A Literature Review of Machine Learning Techniques for Cybersecurity in Data Centers

2021 ◽  
Author(s):  
Evita Roponena ◽  
Janis Kampars ◽  
Andris Gailitis ◽  
Janis Strods
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Data Centers ◽  
Machine Learning Techniques ◽  
Learning Techniques

Literature review: Machine learning techniques applied to financial market prediction

2019 ◽  
Vol 124 ◽  
pp. 226-251 ◽  
Author(s):  
Bruno Miranda Henrique ◽  
Vinicius Amorim Sobreiro ◽  
Herbert Kimura
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Financial Market ◽  
Machine Learning Techniques ◽  
Learning Techniques

Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

2019 ◽  
Vol 892 ◽  
pp. 274-283
Author(s):  
Mohammed Ashikur Rahman ◽  
Afidalina Tumian
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Cardiac Care ◽  
Learning Techniques

Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.

Machine learning techniques for code smell detection: A systematic literature review and meta-analysis

2019 ◽  
Vol 108 ◽  
pp. 115-138 ◽  
Author(s):  
Muhammad Ilyas Azeem ◽  
Fabio Palomba ◽  
Lin Shi ◽  
Qing Wang
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Code Smell

scholarly journals Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

2021 ◽  
Vol 29 (2) ◽  
pp. 403-421
Author(s):  
Faseeha Matloob ◽  
Shabib Aftab ◽  
Munir Ahmad ◽  
Muhammad Adnan Khan ◽  
Areej Fatima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Learning Techniques

Code Clone Detection Using Machine Learning Techniques

2020 ◽  
Vol 11 (2) ◽  
pp. 49-75
Author(s):  
Amandeep Kaur ◽  
Sandeep Sharma ◽  
Munish Saini
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Machine Learning Techniques ◽  
Clone Detection ◽  
Learning Approaches ◽  
Code Clone ◽  
Online Databases ◽  
Learning Techniques ◽  
Tools And Techniques

Code clone refers to code snippets that are copied and pasted with or without modifications. In recent years, traditional approaches for clone detection combine with other domains for better detection of a clone. This paper discusses the systematic literature review of machine learning techniques used in code clone detection. This study provides insights into various tools and techniques developed for clone detection by implementing machine learning approaches and how effectively those tools and techniques to identify clones. The authors perform a systematic literature review on studies selected from popular computer science-related digital online databases from January 2004 to January 2020. The software system and datasets used for analyzing tools and techniques are mentioned. A neural network machine learning technique is primarily used for the identification of the clone. Clone detection based on a program dependency graph must be explored in the future because it carries semantic information of code fragments.

Machine Learning Techniques for Breast Cancer Diagnosis: Literature Review

2020 ◽  
pp. 247-254
Author(s):  
Djihane Houfani ◽  
Sihem Slatnia ◽  
Okba Kazar ◽  
Noureddine Zerhouni ◽  
Abdelhak Merizig ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Literature Review ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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