Machine Learning Frameworks for Industrial Internet of Things (IIoT): A Comprehensive Analysis

Choudhry, Mani Deepak; Jeevanandham, S; Rose, Biji; P, Sruthi Mol

doi:10.1109/iceeict53079.2022.9768630

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Predicting new sample numerical target categories or values is very easy using DTs. That is one of the main advantages of this type of algorithm [45]. Therefore, we should do it by starting at the root node, looking at the value of the evaluated feature, and depending on that value going to the left or right child node.…”

Section: Discussionmentioning

confidence: 99%

The Application of Decision Tree Classification Algorithm on Decision-Making for Upstream Business

Rahman,

Jamaludin,

Zainol

et al. 2023

IJACSA

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In today's rapidly advancing technological landscape and evolving business paradigms, the pursuit of insightful patterns and concealed knowledge beyond conventional big data becomes imperative. This pursuit serves a crucial role in aiding stakeholders, particularly in the realms of tactical decision-making and forecasting, with a particular focus on business strategy and risk management. Strategic and tactical decision-making holds the key to sustaining the longevity, profitability, and continuous enhancement of the oil and gas industry. Therefore, it is paramount to address this need by uncovering the most effective Decision Tree (DT) techniques for various challenges and identifying their practical applications in real-life scenarios. The integration of big data with Machine Learning (ML) stands as a pivotal approach to foster data-driven innovation within the oil and gas sector. This study aims to offer valuable insights and methodologies for efficient decisionmaking, catering to the diverse stakeholders within the oil and gas industry. It focuses on the exploration of optimal DT techniques for specific problems and their relevance in practical situations. By harnessing the potential of machine learning and collaborative efforts among research scientists, big data practitioners, data scientists, and analysts, the study strives to provide more precise and effective data. Furthermore, it is imperative to recognize that not all stakeholders are mathematicians. In project management, a holistic approach that considers humanistic perspectives, such as risk analysis, ethics, and empathy, is crucial. Ultimately, the output and findings of any system must be accessible, comprehensible, and interpretable by humans or human groups. The success of these insights lies not just in their mathematical precision but also in their ability to resonate with and guide human decision-makers. In this light, the study emphasizes the human element in data interpretation and decision-making, acknowledging that the system's output will require human interaction, analysis, and ethical considerations to be truly effective in driving positive outcomes in the industry.

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Section: Discussionmentioning

confidence: 99%

The Application of Decision Tree Classification Algorithm on Decision-Making for Upstream Business

Rahman,

Jamaludin,

Zainol

et al. 2023

IJACSA

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“…У випадку, коли традиційні обчислювальні методи вже не можуть проаналізувати всю необхідну інформацію, алгоритми ML працюють гнучкіше, легко масштабуються та автоматизують громіздкі обчислення. Для аналізу даних, розташованих на різних пристроях, доцільно використовувати розподілені алгоритми машинного навчання [6][7][8].…”

Section: особливості обробки даних у розподілених системахunclassified

Investigation of Distributed Matrix Factorisation Efficiency in the Industrial Systems

Hordiichuk-Bublivska

2023

ICTEE

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The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis. A prime example of an intelligent industrial system is the smart grid, which efficiently distributes electricity to users by considering demand, network parameters, load, etc. Processing large amounts of information necessitates the use of machine learning methods and mathematical data analysis. Matrix factorization serves as an exemplary technique for transforming information into a more convenient form for further processing, establishing relationships between elements, and optimizing outcomes. In particular, the SVD (Singular Value Decomposition) and Funk-SVD algorithms are employed to address big data processing challenges, and they were discussed in this work. The key features of processing large data volumes in industrial smart grid systems were analyzed in the paper. The advantages of distributed computing for more efficient information analysis were identified. The recommendation algorithms that enable faster and more accurate processing of extensive data were explored in the study. Specifically, the SVD and Funk-SVD algorithms, used in recommendation systems for large data processing, were examined. A method of distributed matrix factorization to provide recommendations to smart grid system users was proposed in the paper. This approach involves the exchange of public data between devices and the local processing of private data. The advantages of this distributed model include flexibility in adjusting parameters, improved calculation accuracy through result exchange between nodes, high data processing speed, and scalability were identified. The conclusion that the proposed method can be effectively used in recommendation systems within the smart grid context, enhancing automated management processes and resource distribution was exclaimed.

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“…In an era defined by rapid technological advancements and interconnected digital ecosystems, the convergence of robotic process automation (RPA) and generative AI (GAI) within the context of the Internet of Everything (IoE) presents a paradigm shift that holds the potential to reshape industries fundamentally [1]. This integration stands as a harbinger of profound transformation, offering a unique approach to elevate productivity, while concurrently minimizing inefficiencies and fostering groundbreaking innovation [2].…”

Section: Introductionmentioning

confidence: 99%

Increased Productivity and Reduced Waste with Robotic Process Automation and Generative AI-powered IoE Services

Lo,

Yang,

Zhang

et al. 2024

JWE

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The convergence of robotic process automation (RPA) and generative AI (GAI) within the context of Internet of Everything (IoE) services represents a profound paradigm shift. This fusion of technologies not only streamlines routine tasks but also catalyzes innovation while harnessing the potential of interconnected devices. Such integration empowers organizations to achieve remarkable gains in efficiency and sustainability. This paper embarks on an exploration of these transformative services, designed to elevate productivity, and curtail wasteful practices in contemporary industries. By closely examining intricate case studies, we illuminate the multifaceted advantages of this integrated approach. Our investigation demonstrates how RPA accelerates the execution of repetitive processes, substantially diminishing the margin for human error and amplifying operational efficiency. In contrast, generative AI introduces a disruptive force, generating fresh ideas, designs, and solutions, thereby elevating the quality of products and services. The infusion of these cutting-edge technologies into the fabric of IoE services paves the way for organizations to attain unprecedented levels of automation, intelligence, and connectivity. Furthermore, this paper comprehensively addresses the intricate challenges and considerations associated with the proposed implementation. We delve into ethical concerns, security implications, and the necessary workforce adaptation to offer a balanced perspective on the adoption of these technologies. Additionally, we navigate through potential limitations and constraints, underscoring the imperative need for strategic planning and robust governance.

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Machine Learning Frameworks for Industrial Internet of Things (IIoT): A Comprehensive Analysis

Cited by 8 publications

References 10 publications

The Application of Decision Tree Classification Algorithm on Decision-Making for Upstream Business

The Application of Decision Tree Classification Algorithm on Decision-Making for Upstream Business

Investigation of Distributed Matrix Factorisation Efficiency in the Industrial Systems

Increased Productivity and Reduced Waste with Robotic Process Automation and Generative AI-powered IoE Services

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