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
Authors - Gunjan Tewari, Divyanshi Verma, Simran Negi, Richa Jain Abstract - Cloud computing has completely revolutionized the way of data storage and management in a scalable environment with cost efficiency and scalability. However, the shift from physical to cloud infrastructure raises significant concerns related to security. The major security issues in cloud storage are: data breaches, unauthorized access, hacking of data, and insider threats, various types of cyber-attacks, etc. Many advancements have been made in cloud security but despite that several challenges still exist. Many security frameworks rely on third-party providers creating potential risks of data exposure. This paper addresses these challenges by proposing an approach for secure file storage in the cloud having multiple layers of security. The key derivation for encryption is done uniquely and then AES-256 is used for encryption and decryption. At the time of decryption, OTP authentication is done using RSA signing which provides multi-factor authentication. The method can also be used to encrypt all multimedia data. To provide security, the file password and OTP information are not stored in any database. The proposed work provides a very secure data storage solution that protects the data from any kind of brute-force attacks and other cryptanalysis attacks.
Authors - Jalindar Nivrutti Ekatpure, Dinesh Bhagwan Hanchate Abstract - This review paper gives a thorough look at all the current methods and uses of AI in crop prediction with aerial images. With the development of drone technology and high-resolution satellite imagery, gathering data on farming has been easier. This paper completely analyses the use full uses of Artificial Intelligence techniques in agricultural functions. The real-word ex-ample shows that how artificial intelligence techniques used in aerial imagery technology it may be accurately applied in different agricultural fields. These examples shows capability to develop observing, expect yields, and assist farmers with correct decisions. The next research enterprises have been suggested to address current difficulties and increasing artificial intelligence application in the crop cultivation techniques. This paper aims to train farmers experts, educators, and those who are to know how to use artificial intelligence in the precision farming.
Authors - Rohit Bajirao Khedkar, Bhakti Dudile, Laksh Rupesh Khobragade, Pawan Babanrao Avhad, Santosh Kumar Abstract - This paper presents a comprehensive study on the development and implementation of a virtual mouse system using hand gestures. With the rapid advancement of human-computer interaction (HCI) technologies, touchless interfaces have gained immense popularity. The proposed system eliminates the need for physical input devices by leveraging computer vision and machine learning techniques to interpret hand movements, translating them into cursor control and command execution. The research explores various methodologies, including hand tracking, gesture recognition, system integration, and deployment strategies, highlighting advancements, challenges, and future directions in the field. The objective of this study is to design an intuitive user interface, develop a robust gesture recognition model, and ensure seamless deployment across various platforms to enhance accessibility and usability.
Authors - Mariela Todorova, Tihomir Dovramadjiev, Darina Dobreva, Tsena Murzova, Mariana Murzova, Iliya Iliev, Ventsislav Markov Abstract - The pursuit of enhancing the quality and precision of final design models has become increasingly vital in modern production processes, particularly in the realm of university information signage. This research explores the application of advanced laser engraving and cutting technologies to achieve maximum accuracy in geometric shapes, fine details, and textual elements. A systematic methodology has been developed and implemented, focusing on the production of custom-designed metal information signs tailored to university environments. The article presents a comprehensive overview of the production stages, including optimized workflows for managing digital data, selection of appropriate file formats, and precise laser machine settings. By integrating digital design tools with laser technology, the study demonstrates how to streamline processes while maintaining exceptional quality and durability of signage. The research outcomes not only emphasize the technological advantages of laser systems—such as high-speed production, cost efficiency, and unparalleled precision— but also highlight their potential to transform design practices in educational settings. This study aims to contribute to the scientific and practical development of digital fabrication methods, inspiring wider adoption of laser-based innovations across design disciplines.
Authors - Devashish Sanjay Gaikwad, Aadit Kisanrao Palande, Prasad Padmakar Joshi, Aditya Atul Kode, Rachana Yogesh Patil Abstract - Assessing the solar potential of rooftops is crucial for optimizing photovoltaic (PV) installations and promoting renewable energy adoption. This study presents a methodology for estimating rooftop solar potential using advanced geospatial and machine learning techniques. The proposed framework integrates Mapbox GL for spatial visualization, PVGIS for solar radiation data, and Scikit-Learn for predictive modeling. A web-based application is developed using React.js, HTML, and TailwindCSS for the frontend, with Node.js and Express.js handling backend processes. The system allows users to input rooftop data, analyze solar potential, and generate estimations of energy output based on historical and real-time solar radiation data. By leveraging machine learning algorithms, the model enhances prediction accuracy and enables better decision-making for solar energy investments. The results demonstrate the feasibility and effectiveness of this approach in providing precise and user-friendly solar potential assessments. This research contributes to the growing field of smart energy solutions and supports the transition to sustainable energy sources.
Authors - Shaveta Thakral, JyotiVerma, Suchita Ganage, Dharmendra Ganage, Monika, Shankar Amalraj Abstract - Cognitive Radio (CR) is a revolutionary technology aimed at optimizing the utilization of the electromagnetic spectrum, a limited and valuable resource. Despite its promising potential, the deployment and widespread adoption of CR face several technical, regulatory, and practical challenges. This paper presents a comprehensive study of the key research challenges in Cognitive Radio Networks (CRNs). We begin with a chronological literature survey, highlighting significant advancements and ongoing research efforts. Subsequently, we delve into the current research challenges, including spectrum sensing, dynamic spectrum access, security, energy efficiency, interoperability, and regulatory issues. We also explore potential research opportunities that could address these challenges, thereby paving the way for more robust and efficient CRNs. This review aims to serve as a foundational reference for researchers and practitioners in the field, offering insights into future research directions.
Authors - Sheela Chinchmalatpure, Atharva Bondarde, Atharva Joshi, Archit Bagad, Samyak Dawle, Rajeshwar Chintawar Abstract - Proper waste management is essential for public health and urban sanitation. Conventional garbage collection systems tend to be absent of verification checks to confirm the emptying of waste bins and instead depend on manual records. To improve monitoring of waste collection, this research suggests a smart, technology-based solution that combines Near Field Communication (NFC) with computer vision through a TensorFlow Lite-based YOLO model. The system includes a mobile app that scans NFC tags on trash bins, logging the time and staff member who serviced the bin. The app also includes a lightweight YOLO model in TensorFlow Lite format to check if a bin is full or not using real-time image processing. NFC scanning is only allowed after successful model verification to avoid fraudulent reporting and ensure accountability of sanitation workers. This two-in-one system serves as both a real-time waste collection monitoring system and an automated worker attendance tracking system. Evaluated on a bin image dataset, the solution showed encouraging accuracy in empty and full bin detection. Through the use of AI and IoT-based tracking, this system promotes accountability, transparency, and effectiveness in waste collection, and makes it a scalable and affordable model for smart cities.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room DGOA, India
Authors - Vishal V. Mahale, Sanket R. Malode, Sudarshan M. Pagare, Punit Chaudhari Abstract - Personality prediction plays a key role in understanding human behavior, decision-making, and social interactions. The OCEAN model—comprising Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism is widely used for assessing personality traits. With the rise of machine learning, predicting personality using this model has become a growing interdisciplinary field. This survey paper reviews existing machine learning approaches, such as K-Means and Gaussian Mixture Models, used to analyze personality traits from questionnaire data. It also highlights the limitations of past studies, including lower prediction accuracy and challenges in model interpretation. The aim is to provide a clear overview of current methods and explore how machine learning can improve personality prediction and reveal deeper links between personality traits and behavior.
Authors - Poonkuzhali S, Shreya Sai Prabakar, Sriram Venkat P Abstract - The airline industry is characterized by fluctuating demand, intricate pricing, and inventory management. In that regard, this study is done regarding AI-driven revenue management systems, where many authors engage quite heavily with techniques of demand forecasting, pricing optimization, and inventory control. With Random Forest Regression, Catboost and LightGBM passenger demand can be predicted using fare class, lead time, and seasonality, optimizes seat allocation by fare category so as to maximize revenue. Evaluation is done using several datasets regarding booking patterns and market behavior in order to critically assess the accuracy, efficiency, and adaptability of the model. This study will demonstrate the strengths and trade-offs of AI techniques, indicating to the airline the power of using data for real- time decisions. Its strength lies in the improvement of demand forecasting combined with dynamic pricing and inventory management, maximized profitability, efficiency, and customer satisfaction.
Authors - Vijayalakshmi B, Jayasheela C S Abstract - Postmenopausal women (PW) are at a significantly increased risk of fractures, largely due to estrogen deficiency leading to osteoporosis and altered bone quality. Fracture risk assessment is critical for early intervention and prevention strategies. This review explores advancements in fracture risk evaluation, highlighting experimental methodologies, clinical applications and emerging technologies. Key advanced imaging approaches include the use of dual-energy X-ray absorptiometry (DEXA) for bone mineral density (BMD) measurement, high-resolution peripheral quantitative computed tomography (HR-pQCT) for micro-architectural assessment and biochemical markers like C-terminal telopeptide (CTX) and procollagen type I N-terminal propeptide (PINP) for monitoring bone mass density. Fracture risk assessment (FRA) tools such as FRAX and the Garvan calculator provide practical frameworks for estimating fracture probability by integrating clinical risk factors and BMD data. However, challenges remain. including limited access to advanced imaging, variability in biochemical marker and under representation of diverse populations in validation studies. Future directions emphasize integrating artificial intelligence, expanding population specific validations and combining imaging with dynamic bone mass density data. This comprehensive review emphasizes the significance of a multidisciplinary approach in FRA, aiming to enhance precision, accessibility and clinical outcomes in postmenopausal women.
Authors - Dhruv Aswani, Aman Sande, Praful Pradhan, Rajveer Tolani, Pallavi Saindane Abstract - Security concerns and the empowerment of women remain highly pressing challenges in India, with significant issues evident across urban, semi-urban, and rural regions alike. Women’s mobility is often constrained by the fear of harassment, crime, and social barriers which slows down their movement toward true empowerment. To address these problems, this study focuses on the major drivers of women’s safety and empowerment which include social norms, presence of crime, supportive structures, and women participation in technology. To address these challenges, this paper proposes Agati, an Android application that offers safety and empowerment features specifically tailored for women. Agati combines real-time safety alerts, location tracking, community support networks, and financial literacy modules to foster both security and economic independence. By leveraging data analytics and user feedback, the app aims to build a personalized, data-driven solution that bridges the gap between security and empowerment. Through this integrated approach, Agati seeks to create a safe, supportive environment that promotes social power and holistic growth for women.
Authors - Nikhil Vaishya, Amey Sawant, Mayank Shukla, Suhani Pandey, Vaishali Kosamkar Abstract - Augmented Reality (AR) is transforming the learning experience in anatomy and biology [1, 2]. by providing an engaging and interactive alternative to traditional teaching methods. Understanding complex anatomical structures has historically been challenging due to the limitations of textbooks, static models. AR overcomes these challenges by enabling students to explore high-fidelity 3D representations of the human body in real-time, fostering deeper spatial understanding and retention. The technology allows learners to interact with anatomical structures, receive immediate feedback, and learn at their own pace, beyond the constraints of the classroom. In addition to AR visualization, this project integrates an AI-assisted quiz and learning platform to further enhance anatomy education. By leveraging machine learning algorithms such as ensemble methods like Random Forests and Support Vector Machines (SVMs), coupled with SMOTE for class imbalance handling and cross-validation for robust generalization, the system offers adaptive quizzes, personalized learning recommendations, and real-time feedback. The platform dynamically adjusts to user interactions, ensuring a tailored and effective learning experience. Developed using Unity for AR functionalities, JSON for data management, and machine learning for prediction models, the system bridges the gap between theory and practice while promoting active and self-paced learning. This paper details the design, development, and evaluation of the AR-based Anatomy Learning Platform, highlighting its potential to revolutionize anatomy education by offering an accessible, immersive, and personalized approach. The platform is designed primarily for medical students, but it also supports general learners seeking to enhance their anatomical knowledge through immersive technologies.
Authors - Shital Pawar, Parag Dolhare, Saish Fatangare, Harshdeep Gawhale, Aditya Gadgil Abstract - Environmental, social and governance (ESG) criteria have become essential to assess the sustainability and social impact of companies. This article presents the development of an automated ESG ranking system that uses natural language processing (NLP), sentiment analysis, and machine learning techniques to rank and rate companies based on ESG metrics. Using a pre-existing database of news articles, we used the VADER sentiment analysis tool to assess the polarity of the text data, categorizing it as positive, negative or neutral. Sentiment scores were converted to numerical scores for each ESG component. In addition, Node2Vec is integrated to create network graphs that represent the relationships and interconnections between companies, allowing a comprehensive analysis of potential impacts. The results were visualized with Altair to provide a clear view of ESG trends and relationships that impact the company's performance over time. This study demonstrates the utility of combining NLP and advanced graph analytics for scalable data-driven ESG assessment.
Authors - Chalamalasetty Nishitha, Yelavarti Kalyan Chakravarti, V. Esther Jyothi Abstract - Sensitive domains such as healthcare institutions are increasingly relying on Federated learning for data security. Irrespective of this approach, they are gullible to adversarial attacks such as poisoning attacks and confidentiality breaches. To overcome these hindrances, Blockchain driven Federated learning is put forward, which integrates Secure Multi-Party Computation (SMPC) with Zero-Knowledge Proofs (ZKPs). This framework strives to ensure confidentiality in a distributed training environment. The individual entities train their local AI models with their exclusive datasets and generate Zero Knowledge Proofs to assert the accuracy of the model updates. The SMPC protocol encrypts the model updates, which are later aggregated to enable computing that guarantees privacy. Later, Smart Contracts are used to immutably store these adjustments on the Blockchain ledger, ensuring impenetrable model ensemble. To improve the trade-off between model dependability and precision, privacy noise is dynamically adjusted by employing adaptive differential privacy, based on individual client’s reputation. Extensive experiments prove the fact that the proposed system prominently reduces computing overhead in comparison to the established system while strengthening the attack detection rates. This architecture establishes a benchmark for information security in delicate areas like healthcare systems while designing its data-sensitive Al models.
Authors - Nandana R, Rithika Kannan, Ramgeeth N Nair Abstract - The rise of fintech applications has revolutionized financial decision-making, yet the determinants of risk-taking behavior in these digital platforms remain a critical research area. This study investigates the role of gamification, financial knowledge, and psychological influences in shaping users’ risk-taking behavior. Using a quantitative approach, an Ordinary Least Squares (OLS) regression analysis was conducted on a dataset of 200 fintech users. The results indicate that gamification has a significant positive effect on risk-taking behavior (β = 0.1414, p = 0.001), suggesting that game-like elements in fintech apps encourage users to take greater financial risks. However, certain gamification effects exhibit a negative influence (β = -0.1272, p = 0.005), highlighting that not all gamification strategies lead to in- creased risk-taking. Financial knowledge also emerged as a significant determinant (β = 0.1965, p = 0.001), implying that financially literate users tend to take more calculated risks. Among psychological factors, risk tolerance (β = 0.2754, p < 0.001) was the strongest predictor, demonstrating that individuals predisposed to risk-taking in general extend this behavior to fintech platforms. Additionally, social efficacy (β = 0.2461, p < 0.001) and social influence (β = 0.1598, p = 0.004) significantly contribute to risk-taking, emphasizing the role of self-perceived competence and peer influence in financial decision-making. The model explains approximately 48.1% of the variance in risk-taking behavior (R² = 0.481), confirming the robustness of these deter- minants. The findings underscore the importance of designing fintech applications that balance engagement with responsible financial behavior. Future research should explore the ethical implications of gamification and assess long-term user behavior to ensure sustainable financial decision-making in digital finance ecosystems.
Authors - Ramesh Babu Mutluri, Vinit Kumar Singh, D Saxena Abstract - Rural areas in developing countries are still refrained from continuous and uninterrupted power supply to power their household and run small industries. Thus, we can say that these rural areas are weakly connected to the utility grid. The main reasons for poor power supply are weak infrastructure, lack of adequate generation to fulfill the demand-supply gap, dependency on long-distance transmission, frequent load shedding, and distributed generation. This demand-supply gap can be minimized by installing renewable energy sources with the local load forming rural microgrid and connecting to the utility grid. The grid connection would help to maintain the power supply due to the variable output characteristics of renewable energy sources thus also acting as a buffer to the local power system. This paper presents a novel approach towards modeling of utility connected rural microgrid comprising renewable energy sources considering control architecture for marinating frequency-voltage interdependency. Accordingly, a frequency-based voltage controller is introduced. Further, the model has been verified in view of various scenarios with a fluctuation in load demand and power input to renewables. The controllers are tuned such that in case of increase in load or decrease in power generation, power demand is met from the utility grid, and in case of surplus generation, the power is fed to the grid, therefore, developing microgrid as business unit applicable for power trading. The model has been developed in Simulink/MATLAB. An integral square error criterion has been used for tuning the controllers to mitigate the oscillations.
Authors - Ashwitha A Shetty, Naganna Chetty, Antony P.J Abstract - The poultry industry is a significant and prominent business sector. As the daily intake of chicken meat and eggs is rising globally, poultry farming is gaining significance for providing protein. Additionally, this industry raises the nation's revenue despite being a less expensive protein source. Numerous diseases that harm the chickens are the main issue affecting the poultry business. Due to the high cost of vaccinations, poultry owners are unable to adopt these expensive methods. Consequently, this strategy cannot be used because it requires continuous investment. This paper aims to present one of the prevalent chicken diseases, coccidiosis and the different detection techniques used. In this regard, the study introduces multiple strategies that can be used in tandem to identify coccidiosis-affected fowl hens automatically. The idea behind studying chicken activity monitoring is that it directly connects to the health condition of the chicken. The enhanced future research could result in a system to monitor chicken activity and detect coccidiosis among them
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room DGOA, India
Authors - Piyush Sharma, Harish Patidar, Anuj Kumar Abstract - This research introduces a ResNet-based framework for multiclass classification of mammographic density and mass regions. The framework was rigorously tested using two prominent mammographic datasets, INbreast and DDSM, and benchmarked against other models, including CNNs, Random Forest (RF), Support Vector Machines (SVMs), Logistic Regression (LR), and K-Nearest Neighbors (KNN). ResNet demonstrated superior performance across all critical evaluation metrics—accuracy, precision, recall, F1-score, and AUC—outclassing the comparative models on both datasets. Its proficiency in extracting complex hierarchical features and addressing multiclass classification tasks positions it as a robust choice for breast cancer diagnosis. This framework offers a reliable and efficient tool for automating diagnostic processes, with the potential to significantly improve clinical decision-making and patient care.
Authors - Pranav Bagal, Bhavesh Patil, Shounak Muglikar, Yash Sonavane, Prajakta S. Shinde Abstract - The study addresses dental caries detection and classification using state-of-the-art deep learning architectures. We implemented and compared three pre-trained convolutional neural network models: VGG19, DenseNet169, and ResNet101, to automatically identify and classify dental caries from intraoral clinical image. Our research focused specifically on pediatric populations aged 1 to 14 years, where caries remain a significant health concern despite global prevention efforts. The models were trained and validated on a comprehensive dataset of dental images. Performance metrics demonstrated that DenseNet169 model achieved superior results with an Validation accuracy of 72.22%. These deep learning approaches show promising potential to augment traditional diagnostic methods, particularly in resource-limited settings where expert dental practitioners may be scarce. By enabling earlier and more accurate detection of carious lesions, our proposed system could help address disparities in oral healthcare accessibility and contribute to more effective intervention strategies, especially for underprivileged populations where caries prevalence continues to rise. This research establishes a technological framework that could be integrated into portable diagnostic tools for use in diverse clinical environments.
Authors - Govinda Sambare, Lalit Deore, Harsh Itkar, Onkar Jadhav, Sarthak Joshi Abstract - This research presents the development of an intelligent stock recommendation system that utilizes advanced machine learning models for informed long-term investment decisions. The system addresses the complexities of the stock market, where traditional methods often fall short in accessibility, accuracy, and efficiency. By automating fundamental analysis with models like Long Short-Term Memory (LSTM) networks and the CNN-GRU-XGBoost hybrid model, the system integrates key financial ratios, macroeconomic indicators, and sector performance, providing data-driven insights. The proposed framework optimizes stock selection using XGBoost and forecasts future stock prices with LSTM, offering precise and scalable solutions for diverse investment portfolios. The literature review highlights modern methodologies like TRAN, Bi-LSTM, and hybrid models, which improve stock forecasting and trading strategies by incorporating temporal dependencies and inter-stock relationships. The algorithmic analysis explains LSTM's ability to handle sequential data and the hybrid model's powerful feature extraction and prediction capabilities. This hybrid approach enhances decision-making, saves time, and democratizes financial insights, making advanced analysis accessible to individual investors, robo-advisors, and educational institutions. While offering benefits like scalability and reduced biases, the system also faces challenges, such as computational costs and market volatility. Backtesting results confirm the system's adaptability to dynamic market conditions, ensuring sustainable investment strategies. This project showcases the transformative potential of AI/ML in financial analytics, laying a strong foundation for long-term, informed investment decisions.
