دورية أكاديمية
Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things.
| العنوان: | Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things. |
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| المؤلفون: | Al Shahrani AM; Faculty of Computer Studies, Arab Open University, Riyadh 11681, Saudi Arabia., Alomar MA; Department of Industrial Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia., Alqahtani KN; Department of Industrial Engineering, College of Engineering, Taibah University, Madina 41411, Saudi Arabia., Basingab MS; Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia., Sharma B; Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India., Rizwan A; Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia. |
| المصدر: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Dec 28; Vol. 23 (1). Date of Electronic Publication: 2022 Dec 28. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Basel, Switzerland : MDPI, c2000- |
| مواضيع طبية MeSH: | Internet of Things*, Humans ; Automation ; Industry ; Technology ; Machine Learning |
| مستخلص: | Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of such technology offers businesses and people tremendous assistance in successfully achieving commercial and noncommercial requirements. Organizations are expected to automate industrial processes owing to the significant risk management and inefficiency of conventional processes. Hence, we developed an elaborative stepwise stacked artificial neural network (ESSANN) algorithm to greatly improve automation industries in controlling and monitoring the industrial environment. Initially, an industrial dataset provided by KLEEMANN Greece was used. The collected data were then preprocessed. Principal component analysis (PCA) was used to extract features, and feature selection was based on least absolute shrinkage and selection operator (LASSO). Subsequently, the ESSANN approach is proposed to improve automation industries. The performance of the proposed algorithm was also examined and compared with that of existing algorithms. The key factors compared with existing technologies are delay, network bandwidth, scalability, computation time, packet loss, operational cost, accuracy, precision, recall, and mean absolute error (MAE). Compared to traditional algorithms for industrial automation, our proposed techniques achieved high results, such as a delay of approximately 52%, network bandwidth accomplished at 97%, scalability attained at 96%, computation time acquired at 59 s, packet loss achieved at a minimum level of approximately 53%, an operational cost of approximately 59%, accuracy of 98%, precision of 98.95%, recall of 95.02%, and MAE of 80%. By analyzing the results, it can be seen that the proposed system was effectively implemented. |
| References: | ISA Trans. 2020 Jun;101:379-389. (PMID: 31955949) J Ambient Intell Humaniz Comput. 2021 Feb 21;:1-17. (PMID: 33643481) IEEE Access. 2018;6:. (PMID: 35531371) Sensors (Basel). 2022 Oct 25;22(21):. (PMID: 36365871) |
| فهرسة مساهمة: | Keywords: Internet of Things (IoT); elaborative stepwise stacked artificial neural networks (ESSANN) algorithm; industrial automation; industrial environment; least absolute shrinkage and selection operator (LASSO); machine learning; principal component analysis (PCA); robotics |
| تواريخ الأحداث: | Date Created: 20230108 Date Completed: 20230110 Latest Revision: 20230111 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC9823523 |
| DOI: | 10.3390/s23010324 |
| PMID: | 36616923 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1424-8220 |
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| DOI: | 10.3390/s23010324 |