Review Version 1 This version is not peer-reviewed
: Received: 1 December 2019 / Approved: 3 December 2019 / Online: 3 December 2019 (05:34:17 CET)
Petrillo, A.; Travaglioni, M.; De Felice, F.; Cioffi, R.; Piscitelli, G. Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends and Directions. Preprints 2019, 2019120016 (doi: 10.20944/preprints201912.0016.v1). Petrillo, A.; Travaglioni, M.; De Felice, F.; Cioffi, R.; Piscitelli, G. Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends and Directions. Preprints 2019, 2019120016 (doi: 10.20944/preprints201912.0016.v1).
The history of Artificial Intelligence (AI) development dates to the 40s. The researchers showed strong expectations until the 70s, when they began to encounter serious difficulties and investments were greatly, reduced. With the introduction of the Industry 4.0, one of the techniques adopted for AI implementation is Machine Learning (ML) that focuses on the machines ability to receive data series and learn on their own. Given the considerable importance of the subject, researchers have completed many studies on ML to ensure that machines are able to replace or relieve human tasks. This research aims to analyze, systematically, the literature on several aspects, including publication year, authors, scientific sector, country, institution, keywords. Analyzing existing literature on AI is a necessary stage to recommend policy on the matter. The analysis has been done using Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software have been used to complete them. Literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by USA and the increasing interest after the birth of Industry 4.0.
artificial intelligence; machine learning; systematic literature review; applications; industry 4.0
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