Estimating the CAP greening effect by machine learning techniques: A big data ex post analysis

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

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Predictors of outpatients’ no-show: big data analytics using apache spark

Journal Of Big Data ◽

10.1186/s40537-020-00384-9 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Tahani Daghistani ◽

Huda AlGhamdi ◽

Riyad Alshammari ◽

Raed H. AlHazme

Keyword(s):

Machine Learning ◽

Big Data ◽

Negative Impact ◽

Big Data Analytics ◽

Quality Of Healthcare ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Healthcare Organizations ◽

Data Framework ◽

Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

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Big Data Analytics Processes in Industrial Internet of Things Systems: Sensing and Computing Technologies, Machine Learning Techniques, and Autonomous Decision-Making Algorithms

Journal of Self-Governance and Management Economics ◽

10.22381/jsme7420194 ◽

2019 ◽

Vol 7 (4) ◽

pp. 28 ◽

Cited By ~ 4

Keyword(s):

Machine Learning ◽

Decision Making ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Machine Learning Techniques ◽

Industrial Internet Of Things ◽

Autonomous Decision ◽

Learning Techniques ◽

Industrial Internet

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Prediction of Pneumonia Using Big Data, Deep Learning and Machine Learning Techniques

2021 6th International Conference on Communication and Electronics Systems (ICCES) ◽

10.1109/icces51350.2021.9489188 ◽

2021 ◽

Author(s):

K.R. Swetha ◽

Niranjanamurthy M ◽

Amulya M P ◽

Manu Y M

Keyword(s):

Machine Learning ◽

Big Data ◽

Deep Learning ◽

Machine Learning Techniques ◽

Learning Techniques

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Big Data-Based Spectrum Sensing for Cognitive Radio Networks Using Artificial Intelligence

Big Data Analytics for Sustainable Computing - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-9750-6.ch009 ◽

2020 ◽

pp. 146-159 ◽

Cited By ~ 3

Author(s):

Suriya Murugan ◽

Sumithra M. G.

Keyword(s):

Machine Learning ◽

Big Data ◽

Cognitive Radio ◽

Spectrum Sensing ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Spectrum Utilization ◽

Candidate Solution ◽

Learning Techniques ◽

Efficient Data

Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next generation wireless networks. Spectrum sensing is one of the main challenges encountered by cognitive radio and the application of big data is a powerful way to solve various problems. However, for the increasingly tense spectrum resources, the prediction of cognitive radio based on big data is an inevitable trend. The signal data from various sources is analyzed using the big data cognitive radio framework and efficient data analytics can be performed using different types of machine learning techniques. This chapter analyses the process of spectrum sensing in cognitive radio, the challenges to process spectrum data and need for dynamic machine learning algorithms in decision making process.

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What is Machine Learning? A Primer for the Epidemiologist

American Journal of Epidemiology ◽

10.1093/aje/kwz189 ◽

2019 ◽

Cited By ~ 6

Author(s):

Qifang Bi ◽

Katherine E Goodman ◽

Joshua Kaminsky ◽

Justin Lessler

Keyword(s):

Machine Learning ◽

Big Data ◽

Computer Science ◽

Research Methods ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Epidemiologic Research ◽

Learning Techniques ◽

Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

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