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Daily Archives: November 9, 2024

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Park Ments: A Revolutionary Parking Application for the Modern City

Park Ments: A Revolutionary Parking Application for the Modern City/strong>
Authors:-Nikhil A. Patil, Utkarsha A. Salunkhe, Deepika S. Patil, Pooja S. Wagh, Professor Disha Nagpure

Abstract-Challenge due to limited spaces, high demand, and the difficulty of finding available spots. Park Ments is a cutting-edge mobile application designed to revolutionize parking in urban areas by providing real-time information on parking availability. Park Ments is a mobile application that provides real-time information on parking availability in cities, allowing drivers to find a parking spot quickly and easily. This application uses intelligence probability for finding a perfect parking spot which makes it easy to find a perfect parking spot. This parking spot sorted with the help of distance between the user and parking spot, price and it delivers accurate, up-to-date information to users. Park Ments predicts parking availability based on historical data and real-time traffic patterns, enabling drivers to plan their parking in advance, reducing time and stress. It offers features such as advance reservation, remote payment, and directions to parking spots, enhancing user convenience. For cities and parking operators, Park Ments helps reduce traffic congestion and optimize parking space usage. The user-friendly app will be available for both iOS and Android devices, free to download from the App Store and Google Play, with various pricing options including hourly, daily, and monthly passes. By transforming parking into a more efficient and convenient process, Park Ments aims to significantly improve urban parking experiences.

DOI: 10.61137/ijsret.vol.10.issue5.309
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Explainable AI for Enhanced Safety Signal Detection and Mitigation in Clinical Trials: Unveiling Insights from SDTM Data

Explainable AI for Enhanced Safety Signal Detection and Mitigation in Clinical Trials: Unveiling Insights from SDTM Data
Authors:Lasya Shree Sharma

Abstract-Clinical trials play a crucial role in ensuring the safety and efficacy of emerging drugs and treatments. However, the conventional statistical methods employed for analyzing adverse event (AE) data within Safety Domain Terminology Mapping (SDTM) datasets often lack transparency, posing challenges in interpretation and impeding targeted risk mitigation efforts. Addressing this issue, we propose a novel approach that involves harnessing Explainable AI (XAI) algorithms to discern key features and relationships relevant to specific safety signals within SDTM AE data. This paper delves into the potential transformative impact of employing XAI in conjunction with traditional safety analyses, thereby enhancing our comprehension of safety concerns and the overall effectiveness of risk management techniques. By leveraging XAI, we aim to not only uncover hidden patterns and correlations within the intricate web of AE data but also to provide a more interpretable framework for stakeholders involved in clinical trials. This innovative integration of XAI into safety analyses has the potential to significantly augment our ability to identify and understand safety signals, ultimately contributing to more informed decision-making in the realm of drug development and patient care.

DOI: 10.61137/ijsret.vol.10.issue1.308

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