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Park Ments: A Revolutionary Parking Application for the Modern City

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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

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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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A Short Review on Botany, Phytochemistry and Medicinal Potential of Christ’s Thorn Jujube

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A Short Review on Botany, Phytochemistry and Medicinal Potential of Christ’s Thorn Jujube/strong>
Authors:-Ruwa Talib Arffa, Sivamani Selvaraju

Abstract-Ziziphus spina-christi, commonly known as Christ’s thorn jujube, is a hardy deciduous shrub native to arid and semi-arid regions of Africa and the Middle East. This species is characterized by its thorny branches, small, yellow-green flowers, and edible drupes. Z. spina-christi is of considerable ecological and economic importance; it plays a vital role in soil stabilization and desert reclamation due to its deep root system. Additionally, the plant has various traditional uses, including medicinal applications, as a source of fodder, and for its wood, which is valued for its durability. Recent studies have highlighted its potential in sustainable agriculture and agroforestry, particularly in drought-prone areas. The present review highlights the botanical characteristics, ecological significance, traditional uses, and potential applications of Z. spina-christi , underscoring its value in both cultural practices and environmental conservation.

DOI: 10.61137/ijsret.vol.10.issue5.307
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Vehicle-Focused Traffic Mapping for Forecasting Urban Movement and Detecting Peak Congestion Periods

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Vehicle-Focused Traffic Mapping for Forecasting Urban Movement and Detecting Peak Congestion Periods/strong>
Authors:-Atharva Daga, Aditya Wandhekar

Abstract-Effectively managing urban traffic dynamics is essential for optimized city planning and administration. This research focuses on a vehicle-centric approach to traffic mapping, aiming to predict congestion levels and identify peak traffic times within urban areas. The main objective is to forecast daily traffic density and detect periods of high congestion to support improved traffic management. To achieve this, we analysed real-time CCTV footage from Nasik Smart City Office, collected from key routes—Pathardi Gaon Circle and Golf Club Ground Circle — over a continuous five-day span. The findings confirm that real-time CCTV data delivers accurate congestion predictions and enhances traffic control strategies. By applying this methodology, we provide a reliable solution for traffic authorities, enabling them to take proactive measures to mitigate traffic congestion and improve overall traffic flow. This research contributes to the advancement of intelligent transportation systems, highlighting the value of incorporating real-time data into urban traffic management solutions.

DOI: 10.61137/ijsret.vol.10.issue5.306
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Advanced Multi Model RAG Application

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Advanced Multi Model RAG Application/strong>
Authors:-Professor Disha Nagpure, Sujal Pore, Shardul Deshmukh, Aditya Suryawanshi

Abstract-This paper presents a modular, context-aware multimodal Retrieval-Augmented Generation (RAG) application that leverages both chain-based and agentic execution strategies. Powered by Gemini 1.5 Flash as the core language model, the system integrates Langchain and Langsmith frameworks to enable dynamic document retrieval, task orchestration, and seamless handling of multiple data sources. Key features include a YouTube summarizer using transcript APIs, real-time web search via the Tavily search tool, and support for text, image, and audio inputs, with OpenAI’s Whisper model for speech-to-text conversion. The application’s contextual awareness is enhanced by chat memory fallback functions, ensuring continuous, coherent interaction across sessions. Additionally, vector databases are employed for efficient multimodal retrieval. This system represents a significant advancement in RAG applications, offering flexibility, scalability, and adaptability across various input modalities and real-time tasks.

DOI: 10.61137/ijsret.vol.10.issue5.305
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A Study on Consumer Attitudes towards Organic Skincare Products among Young Adults in Urban Areas

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A Study on Consumer Attitudes towards Organic Skincare Products among Young Adults in Urban Areas/strong>
Authors:-Smeet Raut

Abstract-This study aims to explore consumer attitudes towards organic skincare products, focusing specifically on young adults residing in urban areas. The growing demand for organic products has transformed the skincare industry, with consumers increasingly seeking products that align with their values of health, sustainability, and ethical consumption. This research investigates the motivations, preferences, and purchasing behaviours of young urban consumers, examining how factors such as environmental concerns, health consciousness, and brand perception influence their choices in skincare products. Utilizing a mixed-methods approach, the study employs quantitative surveys and qualitative interviews to gather comprehensive data on consumer attitudes. The survey targets a diverse sample of young adults aged 18 to 35, encompassing various demographics and lifestyles within urban settings. The qualitative component further enriches the findings by providing deeper insights into the underlying motivations behind consumers’ preferences for organic skincare products. Preliminary findings indicate that young adults are significantly influenced by the perceived benefits of organic ingredients, such as their natural composition and lower environmental impact. Additionally, social media and peer recommendations play a crucial role in shaping their purchasing decisions. The study highlights the importance of transparency in marketing and the need for brands to effectively communicate the benefits of organic skincare products to engage this demographic. By understanding the attitudes and behaviours of young consumers towards organic skincare, this research aims to provide valuable insights for marketers and industry stakeholders, ultimately contributing to more effective strategies in the rapidly evolving skincare market. The findings will also pave the way for future research exploring the broader implications of consumer attitudes on the organic product industry as a whole.

DOI: 10.61137/ijsret.vol.10.issue5.304
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Revolutionizing Gratitude Humanizing Tipping Culture and Empowering Unseen Contributors through Digital Recognition

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Revolutionizing Gratitude Humanizing Tipping Culture and Empowering Unseen Contributors through Digital Recognition/strong>
Authors:-Tania, Professor Vanita Rani

Abstract-Tipping culture, a long-standing custom in many service sectors, has changed dramatically as digital platforms and technology have grown in popularity. The core of thankfulness, though, which is to recognize and empower the invisible contributors who work behind the scenes, is still mostly ignored. Using digital recognition, this article investigates the idea of “humanizing” tipping, emphasizing how digital platforms might transform the distribution and expression of gratitude. Blockchain, mobile apps, and peer-to-peer recognition are examples of technical advancements that service providers can use to make sure that frontline and background workers receive just recognition and compensation. In addition to increasing tipping’s monetary worth, this digital revolution fosters an inclusive and appreciative culture. The study highlights the potential socio-economic effects, psychological advantages, and ethical ramifications of strengthening frequently disregarded contributions through a more open and equal tipping ecology.

DOI: 10.61137/ijsret.vol.10.issue5.302
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Build Your Own SOC Lab

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Build Your Own SOC Lab/strong>
Authors:-Monika Sahu, Kanakmedala Kashish, Assistant Professor Neelam Sharma, Dr. Siddhartha Choubey

Abstract-The “Build your SOC Lab” project is designed to address the pressing need for robust cybersecurity measures in today’s digital landscape. It provides a comprehensive guide tailored to organizations and individuals seeking practical resources in digital security. Emphasizing cost-effectiveness, adaptability, and scalability, it offers detailed instructions for setting up a functional SOC lab. Covering essential components like hardware, software tools, and network infrastructure, the project ensures thorough preparation for cybersecurity challenges. It delves into various use cases, including threat detection, incident response, and security monitoring, facilitating hands-on learning in SOC operations. By enhancing stakeholders’ capabilities in safeguarding digital assets and mitigating cyber threats, the project contributes to the resilience and security of modern digital ecosystems. Through practical insights and methodologies, it empowers individuals and organizations to navigate the evolving cybersecurity landscape effectively.

DOI: 10.61137/ijsret.vol.10.issue5.300
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Organic Farming and Climate Change Mitigation

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Organic Farming and Climate Change Mitigation/strong>
Authors:-Rohan Raju Thomas, Dr Gurshaminder Singh

Abstract-Organic farming has gained significant attention as a sustainable agricultural practice with potential benefits for climate change mitigation. This paper presents a comprehensive review of the literature on the role of organic farming in mitigating climate change. The review examines various aspects such as carbon sequestration, reduced greenhouse gas emissions, soil health improvement, biodiversity conservation, and resilience to climate variability. The findings highlight the potential of organic farming practices to contribute positively to climate change mitigation efforts. Key challenges and future research directions in this field are also discussed. The analysis draws upon a range of studies and scholarly articles to support the assertions made regarding the positive role of organic farming in climate change mitigation. Additionally, challenges and future prospects in this field are explored, emphasizing the need for further research and policy support to harness the full potential of organic farming for sustainable agriculture and climate resilience. Organic farming has gained prominence as an environmentally friendly agricultural approach with the potential to mitigate climate change impacts. This paper presents a synthesized overview of the contributions of organic farming practices to climate change mitigation. Climate change is one of the most pressing issues facing the world today, and agriculture is a significant contributor to greenhouse gas emissions. Organic farming has gained popularity as a more sustainable alternative to conventional farming practices, but what impact does it have on mitigating climate change? This essay will explore the impact of organic farming on climate change mitigation, the effectiveness of organic farming in mitigating climate change, and the challenges and limitations of organic farming in mitigating climate change.

DOI: 10.61137/ijsret.vol.10.issue5.301
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Enhancing Cardiovascular Disease Prediction with XAI Technique Using Machine Learning

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Enhancing Cardiovascular Disease Prediction with XAI Technique Using Machine Learning/strong>
Authors:-Assistant Professor Dr.N.Chandrasekhar, P. Sravani, V.Charishma, N.Padmavathi, SK. Abdul Khadar, S.Rajeswari

Abstract-Globally, coronary diseases (CV) are several of the most significant causes of demise, improvements in predictive healthcare technologies are imperative. The goal of this study is to improve the predictability and interpretability of cardiovascular disease prediction models by combining machine learning methods with Explainable Artificial Intelligence (XAI). To create reliable predictive models, we investigate a range of machine learning algorithms, such as ensemble approaches, logistic regression, and XG-Boost. But while though precision is crucial, these predictions’ interpretability is just as crucial for therapeutic use. Our goal is to make model procedures for making decisions concise and intelligible for physicians by utilising XAI techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). Using a real-world CVD dataset, our tests demonstrate that XAI-enhanced models do not not only increase the accuracy of predictions but also identify important variables affecting heart function. By providing a workable framework for using interpretable machine learning models in healthcare, this study advances the discipline and may result in better clinical judgements and more individualised patient care.The accuracy of the Random forest-CARDIO system is assessed against the Framingham heart disease dataset using the Colab Simulator. In the experiment, Random forest demonstrated a significant accuracy score of 91.38%, which is appreciably better than alternative techniques including, XGBoost (90.01%), RNN (85.02%), GRU (85.02%) and RNN+GRU (as a combined model) (86%).

DOI: 10.61137/ijsret.vol.10.issue5.299
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