisture sensors are embedded in the inner part of the cuff to monitor the physiological parameters. An Arduino-based control system was used to control the inflation rate, air pressure, and duration of compression, optimally ensuring patient comfort. The designed pump was tested and shown adaptability when the sensor data changes. An AI-based control framework is also proposed in this work to enhance the performance and to make the pump autonomous and user-friendly. The response of the proposed AI-based control was validated through simulations of the model developed from fundamentals. The simulation results suggest that the AI-based DVT pump is more adaptable to the physiological parameter variations, even when the parameters change rapidly. The AI-driven model provides faster and more precise control of inflation and deflation patterns, preventing overheating, over-compression, and sweating. This study highlights the feasibility of a smart, wearable DVT pump that can adapt to the compression requirements while ensuring safety and comfort.
Associate Professor & Head, Department of Computer Science & Engineering, CSPIT, Charotar University of Science & Technology (CHARUSAT), Gujarat, India
Thursday August 27, 2026 11:30am - 11:32am IST Virtual Room DGOA, India
