Research Methods in Machine Learning: A Content Analysis

Kamiri, Jackson; Mariga, Geoffrey

doi:10.24203/ijcit.v10i2.79

Cited by 21 publications

(17 citation statements)

References 110 publications

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“…Most studies did not mention the selection process of algorithms, and LR was the most frequent machine learning algorithm, followed by RF, SVM, NN, and DT. These results match the study by Kamiri and Mariga [52].…”

Section: Machine Learning Prediction Models Of Pressure Injurysupporting

confidence: 93%

“…It is worth mentioning that there is one study that utilized the experimental design to construct the prediction model of pressure injury. This approach matches a content analysis study by Kamiri and Mariga [52] to discuss the research methodology in machine learning, which revealed that all studies included in the analysis used machine learning in experimental designs.…”

Section: Characteristics Of Included Studiesmentioning

confidence: 87%

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Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms

Barghouthi,

Owda,

Asia

et al. 2023

Diagnostics

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Pressure injuries are increasing worldwide, and there has been no significant improvement in preventing them. This study is aimed at reviewing and evaluating the studies related to the prediction model to identify the risks of pressure injuries in adult hospitalized patients using machine learning algorithms. In addition, it provides evidence that the prediction models identified the risks of pressure injuries earlier. The systematic review has been utilized to review the articles that discussed constructing a prediction model of pressure injuries using machine learning in hospitalized adult patients. The search was conducted in the databases Cumulative Index to Nursing and Allied Health Literature (CINAHIL), PubMed, Science Direct, the Institute of Electrical and Electronics Engineers (IEEE), Cochrane, and Google Scholar. The inclusion criteria included studies constructing a prediction model for adult hospitalized patients. Twenty-seven articles were included in the study. The defects in the current method of identifying risks of pressure injury led health scientists and nursing leaders to look for a new methodology that helps identify all risk factors and predict pressure injury earlier, before the skin changes or harms the patients. The paper critically analyzes the current prediction models and guides future directions and motivations.

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Section: Machine Learning Prediction Models Of Pressure Injurysupporting

confidence: 93%

Section: Characteristics Of Included Studiesmentioning

confidence: 87%

Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms

Barghouthi,

Owda,

Asia

et al. 2023

Diagnostics

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“…Kamiri & Mariga [ 9 ] suggested, that ML is a subfield of artificial intelligence that deals with the creation of algorithms and procedures that allow a computer to learn and gain intelligence through experience. The research methodology used in machine learning research is critical because it influences the accuracy and dependability of the results.…”

Section: Methodsmentioning

confidence: 99%

Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm

Shahid

Alam²,

Su’ud³

2023

PLoS ONE

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The benefits and opportunities offered by cloud computing are among the fastest-growing technologies in the computer industry. Additionally, it addresses the difficulties and issues that make more users more likely to accept and use the technology. The proposed research comprised of machine learning (ML) algorithms is Naïve Bayes (NB), Library Support Vector Machine (LibSVM), Multinomial Logistic Regression (MLR), Sequential Minimal Optimization (SMO), K Nearest Neighbor (KNN), and Random Forest (RF) to compare the classifier gives better results in accuracy and less fault prediction. In this research, the secondary data results (CPU-Mem Mono) give the highest percentage of accuracy and less fault prediction on the NB classifier in terms of 80/20 (77.01%), 70/30 (76.05%), and 5 folds cross-validation (74.88%), and (CPU-Mem Multi) in terms of 80/20 (89.72%), 70/30 (90.28%), and 5 folds cross-validation (92.83%). Furthermore, on (HDD Mono) the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80/20 (87.72%), 70/30 (89.41%), and 5 folds cross-validation (88.38%), and (HDD-Multi) in terms of 80/20 (93.64%), 70/30 (90.91%), and 5 folds cross-validation (88.20%). Whereas, primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80/20 (97.14%), 70/30 (96.19%), and 5 folds cross-validation (95.85%) in the primary data results, but the algorithm complexity (0.17 seconds) is not good. In terms of 80/20 (95.71%), 70/30 (95.71%), and 5 folds cross-validation (95.71%), SMO has the second highest accuracy and less fault prediction, but the algorithm complexity is good (0.3 seconds). The difference in accuracy and less fault prediction between RF and SMO is only (.13%), and the difference in time complexity is (14 seconds). We have decided that we will modify SMO. Finally, the Modified Sequential Minimal Optimization (MSMO) Algorithm method has been proposed to get the highest accuracy & less fault prediction errors in terms of 80/20 (96.42%), 70/30 (96.42%), & 5 fold cross validation (96.50%).

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“…Kamiri and Mariga [ 17 ] suggested that ML is a subfield of artificial intelligence that deals with the creation of algorithms and procedures that allow a computer to learn and gain intelligence through experience. The research methodology used in ML research is critical because it influences the accuracy and dependability of the results.…”

Section: Literature Reviewmentioning

confidence: 99%

Achieving Reliability in Cloud Computing by a Novel Hybrid Approach

Shahid

Alam

Su’ud

2023

Sensors

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Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing’s challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system’s ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.

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Research Methods in Machine Learning: A Content Analysis

Cited by 21 publications

References 110 publications

Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms

Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms

Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm

Achieving Reliability in Cloud Computing by a Novel Hybrid Approach

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