Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management
Managing electronic components efficiently in a laboratory environment poses a tedious problem, and the same can hinder research efficiency and productivity. These challenges stem from the diversity of electronic components, which can range from small passive components like resistors and capacitors...
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2025-01-01
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| author | M. Karthiga Syarifah Bahiyah Rahayu E. Suganya S. Sankarananth S. Sountharrajan K. Venkatesan |
| author_facet | M. Karthiga Syarifah Bahiyah Rahayu E. Suganya S. Sankarananth S. Sountharrajan K. Venkatesan |
| author_sort | M. Karthiga |
| collection | DOAJ |
| description | Managing electronic components efficiently in a laboratory environment poses a tedious problem, and the same can hinder research efficiency and productivity. These challenges stem from the diversity of electronic components, which can range from small passive components like resistors and capacitors to large integrated circuits. This research presents a groundbreaking system for managing electronic components in smart vending machines, leveraging advanced AI and innovative techniques. The Multimodal Annotation Fusion (MAF) method enhances a carefully chosen dataset with various attributes and multimodal annotations, laying the groundwork for a robust and intelligent recognition system. YOLO v8 and transfer learning are integrated along with custom loss functions and model optimisation to produce a model that significantly outperforms existing solutions. The algorithm’s effectiveness is particularly noticeable in its low GPU time consumption, which is important for real-time applications. Ablation experiments in various settings further validate the algorithm’s efficacy, particularly in identifying small electrical components. As an example, the suggested model shows a 3% higher IoU than SSD and a 1% higher IoU than YOLO v7 and Faster RCNN, indicating significant increases in accuracy. The results of this study could greatly improve electronic component management, increasing the usefulness of smart vending machines for professionals and students working in lab environments. Furthermore, the vending machine’s digital twin accurately simulates its real-world performance for virtual testing and improvement prior to real deployment, offering increased effectiveness and convenience. |
| format | Article |
| id | doaj-art-2e2f7b3ef3b34a719a5e5b09e349d92f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2e2f7b3ef3b34a719a5e5b09e349d92f2025-01-14T00:02:30ZengIEEEIEEE Access2169-35362025-01-01136029605310.1109/ACCESS.2024.352377410818664Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component ManagementM. Karthiga0https://orcid.org/0000-0002-7112-8218Syarifah Bahiyah Rahayu1https://orcid.org/0000-0002-1996-5166E. Suganya2S. Sankarananth3S. Sountharrajan4https://orcid.org/0000-0003-4248-3875K. Venkatesan5Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IndiaDepartment of Science Defense, Faculty of Defense Science and Technology, National Defence University Malaysia (UPNM), Kuala Lumpur, MalaysiaDepartment of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, IndiaDepartment of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IndiaDepartment of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IndiaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, IndiaManaging electronic components efficiently in a laboratory environment poses a tedious problem, and the same can hinder research efficiency and productivity. These challenges stem from the diversity of electronic components, which can range from small passive components like resistors and capacitors to large integrated circuits. This research presents a groundbreaking system for managing electronic components in smart vending machines, leveraging advanced AI and innovative techniques. The Multimodal Annotation Fusion (MAF) method enhances a carefully chosen dataset with various attributes and multimodal annotations, laying the groundwork for a robust and intelligent recognition system. YOLO v8 and transfer learning are integrated along with custom loss functions and model optimisation to produce a model that significantly outperforms existing solutions. The algorithm’s effectiveness is particularly noticeable in its low GPU time consumption, which is important for real-time applications. Ablation experiments in various settings further validate the algorithm’s efficacy, particularly in identifying small electrical components. As an example, the suggested model shows a 3% higher IoU than SSD and a 1% higher IoU than YOLO v7 and Faster RCNN, indicating significant increases in accuracy. The results of this study could greatly improve electronic component management, increasing the usefulness of smart vending machines for professionals and students working in lab environments. Furthermore, the vending machine’s digital twin accurately simulates its real-world performance for virtual testing and improvement prior to real deployment, offering increased effectiveness and convenience.https://ieeexplore.ieee.org/document/10818664/Transfer learningsmart vending machinefaster RCNNYOLO v8digital twin |
| spellingShingle | M. Karthiga Syarifah Bahiyah Rahayu E. Suganya S. Sankarananth S. Sountharrajan K. Venkatesan Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management IEEE Access Transfer learning smart vending machine faster RCNN YOLO v8 digital twin |
| title | Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management |
| title_full | Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management |
| title_fullStr | Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management |
| title_full_unstemmed | Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management |
| title_short | Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management |
| title_sort | multimodal fusion and cutting edge ai based smart vending machines for electronic component management |
| topic | Transfer learning smart vending machine faster RCNN YOLO v8 digital twin |
| url | https://ieeexplore.ieee.org/document/10818664/ |
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