A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

Nti, Isaac Kofi; Quarcoo, Juanita Ahia; Aning, Justice; Fosu, Godfred Kusi

doi:10.26599/bdma.2021.9020028

Cited by 88 publications

(30 citation statements)

References 108 publications

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“…The computing platform needs graphical and tensor processing units to reduce the processing time. DL has been applied to big data [ 58 ], medical [ 59 , 60 ], computer vision [ 61 , 62 ], power systems [ 63 ], nuclear power [ 64 ], etc. ( Figure 6 ).…”

Section: Tbi Detection Using Artificial Intelligencementioning

confidence: 99%

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Alouani

Elfouly

2022

Biomedicines

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Traumatic brain injury (TBI) can produce temporary biochemical imbalance due to leaks through cell membranes or disruption of the axoplasmic flow due to the misalignment of intracellular neurofilaments. If untreated, TBI can lead to Alzheimer’s, Parkinson’s, or total disability. Mild TBI (mTBI) accounts for about about 90 percent of all TBI cases. The detection of TBI as soon as it happens is crucial for successful treatment management. Neuroimaging-based tests provide only a structural and functional mapping of the brain with poor temporal resolution. Such tests may not detect mTBI. On the other hand, the electroencephalogram (EEG) provides good spatial resolution and excellent temporal resolution of the brain activities beside its portability and low cost. The objective of this paper is to provide clinicians and scientists with a one-stop source of information to quickly learn about the different technologies used for TBI detection, their advantages and limitations. Our research led us to conclude that even though EEG-based TBI detection is potentially a powerful technology, it is currently not able to detect the presence of a mTBI with high confidence. The focus of the paper is to review existing approaches and provide the reason for the unsuccessful state of EEG-based detection of mTBI.

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Section: Tbi Detection Using Artificial Intelligencementioning

confidence: 99%

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Alouani

Elfouly

2022

Biomedicines

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“…These three properties together (volume, velocity, and variety) form the dimensions of Big Data (or three V of big data). However, as shown in [Figure 1], most studies expand this concept to five key characteristics (5 Vs), namely, volume, velocity, variety, value, and veracity [21].…”

Section: Concepts and Characteristicsmentioning

confidence: 99%

A Web Scraping Framework for Descriptive Analysis of Meteorological Big Data for Decision-Making Purposes

Mhouti¹,

Fahim²,

Soufi³

et al. 2023

IJHIT

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With the large increase in the amount of heterogeneous, complex, and unstructured data issued from Web sources, there is an emerging need to develop Big Data technologies and tools to extract and manage this data. In this context, Web scraping for Big Data is a technique that has gained importance because of its rapidity and efficiency in gathering data for Big Data technology. This study was conducted to propose a Web Scraping framework for descriptive analysis of meteorological Big Data. The introduced framework makes it possible to extract a set of data to process it, present it in a form that is easier to analyze and understand, and use it for decision-making purposes about weather forecasts. The study employed descriptive analysis of available big data as it allows one to easily make quality and effective decisions. The web scraping process takes place in several stages, including data extraction, data archiving in a data warehouse, and finally data filtering and analysis. To test its applicability, the proposed web scraping framework was implemented and tested in the meteorological context to extract and present meteorological data issued from a specialized web source. The proposed system makes it possible to restore the data in the form of statistical models published in a dashboard. The results of the study revealed that the predictive models provided by the system are capable of predicting certain weather-related variables, such as humidity, precipitation, and temperature. The opportunities and implications to leverage the results of this study are many, including weather forecasting and decision support.

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“…In order not to degrade the performance of the network due to degradation caused by nonconstant mapping, a residual network is used in this paper. A residual connection [19] is made for every two mini-modules to obtain the hidden layer h(X i ):…”

Section: Hard Sharing Network For Subcost Intervalmentioning

confidence: 99%

“…However, the overall representation is inadequate owing to the sophisticated correlations among factors. In recent research, deep learning methods [19] outperformed traditional computational methods in various prediction tasks due to their ability to adapt the composition of individual feature factors for better representation [20]. Given the complexity of the components and data dimensions in medical cost prediction [21], deep learning methods can make more accurate and reasonable predictions of overall costs by depicting the correlation between the various costs in addition to predicting individual costs.…”

Section: Introductionmentioning

confidence: 99%

Explainable and Personalized Medical Cost Prediction Based on Multitask Learning over Mobile Devices

Sun

Wang

Hui

et al. 2022

Mobile Information Systems

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Currently, the forecasting of healthcare costs is of significant importance for the finance management of both government and individual citizens. However, the existence of dramatic individual diversity in health status, as well as the extensive complexity of the factors influencing the cost, has made the prediction a challenging task. Thanks to the unprecedented adoption of mobile devices, regular individuals may contribute diverse dimensions of data for the medical cost prediction. Hospitals and healthcare service providers are all setting up their own mobile services and collect user data for analysis. Previous methods usually employed traditional machine learning or simple neural network methods, which are difficult to be applied to the nonlinear medical cost and diverse dimensions of data. Therefore, this paper proposes a multitask learning-based framework for interpretable medical cost interval prediction to address these issues. The framework proposed in this paper first predicts subcost intervals by applying the multidimensional data collected from mobile ends and following the multitask learning paradigm. The total cost interval is then predicted based on this prediction. Simultaneously, the framework derives a decision tree from the parameters of the multitask learning network and calculates the importance of each feature in predicting the cost intervals. This paper demonstrates the method's effectiveness using real-world data experiments.

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A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

Cited by 88 publications

References 108 publications

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

A Web Scraping Framework for Descriptive Analysis of Meteorological Big Data for Decision-Making Purposes

Explainable and Personalized Medical Cost Prediction Based on Multitask Learning over Mobile Devices

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