Classification and Big Data Usages for Industrial Applications

Lim, Sang Boem

doi:10.30534/ijatcse/2019/18842019

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…ML divided in many steps collect data, pre-process data, develop predictive model, validation and test predictive models [14][15].…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning Models for the Prediction the Necessity of Resorting to ICU of COVID-19 Patients

Agieb¹

2020

IJATCSE

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The world is currently facing many unrests and challenges due to the emergence of the COVID-19 Epidemic. The management of medical resources is considered one of the most important challenges posed by the emergence of this epidemic. The intensive care unit (ICU) plays an important role in saving the life of a COVID-19 patient, and therefore work has been done in this research to find models to predict the patient's need to enter ICU or not. The prediction models depend on Machine Learning (ML). Three models will be built to predict the state at which the patient needs to enter the ICU or not. The proposed predictor models based on three types of Supervised machine learning Naive Bayes, K -nearest neighbor (K-NN), and Support Vector Machine (SVM) according to the scarce datasets. Predictor model trained based on Extracted features from patients' X-ray images.

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“…ML divided in many steps collect data, pre-process data, develop predictive model, validation and test predictive models [14][15].…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning Models for the Prediction the Necessity of Resorting to ICU of COVID-19 Patients

Agieb¹

2020

IJATCSE

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“…In contemporary world, Big Data concept became important in different domains especially in business world [18]. Research related to Big Data processing and storage is considered a dominant research were there exist a significant need for solutions that aim to improve the performance [19].…”

Section: Introductionmentioning

confidence: 99%

Performance Optimization for Distributed Hybrid Storage Systems using a Predictive Approach

Assaf¹

2020

IJATCSE

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Distributed data centers are considered very important for data storage services in the contemporary computing world especially with the increased amount of data that are needed to be stored and retrieved. Data retrieval speed performance is a sensitive issue when considering the huge amount of data that need to be retrieved from several nodes over the network. Data prefetch has proved to be an important technique for reducing data reading time from the distributed nodes. In such distributed environment, data fetching time from a node to an another consists of the disk reading time and the network transmission time. Multi-layer (hybrid) storage provides high performance solutions for big data centers. We introduce a solution PPDHSS that implements predictive-probability graph to predictively prefetch the data that are expected to be accessed by the application in the near future from the lower level hard disk of the storage-side nodes (slower) to the top level solid state disk (faster) in parallel which the application data reading requests that comes from the client-side node by taking advantage of the storage system's parallelism. Our performance evaluation in which we used real traces shows that our system can reduce the data fetching time from storage side-nodes to the client-side nodes without the need of using caches of large size.

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Classification and Big Data Usages for Industrial Applications

Cited by 2 publications

References 33 publications

Machine Learning Models for the Prediction the Necessity of Resorting to ICU of COVID-19 Patients

Machine Learning Models for the Prediction the Necessity of Resorting to ICU of COVID-19 Patients

Performance Optimization for Distributed Hybrid Storage Systems using a Predictive Approach

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