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Cardiovascular Disease Prediction Utilizing Machine Learning and Feature Selection with Clonal Selection Algorithm 5

Cardiovascular Disease Prediction Utilizing Machine Learning...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Rahman, Wahidur Aneek, Riasaad Haque Moinuddin, Md. Sakib, Md. Shadman Iqbal, Md. Sadiq Rahman, Mohammad Motiur Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Z.H. Sikder University of Science and Technology Dept. of Computer Science and Engineering Bhedarganj Shariatpur Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Mawlana Bhashani Science and Technology University Dept. of Computer Science and Engineering Tangail Bangladesh

ISBN: (纸本)9798350394290

A substantial and rising number of patients suffer from cardiovascular diseases, including heart attacks, heart failure, and other related illnesses. This case surge places increasing pressure on healthcare professionals and administrators while patients grapple with growing medical costs. To address these challenges, an automated system is necessary within the healthcare sector. In this paper, we present a cardiovascular health monitoring system that incorporates Machine Learning techniques. The study employed feature selection techniques on a secondary dataset. The study has used a nature-inspired technique, namely the Clonal Selection Algorithm (CSA) and Maximum Relevance Minimum Redundancy (mRMR) technique, to identify the most prominent feature in the detection of cardio-vascular illness. This approach was combined with a collection of Machine Learning classifiers. A group of experimental data has been listed to assess the suggested model's effectiveness. The research findings indicated a maximum accuracy rate of 100% when employing the proposed algorithms and orientations. The study also discusses the performance analysis for CSA and mRMR using a set of performance evaluation matrices. Based on the obtained results, it can be inferred that the suggested model will likely exhibit a high level of effectiveness in identifying cardiovascular diseases. © 2023 IEEE.

关键词： Feature Selection

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Enhancing DNA Sequence Classification Through Machine Learning: A Comparative Study on Human and Chimpanzee Genomic Data

Enhancing DNA Sequence Classification Through Machine Learni...

引用

2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Pandya, Kushagra Hathi, Heli Ravikumar, R.N. Nadarajan, Sivakumar Praveen, Manoj Pal, Vinod Kumar Tss Consultancy Pvt. Ltd. Rajkot India Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Velalar College of Engineering and Technology Dept. of Ai & Ds Erode India Marwadi University Dept. of Computer Science Rajkot India

ISBN: (纸本)9798350379716

DNA sequence classification is a major challenge in biological processing of data. The classification of DNA sequences is an important study field in bioinformatics since it allows researchers to perform genomic analysis. Through this research, we tried to analyze the performance of various machine learning (ML) algorithms in identifying DNA genes in humans and chimpanzees to find the most effective algorithm for classification of DNA sequences. The results of this research outperformed earlier studies, which had an accuracy around 98%. The dataset was preprocessed using k-mer counting, which assisted in finding key characteristics for the model's training. Naïve Bayes stood out, with an outstanding accuracy of 98.4% for human DNA and 91.4% for chimpanzee DNA. After improving the parameters, Naïve Bayes continued to improve, with human DNA accuracy increasing to 99% and chimpanzee DNA accuracy to 92% in all categories followed by Logistic Regression at 93.9% for human DNA sequences and Ridge Classifier at 90.2% for chimpanzee DNA sequences. K-Nearest Neighbors, Ridge Classifier, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extra Trees, XGBoost, and Naïve Bayes were among the algorithms tested, with accuracy, F1 score, precision, and recall used in assessing their performance. These findings highlight the necessity of selecting the appropriate models and tweaking their parameters to improve performance. © 2024 IEEE.

关键词： Contrastive Learning

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Next Generation Social Media Platform to Move from Centralization to Decentralization of Data 7

Next Generation Social Media Platform to Move from Centraliz...

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Dhore, M.L. Ratnaparkhi, Soham Sasne, Rohan Surase, Om Chandak, Rushikesh Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Social media has revolutionized global communication, business contracts, and the diffusion of ideas. However, there are several potential drawbacks to this, including the spread of false information, data breaches, identity fraud, effective content monitoring, and digital piracy. Using blockchain protocols and Web3 philosophies, it is possible to protect data privacy, data security, and fraudulent identities. By replacing traditional email/name-password authentication with Metamask authentication, user privacy is strengthened. Storing application media on the Ethereum blockchain, a proof-of-concept for application data can be established, thereby preventing data breaches. According to our research results, blockchain technology has the potential to provide tamper resistance, traceability, and transparency. As a result, this proposed research provides a social media platform that is truly decentralized and built on a network, enhancing its users' privacy. © 2023 IEEE.

关键词： blockchain decentralized network identity fraud Social media Web3

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Deepfake detection for preventing Audio and Video frauds using Advanced Deep Learning Techniques

Deepfake detection for preventing Audio and Video frauds usi...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Bharadwaj, Rakhi Ratnaparkhi, Soham Rajpurohit, Rajendrasingh Rahate, Kashish Pandita, Rahul Thosar, Samarth Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350315455

Deepfakes, powered by generative adversarial networks (GANs), automate the creation of deceptive videos. To combat this menace, the study assembles an advanced model that fuses ResNeXt, Long Short-Term Memory (LSTM), and ResNet architectures, renowned for their efficacy in handling visual and temporal aspects, for the detection of deepfakes in audio and video content. Pre-processing, facilitated by a Multi-Task Cascaded-Convolutional Neural Network (MTCNN), accurately extracts facial regions. The model undergoes rigorous evaluation across three key datasets which are, FaceForensics++(FF-DF), Celeb-DF, and Facebook Deepfake Detection Challenge (DFDC), affirming its real-world readiness and exceptional accuracy. The combined models consistently deliver highly precise results, maintaining their robustness against evolving deepfake technologies. © 2023 IEEE.

关键词： Long short-term memory

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Detection and Classification of Aggressive Comments and Hate Speech

Detection and Classification of Aggressive Comments and Hate...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Nemade, Srushti Mane, Sunil B. Nandgaonkar, Sushma Dept. of Computer Engineering and Information Technology Maharashtra Pune India

ISBN: (纸本)9798350323917

The prevalence of social media has led to an increase in aggressive online behaviour such as cyberbullying, trolling, hate speech, and flaming. The anonymity and convenience of social media platforms have allowed individuals to express their aggressive tendencies without fear of retaliation. This issue is not limited to certain sections of society and has impacted people globally. Cyberbullies now have the power to affect the lives of millions of people worldwide, which can be detrimental to the internet's importance as a communication and networking tool. Therefore, it is critical to recognize and restrict this negative behaviour. It is essential to detect and prevent cyberbullying and hate speech in Indian native languages, considering India's diverse population and multiple languages. Social media platforms have brought about significant benefits, including improved communication and networking, but the increased use of these platforms has led to an increase in harmful behaviour that can negatively impact mental health. Additionally, misinformation and propaganda can cause severe consequences for society. This paper discusses the detection and classification of aggressive comments in Hindi-English code-mixed and Hindi data from popular social media platforms such as Facebook, Twitter, and Instagram. © 2023 IEEE.

关键词： Social networking (online)

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Decentralized Query Solving Educational Website 2

Decentralized Query Solving Educational Website

引用

2nd International Conference on Informatics, ICI 2023

作者： Jadhav, Swati Bhavik, Manasi Barde, Rohan Bhosale, Vaibhavi Bura, Rishita Amune, Amruta Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350343830

With the blockchain technology's quick advancement and the rising need for the Internet's partial decentralization, the use of underlying technology built on the blockchain has drawn a lot of attention. This research paper presents the design and implementation of a decentralized query-solving educational website, leveraging blockchain technology and a stack of complementary technologies including Solidity, ThirdWeb, React, ***, MetaMask, and Hardhat. The proposed platform aims to revolutionize the traditional online learning experience by addressing challenges related to data integrity, trustworthiness of content, and query-solving efficiency. This study delve into the technical aspects of implementing a blockchain-based query-solving educational website, including the design considerations, data structure, consensus mechanisms, and privacy enhancements. The research paper highlights the technical considerations and challenges encountered during the implementation process, including the design of smart contracts, integration of ThirdWeb technologies, development of the user interface, and integration with MetaMask for secure authentication and transaction handling. © 2023 IEEE.

关键词： Smart contract

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World Coin Currency Detection: A Comparative Study of Machine Learning and Deep Learning

World Coin Currency Detection: A Comparative Study of Machin...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Dumka, Ankur Pandey, Arnav Mishra, Amit Kumar Bhaskarrao Bahadure, Nilesh Tripathi, Neha Pandey, Neeraj Kumar Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India St. Joseph's Academy Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Nagpur Campus Pune India Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

In the economy where trade and finance are closely linked it is essential to accurately identify and authenticate various currencies, especially in industries such, as banking, tourism and retail. Conventional methods, for identifying coins typically require inspections or simple automated processes, which might take up a lot of time and are susceptible, to mistakes. The advancements, in machine learning (ML) and deep learning (DL) technologies, over the years have revolutionized currency detection providing efficient solutions. This article discusses the evaluation of machine learning and deep learning approaches to identify types of coins. It explores how algorithms, like MobileNetV2, Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform in distinguishing and classifying coin denominations across currencies. The results of experiments conducted on data sets show how these methods compare in terms of accuracy, effectiveness, and reliability. Furthermore, we examine the advantages and constraints of each approach while suggesting directions for advancing research, in the realm of identifying cryptocurrency. © 2024 IEEE.

关键词： Convolutional neural networks

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Relating Muscle Activity and Mouse Sensitivity in FPS Game Players Using Surface EMG Signals 5

Relating Muscle Activity and Mouse Sensitivity in FPS Game P...

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5th International Conference on Biomedical engineering, IBIOMED 2024

作者： Prajapati, Vaibhav Turlapaty, Anish C. Sarma, Himangshu Ghorai, Mrinmoy Indian Institute of Information Technology Dept. of Electronics and Communication Engineering Sri City India Indian Institute of Information Technology Dept. of Computer Science and Engineering Sri City India

ISBN: (纸本)9798350389265

In the domain of first-person shooter (FPS) gaming, mouse sensitivity is crucial for performance. This paper presents an analysis showing the correlation between specific muscle activity and mouse movement. We analyzed mouse movements from twelve subjects with varying sensitivity levels. A new dataset electromyography analysis of human activity database 8 (EMAHA-DB8), is introduced which involved four mouse designs and two game modes: Medium (normal movement) and Hard (aggressive movement). Sensitivity settings were standardized using effective dots per inch (eDPI), calculated as Mouse DPI multiplied by in-game sensitivity. Hand muscle activity was measured using five Noraxon Ultium wireless surface electromyography (sEMG) sensors with Ag/AgCl electrodes. Various regression models, including Random Forest, Support Vector Regressor, K-Nearest Neighbors Regressor, MLP Regressor, and Gradient Boost Regressor, were used to predict eDPI from muscle movement patterns. Sequential feature selection identified the top ten features, highlighting the most significant muscles for predicting sensitivity. The results demonstrate that the Extensor Carpi Radialis (ECR) is the most active muscle, followed by the Extensor Digitorum (ED), while playing the chosen FPS game. These findings give insights for ergonomic and personalized equipment design to enhance gaming experience and performance. © 2024 IEEE.

关键词： Decision trees

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Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction

Exploring the Impact of Univariate Feature Selection Method ...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Islam, Mainul Islam, Rafiqul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

This study compares various machine learning techniques such as logistic regression, support vector machine (SVM), random forest, Naïve Bayes, and decision tree. Later, a feature selection approach that considers the Univariate Feature Selection (UFS) method was applied to select the best features based on statistical testing. In this research, we employed the ANOVA F-test to pick the most significant features, and we used a cutting-edge dataset for experimental analysis. The ANOVA F-test was used to exploit the target variable's dependency on the continuous variables. The results show that employing the UFS approach for picking features improves the performance of the algorithms. According to the experimental results, using UFS with the logistic regression technique yields 93.3% accuracy, whereas the standalone Naïve Bayes algorithm achieved the best accuracy. © 2023 IEEE.

关键词： Feature Selection

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Brain Tumor Detection with Ensemble of Convolutional Neural Networks and Vision Transformer 2

Brain Tumor Detection with Ensemble of Convolutional Neural ...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Yurdakul, Mustafa Tasdemir, Sakir Kirikkale University Engineering Faculty Computer Engineering Dept. Kirikkale Turkey Selçuk University Technology Faculty Computer Engineering Dept. Konya Turkey

ISBN: (纸本)9798350373363

Brain tumors are recognized as one of the most lethal cancer types worldwide. Detecting brain tumors using medical imaging techniques is a challenging task due to their complex anatomical structures. Traditional methods rely on specialists meticulously examining MRI scan images. However, this approach is not only time-consuming but also carries a significant risk of error. Therefore, there is a need for more effective methods to detect brain tumors from MRI images. In this study, an ensemble model was proposed for classifying tumor types using MRI scans. Initially, sixteen well-known Convolutional Neural Network (CNN) models and four Vision Transformer (ViT) models were trained on the Brain Tumor Dataset, which contains 3264 MRI scan images. Subsequently, by combining the top three high-performing models, we achieved a robust classification performance. Experimental results demonstrate that our proposed model provides a satisfactory performance comparedto existing methods. © 2023 IEEE.

关键词： Convolutional neural networks

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