Graph Neural Networks for Anomaly Anticipation in HPC Systems

Molan, Martin; Khan, Junaid; Borghesi, Andrea; Bartolini, Andrea

doi:10.1145/3578245.3585335

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Moreover, a type of ML technique inspired by the structure and functioning of the human brain, called NN, also received considerable attention. There were 11 articles, which are [31], [32], [34], [41], [56], [65], [69], [71], [73], [81], [87], found in this study. In the context of business plans and logistics decision processes, five articles address various challenges and research problems in improving production processes and decisionmaking using AI in Industry 4.0.…”

mentioning

confidence: 65%

“…We have summarized 41 articles that do not specify specific industries in the discussion. While the rest of the articles defined specific industries, and this review categorized them into 14 types of industries, such as aero-engine [43], automotive [32], [33], electronics [23], [39], [77], fast-moving consumer goods [69], garment [30], machining [62], [67], [68], [76], [80], [82], metallurgies [78], [79], [63], milling [26], [59] and gas [50], semiconductor [49], water treatment plant [73], tobacco [86], transportation [54], small-medium enterprises [52]. The correlation between IoT and AI technologies with the categorized industries type is shown in Figure 5.…”

Section: Answering Rq4: Type Of Manufacturing System Decision-making ...mentioning

confidence: 99%

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Artificial intelligence and internet of things in manufacturing decision processes

Wijaya,

Hermanto Rudy,

Debora

et al. 2024

IJ-AI

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This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.

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