Emerging Trends in Data Science and AI

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 3973

Special Issue Editors

ISEG – Lisbon School of Economics and Management, Universidade de Lisboa, 1200-781 Lisboa, Portugal
Interests: data science; data science and management; machine learning in finance; gamification; information systems
Special Issues, Collections and Topics in MDPI journals
NOVA IMS Information Management School, Universidade Nova de Lisboa Campus de Campolide, 1070-312 Lisboa, Portugal
Interests: data science; artificial intelligence; information systems; e-learning; digital transformation; gamification; e-commerce
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Significant advances in artificial intelligence (AI) have led to new challenges and opportunities in the field. Data science is a rapidly growing area of study and a professional discipline. It is thus critical to investigate this new reality from a social and corporate standpoint. Abundant information about data science and AI and how they may be used to solve economic and societal problems exists. However, in order to realize the widespread use of data science and AI in business and everyday life, their efficacy must be objectively assessed. This Special Issue aims to gather contributions from academics investigating a variety of subjects and viewpoints, including AI-related management, social sciences, and engineering. Given the present level of AI and data science, three forms are of particular interest: machine learning, natural language processing, and robotics. Submissions considering other relevant topics will also be considered.

Dr. Carlos J. Costa
Dr. Manuela Aparicio
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data science applications
  • AI applications
  • machine learning applications
  • NLP applications
  • AI trends
  • data science trends

Published Papers (1 paper)

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Research

8 pages, 1995 KiB  
Communication
A Comparison of Machine Learning Approaches for Predicting Employee Attrition
by Filippo Guerranti and Giovanna Maria Dimitri
Appl. Sci. 2023, 13(1), 267; https://0-doi-org.brum.beds.ac.uk/10.3390/app13010267 - 26 Dec 2022
Cited by 3 | Viewed by 3256
Abstract
Employee attrition is a major problem that causes many companies to incur in significant costs to find and hire new personnel. The use of machine learning and artificial intelligence methods to predict the likelihood of resignation of an employee, and the quitting causes, [...] Read more.
Employee attrition is a major problem that causes many companies to incur in significant costs to find and hire new personnel. The use of machine learning and artificial intelligence methods to predict the likelihood of resignation of an employee, and the quitting causes, can provide HR departments with a valuable decision support system and, as a result, prevent a large waste of time and resources. In this paper, we propose a preliminary exploratory analysis of the application of machine learning methodologies for employee attrition prediction. We compared several classification models with the goal of finding the one that not only performs best, but is also well interpretable, in order to provide companies with the possibility of improving those aspects that have been shown to produce the quitting of their employees. Among the proposed methods, Logistic Regression performs the best, with an accuracy of 88% and an AUC-ROC of 85%. Full article
(This article belongs to the Special Issue Emerging Trends in Data Science and AI)
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