Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things
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 tremen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/324 |
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| author | Ali M. Al Shahrani Madani Abdu Alomar Khaled N. Alqahtani Mohammed Salem Basingab Bhisham Sharma Ali Rizwan |
| author_facet | Ali M. Al Shahrani Madani Abdu Alomar Khaled N. Alqahtani Mohammed Salem Basingab Bhisham Sharma Ali Rizwan |
| author_sort | Ali M. Al Shahrani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T09:41:12Z |
| format | Article |
| id | doaj.art-e1e2181d6c624aa4b2716488db4ad331 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:41:12Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e1e2181d6c624aa4b2716488db4ad3312023-12-02T00:55:36ZengMDPI AGSensors1424-82202022-12-0123132410.3390/s23010324Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of ThingsAli M. Al Shahrani0Madani Abdu Alomar1Khaled N. Alqahtani2Mohammed Salem Basingab3Bhisham Sharma4Ali Rizwan5Faculty of Computer Studies, Arab Open University, Riyadh 11681, Saudi ArabiaDepartment of Industrial Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Industrial Engineering, College of Engineering, Taibah University, Madina 41411, Saudi ArabiaDepartment of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, IndiaDepartment of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaIndustrial 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.https://www.mdpi.com/1424-8220/23/1/324industrial automationroboticsInternet of Things (IoT)machine learningelaborative stepwise stacked artificial neural networks (ESSANN) algorithmindustrial environment |
| spellingShingle | Ali M. Al Shahrani Madani Abdu Alomar Khaled N. Alqahtani Mohammed Salem Basingab Bhisham Sharma Ali Rizwan Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things Sensors industrial automation robotics Internet of Things (IoT) machine learning elaborative stepwise stacked artificial neural networks (ESSANN) algorithm industrial environment |
| title | Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things |
| title_full | Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things |
| title_fullStr | Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things |
| title_full_unstemmed | Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things |
| title_short | Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things |
| title_sort | machine learning enabled smart industrial automation systems using internet of things |
| topic | industrial automation robotics Internet of Things (IoT) machine learning elaborative stepwise stacked artificial neural networks (ESSANN) algorithm industrial environment |
| url | https://www.mdpi.com/1424-8220/23/1/324 |
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