<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Intelligent manufacturing system incorporates a number of sensors including IoT devices, cameras, and scanners, for capturing real-time data about the manufacturing process. Based on their physical properties, colours, dimensions, or other relevant characteristics, these sensors can be used to track and identify waste objects. Waste object classification in intelligent manufacturing includes the usage of recent systems and technologies to detect and classify waste materials or objects produced during the manufacturing process. The objective is to enable effective waste management and recycling practices, optimizing resource utilization and reducing environmental impact. Manual waste classification is a laborious and expensive task, which results in the development of automatic waste classification models using artificial intelligence (AI) techniques. It remains a challenging process due to the significant variations in the solid waste because of varying shapes, colours, and sizes. Therefore, recent advances in deep learning (DL) methods can be employed to accomplish the solid waste classification process. The study introduces a chaotic African vulture optimization algorithm with a deep learning-based solid waste classification (CAVOA-DLSWC) system. The CAVOA-DLSWC technique aims to automatically detect waste objects and classify them into different categories using DL models. In the presented CAVOADLSWC approach, two major processes are involved such as object classification and detection. For the object detection method, the CAVOA-DLSWC technique uses a lightweight RetinaNet model with CAVOA based hyperparameter tuning process. The CAVOA is derived by integrating the chaotic concepts into the initial iteration values of the AVOA. Once the waste objects are identified, the classification process can be performed by the use of convolutional long short-term memory (CLSTM) network. The experimental values of the CAVOA-DLSWC approach can be tested employing the solid waste database including diverse kinds of waste objects. The comparative results show the remarkable performance of the CAVOA-DLSWC method over other techniques.</p>
</abstract>

Intelligent manufacturing system incorporates a number of sensors including IoT devices, cameras, and scanners, for capturing real-time data about the manufacturing process. Based on their physical properties, colours, dimensions, or other relevant characteristics, these sensors can be used to track and identify waste objects. Waste object classification in intelligent manufacturing includes the usage of recent systems and technologies to detect and classify waste materials or objects produced during the manufacturing process. The objective is to enable effective waste management and recycling practices, optimizing resource utilization and reducing environmental impact. Manual waste classification is a laborious and expensive task, which results in the development of automatic waste classification models using artificial intelligence (AI) techniques. It remains a challenging process due to the significant variations in the solid waste because of varying shapes, colours, and sizes. Therefore, recent advances in deep learning (DL) methods can be employed to accomplish the solid waste classification process. The study introduces a chaotic African vulture optimization algorithm with a deep learning-based solid waste classification (CAVOA-DLSWC) system. The CAVOA-DLSWC technique aims to automatically detect waste objects and classify them into different categories using DL models. In the presented CAVOADLSWC approach, two major processes are involved such as object classification and detection. For the object detection method, the CAVOA-DLSWC technique uses a lightweight RetinaNet model with CAVOA based hyperparameter tuning process. The CAVOA is derived by integrating the chaotic concepts into the initial iteration values of the AVOA. Once the waste objects are identified, the classification process can be performed by the use of convolutional long short-term memory (CLSTM) network. The experimental values of the CAVOA-DLSWC approach can be tested employing the solid waste database including diverse kinds of waste objects. The comparative results show the remarkable performance of the CAVOA-DLSWC method over other techniques.

Modelling of African Vulture Optimization Algorithm with Deep Learning-based Object Classification for Intelligent Manufacturing Systems

Journal of Smart Internet of Things

This work is licensed under the Creative Commons Attribution 4.0 International License.

Intelligent manufacturing system incorporates a number of sensors including IoT devices, cameras, and scanners, for capturing real-time data about the...

Modelling of African Vulture Optimization Algorithm with Deep Learning-based Object Classification for Intelligent Manufacturing Systems

Data publikacji: 15 gru 2023

Zakres stron: 62 - 83

Otrzymano: 05 wrz 2023

Przyjęty: 22 paź 2023

DOI: https://doi.org/10.2478/jsiot-2023-0011

Słowa kluczowe
Intelligent manufacturing, Solid waste management, Deep learning, Computer vision, Waste recycling, Plastic waste

© 2023 Ameer N. Onaizah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Modelling of African Vulture Optimization Algorithm with Deep Learning-based Object Classification for Intelligent Manufacturing Systems

Data publikacji: 15 gru 2023

Zakres stron: 62 - 83

Otrzymano: 05 wrz 2023

Przyjęty: 22 paź 2023

DOI: https://doi.org/10.2478/jsiot-2023-0011

Słowa kluczoweIntelligent manufacturing, Solid waste management, Deep learning, Computer vision, Waste recycling, Plastic waste

© 2023 Ameer N. Onaizah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Intelligent manufacturing, Solid waste management, Deep learning, Computer vision, Waste recycling, Plastic waste