Authors - Rohith Aryan TG, Kashish Sharma, Sunil Kumar, Abhay Sharma Abstract - A Revolution in the Diagnosis and Treatment Landscape. This paper brings forward how AI technologies can be utilized in earlier detection of mental illnesses, and how robotics provide therapeutic support to patients. Examining the various models, results and challenges on these technologies is aimed at putting attention on their perceived benefits and limitations in augmenting the health care of the mind. Comparative studies that include traditional treatment modalities will also be featured, along with recommendations for future research directions.
Authors - Vinayak Kanhegaonkar, Ayush Mahant, Ali Sayyed, Abha Shah, Aparna Junnarkar Abstract - India’s farmers battle many challenges before they are able to sell their produce to the consumer, one such challenge is related to middlemen and market access. The current scenario is such that middlemen hold significant influence over the market. Middlemen are the intermediaries that farmers sell their produce to because of reasons such as lack of infrastructure, price fluctuations, limited storage facilities, lack of information and more. This causes farmers to lose much of their profit and overall reduces their income. Middlemen take substantial profits from their work; they buy for less from farmers and sell for more to consumers. Through our research we aim to create an app, KrishiMitra, for farmers to facilitate direct access to the market. KrishiMitra will be able to reflect real-time market prices, provide multilingual support, and have a bidding system. With this app we hope to provide farmers a platform to get fair prices, some bargaining power, proper and complete information about market prices and current trends. In the future, the application can be integrated with more services that address other challenges faced by farmers such as limited storage space for produce and climate change impacts on produce.
Authors - Madhuri Wakode, Geetanjali Kale Abstract - Deep learning has many applications in healthcare especially for disease prediction from complex images. One such application is to predict diseases from chest X-rays (CXR). These models need huge amounts of data available for training. A single healthcare facility may struggle to collect sufficient data to build robust and efficient models. The apparent solution is collaboration among multiple healthcare facilities to use their data to build efficient models together. However, facilities may not want to share the patient’s sensitive data with other facilities or with the central server. Federated Learning (FL) allows multiple parties to build models without sharing their data with each other. FL allows parties to train the model locally on their private data and only share the trained model parameters to the server. Server averages the model parameters sent by all the parties to build a robust global model. Server sends this updated model to each party who then again trains the model locally. This process continues till the model convergence. We propose using federated learning average on a large CXR dataset for multi-label classification. Our results show that federated learning achieves the accuracy of ~82% as compared to ~91% of that of traditional centralized training method. FL with more robust algorithms and larger datasets, can achieve performance comparable to centralized approach with an added advantage of collaborative learning with privacy preservation.
Authors - Hepin Gondaliya, Parth Monpara, Dhaval Shah Abstract - Multiplier is a crucial factor influencing processor performance, making its optimization vital for efficient computing. This paper introduces a multiplier design that achieves remarkable improvements, reducing resource utilization by more than half, logic delay to nearly a quarter and total power consumption to less than a quarter of compared to conventional radix-2 booth’s multiplied The proposed design was synthesized and implemented using Xilinx Vivado on a Zedboard FPGA, demonstrating its effectiveness and scalability for modern FPGA-based systems.. . .
Authors - R. R. Bhoge, Ranjit R. Keole, Pravin P. Karde Abstract - In the rapidly evolving landscape of network security, Intrusion Detection Systems (IDS) play an indispensable role in distinguishing normal network traffic from anomalies. While traditional machine learning models have achieved notable success, the introduction of quantum-assisted techniques opens new avenues for improving accuracy and reliability. This review delves into the application of quantum supervised learning for Intrusion Detection, emphasizing recent breakthroughs and comparing the advantages of quantum methods to classical counterparts. As digital networks become increasingly interconnected, the demand for robust Intrusion Detection Systems has grown exponentially. This paper investigates quantum-enhanced cybersecurity solutions, particularly the incorporation of quantum supervised learning, to improve the detection and classification of network intrusions. The review discusses the theoretical principles of quantum supervised learning, its unique attributes, and the advancement of quantum-enabled IDS. This paper provides a detailed overview of how quantum computing is revolutionizing machine learning for cybersecurity, with an emphasis on the enhanced capabilities of IDS through quantum-assisted methodologies.
Authors - Prasanna Tupe, Parikshit N. Mahalle Abstract - This study presents a machine learning-based method for using the K-Nearest Neighbors (KNN) algorithm to optimize swing trading strategies. The algorithm forecasts stock price fluctuations over the next seven days using historical stock market data and offers various levels of nuanced trading signals. By empowering traders to make more accurate and knowledgeable judgments, this method outperforms conventional binary buy-and-sell recommendations. The KNN technique was selected due to its instance-based learning, non-parametric nature, and simplicity, which make it interpretable and computationally efficient. The model's accuracy rate demonstrated how well it could forecast changes in stock prices. This study provides a workable approach for swing traders looking to optimize their profits while highlighting the important role that machine learning plays in tackling the difficulties associated with stock market prediction. This study lays the groundwork for using machine learning to enhance trading tactics and financial market decision-making.
Authors - Atharva Madhukar Nimbalkar, Madhukar Nimbalkar, Madhura Gondhalekar, Yashvendra Singh Dhandal, Parikshit Mahalle, Pankaj Chandre Abstract - The digital divide continues to pose significant challenges to delivering quality education, particularly in remote and underserved regions with limited or no internet connectivity. This paper explores the innovative integration of three advanced technological solutions within the Spacelink: Educational System, designed to bridge this gap. The system incorporates an AI-driven transcription and translation model using Whisper, a web-based Learning Management System (LMS), and a real-time communication platform utilizing WebRTC. These tools are tailored for offline functionality, operating effectively through LAN-based communication to ensure uninterrupted educational services. The AI transcription system enhances accessibility by converting spoken content into text and supporting multiple languages, addressing linguistic diversity and inclusivity. The LMS provides a robust offline platform for content delivery, course management, and student assessment, synchronizing data seamlessly when connectivity is restored. Meanwhile, the WebRTC-based communication tool facilitates real-time audio and video interactions optimized for low-bandwidth environments, promoting interactive learning experiences. Together, these technologies offer a holistic solution to overcome educational barriers, enabling equitable access to high-quality learning resources and fostering collaborative education in disconnected settings.
