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Reliable Industry 4.0 Based on Machine Learning and IoT
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Reliable Industry 4.0 Based on Machine Learning and IoT

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 12805

Special Issue Editors

Dr. Mahmoud Elsisi

E-Mail Website
Guest Editor
1. Industry 4.0 Implementation Center, Center for Cyber-physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
2. Department of Electrical Engineering, Faculty of Engineering (Shoubra), Benha University, 108 Shoubra St., B. O. Box 11241 Cairo, Egypt
Interests: artificial intelligence techniques; machine learning; deep learning; internet of things (IoT); cybersecurity, Industry 4.0; model predictive control; decentralized control of large scale systems; neural networks and fuzzy logic; robotic control; autonomous vehicle control; renewable energy; power system dynamics: stability&control; nuclear power plant control; wind energy conversion systems (WECs)
Special Issues, Collections and Topics in MDPI journals
Dr. Minh-Quang Tran

E-Mail Website
Guest Editor
1. Industry 4.0 Implementation Center, Center for Cyber-physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
2. Department of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam
Interests: tool condition monitoring; machining dynamics; AGV; industrial IoT; big data analytics; machine learning; intelligent systems for industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The fourth industrial revolution, known as Industry 4.0, can provide and integrate many advanced technologies for automation so as to contribute to the operational efficiency and effectiveness of production processes, particularly, the process of combining smart machines and systems. The key technologies of Industry 4.0 are cyber-physical systems, Internet of things (IoT), big data analytics, cloud computing, machine learning, artificial intelligence, visualization, virtual reality, and autonomous robots towards practical applications in many industrial areas. To enhance the reliability of Industry 4.0, researchers from many fields and industries have to work together applying the new technologies in practical applications to provide secure online monitoring and control. This special issue aims to encourage scholars and researchers to present research achievements of state-of-the-art technologies with respect to reliable Industry 4.0 based on machine learning and IoT.

Authors are encouraged to submit papers in any of the following potential topics or related areas of Industry 4.0.

Dr. Mahmoud Elsisi
Dr. Minh-Quang Tran
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Cyber-physical systems
  • Industrial Internet-of-Things (I-IoT)
  • Advanced robotics (collaborative and adaptive robots)
  • Additive manufacturing, hybrid manufacturing, and 3D printing
  • Smart manufacturing
  • Autonomous vehicles and drones
  • Industrial big data and data analytics
  • Machine learning (ML) and Artificial Intelligence (AI)
  • Cloud computing for Industry 4.0
  • Augmented reality (AR) and virtual reality (VR) technologies
  • Distributed manufacturing
  • Planning and scheduling in Industry 4.0
  • Energy in Industry 4.0

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Published Papers (2 papers)

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13 pages, 3905 KiB  
Article
Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network
by Gnanavel Sakkarvarthi, Godfrey Winster Sathianesan, Vetri Selvan Murugan, Avulapalli Jayaram Reddy, Prabhu Jayagopal and Mahmoud Elsisi
Electronics 2022, 11(21), 3618; https://doi.org/10.3390/electronics11213618 - 6 Nov 2022
Cited by 62 | Viewed by 9196
Abstract
Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Leaf disease detection and categorization employ a variety of deep learning approaches. Tomatoes are one of the most popular vegetables and can be found in every [...] Read more.
Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Leaf disease detection and categorization employ a variety of deep learning approaches. Tomatoes are one of the most popular vegetables and can be found in every kitchen in various forms, no matter the cuisine. After potato and sweet potato, it is the third most widely produced crop. The second-largest tomato grower in the world is India. However, many diseases affect the quality and quantity of tomato crops. This article discusses a deep-learning-based strategy for crop disease detection. A Convolutional-Neural-Network-based technique is used for disease detection and classification. Inside the model, two convolutional and two pooling layers are used. The results of the experiments show that the proposed model outperformed pre-trained InceptionV3, ResNet 152, and VGG19. The CNN model achieved 98% training accuracy and 88.17% testing accuracy. Full article
(This article belongs to the Special Issue Reliable Industry 4.0 Based on Machine Learning and IoT)
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23 pages, 4128 KiB  
Article
IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0
by Anil Verma, Divya Anand, Aman Singh, Rishika Vij, Abdullah Alharbi, Majid Alshammari and Arturo Ortega Mansilla
Electronics 2022, 11(9), 1436; https://doi.org/10.3390/electronics11091436 - 29 Apr 2022
Cited by 2 | Viewed by 2034
Abstract
Education 4.0 imitates Industry 4.0 in many aspects such as technology, customs, challenges, and benefits. The remarkable advancement in embryonic technologies, including IoT (Internet of Things), Fog Computing, Cloud Computing, and Augmented and Virtual Reality (AR/VR), polishes every dimension of Industry 4.0. The [...] Read more.
Education 4.0 imitates Industry 4.0 in many aspects such as technology, customs, challenges, and benefits. The remarkable advancement in embryonic technologies, including IoT (Internet of Things), Fog Computing, Cloud Computing, and Augmented and Virtual Reality (AR/VR), polishes every dimension of Industry 4.0. The constructive impacts of Industry 4.0 are also replicated in Education 4.0. Real-time assessment, irregularity detection, and alert generation are some of the leading necessities of Education 4.0. Conspicuously, this study proposes a reliable assessment, irregularity detection, and alert generation framework for Education 4.0. The proposed framework correspondingly addresses the comparable issues of Industry 4.0. The proposed study (1) recommends the use of IoT, Fog, and Cloud Computing, i.e., IFC technological integration for the implementation of Education 4.0. Subsequently, (2) the Symbolic Aggregation Approximation (SAX), Kalman Filter, and Learning Bayesian Network (LBN) are deployed for data pre-processing and classification. Further, (3) the assessment, irregularity detection, and alert generation are accomplished over SoTL (the set of threshold limits) and the Multi-Layered Bi-Directional Long Short-Term Memory (M-Bi-LSTM)-based predictive model. To substantiate the proposed framework, experimental simulations are implemented. The experimental outcomes substantiate the better performance of the proposed framework, in contrast to the other contemporary technologies deployed for the enactment of Education 4.0. Full article
(This article belongs to the Special Issue Reliable Industry 4.0 Based on Machine Learning and IoT)
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