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Machine Learning Models for Heart Disease Prediction: Balancing Accuracy and Transparency 2

Machine Learning Models for Heart Disease Prediction: Balanc...

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Gupta, Sukriti Tripathi, A. Srivastava, Chaynika Buddha Institute of Technology Department of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Heart disease continues to be the world's leading cause of death, so precise and trustworthy predictive models are required to enable early diagnosis and individualized treatment. This paper addresses the escalating global burden of heart failure, marked by increasing morbidity, mortality, and strain on healthcare systems, stemming from left ventricular systolic and diastolic dysfunction. When compared to conventional methods, machine learning proves to be a more accurate tool for predicting heart diseases. Machine learning (ML) models have shown great promise in predicting heart disease, but their complexity often results in a trade-off between accuracy and interpretability. This study investigates various interpretable machine learning approaches, including decision trees, Mtree, Random Forest, K-means, to predict heart disease, while maintaining transparency. Utilizing a dataset comprising patient health records, including attributes like age, blood pressure, cholesterol levels, and electrocardiogram results, preprocess the data through normalization and feature selection to enhance model accuracy. Our study showcases the implementation of distinct algorithms, demonstrating their effectiveness in accurately detecting heart failure with an impressive accuracy range of approximately 89% to 92%. The study underscores the importance of balancing accuracy with transparency in developing ML models for healthcare applications, particularly in high-stakes scenarios like heart disease prediction. Keywords: Heart disease, Machine learning (ML), random forest, health records, decision trees, MTree. © 2024 IEEE.

关键词： Heart

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Performance Analysis of Machine Learning Algorithms for Covid-19 Detection using Cough Analysis

Performance Analysis of Machine Learning Algorithms for Covi...

引用

2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Subitha, D. Kavitha, J.C. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309140

With an alarming rate of Covid-19 cases worldwide, the challenge for early detection of this disease is growing. There are many methods available for the detection of Covid-19 but they can be tiresome and difficult. The more common method of testing is reverse transcriptase-polymerase chain reaction (RTPCR) testing which requires direct contact with the patient during the testing that further causes more chance for disease spread. But, the method of testing that uses coughing sounds was found to be more safe method of testing as this does not require the presence of patient in the lab rather the pre-recorded audio wave is enough to perform the testing. Machine Learning algorithms are a great promise for detection of Covid-19 disease. There are many different approaches for detection involving wide range of inputs. Likewise, this paper shows about the possibility of detecting Covid-19 using cough sound of the patient, by extracting required input features from the audio file using short time Fourier transform method. Simulation results shows the performance of different classification algorithms in terms of Accuracy, Precision, Recall and F1-Score for the detection of Covid-19. This paper has developed a web application which can detect the presence of corona virus. The audio recording of cough is loaded as the input in to the app which is then sent to deep learning model at the back end that returns the Covid infection result at the front end of the application. This also shows the accuracy of various algorithms in the web application. © 2024 IEEE.

关键词： COVID-19

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Elevating Medical Training: A Synergistic Fusion of AI and VR for Immersive Anatomy Learning and Practical Procedure Mastery

Elevating Medical Training: A Synergistic Fusion of AI and V...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Harinee, S. Vimal Raja, R. Mugila, E. Govindharaj, I. Sanjaykumar, V. Ragavendhiran, T. Computer Science and Engineering C.K. College of Engineering and Technology Cuddalore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Virtual reality with its 3D visualization have brought an overwhelming change in the face of medical education, especially for courses like human anatomy. The proposed virtual reality system to bring massive improvements in the education received by a medical student studying for their degree courses. The project puts forward the text-to-speech and speech-to-text aligned system that simplifies the usage of a chatbot empowered by OpenAI GPT-4 and allows pupils to vocally speak with Avatar, the set virtual assistant. Contrary to the current methodologies, the setup of virtual reality is powered by avatars and thus covers an enhanced virtual assistant environment. Avatars offer students the set of repeated practicing of medical procedures on it, and the real uniqueness in the proposed product. The developed virtual reality environment is enhanced over other current training techniques where a student should interact and immerse in three-dimensional human organs for visualization in three dimensions and hence get better knowledge of the subjects in greater depth. A virtual assistant guides the whole process, giving insights and support to help the student bridge the gap from theory to practice. Then, the system is essentially Knowledge based and Analysis based approach. The combination of generative AI along with embodied virtual agents has great potential when it comes to customized virtual conversation assistant for much wider range of applications. The study brings out the value of acquiring hands-on skills through simulated medical procedures and opens new frontiers of research and development in AI, VR, and medical education. In addition to assessing the effectiveness of such novel functionalities, the study also explores user experience related dimensions such as usability, task loading, and the sense of presence in proposed virtual medical environment. © 2024 IEEE.

关键词： Virtual environments

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Parallel Katz Centrality On Evolving Graphs 31

Parallel Katz Centrality On Evolving Graphs

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sharma, Dishit Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

Katz centrality measures a node’s influence within a network, considering the total number of walks between pairs of nodes rather than solely the shortest paths. This paper presents an algorithm for dynamically updating Katz centrality scores as the underlying graph evolves. Our method employs a frontier-based breadth-first search (BFS) to efficiently derive updated Katz scores in dynamic graphs. Notably, our algorithm accommodates node insertions and deletions, addressing a significant limitation of current state-of-the-art methods. © 2024 IEEE

关键词： Graph algorithms

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Enhancing Intrusion Detection Systems for 5G Networks using AI 1

Enhancing Intrusion Detection Systems for 5G Networks using ...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Gurushanker, Abirami Anandakumaran, Kirtanaa Christopher Columbus, C. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

As 5G networks advance, cybersecurity vulnerabilities in IoT devices are increasingly exposed. While intrusion detection systems (IDS) have progressed, there is a notable gap in IDS specifically designed for the unique protocols and complexities of 5G networks. Current research often emphasises binary classification, missing the nuanced multiclass classification needed for effective threat detection. This study addresses these deficiencies by integrating machine learning and deep learning techniques within the 5G core architecture to develop an advanced IDS. Utilising TCP/IP flow statistics and PFCP signalling attack data, and focusing on control plane signalling between the Session Management Function (SMF) and User Plane Function (UPF), various models are evaluated for detecting anomalous activities. The study achieves 97% accuracy with TCP/IP data and 69% with PFCP data. The proposed IDS, tailored for 5G-specific workloads and trained on PFCP protocol data, enhances detection capabilities and addresses significant security challenges, improving the resilience of IoT devices in 5G networks. © 2024 IEEE.

关键词： Network intrusion

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Complementarity Fusion RGB-T Tracking Based on Siamese Network 9

Complementarity Fusion RGB-T Tracking Based on Siamese Netwo...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Wang, Wei Yang, Congxian Yan, Zhengbang Quan, Wenjun School of computer science and engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9798350376548

In recent years, the Siamese network has gained popularity in RGB-T tracking primarily due to its notable success in RGB object tracking. Although the speed of the existing RGB-T Siamese tracker is faster than the real-time speed, it suffers poor robustness and low accuracy when encountering tracking challenges such as low illumination and deformation. To tackle these challenges, we introduce a novel RGB-T tracking approach utilizing complementarity fusion based on Siamese network framework. In addition, we design dual-modal complementary features fusion module (referred to as CFF) to effectively capture information from RGB images and thermal infrared images. Our newly proposed tracker demonstrates outstanding performance, validated through a large number of experiments on public RGB-T tracking benchmark datasets. Our tracker achieves an impressive tracking speed surpassing 35 frames per second. © 2024 IEEE.

关键词： Object tracking

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Revolutionizing Healthcare: Blockchain's Transformative Applications for Data Security, Privacy, and Interoperability 9

Revolutionizing Healthcare: Blockchain's Transformative Appl...

引用

9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Raj, Riya Raja, S.P. Vellore Institute of Technology School of Computer Science And Engineering Vellore India

ISBN: (纸本)9798350394474

In a rapidly evolving digital era, the convergence of blockchain technology and healthcare is poised to revolutionize the industry in unprecedented ways. This research paper presents an innovative exploration of the transformative potential of blockchain within healthcare, unveiling a new paradigm of patient-centric, secure, and interoperable healthcare systems. Blockchain, renowned for its decentralized architecture and cryptographic security, addresses the perennial challenges of data integrity, patient privacy, and healthcare system interoperability. In this paper, we embark on a journey to unveil the diverse applications of blockchain technology in healthcare, ranging from enhancing medical records management (MRMs) to enabling groundbreaking advances in telemedicine, drug traceability, clinical trials and organ transplantation. Our research deploys a multidimensional analysis, we delve into blockchain's ability to empower patients with unprecedented control over their health data, fostering transparency and trust within the healthcare ecosystem. As we navigate the landscape of blockchain in healthcare, we also confront the formidable challenges that lie ahead, such as regulatory compliance, scalability, and the need for industry-wide adoption. In conclusion, this research paper is an innovative exploration into the fusion of blockchain technology and healthcare, reimagining the possibilities of a digitally transformed healthcare landscape. By unveiling novel applications, addressing challenges, and presenting practical solutions, this work inspires a new wave of research and innovation in the intersection of blockchain and healthcare, paving the way for a healthier, more secure, and patient-centric future. © 2024 IEEE.

关键词： Blockchain

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Adopting Ensemble Learning and Machine Learning Techniques for Predictive Modeling in Traffic Data Analysis 3

Adopting Ensemble Learning and Machine Learning Techniques f...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Santhosh Narayanan, B. Shyamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

In this study, the trends of traffic data are analyzed using predictive analysis techniques applied to the NYC taxi dataset. Various models were tested for forecasting travel time, fare, and speed. Notably, the Decision Tree Regression model demonstrated outstanding performance with an impressive R2 score of 0.9995 for travel time prediction. Additionally, an ensemble model consisting of Linear Regression and Decision Tree Regression achieved an accuracy of 0.93 for fare prediction. Furthermore, another ensemble model combining LSTM and DBN attained an R2 score of 0.90 for speed prediction. This study highlights the effectiveness of diverse predictive modeling techniques in accurately forecasting these features. © 2024 IEEE.

关键词： Long short-term memory

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AVATAR-RETAIL: The Digital ShoppingWorld 15

AVATAR-RETAIL: The Digital ShoppingWorld

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Gahana, R. Ekeshwar, J. Nihal Jain, T.P. Shanthakumara, A.H. Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

ISBN: (纸本)9798331300579

With cutting-edge functionality, 3D Shopping (Avatar Retail) promises to revolutionize the online shopping experience by seamlessly integrating gamification, social media, and augmented reality features. By immersing users in a realistic virtual shopping environment, the application aims to bridge the gap between traditional in-store purchases and the convenience of online buying. Through dynamic 3D navigation, avatar customization, and gamification components, Avatar Retail enhances user engagement and ensures seamless performance scaling on various platforms. Augmented reality plays a central role, allowing users to interact with products in a fully immersive online environment while facilitating instant communication and collaboration. Extensively tested across multiple platforms and devices, Retail Realm sets new standards for immersive online shopping, combining technology and user-centric design to make a significant impact on the e-commerce sector. © Grenze Scientific Society, 2024.

关键词： Gamification

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Intelligent Computing Methods for Knowledge Discovery and Management: Analysis, Comparison, and Application Domains 2

Intelligent Computing Methods for Knowledge Discovery and Ma...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Ahamad, Rayees K.N., Mishra Birla Institute of Technology Department of Computer Science and Engineering Mesra India

ISBN: (纸本)9798350370188

Knowledge Management (KM) and Artificial Intelligence (AI) at their core are about knowledge. AI provides the mechanism that allows a machine to obtain, acquire, process, and use information to execute tasks, as well as reveal or unlock knowledge that may be transmitted to people for improved strategic decision-making. The use intelligent approaches in the process of KDM (Knowledge Discovery and Management) can improve efficiency in the sense of time and accuracy. Intelligent approaches especially soft computing approaches have the ability to learn in any environment with the help of logic, reasoning, and other computing abilities. In this paper authors have analyses single ICM as well as Combined ICM those provide the correct and reasonable solutions in the form of knowledge. The main aim of this study is to apply intelligent techniques to solve the day-to-day problems of our rural and smart digital societies. © 2024 IEEE.

关键词： Decision making

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