Progressive Study and Investigation of Machine Learning Techniques to Enhance the Efficiency and Effectiveness of Industry 4.0

Abstract

The goal of this article is to assess the most recent work on Industry 4.0 as well as the present state of science on Industry 4.0 through papers produced between January 2017 and March 2020.A systematic review process with a 5-step approach to article selection was employed, which included the following steps: 1) Selection of database 2) Research of keyword 3) Collection of articles 4) Inclusion/Exclusion criteria 5) Examining Selected Articles. It is noticed that much of the research is philosophical or case-based in character. The prospective study direction described in this paper may be useful to researchers interested in the field of industry 4.0 for research. The paper's future study directions must undoubtedly be beneficial to researchers.

EPSSNet

“…For instance, supervised classification leveraging machine learning techniques has been explored (Salhi et al, 2021). Text analysis has notably benefited from deep learning methodologies (Singh & Sachan, 2021;Ismail et al, 2022;Gu et al, 2022), alongside sentiment analysis (Mohammed et al, 2022), industrial applications (Sharma et al, 2022), medical diagnostics , disease safety detection (Nguyen et al, 2021), and image enhancement tasks like defogging . The metaverse (Deveci et al, 2022) emerges as a groundbreaking platform for experimenting with autonomous driving, heavily reliant on deep learning for its core technology.…”

Similarity Retrieval Based on Image Background Analysis

“…Withtherapiddevelopmentofscienceandtechnology,artificialintelligenceindustryhasexperienced rapiddevelopmentformorethantenyears.Atpresent,China'sAItechnologyisdevelopingrapidly, and the country should set reasonable short-term goals for AI promotion policies, and start to pragmaticallydevelopAIinfrastructurefromthreeaspects:improvingthesystem,makingupfor theshortcomingsandimprovingtheregulation (ZhangXin&WangMinghui,2019).AIisapplied to various industries, such as social and smart homes (Kar, 2022;Sharma et al, 2022). Machine learning,asapartofthethemeofartificialintelligence,hasbroughtsignificantchangestotheindustry andmadeasignificantcontributiontothedetectionofphishingwebsites (Almomanietal.,2022;Madhuetal.,2022).Theimageretrievaltechnologyusedinthispaperisapartofmachinelearning.…”

Examining the Influence of AI Chatbots on Semantic Web-Based Global Information Management in Various Industries

“…AI chatbots use semantic web technologies to enhance global information management by incorporating advanced capabilities in understanding and processing data. The integration of semantic web technologies, such as knowledge graphs, ontologies, and linked data, enables AI chatbots to access and interpret information in a structured and meaningful way (Sermet & Demir, 2021;Sharma et al, 2022). By using semantic web technologies, AI chatbots can enhance their ability to comprehend user queries, extract relevant information from diverse sources, and provide accurate responses based on contextual understanding.…”