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A Review on data Analytical Techniques for Predicting HIV Treatment Outcome Using Selected Biomarkers

A Review on Data Analytical Techniques for Predicting HIV Tr...

引用

2024 international conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adeyemo, Joke Oluwatimilehin Rontala Subramaniam, Prabhakar University of KwaZulu-Natal Information Systems & Technology Durban South Africa

ISBN: (纸本)9798350358155

Analytical techniques for the prediction of treatment outcomes in HIV-infected patients to a state of HIV disease (AIDS) have been examined over more than two decades. Previously, limited knowledge hindered using data analytics techniques to track clinical treatment outcomes. This paper reviews recent studies on HIV treatment prediction over the last five years. A comprehensive search on Google Scholar centered around analytical methods, biomarkers, and their role in predicting the outcomes of HIV treatment. The inclusion criteria were articles published in English between 2019 and March 2024. The search yielded 420 articles limited to the adult population. The evaluation criteria included research design, number of data analytic techniques studied, technique accuracy/efficiency, biomarker assessment methods, impact on data science prediction research, and sample size. A total of 10 studies met the above criteria. These consist of two reviews, three comparative studies, three cross-sectional studies, one cohort design, and one case-control study - the results from various analytic techniques mainly combined with statistical learning, machine learning, and other approaches. However, further evaluation and replication of these novel techniques in more significant, long-term efficacy trials are necessary. Therefore, the objective(s) of this paper review is to discover newer learning algorithms for HIV disease treatment outcomes. A learning algorithm alert or feedback, an innovative concept, may be applied in further research on HIV infection prevention or decreasing the cause of the illness. © 2024 IEEE.

关键词： analytical techniques biomarkers HIV prediction treatment outcome

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A Comparative Study on Prosumers Load Demand and Production Forecasting Using machine learning and Time Series Techniques 16

A Comparative Study on Prosumers Load Demand and Production ...

引用

16th international conference on applied and Theoretical Electricity, ICATE 2024

作者： Solea, Claudiu Hera, Corina Florea, Adrian Morariu, Daniel Stanescu, Dorel Vintan, Maria Lucian Blaga University of Sibiu Computer Science and Electrical Engineering Department Sibiu Romania

ISBN: (纸本)9798350388107

A data-driven and realistic policy could overcome the current turmoil in the intricate energy landscape. With solar and wind expanding faster than liquified natural gas or nuclear ever have, local, bottom-up solutions, such as organising collaboratively residential prosumers into electricity trading communities and the efficient use of aggregators, are one way to resilience and control over these resources. This paper analyses the features and characteristics of some prosumers and facilitates the identification of relevant algorithms for the specific tasks of both load demand and production forecasting using machine learning and time series techniques, thus enabling them to operate on a level playing field in the market. Load demand and production forecast simulations on two data sets, one collected in 2019, corresponding to 50 residential households and a public building from Portugal, and the other from 2023, that aggregates values from 29 household prosumers from Romania, show that both simple and double exponential smoothing techniques are more accurate than Markov predictors. Provided that the external contingencies influencing either the consumption or the production are considered, the latter gives a better prediction accuracy for energy production, whereas the former provides a better forecast for the consumption. © 2024 IEEE.

关键词： Demand response

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Trends in Computational science: Natural Language Processing and Network Analysis of 23 Years of ICCS Publications 24th

Trends in Computational Science: Natural Language Processing...

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24th international conference on Computational science (ICCS)

作者： Luo, Lijing Kovalchuk, Sergey Krzhizhanovskaya, Valeria Paszynski, Maciej de Mulatier, Clelia Dongarra, Jack Sloot, Peter M. A. Univ Amsterdam Amsterdam Netherlands AGH Univ Krakow Krakow Poland Univ Tennessee Knoxville TN USA

ISBN: (纸本)9783031637537;9783031637513

We analyze 7826 publications from the international conference on Computational science (ICCS) between 2001 and 2023 using natural language processing and network analysis. We categorize computer science into 13 main disciplines and 102 sub-disciplines sourced from Wikipedia. After lemmatizing full texts of these papers, we calculate the similarity scores between the papers and each sub-discipline using vectors built with TF-IDF evaluation. Among the 13 main disciplines, machine learning & AI have become the most popular topics since 2019, surpassing parallel & distributed computing, which peaked in the early 2010 s. Modeling & simulation, and algorithms & data structure have always been popular disciplines in ICCS over the past 23 years. The most frequently researched sub-disciplines, on average, are algorithms, numerical analysis, and machine learning. Deep learning shows the most rapid growth, while parallel computing has declined over the past 23 years in ICCS publications. The network of sub-disciplines exhibits a scale-free distribution, indicating certain disciplines are more connected than others. We also present correlation analysis of sub-disciplines, both within the same main disciplines and between different main disciplines.

关键词： natural language processing topic modelling computational science graph theory network analysis scientometrics ICCS

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Cardiovascular Disease Prediction Using machine learning Techniques: A Review

Cardiovascular Disease Prediction Using Machine Learning Tec...

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2024 international conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Enihe, Raphael Ozighor Prasad, Rajesh Ogwueleka, Francisca Nonyelum Sanusi, Muhammad Baze University Department of Computer Science Abuja Nigeria African University of Science and Technology Department of Computer Science Abuja Nigeria University of Abuja Department of Computer Science Nigeria

ISBN: (纸本)9798350358155

Cardiovascular Disease (CVD), encompassing various conditions, still stands as the foremost worldwide contributor to mortality, accounting for 17.9 million fatalities in 2019 (comprising 32% of deaths globally). with 85% attributed to heart attacks and strokes. Low- and middleincome countries bear a significant burden, contributing to disability and reduced quality of life. Diabetes, physical inactivity, high blood pressure, obesity, smoking, high cholesterol, and unhealthy eating habits are risk factors that can be changed over time. are Accurate and timely heart disease diagnosis is crucial, with traditional methods often lacking accuracy and efficiency. Advanced diagnostic systems are urgently needed for better detection. machine learning technology is capable of improving diagnosis and management through accurate data analysis and prediction. This paper provides an overview of advanced machine learning methods suggested by different researchers for predicting cardiovascular disease and offers analytical conclusions. The investigation uncovers that a variety of classification algorithms, such as Logistics Regression, Decision Tree, Naïve Bayes, Artificial Neural Network, Support Vector machine, and others, have been extensively employed, yielding varying prediction accuracies. Furthermore, it is observed that combining multiple algorithms through ensemble techniques can significantly enhance the prediction accuracy of cardiovascular disease. This paper showcases how cutting-edge machine learning techniques are being harnessed to address real-world challenges in cardiovascular disease prevention and management. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-Driven DDoS Mitigation at the Edge: Leveraging machine learning for Real-Time Threat Detection and Response 2

AI-Driven DDoS Mitigation at the Edge: Leveraging Machine Le...

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2nd IEEE international conference on data science and Network Security, ICDSNS 2024

作者： Arora, Sahil Khare, Pranav Gupta, Sandeep Ggsipu It Department New Delhi India Bhopal MP Bhopal India Techieshubhdeep It Solutions Pvt. Ltd M.P. Gwalior India

ISBN: (纸本)9798350373110

As cyber threat actors develop increasingly sophisticated strategies, cutting-edge cyber security is necessary for industry organizations and government agencies. A security threat model must consider these developments since they might bring new cyber dangers. Many attacks have been cropping up on the Internet as it has grown. Today's most prevalent attacks are viruses, distributed denial of service (DDoS) attacks, service interruptions, code injection, and spoofing. Several new techniques for DDoS detection have been proposed, including AI-based machine learning and deep learning tactics. The most recent developments in cybersecurity's use of AI and ML are covered extensively in this article. Using the most current dataset, CICDDoS2019, this research compares and contrasts the performance of deep learning models that identify DDoS attacks, such as RNNs and GRUs. To measure how well the model detects DDoS assaults, use evaluation measures, including recall, accuracy, precision, and F1 score. Models exhibit comparable performance on the CICDDoS2019 dataset, as shown by the experimental findings, which show an accuracy score of 99.9%. The study highlights the effectiveness of AI- driven methods in improving cybersecurity resilience in network infrastructures to new and changing edge DDoS assaults. © 2024 IEEE.

关键词： Cyber attacks

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Proposing Model for Classification of Malicious SQLi Code Using machine learning Approach 1

Proposing Model for Classification of Malicious SQLi Code Us...

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1st international conference on Innovative Engineering sciences and Technological Research, ICIESTR 2024

作者： Shah, Imdad Ali Jhanjhi, N.Z. Brohi, Sarfraz Nawaz Taylor's University School of Computing Science Malaysia Scs Taylor's University School of Computer Science Malaysia University of Bristol United Kingdom

ISBN: (纸本)9798350348637

In today's digital landscape, we need to pay significant attention to identifying flaws in web-based applications. Most online applications use browsers to access data that belongs to web-based applications. The victim industry and industry losses increase in the perspective of SQL injection attacks. at the same time, the software market size has increased from 2019 to 2023, as the number of SQL injection attacks has grown in the same years. Many database-driven websites work in the market, and there is no mechanism to block another input, and there is a longer chance of injecting malicious SQL code into the database. The hackers injected through string commands and functions of SQL (Structure Query Language) due to the dynamic disregard of this command in the runtime. The security community has developed several methods for detecting software vulnerabilities, such as supervised, semi-supervised, deep learning, and machine learning. Given our literature, successful and effective vulnerability identification models require for the users. OWASP has the top 10-2021 vulnerabilities, as the numbers 07 and 08 were significant code injection attacks found in our literature review as critical, and 20% of detection models work in the software industry on machine learning basis. The review's findings demonstrated that studies create new SQL injection attack datasets using machine learning techniques and *** proposed model is working on a machine learning basis to identify two vulnerabilities of OWASP's top-ten 2021 No: 7 and 8: (1) identification and authentication failures and (2) software and data integrity failures. Both vulnerabilities are related to SQL injection. Further, this model works as a tool for identifying vulnerabilities in input data in the runtime. Support Vector machine (SVM), K-nearest neighbours Random Forest (RF) and Random Forest (RF). RF provides higher accuracy due to its feature extraction and classification process and trained for classificatio

关键词： Adversarial machine learning

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Rigidity dependence of long-term cosmic ray variation 38

Rigidity dependence of long-term cosmic ray variation

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38th international Cosmic Ray conference, ICRC 2023

作者： Kato, C. Munakata, K. Kadokura, A. Kataoka, R. Miyake, S. Kozai, M. Hayashi, Y. Masuda, Y. Matsumoto, M. Shinshu University Faculty of Science 3-1-1 Asahi Matsumoto Japan National Institute of Polar Research 10-3 Midori-cho Tachikawa Japan Ibaraki College 866 NakaneHitachinaka-shi Ibaraki-ken312-8508 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems 10-3 Midori-cho Tachikawa Japan

Investigation of rigidity dependence of long-term cosmic ray variation with cosmic ray muon count rate on the ground requires correction for atmospheric pressure and temperature effect. Although a correction method developed by Mendoça [? ] works well, we tried out machine learning technique as an alternative and confirmed it works for several years data[? ]. In this study, we apply the machine learning technique to 50 years of long-term muon data. We demonstrate that correction with machine learning technique works well by comparing to Mendoça’s method and by showing the variation amplitude is approximately proportional to the reciprocal of the median rigidity. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Tourism Demand Forecasting with Multi-terminal Search Query data and Deep learning 23rd

Tourism Demand Forecasting with Multi-terminal Search Query ...

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23rd Wuhan international conference on E-business (WHICEB)

作者： Hu, Zhongyi Li, Xue Misir, Mustafa Wuhan Univ Wuhan 430072 Peoples R China Duke Kunshan Univ Suzhou 215316 Peoples R China

ISBN: (纸本)9783031603266;9783031603242

This study aims to explore the effectiveness of multiple devices' search query in tourism demand forecasting. Accordingly, this study collects search data from computer and mobile devices, and proposed a hybrid deep learning model, namely MUL-CNN-LSTM, with improved structure accordingly. In the model, a dual CNN module is employed to extract deep features of search queries from multi-devices. Subsequently, the LSTM is applied to generate the prediction of tourism demand. By taking JiuZhaiGou Valley as a case, the empirical results of three groups of comparisons demonstrate that the proposed deep learning model, along with the multi-terminal search query data, significantly enhances forecasting performance.

关键词： Tourism demand forecasting Deep learning Search query data

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Accidental Prone Area Detection in Dhaka Metropolitan City Using machine learning Models 5

Accidental Prone Area Detection in Dhaka Metropolitan City U...

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5th IEEE international conference on Recent Trends in Computer science and Technology, ICRTCST 2024

作者： Jerin, Foyjunnesa Akhi, Amatul Bushra Tasin, Mobin Mohtadi Sultana, Naznin Akbar, Md. Atef Sharmin, Shayla Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh North South University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350351378

Dhaka, known for its status as one of the world's most densely populated areas, grapples with numerous challenges associated with urbanization and safety. This study identifies accident-prone zones within the Dhaka Metropolitan City by analyzing data collected from 2019 to 2023. Ten specific locations were investigated using various machine learning models: Decision Tree Classifier, Gradient Boosting Classifier, Random Forest, Gaussian Naive Bayes, and Logistic Regression. Evaluation metrics, including precision, recall, accuracy, F1-score, and standard deviation, were utilized, with the Random Forest classifier exhibiting the highest accuracy at 94.39%. The analysis encompassed both monthly and annual accident data, revealing trends and probabilities associated with different incident types. The study's implications suggest potential advancements in accident investigation by incorporating advanced machine learning techniques, integrating diverse datasets, and expanding the geographical coverage to encompass the entire metropolitan area. This comprehensive approach aims to bolster accident prevention measures and enhance urban safety. Overall, this research contributes to a deeper understanding of accident-prone regions and proposes strategies for targeted interventions to improve safety within metropolitan areas. © 2024 IEEE.

关键词： Accidents

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Using learning Analytics to Improve Students' Enrollments in Higher Education 2

Using Learning Analytics to Improve Students' Enrollments in...

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2nd international conference on Innovative Research in applied science, Engineering and Technology (IRASET)

作者： Sghir, Nabila Adadi, Amina El Mouden, Zakariyaa Ait Lahmer, Mohammed Moulay Ismail Univ Higher Sch Technol Informat & Commun Syst Engn Meknes Morocco

ISBN: (纸本)9781665422093

In the last years there has been a growing interest in adopting learning analytics (LA) in higher and further education systems. LA assists the institutional stakeholders to enhance the learning process, ameliorate the teaching activities, make adequate decisions and take appropriate actions based on the collection, analysis, and reporting of data generated from individual learners. The learning analytics approach aims to achieve many objectives, one of them is prediction which is the center of this research. In this paper, we conduct a comparative study between three machine learning algorithms;Decision Tree (DT), Random Forest (RF), and Support Vector machine (SVM);to predict the stream of new enrollments in the first year of higher education. As a case study, the predictive model is applied to new enrollments in the first year of the University Diploma of Technology (DUT) at the Higher School of Technology in Meknes, Morocco (ESTM). The performance of the classifiers is tested with and without the use of SMOTE data balancing on a dataset of 53554 students collected between 2016 and 2019. The obtained results show the best algorithm to predict the most accurate enrollments of students.

关键词： learning analytics Prediction machine learning Decision Tree Random Forest Support Vector machine

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