The integration of emerging technologies in Internet of Things (IoT) systems focused on Industry 4.0 has emerged as a topic of relevance in the technological field of digital transformation, this process offers opport...
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ISBN:
(纸本)9783031840777;9783031840784
The integration of emerging technologies in Internet of Things (IoT) systems focused on Industry 4.0 has emerged as a topic of relevance in the technological field of digital transformation, this process offers opportunities to improve operational efficiency, decision making and process optimization. This article focuses on the systematic literature review (SLR), for a detailed understanding on the different approaches, challenges and opportunities related to the implementation of advancedtechnologies with IoT in various Industry 4.0 environments. Seventy-three articles published between 2020 and 2024 were reviewed, of which 26 primary studies were identified that address issues of most used technologies in integration with IoT for Industry 4.0 such as Blockchain technologies, Digital twins, Artificial Intelligence, Big Data, Edge Computing, Cyberphysical systems, Smart devices, sensors, robotics, and wireless sensor networks. The review revealed the need for reference frameworks, standards and protocols for the integration of advancedtechnologies with IoT. In conclusion, the diversity of approaches underlines the importance of leveraging emerging IoT technologies to drive the Industry 4.0 transformation.
The work hereby suggests the use of machine learning toward the prediction of concrete workability based on the available large dataset from literature studies. Of the four ML models, the XGBoost Regressor gives the b...
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The Internet of Things (IoT) heralds a innovative generation in communication via enabling regular gadgets to supply, receive, and percentage records easily. IoT applications, which prioritise venture automation, aim ...
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This work explores advanced data structures as a means of optimizing algorithmic efficiency in high-performance computing. The search for faster and more scalable algorithms becomes essential as computing demands rise...
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Ensuring road safety in this technologically advanced society is critical. This study presents an innovative approach for identifying traffic signs using convolutional neural networks (CNNs). Here, the existing traffi...
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Real-time image restoration is a cutting-edge tech-nology that accelerates or rebuilds images as soon as they are captured, processed, or released to correct issues such as noise, blur, and compression. This field is ...
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Deep learning technology establishes advanced methods for enhancing Human-computer Interaction systems by providing machines with the ability to sense and understand human emotional states. The capability's value ...
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Curricular advising takes up a significant amount of time for both students and faculty. Given the prerequisite graph of courses, and one's career goals, the course selections every semester should be almost deter...
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ISBN:
(纸本)9798350300543
Curricular advising takes up a significant amount of time for both students and faculty. Given the prerequisite graph of courses, and one's career goals, the course selections every semester should be almost deterministic. Yet, no viable tools exist to advise students automatically, or remotely. Given the rise of online learning platforms, and a significant interest in non-standard degrees, the need for an institution independent and online educational advising system has emerged. Additionally, such a system can play a significant role in a learner's career progression in a rapidly changing career landscape. In this paper, we explore the design and implementation of an advising system that can serve as a blueprint for a smart advising system.
The rapid evolution of emerging technologies has generated growing interest in their potential to transform customer loyalty into digital environments. This study aims to conduct a systematic literature review (SLR) t...
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In many machine learning applications, high-dimensional data complicates processing, making feature selection critical. This work aims to review and compare existing feature selection methods for regression tasks usin...
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