Internet of Things (IoTs) Evolutionary Computation, Enterprise Modelling and Simulation

Haldorai, Anandakumar; Ramu, Arulmurugan; Suriya, M.

doi:10.1007/978-3-030-44407-5_1

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…The identification of AI methods is based on [19,20,31], focusing on machine learning. Here, various methods were assigned to different tasks and clusters (Table 1) for application as the basis for the subsequent analysis.…”

Section: Identification Of Ai Methodsmentioning

confidence: 99%

“…Generally, three main categories of learning methods are applied for state-of-the-art AI agents with machine learning algorithms (supervised, unsupervised and reinforcement learning). Supervised learning, as the least autonomous method, utilizes expert knowledge or given regularities to verify hypotheses derived from data analyzation [19,20]. Humans predefine and label the utilized data beforehand [21].…”

Section: Definition Of Aimentioning

confidence: 99%

“…Reinforcement learning is also known as semi-supervised learning as it is a mixture of the two learning methods described above. The input data are labelled beforehand; however, it might occur that the labelling is changed over the course of the learning process [20]. The reinforcement is obtained from rewards emitted from the affected environment.…”

Section: Definition Of Aimentioning

confidence: 99%

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Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

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Section: Identification Of Ai Methodsmentioning

confidence: 99%

Section: Definition Of Aimentioning

confidence: 99%

Section: Definition Of Aimentioning

confidence: 99%

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Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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“…Nevertheless, several investigations have documented the use of systematic literature review (SLR) methodologies using a solitary scientific database. In their study, [8] examined the benefits associated with the use of numerous databases in the context of SLR (systematic literature review) methodologies. Industry 4.0 is a burgeoning area of investigation that has garnered significant attention from both practitioners and scholars.…”

Section: Overview Of Researchmentioning

confidence: 99%

Achievement of Sustainable Manufacturing From Industry 4.0 Technologies – Future Perspective

Badurashvili

2023

JEBI

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Throughout history, manufacturing has consistently been at the forefront of technical progress, seeing the evolution from steam engines through cyber-physical systems, electricity, IoT, microprocessors, AI, automation, computers, and now. In the context of promoting growth of economy and generating lasting value in industries, sustainable manufacturing comprises the three essential components of manufacturing, namely processes, products, and systems. In order for manufacturing to be deemed sustainable, it is essential that these three components, when examined individually, illustrate the advantages in terms of environmental, economic, and social aspects. The primary objective of sustainable manufacturing is to produce things of superior quality while minimizing resource consumption and ensuring the safety of customers, employees, and local communities. This article explores the future direction of research in the domains of Industry 4.0 and sustainable manufacturing technology. Upon reviewing the extant literature, six key areas emerge as important subjects for further inquiry. These focal points are elucidated, along with the identified gaps in knowledge that need more exploration. Relevant papers for this research were identified using keywords such as "Sustainability," "Industry 4.0," "sustainable manufacturing," "manufacturing sustainability," or "smart manufacturing."

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Predicting Trends and Research Patterns of Smart Cities: A Semi-Automatic Review Using Latent Dirichlet Allocation (LDA)

Sharma¹,

Batra

Sharma³

et al. 2022

IEEE Access

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Smart cities are a current worldwide topic requiring much scientific investigation. This research instigates the necessity of an organized review to a heedful insight of the research trends and patterns prevailing in this domain. The string is formulated to extract the corpus from Scopus largest database of publications. The corpus of 8320 articles published from 2010 to 2022 is processed using Latent Dirichlet Allocation. Two, five, and ten topics have been extracted to provide the recent trends for IoT in smart cities. There has been an increased recognition that more attention needs to be paid to the area of smart cities so a complete overview of the topic of smart cities research, including the most prominent nations (institutions, sources, and authors) and noteworthy research directions has been presented in this paper. The scientific collaboration across countries (regions), organizations, and authors has also been widely discussed. A detailed and comprehensive overview and visualization of the trends and research patterns used to integrate the Internet of Things in Smart Cities. This data based experimental study signifies a roadmap of the research trends in Smart Cities by implementing topic modeling technique that has never been used in this domain. Based upon the topic modeling using LDA, authors have formulated three research questions and answered those question based on the in-depth research. At the end this study concludes the areas suggested are at the growing phase and need more insight for their growth.

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Internet of Things (IoTs) Evolutionary Computation, Enterprise Modelling and Simulation

Cited by 7 publications

References 29 publications

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Achievement of Sustainable Manufacturing From Industry 4.0 Technologies – Future Perspective

Predicting Trends and Research Patterns of Smart Cities: A Semi-Automatic Review Using Latent Dirichlet Allocation (LDA)

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