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Heart Disease Predictive Analysis Using Machine Learning Approaches 6

Heart Disease Predictive Analysis Using Machine Learning App...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Rana, Anamika Malik, Sushma Chauhan, Madhu Kumar, Prashant Maharaja Surajmal Institute Department of Computer Application New Delhi India Institute of Innovation in Technology & Management Department of Computer Application New Delhi India Jagannath International Management School New Delhi India

ISBN: (纸本)9798350304480

Machine Learning (ML) has found widespread applications in the healthcare sector worldwide, including the diagnosis and treatment of heart diseases, locomotor disorders, and various other medical conditions. ML methods have revolutionized healthcare by enabling the analysis of large and complex medical datasets, leading to valuable insights and predictions that aid healthcare professionals in providing better patient care. ML's ability to analyze vast amounts of healthcare data, uncover patterns, and make predictions has significant potential to improve patient outcomes, optimize medical workflows, and advance medical research. However, it's essential to address privacy and ethical considerations when using ML in healthcare, ensuring the responsible and secure use of sensitive patient information. Supervised Learning methods like SVM, Random Forest, and Logistic Regression are used for the analysis of the dataset downloaded from Kaggle. The various performance parameters such as Precision, F-1 score, Accuracy, and Recall were used to compare the performance of different ML classification techniques. Among the various methods evaluated, the Random Forest classification algorithm was found to outperform the other methods across the fourteen available parameters. © 2023 IEEE.

关键词： Heart Disease Logistic Regression Machine Learning (ML) Random Forest SVM

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Sentiment Analysis of Makeup Alley Website Cosmetic Reviews Using SVM and TF-IDF 9

Sentiment Analysis of Makeup Alley Website Cosmetic Reviews ...

引用

9th International Conference on Information Management and technology, ICIMTech 2024

作者： Putri, Christine Salim, Claudya Veronica Dwiyanti, W.K. Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This paper focuses on performing sentiment analysis of cosmetic reviews using a machine learning algorithm. The study aims to classify reviews into positive, neutral, and negative opinions using aspect-based sentiment analysis with TF-IDF and SVM algorithms. The research questions include understanding user opinions on cosmetic products and identifying the most common positive and negative sentiments in reviews. Data cleaning, tokenization, stopwords removal, normalization, and lemmatization techniques are employed to achieve these goals. The study uses the Makeup Alley website as a data source and employs web scraping to obtain the data. The Natural Language Toolkit (NLTK) is used to simplify the removal of stopwords. The research seeks to document the step-by-step process of developing a sentiment analysis application tailored to cosmetic product reviews. © 2024 IEEE.

关键词： Support vector machines

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Review on Enhancing Predictive Accuracy in Alzheimer Detection: A Hybrid Approach 3

Review on Enhancing Predictive Accuracy in Alzheimer Detecti...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ambooj Khan, Ihtiram Raza Agarwal, Parul Kaur, Harleen School of Engineering Sciences & Technology Jamia Hamdard New Delhi India Bvicam Department of Computer Science and Application New Delhi India

ISBN: (纸本)9798331518592

Effective early detection techniques are essential because to the tripled global prevalence of dementia, which is mostly caused by Alzheimer's disease (AD) and is predicted to reach 152 million by 2050. AD pathology, which has a preclinical phase lasting 10-20 years before severe cognitive impairment, is associated to about 70% of dementia cases. Early detection of AD is essential since medication trial success rates may be increased and risk factor management may avoid 40% of cases. Currently, lengthy cognitive tests that are insensitive to the pre-clinical stage are used to detect dementia. Initiatives to make open data available have gathered so much data on AD that it is now too much for humans to analyze. In order to diagnose AD accurately, machine learning (ML) has become essential, especially when it comes to early-stage AD prediction. AI and ML-driven healthcare research places a strong emphasis on automated disease prediction systems, including dementia detection. Finding those who is susceptible of 'Alzheimer's disease' (AD) progression is still difficult, especially in light of the disease's severity and fast advancement. Preventing the progression of the disease and planning the course of treatment depend on early detection. Reviewing AD detection research conducted since 2016 has highlighted the contribution of machine learning (ML) to improving prediction accuracy. The reviews provide information on participant demographics, data modalities, feature extraction strategies, and ML models used. By combining earlier studies, promoting critical analysis, and directing future investigations for early AD diagnosis, this thorough review helps researchers. © 2024 IEEE.

关键词： Adversarial machine learning

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Implementation of Gamification to Help Teenagers Prevent Diseases 9

Implementation of Gamification to Help Teenagers Prevent Dis...

引用

9th International Conference on Information Management and technology, ICIMTech 2024

作者： Haryanto, Michael Djaja, Fabian Putera Chrisandy, Michael Achmad, Said Moniaga, Jurike V Bina Nusantara University Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Adolescence is an important part of growing up to become an adult;keeping adolescent physique healthy is very important because poor dietary choices and a sedentary lifestyle will lead to diseases that they shouldn't have experienced yet. That is why teenagers need to be engaged to do more health- promoting behaviors, one way is by implementing gamification. This paper seeks to study which kind of gamification elements can be implemented from past research and whether the results were effective or not to prove whether gamification is a valid and effective strategy. At the same time, we are creating a gamified prototype for the mobile platform. The methodology starts with a systematic review of PubMed, MDPI, and Google Scholar to get a general idea of which gamification elements are the most implemented. Then a prototype will be made with the most used gamified elements from the systematic review. And then participants ranging from 10 - 19 years old will test the prototype and will give their opinions via a survey, From this, it is proven by our own gamified prototype survey results that using gamification as a way to engage teenagers is a fun and engaging way to do more health-promoting behaviors with a mostly positive feedbacks with the average rating of 8.33 out of 10 given from the participants. © 2024 IEEE.

关键词： Gamification

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Trend analysis in competitive programming using topic modeling 9th

Trend analysis in competitive programming using topic modeli...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Ramahalim, Albert Yulius Karuniawan, Christoffer Edbert Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora. © 2024 The Authors.

关键词： Modeling languages

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Enhancing User Interface and Experience in Skin Type Classification and Skincare Product Recommendation System for Young Adults 9

Enhancing User Interface and Experience in Skin Type Classif...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Himawan, Felicia Soerjadi, Vania Paramitha Handoyo, Yonathan Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The escalating interest of skincare among Indonesian young adults reflects the rising trend in the skincare industry. However, this heightened interest also brings several challenges, like the lack of knowledge and confusion when selecting the right skincare products for our skin type. This underscores the need for an effective and efficient skin type classification and product recommendation system. This paper addresses this challenge by focusing on enhancing a skincare application's user interface and overall experience. This research employed User-Centered Design (UCD), implementing Design Sprint, and Goal-Directed Design (GDD) methodologies to achieve a high value of user satisfaction. The Sprint process is used to create the design prototype efficiently, and the Goal-Directed method is performed to create specific, measurable goals. Our evaluation shows that an iterative design process based on user feedback results in higher user satisfaction. The analysis indicated a significant increase of approximately 25% in both effectiveness and efficiency among users when using the application. Initially, the first iteration reveals a low value of approximately 59% in overall effectiveness and efficiency. However, a substantial boost to around 83.5% was seen after a redesign. This improvement proved that User-Centered Design methodologies highly influence user perception and satisfaction. © 2024 IEEE.

关键词： design sprint goal-directed design skincare UI/UX user-centered design

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AI-Powered Mobile application for Image-Based Food Ingredient Detection and Recipe Generation 9

AI-Powered Mobile Application for Image-Based Food Ingredien...

引用

9th International Conference on Information Management and technology, ICIMTech 2024

作者： Rosaline, Thessalonica Maria Luisse Suciawan, Calvin Anacia Ng, William Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This research explores the critical impact of dietary habits on health, addressing the lack of awareness regarding nutritional content that leads to issues such as malnutrition. The proposed mobile application utilizes the YOLOv8 algorithm with a deep convolutional neural network (CNN) architecture, deployed on the Roboflow server, a local MySQL database server, and ChatGPT. The architectural framework of this application is integrated through the NodeJs server via an API system embedded within the Android application. Despite this approach, the study highlights the need for further refinement in the accuracy of the ingredients' object detection model. The refined YOLOv8 model, trained on an extensive dataset of 24,583 images across 13 food ingredient classes, outperforms the initial YOLOv8 model trained on 9,867 images. Improvements are notable, particularly in addressing misclassifications of ingredients such as beef, pork, onion, and egg in the initial model. However, the YOLOv8 model can still be improved by introducing more ingredient variations and expanding the training dataset. This application can detect multiple food ingredients from an image and evaluate their nutritional value, specifically in calorie count. It also provides tailored recipes to help users meet their daily nutritional requirements, offering a practical solution for users to achieve their dietary goals. This research connects computer vision and nutrition, providing insights for developing practical solutions in this domain. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigating the use of Neural Networks in Enhancing Music Composition and Sound Quality

Investigating the use of Neural Networks in Enhancing Music ...

引用

2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Gupta, Sachin Inzimam Manivasagam Sanskriti University Department of Management Mathura India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350388800

An exciting new area of study in the world of music technology is the use of neural networks to the improvement of sound quality and the automation of musical creation. The goal of this research is to evaluate the potential of neural networks in boosting musical creation and reception. To determine whether algorithms based on neural networks can help us create groundbreaking music, we do extensive research into the field. First, the study presents a recurrent neural network (RNN)-based approach to composing melodic sequences of notes. This method draws on the remarkable pattern-recognition skills of neural networks to motivate richer musical production. Listening to a big library of pieces, the RNN model is able to learn how to construct new pieces that are enjoyable to human hearing. Synthesizing high-quality audio waveforms is crucial in modern music composition, and this study investigates the use of Generative Adversarial Networks (GANs) to this problem. The AudioWaveGAN approach utilizes GANs to produce high-quality sound, filtering out unwanted noise while also composing immersive pieces. Our study compares the proposed method to various established rivals. The criteria for evaluation are based on genuine conventional methods. The findings illustrate the superiority of the suggested technique in terms of originality, sound quality, and a combined score, emphasizing its potential to change music creation and production. This study represents a major advance toward using neural networks in music creation and audio processing. The research shows that these fresh methods open up exciting opportunities for artists, composers, and music producers who want to release hitherto unattainable wellsprings of inspiration and provide their listeners with fully enveloping aural journeys. © 2024 IEEE.

关键词： Generative adversarial networks

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Gap estimation method for specular target based on binocular vision 9

Gap estimation method for specular target based on binocular...

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9th Symposium on Novel Photoelectronic Detection technology and applications

作者： Li, Ye Li, Dongxue Gao, Wenfei Xu, Kaiseng Qing, Dangsheng Zhang, Jinming Wang, Hongyu School of Artificial Intelligence Shenyang University of Technology Shenyang China Beijing Institute of Computer Technology and Application Beijing100854 China

ISBN: (纸本)9781510664432

OSR is a photosensitive material commonly used in aerospace photovoltaic displays, and it is difficult to detect the quality of OSR sheets due to its extremely high specular reflectivity. The mirror target is a common target in industrial production, such as the patch on the surface of photovoltaic cells, and its compact and neat arrangement affects the photosensitive quality. Therefore, the automatic perception and measurement of its gap is of great significance. In this paper, the gap measurement method of specular target based on binocular vision is proposed by taking the gap of a high reflectivity OSR sheet as the measurement target. In this method, the binocular camera is first calibrated to determine the relative geometric position of the binocular camera and its structural parameters. Then, the Canny algorithm that uses bilateral filtering instead of Gaussian filtering extracts the edge information in the image, and the LSD straight line detection algorithm is used to extract the extracted edge information in a straight line. Finally, the parallax map or depth map is obtained through the parallax change of the binocular camera, the virtual point is synthesized, and the fixed height constraint binocular matching method and triangulation method are used to solve the periodic blurring problem of boundary registration, and the gap width of the high reflectivity specular target is calculated. Binocular visual quality inspection experiments were designed to verify the feasibility and practicability of the proposed method. © 2023 SPIE.

关键词： Triangulation

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Enhanced face recognition using rank-level fusion with multi-feature vectors and interval type-3 fuzzy set 3

Enhanced face recognition using rank-level fusion with multi...

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3rd IEEE Applied Signal Processing Conference, ASPCON 2023

作者： Ghosh, Manas Sing, Jamuna Kanta Rcc Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350325881

In the near past, interval type-3 fuzzy set theory has been introduced as a more effective means of addressing uncertainty in decision-making than its fuzzy set counterparts. This paper suggests an approach to face recognition which integrates multiple feature vectors and interval type-3 fuzzy sets for fuzzy rank-level fusion. Use of multiple feature vectors resolves inter-class resemblances while incorporating interval type-3 fuzzy membership function in generating fuzzy rank reduces the uncertainty due to intra-class divergences. The output of the classifier is regarded as confidence factor. For each feature vector, the fuzzy ranks are generated by formulating an interval type-3 fuzzy membership function for each of top-ordered H classes based on confidence factors utilizing all training feature vectors and those of the training samples of the respective class. Multiple fuzzy ranks, built upon interval type-3 fuzzy set, are generated by employing of multiple feature vectors. To determine the final score of a class, summation of the fuzzy rank complements is combined with the complement of arithmetic mean of respective classifier's output. Final scores are evaluated to conclude test image's class. Two publicly accessible face databases are used to evaluate the method and it has shown that the solution, advocated here, exhibits an improved effectiveness over its existing related counterparts. © 2023 IEEE.

关键词： Face recognition

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