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Author Archives: Kajal Tripathi

Heart Disease Prediction Using Machine Learning Techniques in Python: A Review

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Heart Disease Prediction Using Machine Learning Techniques in Python: A Review/strong>
Authors:-Tanmay Deshmukh, Supriya Kharatmol, Professor Nishant Patil

Abstract-As the global incidence of heart disease escalates daily, there is an urgent imperative to accurately predict and diagnose these conditions efficiently. Heart illness, also referred to as cardiovascular disease, is a broad category of conditions that affect the heart, including congenital abnormalities, vascular problems, and cardiac arrhythmias. In recent decades, it has emerged as one of the world’s top causes of death. Thus, it is imperative to create accurate and trustworthy techniques for early disease detection .Heart illness, also referred to as cardiovascular disease, is a broad category of conditions that affect the heart, including congenital abnormalities, vascular problems, and cardiac arrhythmias

DOI: 10.61137/ijsret.vol.10.issue5.317
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Space Debris Tracking and Prediction Models

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Space Debris Tracking and Prediction Models/strong>
Authors:-Sakshi Khedekar, Jayesh Jadhav, Jiya Mokalkar, Pratik Patil, Professor Manisha Mali

Abstract-In a growing risk for space activities intentionally located or accidentally resulting from the creation of space debris, monitoring and forecasting are indispensable for the protection of both crewed and uncrewed space missions. The paper presents the assessment of eight most widespread space debris tracking and prediction models: TLE based SGP4, ORDEM, MASTER, Debrisat, SDebrisNet, SDTS, CARA, SSN. For every model, a multi-faceted approach with respect to its various characteristics, accuracy, complexity, data requirement, adaptability, reliability, and usability is employed. This appraisal provides the benefits and associated drawbacks of each methodology in tackling the major issues of data, computation and construction of the complete system. The research further considers the progress of tracking devices and existing systems as well as possibilities of their improvement for the realtime challenges. The comparative assessment of the models presented in this paper will help to strategically improve current approaches to space debris control instruments, thus supporting safety and long-term operating trends in outer space. This study has been carried out in order to devise strategies that will fit the growing and dynamic endeavors of exploring space, by tracking debris with the utmost efficiency and precision.

DOI: 10.61137/ijsret.vol.10.issue5.316
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ECG Signal Classification Using Fine-Tuned MobileNetV2 for Cardiovascular Disease Detection

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ECG Signal Classification Using Fine-Tuned MobileNetV2 for Cardiovascular Disease Detection/strong>
Authors:-Assistant Professor Nadikatla Chandrasekhar, Chennapragada Tarun, Gorle vassudeva rao, Burada Jeevan

Abstract-Cardiovascular disease, otherwise referred to as heart disease, represents one of the most common and fatal illnesses that entails injuring the heart as well as the blood vessels. These, in turn, can cause a range of complications such as myocardial ischemia, for instance, coronary artery disease, or heart failure. The appropriate and timely identification of heart conditions. Clinical practice is determined by the relevance of the illness. The sickness known as heart disease, also known as cardiovascular disease, is common and, sadly, harmful. This condition deals with the morbidity and mortality associated with the heart and blood vessels. This can cause numerous issues such as myocardial ischemia, coronary heart problems and heart failure. A timely and correct identification of heart ailments. clinical practice is guided by the relevance of the disease. Being able to identify those at risk allows for preventative measures, preventative actions, and individualized treatment plans to lessen the negative effects and slow the disease’s course. The identification of cardiac disease has seen significant growth in recent years. major improvements as a result of the incorporation of the complex. Technology and methods based on computation. Among them is the machine. predictive modeling, data mining methods, and learning algorithms frameworks that make extensive use of physiological and clinical data information.

DOI: 10.61137/ijsret.vol.10.issue5.315
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A Cost-Benefit Analysis of Material Handling on the Productivity of Food and Beverage Manufacturing Industries

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A Cost-Benefit Analysis of Material Handling on the Productivity of Food and Beverage Manufacturing Industries/strong>
Authors:-Ms. Krupa Shetty

Abstract-Food and Beverage manufacturing companies face challenges today due to their high competitiveness, poor working conditions, and more stringent regulations. Growing demand for high quality products and frequent changes in the variety of products by the consumers had an impact on the viability of the food and beverages manufacturing sector. Working conditions for many food and beverages operatives are difficult, as it requires large number of labours for handling. In the present scenario, the production cost increases due to the handling of material from one place to another inside the factory by using the unskilled labour. Due to shortage of labour majority of the manufacturing industries are facing problem and there is a drastic reduction in total output and not achieving the required target is a common weakness In most of the small scale food and beverage manufacturing companies manual handling is adopted to transfer the raw material from one place to other, transfer of semi- finished material from one equipment to other and finally transfer the final finished products to the packing section and storage division. In all these stages movement of material takes place with help of semi-skilled workers. Because of this required quality is not achieved. Finally cost of the product increases, which they are not in a position to match the competitive market. One of the major reasons for slow growth of the Indian Economy is the improper handling of materials and unnecessary costs incurred. This research paper focuses on the benefits of utilising the material handling system with properly planed plant layout and automation, there is a drastic reduction labour cost and it avoids the damage caused by manual movement of material, which results in better- quality product with less cost of production. Good handling system also improve inventory control, less fatigue of workers, greater industrial safety with less accident potential and disruption of work, improved morale of workers.

DOI: 10.61137/ijsret.vol.10.issue5.314
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Student Voting Election Portal

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Student Voting Election Portal/strong>
Authors:-Professor Swati Shinde, Vaishnavi Borse, Resham Umale, Shraddha Sonwane

Abstract-With advancements in technology, traditional voting methods are evolving, offering more advanced solutions like online voting portals. A Student Voting Election Portal provides a modern and secure way for students to participate in elections from any location with internet access, eliminating the need for physical polling stations. This online system offers several benefits, such as improved accessibility, time and resource efficiency, greater accuracy, and transparency, making the voting process more democratic. Critical to the success of such a platform are proper voter verification and the accurate management of student information. While online voting systems have been implemented successfully in various contexts, there are still challenges and limitations to overcome for widespread adoption. This paper will explore different types of electronic voting, examine successful implementations of student election portals, and compare them to traditional voting methods, highlighting current trends and potential future developments.

DOI: 10.61137/ijsret.vol.10.issue5.313
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Easy Trade: Forex Trading bot Using Artificial Intelligence

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Easy Trade: Forex Trading bot Using Artificial Intelligence/strong>
Authors:-Professor Alim Khan, Rudransh Sharma, Abhinav Shukla, Kshitij Khare, Shivam Shukla

Abstract-The foreign exchange (forex) market, with its high liquidity and 24/5 trading hours, presents significant opportunities for investors. This paper discusses the development of a forex trading AI bot by a group of four college students, leveraging Python for programming and various analytical sources for strategy formulation. The project aims to create an automated trading system that utilizes machine learning algorithms and technical indicators to make informed trading decisions.

DOI: 10.61137/ijsret.vol.10.issue5.312
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Social Media Insights

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Social Media Insights/strong>
Authors:-Saniya M. Kadmude, Shrutika D. Bansode, Vedant S. Joge, Professor Prachi Tamhan

Abstract-This research presents a comprehensive sentiment analysis system tailored for social media comments, aiming to classify user sentiments into positive, negative, or neutral categories. With Social media’s vast user engagement—over 1 billion unique users generating extensive comment data—there exists a significant opportunity to derive insights into public opinions. This study addresses challenges inherent in analyzing social media comments, including the high volume of data, diverse linguistic expressions, the use of slang, emojis, sarcasm, and the presence of spam. We leverage a constructed annotated corpus comprising 1500 citation sentences, which underwent rigorous data normalization to enhance quality and consistency. Six machine learning algorithms— Naïve Bayes, Support Vector Machine (SVM), Logistic Regression, Decision Tree, K-Nearest Neighbor (KNN), and Random Forest (RF)—were implemented for sentiment classification. The performance of these algorithms was evaluated using various metrics, including F-score and accuracy, demonstrating a correlation between sentiment trends and real-world events associated with specific keywords. This work contributes to the field of sentiment analysis by providing insights that can aid researchers in identifying quality research papers and understanding user attitudes towards video content.

DOI: 10.61137/ijsret.vol.10.issue5.311
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Fake Profile Identification and Classification Using Machine Learning

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Fake Profile Identification and Classification Using Machine Learning/strong>
Authors:-Professor Disha Nagpure (HOD), Professor Shilpa Shide (Guide) Vaishnavi Gaikwad, Vaishnavi Panchal, Vikrant Kothimbire, Vinay Makwana

Abstract-This paper details the design and implementation of Social media platforms are essential for communication today, allowing people to connect, share, and interact. However, the rise of fake profiles on sites like Instagram creates significant challenges related to user privacy, security, and trust. This research proposes a new approach to identify and classify these fake profiles using machine learning techniques. The findings contribute to ongoing efforts to combat fake accounts, promoting a safer and more trustworthy online environment. By leveraging machine learning and a thorough set of features, the model shows promising results in detecting and categorizing fake profiles. This research also opens up opportunities for further exploration, such as integrating different data sources and adapting the model for use on other social media platforms.

DOI: 10.61137/ijsret.vol.10.issue5.310
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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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