Reliance on Artificial Intelligence, Machine Learning and Deep Learning in the Era of Industry 4.0

Rao, T. Venkat Narayana; Gaddam, Akhila; Kurni, Muralidhar; Saritha, K.

doi:10.1002/9781119792253.ch12

Cited by 28 publications

(18 citation statements)

References 18 publications

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“…This interactive visual map facilitates the identification of key authors, influential papers, collaboration trends, and emerging research areas [4]. VOSviewer has become a popular tool for scientometric research in various fields, including thermal comfort and building control [41], sustainable urban development [42], circularity in construction [43], autonomous vehicles [44], and smart homes [45]-just to name a few. In this study, VOSviewer was used to analyze AI in construction literature and create a range of visual diagrams, such as co-authorship network maps, citation-based network maps, and co-occurrence network maps, to help visualize and qualitatively analyze the literature data [39].…”

Section: Methodsmentioning

confidence: 99%

Mapping Two Decades of AI in Construction Research: A Scientometric Analysis from the Sustainability and Construction Phases Lenses

Regona,

Yigitcanlar,

Hon

et al. 2023

Buildings

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The construction industry plays a vital role in the urbanization process and global economy, and there is a growing interest in utilizing artificial intelligence (AI) technologies to improve sustainability, productivity, and efficiency. However, there is a lack of comprehensive analysis regarding the progression of AI in the construction context, particularly from the sustainability angle. This study aims to fill this gap by conducting a scientometric analysis of AI research in construction by focusing on historical clusters, emerging trends, research clusters, and the correlation between sustainability pillars and key project stages. A Scopus search, between January 2000 and July 2023, was conducted that used 25 construction industry-related keywords, resulting in a total of 9564 publications. After evaluating practical AI applications in construction, 3710 publications were selected for further analysis using VOSviewer for visual diagrams and to further understand connections and patterns between literature. The findings revealed that: (a) Literature on AI in construction has experienced steady growth over the past two decades; (b) Machine learning, deep learning, and big data are seen as the key enabling digital technologies in the construction sector’s performance; (c) Economic and governance pillars of sustainability exhibit the highest potential for AI adoption; (d) Design and construction phases demonstrate substantial advantages for AI adoption; (e) AI technologies have become, despite adoption challenges, a strong driver of construction industry modernization, and; (f) By incorporating AI, the construction industry can advance towards a more sustainable future by consolidating its processes and practices.

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Section: Methodsmentioning

confidence: 99%

Mapping Two Decades of AI in Construction Research: A Scientometric Analysis from the Sustainability and Construction Phases Lenses

Regona,

Yigitcanlar,

Hon

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

“…The problems are solved by learning from proficient data and improve results without using obvious programming. ML evaluates the practical circumstances, recognizes the pattern and gives an appropriate solution (Rao et al, 2022). In Industry 4.0, machine learning is applicable in various sectors like manufacturing, health automation, finance and telecommunication.…”

Section: Machine Learningmentioning

confidence: 99%

Section: Fundamental Pillars For Industry 40 Developmentmentioning

confidence: 99%

Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment

Singh

Goyat

Panwar

2023

TQM

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PurposeAt the present time, Industry 4.0 has proven its effectiveness and significance in automation and data exchange within industries across different sectors worldwide. In the current literature, there is still a lack of research on adopting Industry 4.0 in the manufacturing setting in developing economies. The main purpose of the present study is to explore the fundamental pillars and framework for ease of adoption of Industry 4.0 in manufacturing environments, along with highlighting the benefits and challenges.Design/methodology/approachIn this study, a systematic literature review has been conducted through protocol, search, appraisal, synthesis, analysis, report (PSALSAR) model. In the literature, the articles are included within time span of 2008–2022, consisting keywords like Industry 4.0, blockchain, machine learning, artificial intelligence, Internet of Things, 3D printing, big data analytics, etc. Based on available literature, conceptual implementation framework of Industry 4.0 is proposed.FindingsThis study explored the key ingredients that play an essential role to bridge the gap and construct a strong relationship among physical and cyber world. The results reveals that the emerging technologies such as IoT, blockchain, artificial intelligence, augmented reality, 3D printing, big-data analytics, cloud-computing join hands to accomplish success in Industry 4.0 by reducing human interference for effective and efficient systems. In addition, the study also explored the possible benefits of emerging technologies with challenges faced by manufacturing setting during adaptation of Industry 4.0.Originality/valueAs per the authors' best knowledge, no research articles are found in literature which explore various emerging technologies in Industry 4.0 with its implementation framework in the manufacturing setting in developing economies. The main focus of the present study is to discover the literature review in defined area and find the research gap among current scenario and future trend for execution of Industry 4.0 in manufacturing environment.

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“…Computer vision is crucial for tasks such as image recognition, object detection, and autonomous navigation. AI and robotics converge to create intelligent machines capable of performing physical tasks (Rao et al, 2022;Mindell and Reynolds, 2023). Advanced robots equipped with AI can navigate dynamic environments, make decisions, and collaborate with humans AI algorithms to analyse medical images, aiding in the early detection of diseases like cancer.…”

Section: Economic and Social Implications Of Limited Electrificationmentioning

confidence: 99%

Role of Artificial Intelligence in Electrification of Africa

Ibegbulam C.M,

Aigbovbiosa, O.J,

Olowonubi, J.A

et al. 2023

Eng. sci. technol. j.

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The research explores the relationship between Artificial Intelligence (AI) and electrification in Africa, focusing on the challenges and emerging trends. The electrification deficit in Africa poses a significant impediment to economic development and social progress. This paper explores the pivotal role that Artificial Intelligence (AI) plays in addressing the challenges associated with electrification initiatives across the African continent. With its capacity for innovation and optimization, AI emerges as a transformative force capable of revolutionizing the planning, deployment, and management of electrification projects in a region characterized by diverse geographical landscapes and economic constraints. The paper investigates the potential of AI in addressing financial barriers associated with electrification projects. By facilitating innovative financing models, reducing operational costs, and attracting investments, AI contributes to creating sustainable and economically viable electrification solutions. The focus extends to decentralized energy systems and microgrids, exploring how AI can empower remote and underserved communities with reliable access to electricity. The socio-economic impact of AI-driven electrification initiatives is also scrutinized, emphasizing the potential for job creation, economic growth, and improved living standards. The paper discusses the importance of capacity building and local empowerment to ensure that AI technologies are effectively integrated into electrification projects while fostering inclusive and sustainable development. It highlights the role of AI in revolutionizing electrification, predicting electricity consumption and enabling decentralized solutions. AI-driven electrification has shown economic and social benefits, including enhanced productivity, improved quality of life, and increased market access. However, it also raises ethical concerns and privacy implications. The research emphasizes the need for proactive mitigation strategies and collaborations across sectors to drive regulatory frameworks, technological innovations, and global impact. The research envisions a future where AI plays a central role in fostering inclusive growth, connecting communities, and illuminating a digitally transformed and sustainable continent. Keywords: Artificial Intelligence, Electrification, Africa, Electricity, Energy

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Reliance on Artificial Intelligence, Machine Learning and Deep Learning in the Era of Industry 4.0

Cited by 28 publications

References 18 publications

Mapping Two Decades of AI in Construction Research: A Scientometric Analysis from the Sustainability and Construction Phases Lenses

Mapping Two Decades of AI in Construction Research: A Scientometric Analysis from the Sustainability and Construction Phases Lenses

Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment

Role of Artificial Intelligence in Electrification of Africa

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