Data Mining and Machine Learning Techniques for Bank Customers Segmentation: A Systematic Mapping Study

2020 ◽  
pp. 666-684
Author(s):  
Maricel Monge ◽  
Christian Quesada-López ◽  
Alexandra Martínez ◽  
Marcelo Jenkins
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Machine Learning Techniques ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Learning Techniques ◽  
Customers Segmentation

scholarly journals Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Abderrahim El hafidy ◽  
Taoufik Rachad ◽  
Ali Idri ◽  
Ahmed Zellou
Keyword(s):  
Machine Learning ◽  
Mobile Applications ◽  
Mobile Apps ◽  
Driving Behavior ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Learning Techniques

Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.

scholarly journals Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study

2021 ◽  
Vol 101 ◽  
pp. 107050
Author(s):  
Michał Choraś ◽  
Konstantinos Demestichas ◽  
Agata Giełczyk ◽  
Álvaro Herrero ◽  
Paweł Ksieniewicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Systematic Mapping Study ◽  
Fake News ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Learning Techniques ◽  
News Online

Machine Learning Techniques for Code Smells Detection: A Systematic Mapping Study

2019 ◽  
Vol 29 (02) ◽  
pp. 285-316 ◽  
Author(s):  
Frederico Luiz Caram ◽  
Bruno Rafael De Oliveira Rodrigues ◽  
Amadeu Silveira Campanelli ◽  
Fernando Silva Parreiras
Keyword(s):  
Machine Learning ◽  
Empirical Studies ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Systematic Mapping Study ◽  
Code Smells ◽  
Mapping Study ◽  
Learning Techniques ◽  
Wide Range ◽  
High Level

Code smells or bad smells are an accepted approach to identify design flaws in the source code. Although it has been explored by researchers, the interpretation of programmers is rather subjective. One way to deal with this subjectivity is to use machine learning techniques. This paper provides the reader with an overview of machine learning techniques and code smells found in the literature, aiming at determining which methods and practices are used when applying machine learning for code smells identification and which machine learning techniques have been used for code smells identification. A mapping study was used to identify the techniques used for each smell. We found that the Bloaters was the main kind of smell studied, addressed by 35% of the papers. The most commonly used technique was Genetic Algorithms (GA), used by 22.22% of the papers. Regarding the smells addressed by each technique, there was a high level of redundancy, in a way that the smells are covered by a wide range of algorithms. Nevertheless, Feature Envy stood out, being targeted by 63% of the techniques. When it comes to performance, the best average was provided by Decision Tree, followed by Random Forest, Semi-supervised and Support Vector Machine Classifier techniques. 5 out of the 25 analyzed smells were not handled by any machine learning techniques. Most of them focus on several code smells and in general there is no outperforming technique, except for a few specific smells. We also found a lack of comparable results due to the heterogeneity of the data sources and of the provided results. We recommend the pursuit of further empirical studies to assess the performance of these techniques in a standardized dataset to improve the comparison reliability and replicability.

scholarly journals Recent Progress in Automated Code Generation from GUI Images Using Machine Learning Techniques

2020 ◽  
Vol 26 (9) ◽  
pp. 1095-1127
Author(s):  
Daniel Baulé ◽  
Christiane Gresse von Wangenheim ◽  
Aldo Wangenheim ◽  
Jean Carlo Rossa Hauck
Keyword(s):  
Machine Learning ◽  
Code Generation ◽  
Interface Design ◽  
Machine Learning Techniques ◽  
Systematic Mapping Study ◽  
Learning Approaches ◽  
Systematic Mapping ◽  
Learning Techniques ◽  
Performance Results ◽  
Automated Code Generation

The manual transformation of a user interface design into code is a costly and time-consuming process. A solution can be the automation of the generation of code based on sketches or GUI design images. Recently, Machine Learning approaches have shown promising results in detecting GUI elements for such automation. Thus, to provide an overview of existing approaches, we performed a systematic mapping study. As a result, we identified and compared 20 approaches, that demonstrate good performance results being considered useful. These results can be used by researchers and practitioners in order to improve the efficiency of the GUI design process as well as continue to evolve and improve approaches for its support.

scholarly journals Dr. Phish: Phishing Website Detector

2021 ◽  
Vol 297 ◽  
pp. 01032
Author(s):  
Harish Kumar ◽  
Anshal Prasad ◽  
Ninad Rane ◽  
Nilay Tamane ◽  
Anjali Yeole
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Cyber Crime ◽  
Data Mining Algorithms ◽  
Learning Techniques ◽  
Mining Algorithms ◽  
Host Properties ◽  
New Strategies

Phishing is a common attack on credulous people by making them disclose their unique information. It is a type of cyber-crime where false sites allure exploited people to give delicate data. This paper deals with methods for detecting phishing websites by analyzing various features of URLs by Machine learning techniques. This experimentation discusses the methods used for detection of phishing websites based on lexical features, host properties and page importance properties. We consider various data mining algorithms for evaluation of the features in order to get a better understanding of the structure of URLs that spread phishing. To protect end users from visiting these sites, we can try to identify the phishing URLs by analyzing their lexical and host-based features.A particular challenge in this domain is that criminals are constantly making new strategies to counter our defense measures. To succeed in this contest, we need Machine Learning algorithms that continually adapt to new examples and features of phishing URLs.

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