Authors - Rajlaxmi Sunil Sangve, Riya Jha, Bhagyashri Narale, Sakshi Hosamani Abstract - Kidney disease is an asymptomatic disease, which leads to severe complications or even mortality if not diagnosed early. Routine diagnostic methods, such as serum-based tests and biopsies, are either less effective in the early stages of the disease. This paper proposes an automatic detection of kidney disease using CNNs applied to medical imaging data. Our model is designed to analyze computed tomography (CT) images for the identification of kidney disease, classifying normal and tumors. The proposed CNN architecture leverages deep learning techniques to extract features from these images and classify them with high accuracy. This paper aims to build a system for detection of kidney disease using CNN, based on a public dataset sourced from Kaggle. The paper involves several key stages, initiated from raw data preprocessing and feature selection, followed by training and evaluating machine learning model using CNN. Our proposed model demonstrated superior performance in kidney disease detection, achieving an accuracy of 95%.
Authors - Aman Bhimrao Kamble, Shafi Pathan Abstract - Deep Q-Learning (DQL) has emerged as a promising method for enhancing Network Intrusion Detection Systems (NIDS) by enabling dynamic and adaptive detection of evolving network threats. This review examines the strengths, limitations, and potential enhancements of using DQL in NIDS. Even while DQL increases the accuracy of anomaly detection and manages massive amounts of network data, it has drawbacks such slow convergence, high processing costs, and vulnerability to adversarial attacks. This study proposes improvements to overcome these problems, including efficient reward systems, hybrid architectures that combine DQL with other machine learning models, and continuous learning to adapt to changing threats. Recommendations for further research to enhance DQL's efficacy in real-time intrusion detection are included in the study's conclusion.
Authors - Shannon D’Souza, Rashmy Moray, Sridevi Chenammasetti, Shikha Jain Abstract - This study explores factors affecting investors' intentions to utilize Peer-to-Peer (P2P) lending platforms using the DeLone and McLean Information Systems Success (ISS) model and the Technology Acceptance Model (TAM). Data was collected through an online survey, yielding 283 valid responses from 350 distributed questionnaires, using snowball and convenience sampling. Structural Equation Modelling (SEM) with SmartPLS validated relationships between system quality, information quality, service quality, user satisfaction, social influence, and continuous intention to use. Findings indicate that system quality and service quality have a significant positive predictive effect on user satisfaction, while social influence positively affects ongoing usage intentions. Improved system performance, information accuracy, and service responsiveness can foster investor trust, platform adoption, and retention, guiding stakeholders and regulators toward an environment of stable and successful P2P lending.
Authors - Kumbhar Sanjivanee Rajan, Kulkarni Prachi Prashant, Pawar Prachi Baghwan, Patara Diya Milan, Abira Banik Abstract - “Holistic Heal” is an app developed using Flutter, tailored to address the growing interest in alternative healthcare options such as Ayurveda, Yoga, and Naturopathy. The app simplifies the process of discovering and accessing these specialized hospitals and wellness centers, making holistic healthcare more accessible to users. Harnessing the capabilities of geolocation services enables users to find the nearest facilities, offering detailed information for each. The app also ensures a user-friendly experience. Built with a focus on promoting holistic well-being, “Holistic Heal” showcases the potential of technology to enhance traditional healing practices and empower individuals in their journey towards a healthier, more balanced lifestyle. Depending on their demands, the patient can search the hospital. Upon the patient’s request, this application offers the hospital and physician details that are currently accessible. A suggested application has been designed to find the closest hospital with the requested medical specialty. Hospitals’ nearest locations are identified using the Global Positioning System (GPS), which provides real-time geographic data by triangulating signals from satellites. This data, integrated into smartphones, is combined with Google Maps Application Programming Interfaces (APIs) to determine optimal routes from the user’s current location to hospitals, accounting for road networks, traffic, and travel modes. A patient can use this application to discover the closest hospital based on the availability of expert consultants. This application that was built is easy to use and effectively gives patients the necessary information.
Authors - Malay Shah, Sayal Goyal, Rashmi Rane, Ruhi Patankar, Sarika Bobde, Arnav Jain Abstract - This study investigates the impact of risk-taking on football match outcomes, focusing on player substitutions. The analysis reveals that risk-taking propensity peaks when a team is trailing by 2-3 goals and diminishes when leading by the same margin. Younger managers outperform middle-aged ones in risky decisions, while older managers excel in later substitutions. Additionally, a manager's tenure with the team increases the effectiveness of risk-taking, particularly in earlier substitutions and stronger teams. This study also emphasizes the importance of mental state in player performance, proposing a framework combining Match Score Analysis (Kaplan-Meier Fitter) and Score Analysis to evaluate players' mental stability and survival rates during the game. By integrating these models, teams can make better-informed decisions regarding substitutions, considering both past performance and mental health, ultimately enhancing match outcomes. This research underscores the synergistic potential of combining black-box causal machine learning with interpretable models, offering valuable insights for football management and beyond.
Authors - Sanjana, Sukanya Sharma, Dipty Tripathi Abstract - Handwritten digit recognition is a key application in image processing and pattern recognition, with wide usage in areas such as postal services, banking, and mobile applications. This research paper presents a performance comparison between traditional machine learning models and deep learning models for accurate handwritten digit classification. The study focuses on developing a mobile application using Flutter integrated with TensorFlow Lite and Firebase to deliver real-time predictions. The app performs preprocessing on input images and employs model inference for efficient and accurate digit recognition. The objective is to determine the most effective model in terms of speed and accuracy for on-device predictions, emphasizing usability and real-time response
