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Volume 13 | Issue 6 |

Volume 13 | Issue 6 | Month  
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  Paper Title: Advanced Rail Track Defect Detection Using Deep Learning

  Author Name(s): Gourav, Ms. Ruchi Patira, Ms. Monika Saini

  Published Paper ID: - IJCRT2506026

  Register Paper ID - 288371

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506026 and DOI :

  Author Country : Indian Author, India, - , -, - , | Research Area: Science and Technology

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506026
Published Paper PDF: download.php?file=IJCRT2506026
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506026.pdf

  Your Paper Publication Details:

  Title: ADVANCED RAIL TRACK DEFECT DETECTION USING DEEP LEARNING

 DOI (Digital Object Identifier) :

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Science and Technology

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a226-a239

 Year: June 2025

 Downloads: 108

  E-ISSN Number: 2320-2882

 Abstract

Railway infrastructure is a fundamental pillar of modern transportation networks, playing a critical role in facilitating the movement of goods and passengers across vast geographical regions. Its reliability, cost-efficiency, and ability to handle large volumes make it indispensable for both urban and rural connectivity. However, the continuous exposure to dynamic loads, environmental stressors, and operational wear renders rail tracks susceptible to a wide range of structural defects, such as cracks, surface wear, and misalignments. These defects, if not identified and addressed promptly, can escalate into severe safety hazards, potentially leading to derailments, delays, or costly repairs. Traditionally, rail track inspection has relied heavily on manual monitoring by field personnel or basic mechanical systems. While effective to a degree, these methods are inherently limited by human fatigue, subjective judgment, and the inability to conduct continuous or large-scale inspections efficiently. As a result, there has been a growing emphasis on adopting intelligent, automated systems that can offer real-time, high-precision defect detection.


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Advanced Rail Track Defect Detection Using Deep Learning

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  Paper Title: Comprehensive Review of Machine Learning Techniques for Credit Card Fraud Detection: Challenges, Solutions, and Future Directions.

  Author Name(s): Ravindra Aggarwal, Suraj Kumar, Ketan Jain, Divyanka Rai, Prem Sunka

  Published Paper ID: - IJCRT2506025

  Register Paper ID - 287635

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506025 and DOI :

  Author Country : Indian Author, India, 410210 , mumbai, 410210 , | Research Area: Science and Technology

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506025
Published Paper PDF: download.php?file=IJCRT2506025
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506025.pdf

  Your Paper Publication Details:

  Title: COMPREHENSIVE REVIEW OF MACHINE LEARNING TECHNIQUES FOR CREDIT CARD FRAUD DETECTION: CHALLENGES, SOLUTIONS, AND FUTURE DIRECTIONS.

 DOI (Digital Object Identifier) :

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Science and Technology

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a218-a225

 Year: June 2025

 Downloads: 125

  E-ISSN Number: 2320-2882

 Abstract

Credit card fraud has become a significant threat in the digital age, necessitating the development of robust and intelligent detection systems. This paper presents a comprehensive review of machine learning techniques applied to credit card fraud detection, analyzing their strengths, limitations, and real-world applicability. Various supervised, unsupervised, and hybrid approaches are critically examined, with a focus on performance metrics, data imbalance handling, and adaptability to evolving fraud patterns. The review also explores current challenges such as data privacy, scalability, and interpretability, while proposing future research directions to enhance detection accuracy and efficiency. This study aims to provide researchers and practitioners with valuable insights for developing more effective and resilient fraud detection frameworks.


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 Keywords

Credit Card Fraud Detection, Machine Learning, Supervised Learning, Unsupervised Learning, Data Imbalance, Fraud Analytics, Anomaly Detection, Model Interpretability, Cybersecurity, Financial.

  License

Creative Commons Attribution 4.0 and The Open Definition


  Paper Title: Healthcare

  Author Name(s): Prof.Kamble S.A., Prerana Misal, Pragati Sawant, Aishwarya Gadekar, Pooja Ghogare

  Published Paper ID: - IJCRT2506024

  Register Paper ID - 286225

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506024 and DOI :

  Author Country : Indian Author, India, 413504 , Bhoom, 413504 , | Research Area: Science and Technology

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506024
Published Paper PDF: download.php?file=IJCRT2506024
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506024.pdf

  Your Paper Publication Details:

  Title: HEALTHCARE

 DOI (Digital Object Identifier) :

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Science and Technology

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a213-a217

 Year: June 2025

 Downloads: 107

  E-ISSN Number: 2320-2882

 Abstract

This paper presents an Android-based healthcare application designed to enhance accessibility to medical services for patients and healthcare providers. The application allows users to book appointments, maintain digital health records, receive medication reminders, and consult doctors remotely. It aims to simplify the interaction between patients and healthcare professionals, especially in remote or underserved areas. The system leverages mobile technology to provide a user-friendly interface, real-time updates, and secure data handling. This solution promotes efficiency, reduces paperwork, and supports digital transformation in the healthcare sector.


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 Keywords

Android Application, Healthcare, Firebase, Patient Management, Telemedicine.

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Creative Commons Attribution 4.0 and The Open Definition


  Paper Title: Emotion Meets Motion: A Unified, Context-Aware Music Recommender Leveraging Real-Time Facial Analysis and Video-Based Activity Detection

  Author Name(s): Dnyaneshwari Dhumal, Aarya Joshi, Akanksha Ghadge, Abhimanyu Giri, Balaji Chaughule

  Published Paper ID: - IJCRT2506023

  Register Paper ID - 286683

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506023 and DOI :

  Author Country : Indian Author, India, 412307 , Pune, 412307 , | Research Area: Science and Technology

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506023
Published Paper PDF: download.php?file=IJCRT2506023
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506023.pdf

  Your Paper Publication Details:

  Title: EMOTION MEETS MOTION: A UNIFIED, CONTEXT-AWARE MUSIC RECOMMENDER LEVERAGING REAL-TIME FACIAL ANALYSIS AND VIDEO-BASED ACTIVITY DETECTION

 DOI (Digital Object Identifier) :

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Science and Technology

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a198-a212

 Year: June 2025

 Downloads: 114

  E-ISSN Number: 2320-2882

 Abstract

: Personalized media experiences are rapidly evolving from static, preference-based models to dynamic, context-aware systems that respond in real-time to users' emotional states and activities. In this paper, we present a novel, integrated pipeline that fuses real-time facial emotion detection (captured via webcam) and offline activity recognition (analyzing uploaded video files) to drive a contextual song recommendation engine. The system comprises three tightly coupled modules: a Kivy-based GUI application leveraging OpenCV and DeepFace for low-latency facial affect analysis; a Flask web service for user management, video ingestion, and recommendation logic; and an offline video processor employing an Ultralytics YOLOv5 model fine-tuned for "running" and "sleeping" activities. We detail data collection and annotation procedures, model architectures and training regimes, algorithmic pseudocode, deployment via container orchestration, and front-end integration. Quantitative evaluation demonstrates 87-90% accuracy in seven-class emotion classification, 90.1% mAP in two-class activity detection, and round-trip latencies under 100 ms for emotion feedback. A user study with thirty participants reports 92% satisfaction with recommendation relevance and 4.6/5 mean perceived utility. Compared to standalone emotion- or activity-based recommenders, our unified approach yields a 25% uplift in personalization metrics. We conclude by mapping future research avenues: expanding affective and activity taxonomies, reinforcement-learning driven playlist adaptation, multimodal sensor fusion, and on-device inference for privacy.


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 Keywords

: Convolutional Neural Networks, Facial Expression Recognition, Activity-Based Learning, Machine Learning, Emotion Identification, Mood-Based Music Recommendation, Personalized Audio Experience.

  License

Creative Commons Attribution 4.0 and The Open Definition


  Paper Title: Plant-Based Antimicrobials In Paediatric Dentistry: Exploring A Natural Approach To Oral Health

  Author Name(s): Manib Ratnam Deka Sinha, Manohar Bhat, Abhishek Khairwa, Karn Anjali Yateenra, Sandeep Mukherjee

  Published Paper ID: - IJCRT2506022

  Register Paper ID - 285212

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506022 and DOI :

  Author Country : Indian Author, India, 781016 , Guwahati, 781016 , | Research Area: Humanities All

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506022
Published Paper PDF: download.php?file=IJCRT2506022
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506022.pdf

  Your Paper Publication Details:

  Title: PLANT-BASED ANTIMICROBIALS IN PAEDIATRIC DENTISTRY: EXPLORING A NATURAL APPROACH TO ORAL HEALTH

 DOI (Digital Object Identifier) :

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Humanities All

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a190-a197

 Year: June 2025

 Downloads: 108

  E-ISSN Number: 2320-2882

 Abstract

Oral diseases have a significant impact on the quality of life of children. Early exposure to irritants in the infant's environment (e.g., bacteria or sugars) can cause oral problems. Many synthetic compounds have strong antimicrobial activity and consequently are widely utilized in pediatric medicine, they may have side effects such as the disruption of the natural micro-flora, leading to microbial resistance. These aspects thus suggest the need for studies and the development of alternative antimicrobials. Potable plant extracts have been widely used as therapeutic agents in oral health, with an important number of active components. The antimicrobial activities of these agents have been tested side by side with conventional antibiotic treatments. Furthermore, the introduction of plant-derived antimicrobials is receiving a growing interest from the pharmaceutical industry because of their effectiveness and increased safety margin as compared to their synthetic analogues. Plant-based antimicrobials hold promise for improving pediatric oral health by providing safe and effective alternatives to synthetic agents. However, further research and development are necessary to fully realize their potential.


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 Keywords

Antimicrobial agents, alternative antimicrobials, plant extracts, Microbial Ecology, Antimicrobial Resistance, Flavonoids, Terpenoids, Alkaloids

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Creative Commons Attribution 4.0 and The Open Definition


  Paper Title: Language as a Soft Power : A Case Study of Hindi Language Influence in China

  Author Name(s): INDRAJEET MISHRA

  Published Paper ID: - IJCRT2506021

  Register Paper ID - 288414

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506021 and DOI : https://doi.org/10.56975/ijcrt.v13i6.288414

  Author Country : Indian Author, India, 442001 , Wardha, 442001 , | Research Area: Languages

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506021
Published Paper PDF: download.php?file=IJCRT2506021
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506021.pdf

  Your Paper Publication Details:

  Title: LANGUAGE AS A SOFT POWER : A CASE STUDY OF HINDI LANGUAGE INFLUENCE IN CHINA

 DOI (Digital Object Identifier) : https://doi.org/10.56975/ijcrt.v13i6.288414

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Languages

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a183-a189

 Year: June 2025

 Downloads: 117

  E-ISSN Number: 2320-2882

 Abstract

In today's world, the role of language has evolved from merely a communication tool to a powerful asset in the soft power arsenal. Attempts to globalize a language can be beneficial for effectively deploying other soft power instruments, since language is the vehicle of culture, ideas, and vision. It thereby contributes to economic and political influence in an increasingly globalized world. In this paper, based on Joseph Nye's theory of soft power, I will explore the role of language as a constituent of soft power, with a focus on examining the competence of the Hindi language as a soft power tool and its potential influence in China.


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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Soft-power, Language, Hindi, China

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Creative Commons Attribution 4.0 and The Open Definition


  Paper Title: The Role Of Artificial Intelligence In Transforming Retail And Supply Chain Management

  Author Name(s): Tawheed, Sushma Swaraj

  Published Paper ID: - IJCRT2506020

  Register Paper ID - 287949

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506020 and DOI :

  Author Country : Indian Author, India, 560068 , Bengaluru, 560068 , | Research Area: Management All

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506020
Published Paper PDF: download.php?file=IJCRT2506020
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506020.pdf

  Your Paper Publication Details:

  Title: THE ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING RETAIL AND SUPPLY CHAIN MANAGEMENT

 DOI (Digital Object Identifier) :

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Management All

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a175-a182

 Year: June 2025

 Downloads: 129

  E-ISSN Number: 2320-2882

 Abstract

This paper examines the integration of Artificial Intelligence (AI) in the retail and supply chain sectors across India and globally. Drawing from 40 research studies, it delves into AI applications in customer experience, inventory and demand forecasting, logistics optimization, and sustainability initiatives. It highlights benefits like improved efficiency and personalization, while also addressing challenges such as data quality, ethics, and costs. The paper includes case studies and concludes with actionable recommendations for leveraging AI in modern retail and logistics.


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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Retail, Supply Chain, Customer Experience, Demand Forecasting, Sustainability, Automation, Predictive Analytics

  License

Creative Commons Attribution 4.0 and The Open Definition


  Paper Title: The Pharmacology of Cannabis: A Review of its Therapeutic Potential

  Author Name(s): Adinath Mahendra Deokate, Kiran Kashinath Jadhav, Snehal Prabhakar Jadhav, Nikita Gulab Pawar, Pallavi Rajendra Pise

  Published Paper ID: - IJCRT2506019

  Register Paper ID - 288056

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506019 and DOI :

  Author Country : Indian Author, India, 415509 , MHASWAD, 415509 , | Research Area: Pharmacy All

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506019
Published Paper PDF: download.php?file=IJCRT2506019
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506019.pdf

  Your Paper Publication Details:

  Title: THE PHARMACOLOGY OF CANNABIS: A REVIEW OF ITS THERAPEUTIC POTENTIAL

 DOI (Digital Object Identifier) :

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Pharmacy All

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a165-a174

 Year: June 2025

 Downloads: 116

  E-ISSN Number: 2320-2882

 Abstract

The legalization of cannabis for medical purposes is an increasingly global trend, supported by a growing body of scientific evidence demonstrating its therapeutic efficacy for a variety of conditions. Concurrently, many prescribers have voiced concerns that this increased utilization may lead to the development of cannabis use disorder in patients. While cannabis use disorder has been extensively studied in recreational users, with findings often extrapolated to medical cannabis patients, research specifically addressing dependence on medical cannabis remains limited, and standardized methodologies for assessing this phenomenon are lacking. This article presents a narrative review of existing research, aiming to determine the relevance and applicability of concerns regarding dependence in recreational cannabis users to patients prescribed medical cannabis. The review focuses on key factors related to medical cannabis and dependence, including the influence of dosage, potency, cannabinoid composition, pharmacokinetics, administration route, frequency of use, and the crucial role of set and setting. Significant differences between medical and recreational cannabis use are highlighted, underscoring the difficulties inherent in extrapolating data from recreational use studies. Given the numerous unanswered questions surrounding the potential for dependence arising from medical cannabis use, it is imperative that these issues be addressed to effectively minimize potential harms. This review culminates in seven recommendations designed to enhance the safety of medical cannabis prescribing practices. It is anticipated that this review will contribute to a deeper understanding of the complexities surrounding medical cannabis dependence.


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 Keywords

Medical cannabis, Dependence, Recreational cannabis use, Dosage, Potency, Prescribing practices, Cannabinoid composition, Pharmacokinetics, Administration route, Frequency of use, Cannabis use disorder, Harm reduction, Recommendations, Narrative review.

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Creative Commons Attribution 4.0 and The Open Definition


  Paper Title: Simulation & Optimization of Communicable Fault Passage Indication System

  Author Name(s): Chetan Biradar, Komal Tidke, Shivraj Gaikwad, Mrs.Rani Phulpagar

  Published Paper ID: - IJCRT2506017

  Register Paper ID - 288389

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506017 and DOI :

  Author Country : Indian Author, India, 412207 , Pune, 412207 , | Research Area: Science and Technology

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506017
Published Paper PDF: download.php?file=IJCRT2506017
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506017.pdf

  Your Paper Publication Details:

  Title: SIMULATION & OPTIMIZATION OF COMMUNICABLE FAULT PASSAGE INDICATION SYSTEM

 DOI (Digital Object Identifier) :

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Science and Technology

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a152-a158

 Year: June 2025

 Downloads: 124

  E-ISSN Number: 2320-2882

 Abstract

Accurate and rapid location of fault in distribution network is of great significance to improve the reliability of power supply in distribution network. At present, the fault location of distribution management system main station requires high data quality of line terminals, and there are problems such as poor fault tolerance and low accuracy of fault location, and it is not suitable for fault location of multi-point simultaneous fault.


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 Keywords

Fault location, Distribution network, Power supply reliability, Distribution Management System (DMS), Data quality, Line terminals, Fault tolerance, Location accuracy.

  License

Creative Commons Attribution 4.0 and The Open Definition


  Paper Title: MEASURING THE HEART ATTACK POSSIBILITY USING DIFFERENT TYPING OF MACHINE LEARNING ALGORITHMS

  Author Name(s): K.Arunpandi, V.Karthik

  Published Paper ID: - IJCRT2506016

  Register Paper ID - 288398

  Publisher Journal Name: IJPUBLICATION, IJCRT

  DOI Member ID: 10.6084/m9.doi.one.IJCRT2506016 and DOI :

  Author Country : Indian Author, India, 606710 , Thiruvannamalai, 606710 , | Research Area: Science and Technology

Published Paper URL: http://ijcrt.org/viewfull.php?&p_id=IJCRT2506016
Published Paper PDF: download.php?file=IJCRT2506016
Published Paper PDF: http://www.ijcrt.org/papers/IJCRT2506016.pdf

  Your Paper Publication Details:

  Title: MEASURING THE HEART ATTACK POSSIBILITY USING DIFFERENT TYPING OF MACHINE LEARNING ALGORITHMS

 DOI (Digital Object Identifier) :

 Pubished in Volume: 13  | Issue: 6  | Year: June 2025

 Publisher Name : IJCRT | www.ijcrt.org | ISSN : 2320-2882

 Subject Area: Science and Technology

 Author type: Indian Author

 Pubished in Volume: 13

 Issue: 6

 Pages: a144-a151

 Year: June 2025

 Downloads: 120

  E-ISSN Number: 2320-2882

 Abstract

: Heart disease remains one of the leading causes of mortality globally, necessitating the development of early and accurate diagnostic tools. This project focuses on predicting the likelihood of heart attacks using various machine learning (ML) algorithms. A publicly available clinical dataset, including features such as age, gender, chest pain type, blood pressure, cholesterol, and ECG results, is used for training and evaluation. The dataset undergoes preprocessing steps including data cleaning, normalization, and feature encoding. Supervised learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, and Random Forest are implemented and compared based on performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The Random Forest algorithm outperformed others in terms of accuracy and generalization ability. The system is integrated into an Android application using Firebase as a backend service, enabling real-time user interaction and prediction delivery. The study demonstrates that ensemble learning methods offer robust and interpretable solutions for heart disease prediction, which can support clinical decision-making and preventive care. Future enhancements may include integration with wearable devices and deployment in real-time hospital environments.


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 Keywords

Heart Disease Prediction, Machine Learning, Random Forest, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Clinical Data, Android Application, Firebase Integration, Healthcare Analytics

  License

Creative Commons Attribution 4.0 and The Open Definition



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