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2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Alshakrani, Sara Taha, Ranyah Hewahi, Nabil Big Data Science and Analytics College of Science University of Bahrain Bahrain College of Information Technology University of Bahrain Department of Computer Science Bahrain

ISBN: (纸本)9781665440325

One of the important health problems is chronic kidney disease (CKD). It is increasing every day as a result of poor eating habits, insufficient water consumption, and lack of health awareness. CKD is a condition in which the kidneys are damaged over time due to a variety of causes, resulting in a progressive and irreversible loss of kidney function. Technological advancements, such as Machine Learning (ML), have a significant influence on the health sector by giving more accurate detection and successful treatment of many chronic diseases. This research paper explores a variety of ML techniques to predict kidney disease. The objective is to identify which ML classifier could be the best classifier for detecting and predicting CKD. In this work, seven ML approaches are used to predict kidney disease, namely, Gaussian Nave Bayes (GNB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Logistic Regression (LR), AdaBoost and Gradient Boosting. RF and GNB scored better than the other classifiers, with 100% accuracy. © 2021 IEEE.

关键词： Adaptive boosting

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Instantons in ϕ4 theories: Transseries, virial theorems, and numerical aspects

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Physical Review D 2024年第3期110卷 036003-036003页

作者： Ludovico T. Giorgini Ulrich D. Jentschura Enrico M. Malatesta Tommaso Rizzo Jean Zinn-Justin Department of Physics Department of Computing Sciences Institute for Data Science and Analytics Dipartimento di Fisica Rome unit Piazzale Aldo Moro 5 00185 Rome Italy /

We discuss numerical aspects of instantons in two- and three-dimensional ϕ4 theories with an internal O(N) symmetry group, the so-called N-vector model. By combining asymptotic transseries expansions for large arguments with convergence acceleration techniques, we obtain high-precision values for certain integrals of the instanton that naturally occur in loop corrections around instanton configurations. Knowledge of these numerical properties is necessary in order to evaluate corrections to the large-order factorial growth of perturbation theory in ϕ4 theories. The results contribute to the understanding of the mathematical structures underlying the instanton configurations.

关键词： Feynman diagrams Particle interactions Path integrals Resummation methods Quantum field theory Spontaneous symmetry breaking

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Fast Moving Consumer Goods Object Detection Using YOLOV8

Fast Moving Consumer Goods Object Detection Using YOLOV8

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： A. Umamageswari Mukund. P.U Abhishek. V.B Y. Devi Vaishnavi Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering With Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The basis of this project is to investigate whether the YOLO, an object detection algorithm where 'You Only Look Once' constitutes the name, could be applied to develop FMCG management; together with the management of FMCG, it becomes the gateway to increasing retail precision, efficiency, and agility. Under real-time processing, the system detects and counts FMCG products on retail shelves with an accuracy rate of 92.5%. This accuracy ensures that the optimum stock levels are maintained and there will be no discrepancies. The system processes video feeds at 0.08 seconds per frame, thus allowing inventory data to be updated in real-time to enable quick, data-driven decisions. YOLO's flexibility ensures consistent performance across different store layouts and product types. This system integrates with other retail management tools and allows integrating inventory data in the real time. This means that it would be made available with invaluable insights which would prevent stock depletion while helping to curtail costs. This project takes YOLO ahead of just being a game changer in the FMCG inventory management while operating to reduce expenses and customer satisfaction due to product availability.

关键词： YOLO Training Accuracy Scalability Refining Streaming media Logic gates Real-time systems Mobile handsets Monitoring

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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Cybernation and Computation (CYBERCOM), International Conference on

作者： M.S. Minu Srikar Talapalli Saahithi Chevuri Medhasri T.S. Department of Computer Science Engineering SRM Institute of Science And Technology Ramapuram Chennai India Department of Computer Science Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience.

关键词： Hands Filtering Collaborative filtering Transforms Machine learning Motion pictures User experience Libraries Hamming distances Recommender systems

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Machine Learning Model for Sentimental Analysis of Amazon Reviews

Machine Learning Model for Sentimental Analysis of Amazon Re...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： A. Umamageswari R.J. Pratishwaran M. Poojitha Reddy R. Yuvan Sankar Raj Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering With Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The goal of this project is to draw a deeper understanding of the subjective nature behind online product reviews, largely by examining a large dataset received from Amazon that contains numerous star ratings and comments. Since there is such a massive amount of data on the Amazon platform, it was perfect for the analysis. Our methodology is platform-agnostic and can be easily extended to other comparable datasets from other web platforms with comparable guarantees in achieving wide applicability. Our primary motivation is improving the dependability of online shopping. Since product reviews are currently widely used by many consumers as guides in making decisions to buy products, such reviews ought to be fair representations of the actual dependability and quality of the products. The goal of our effort is to identify patterns in the language and ratings of such reviews that would directly reflect the real customer sentiment and also the reliability of the product. Our approach has yielded already a rate of about 92%, which shows it has the potential greatly to raise the legitimacy of online reviews. We'd like to make our customers feel more confident and better equipped with information to make better and safer purchases on a website like Amazon and others. In the final analysis, we strive to make online shopping for our customers worldwide safer and more trustworthy.

关键词： Analytical models Sentiment analysis Reviews Stacking Stars Predictive models Boosting Ensemble learning Electronic commerce Random forests

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： M.S. Minu Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system’s scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce.

关键词： Text mining Machine learning algorithms Scalability Systems architecture Machine learning Chatbots User experience Real-time systems Electronic commerce Time factors

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A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits

arXiv

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arXiv 2024年

作者： Musaaed, Elham Hewah, Nabil Alasaadi, Abdulla MSc. in Big Data Science and Analytics College of Science University of Bahrain Al-Riffa Bahrain Department of Information Systems College of Information Technology University of Bahrain Al-Riffa Bahrain

In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for these algorithms such as modeling of diseases. The majority of these applications employ supervised rather than unsupervised ML algorithms. In addition, each year, the amount of data in medical science grows rapidly. Moreover, these data include clinical and Patient-Related Factors (PRF), such as height, weight, age, other physical characteristics, blood sugar, lipids, insulin, etc., all of which will change continually over time. Analysis of historical data can help identify disease risk factors and their interactions, which is useful for disease diagnosis and prediction. This wealth of valuable information in these data will help doctors diagnose accurately and people can become more aware of the risk factors and key indicators to act proactively. The purpose of this study is to use six supervised ML approaches to fill this gap by conducting a comprehensive experiment to investigate the correlation between PRF and Diabetes, Stroke, Heart Disease (HD), and Kidney Disease (KD). Moreover, it will investigate the link between Diabetes, Stroke, and KD and PRF with HD. Further, the research aims to compare and evaluate various ML algorithms for classifying diseases based on the PRF. Additionally, it aims to compare and evaluate ML algorithms for classifying HD based on PRF as well as Diabetes, Stroke, Asthma, Skin Cancer, and KD as attributes. Lastly, HD predictions will be provided through a Web-based application on the most accurate classifier, which allows the users to input their values and predict the output. Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbor (KNN), Extreme Gradient Boost (XGB), and Support Vector Machine (SVM) were the algorithms used. The dataset was obtained from the Kaggle repository. The attributes are divided into PRF and diseases. The selected algorithms were imple

关键词： Support vector machines

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BIM: Improving Graph Neural Networks with Balanced Influence Maximization 40

BIM: Improving Graph Neural Networks with Balanced Influence...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Wentao Gao, Xinyi Yang, Ling Cao, Meng Huang, Ping Shan, Jiulong Yin, Hongzhi Cui, Bin Peking University Center for Machine Learning Research China Institute of Advanced Algorithms Research Shanghai China National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Peking University Key Lab of High Confidence Software Technologies China Apple Inc. Institute of Computational Social Science Peking University Qingdao China

ISBN: (纸本)9798350317152

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score. © 2024 IEEE.

关键词： Graph neural networks

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The risk of endocarditis in hemodialysis patients who have undergone invasive dental treatment: a cohort case–control analysis of the Taiwan National Health Insurance database

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Clinical Oral Investigations 2023年第1期27卷 203-211页

作者： Yu, I.-Chen Chang, Shang-Hung Hong, Hsiang-Hsi Huang, Yu-Tung Fang, Ji-Tseng Department of Nursing Chang Gung University of Science and Technology Tao-Yuan Taiwan Department of Nephrology Chang Gung Memorial Hospital in Linkou Tao-Yuan Taiwan Cardiovascular Medicine Chang Gung Memorial Hospital in Linkou Tao-Yuan Taiwan Center for Big Data Analytics and Statistics Chang Gung Memorial Hospital in Linkou Tao-Yuan Taiwan School of Medicine Chang Gung University Tao-Yuan Taiwan Department of Periodontics Chang Gung Memorial Hospital in Linkou Tao-Yuan Taiwan Chang Gung University Tao-Yuan Taiwan Center for Big Data Analytics and Statistics Department of Medical Research and Development Chang Gung Memorial Hospital Tao-Yuan Taiwan

Objective: The objective of this study was to determine if hemodialysis patients who have undergone an invasive dental treatment are at risk of developing infective endocarditis. Materials and methods: This study was a cohort case–control design and used secondary data collected from the National Health Insurance Research database of Taiwan. The case group and the control group were each comprised of 19,602 hemodialysis patients. The control group was matched for four variables: age, gender, a medical history of diabetes mellitus, and a cerebrovascular event. After matching, the case group and the control group were each comprised of 19,602 hemodialysis patients. Cox regression analysis determined hazard ratios and 95% confidence intervals. Results: Patients were followed up at 1 month and 3 months after receiving invasive dental treatment. The results showed the cohort case–control hazard ratio was 0.88 (95% CI, 0.49, 1.57) 1 month after receiving invasive dental treatment. Three months after receiving IDT, the cohort case–control hazard ratio was 1.04 (95% CI, 0.71, 1.52). Hazard ratios did not differ significantly between groups. Conclusions: Hemodialysis patients who received invasive dental treatment had no greater risk of developing infective endocarditis than matched control patients. The results of this study should alleviate concerns for hemodialysis patients and dentists about invasive dental treatment procedures. We recommend hemodialysis patients undergo invasive dental treatment when needed. Clinical relevance: The results of this study showed that invasive dental treatment did not increase their risk of developing infective endocarditis. Hemodialysis patients in need of an invasive dental procedure should be encouraged to undergo treatment if the dentist deems it necessary. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： database Hemodialysis Infective endocarditis Invasive dental treatment

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Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19:A Large-Scale Social Media Analysis with Natural Language Processing

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Health data science 2024年第1期4卷 152-161页

作者： Zhiyun Zhang Yining Hua Peilin Zhou Shixu Lin Minghui Li Yujie Zhang Li Zhou Yanhui Liao Jie Yang Department of Big Data in Health Science School of Public Health Zhejiang University School of MedicineHangzhouChina Department of Epidemiology Harvard T.H.Chan School of Public HealthBostonMAUSA Department of Biomedical Informatics Harvard Medical SchoolBostonMAUSA Division of General Internal Medicine and Primary Care Department of MedicineBrigham and Women’s HospitalBostonMAUSA Thrust of Data Science and Analytics The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina Department of Psychiatry Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina

Background:The COVID-19 pandemic has caused a disproportionate impact on the sexual and genderdiverse(SGD)*** with non-SGD populations,their social relations and health status are more vulnerable,whereas public health data regarding SGD are ***:To analyze the concerns and health status of SGD individuals,this cohort study leveraged 471,371,477 tweets from 251,455 SGD and 22,644,411 non-SGD users,spanning from 2020 February 1 to 2022 April *** outcome measures comprised the distribution and dynamics of COVID-related topics,attitudes toward vaccines,and the prevalence of ***:Topic analysis revealed that SGD users engaged more frequently in discussions related to“friends and family”(20.5%vs.13.1%,P<0.001)and“wear masks”(10.1%vs.8.3%,P<0.001)compared to non-SGD ***,SGD users exhibited a marked higher proportion of positive sentiment in tweets about vaccines,including Moderna,Pffzer,AstraZeneca,and Johnson&*** 102,464 users who self-reported COVID-19 diagnoses,SGD users disclosed signiffcantly higher frequencies of mentioning 61 out of 69 COVID-related symptoms than non-SGD users,encompassing both physical and mental health ***:The results provide insights into an understanding of the unique needs and experiences of the SGD community during the pandemic,emphasizing the value of social media data in epidemiological and public health research.

关键词： COV discussions users

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