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.
