Machine learning approaches to IoT security: A systematic literature review

2021 ◽  
Vol 14 ◽  
pp. 100365
Author(s):  
Rasheed Ahmad ◽  
Izzat Alsmadi
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Learning Approaches ◽  
Iot Security

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.

scholarly journals Ten-year evolution on credit risk research: a systematic literature review approach and discussion

2020 ◽  
Vol 40 (2) ◽  
Author(s):  
Fernanda Medeiros Assef ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Credit Risk ◽  
Systematic Literature Review ◽  
Academic Research ◽  
Machine Learning Techniques ◽  
Major Influence ◽  
Learning Approaches ◽  
Main Research ◽  
Credit Risk Analysis

Given its importance in financial risk management, credit risk analysis, since its introduction in 1950, has been a major influence both in academic research and in practical situations. In this work, a systematic literature review is proposed which considers both “Credit Risk” and “Credit risk” as search parameters to answer two main research questions: are machine learning techniques being effectively applied in research about credit risk evaluation? Furthermore, which of these quantitative techniques have been mostly applied over the last ten years of research? Different steps were followed to select the papers for the analysis, as well as the exclusion criteria, in order to verify only papers with Machine Learning approaches. Among the results, it was found that machine learning is being extensively applied in Credit Risk Assessment, where applications of Artificial Intelligence (AI) were mostly found, more specifically Artificial Neural Networks (ANN). After the explanation of each answer, a discussion of the results is presented.

scholarly journals Federated Learning in a Medical Context: A Systematic Literature Review

2021 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Bjarne Pfitzner ◽  
Nico Steckhan ◽  
Bert Arnrich
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Privacy ◽  
Research Area ◽  
Learning Models ◽  
Related Data ◽  
Private Data ◽  
Large Databases ◽  
Machine Learning Models

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

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