Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization
Abstract Nowadays, cost-sensitive customers need customized products that demand consumption-based production. The Internet of Things (IoT) makes ubiquitous sensing and data more available, integrating with the semantic web and advanced sensor technologies. Augmented reality (AR) is a collaborative...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-13224-8 |
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| author | Amnah Alshahrani Mukhtar Ghaleb Hany Mahgoub Achraf Ben Miled Nojood O. Aljehane Mohammed Yahya Alzahrani Hasan Beyari Sultan Alanazi |
| author_facet | Amnah Alshahrani Mukhtar Ghaleb Hany Mahgoub Achraf Ben Miled Nojood O. Aljehane Mohammed Yahya Alzahrani Hasan Beyari Sultan Alanazi |
| author_sort | Amnah Alshahrani |
| collection | DOAJ |
| description | Abstract Nowadays, cost-sensitive customers need customized products that demand consumption-based production. The Internet of Things (IoT) makes ubiquitous sensing and data more available, integrating with the semantic web and advanced sensor technologies. Augmented reality (AR) is a collaborative technology that boosts user experience by coating virtual digital content into reality. Holographic communication is a transformative technology that redefines digital interaction by enabling immersive, realistic, and collaborative 3D experiences. It utilizes advanced holography to create virtual projections in real-time environments. Object detection (OD) is the most significant and challenging problem in computer vision (CV). The massive developments in deep learning (DL) models have recently considerably speeded up the OD momentum for consumer goods utilizing holographs. This article presents Object Detection with Holographic Visualization for Consumer products using a Hippopotamus Optimization Algorithm and Deep Learning (ODHVCP-HOADL) model. The aim is to develop an effective IoT-based OD system for consumer products integrated with a holographic display to provide an interactive and immersive visualization experience. Initially, the wiener filtering (WF) is utilized for image pre-processing to enhance image quality by removing unwanted noise. Furthermore, the Faster R-CNN model is employed for the OD process. The SqueezeNet model extracts and isolates relevant features from raw data. Moreover, the convolutional autoencoder (CAE) model is implemented for classification. Additionally, the hippopotamus optimization algorithm (HOA)-based hyperparameter selection model is implemented to improve the classification result of the CAE technique. Finally, the holographic process is performed by using the binary amplitude hologram (BAH) generation. The performance validation of the ODHVCP-HOADL approach is examined under the Indoor OD dataset. The comparison study of the ODHVCP-HOADL approach portrayed a superior accuracy value of 99.64% over existing models. |
| format | Article |
| id | doaj-art-c436a6d04e4f4a5aa85b54b5e9ab3d49 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c436a6d04e4f4a5aa85b54b5e9ab3d492025-08-20T03:43:57ZengNature PortfolioScientific Reports2045-23222025-08-0115112010.1038/s41598-025-13224-8Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimizationAmnah Alshahrani0Mukhtar Ghaleb1Hany Mahgoub2Achraf Ben Miled3Nojood O. Aljehane4Mohammed Yahya Alzahrani5Hasan Beyari6Sultan Alanazi7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityCollege of Computing and Information Technology, University of BishaDepartment of Computer Science, Applied College at Mahayil, King Khalid UniversityDepartment of Computer Science, College of Science, Northern Border UniversityDepartment of Computer Science, Faculty of Computers and Information Technology, University of TabukFaculty of Computing and Information, Al-Baha UniversityDepartment of Administrative and Financial Sciences, Applied College, Umm Al- Qura UniversityDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz UniversityAbstract Nowadays, cost-sensitive customers need customized products that demand consumption-based production. The Internet of Things (IoT) makes ubiquitous sensing and data more available, integrating with the semantic web and advanced sensor technologies. Augmented reality (AR) is a collaborative technology that boosts user experience by coating virtual digital content into reality. Holographic communication is a transformative technology that redefines digital interaction by enabling immersive, realistic, and collaborative 3D experiences. It utilizes advanced holography to create virtual projections in real-time environments. Object detection (OD) is the most significant and challenging problem in computer vision (CV). The massive developments in deep learning (DL) models have recently considerably speeded up the OD momentum for consumer goods utilizing holographs. This article presents Object Detection with Holographic Visualization for Consumer products using a Hippopotamus Optimization Algorithm and Deep Learning (ODHVCP-HOADL) model. The aim is to develop an effective IoT-based OD system for consumer products integrated with a holographic display to provide an interactive and immersive visualization experience. Initially, the wiener filtering (WF) is utilized for image pre-processing to enhance image quality by removing unwanted noise. Furthermore, the Faster R-CNN model is employed for the OD process. The SqueezeNet model extracts and isolates relevant features from raw data. Moreover, the convolutional autoencoder (CAE) model is implemented for classification. Additionally, the hippopotamus optimization algorithm (HOA)-based hyperparameter selection model is implemented to improve the classification result of the CAE technique. Finally, the holographic process is performed by using the binary amplitude hologram (BAH) generation. The performance validation of the ODHVCP-HOADL approach is examined under the Indoor OD dataset. The comparison study of the ODHVCP-HOADL approach portrayed a superior accuracy value of 99.64% over existing models.https://doi.org/10.1038/s41598-025-13224-8Object detectionHolographic visualizationConsumer productHippopotamus optimization algorithmBinary amplitude hologramIoT |
| spellingShingle | Amnah Alshahrani Mukhtar Ghaleb Hany Mahgoub Achraf Ben Miled Nojood O. Aljehane Mohammed Yahya Alzahrani Hasan Beyari Sultan Alanazi Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization Scientific Reports Object detection Holographic visualization Consumer product Hippopotamus optimization algorithm Binary amplitude hologram IoT |
| title | Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization |
| title_full | Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization |
| title_fullStr | Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization |
| title_full_unstemmed | Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization |
| title_short | Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization |
| title_sort | internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization |
| topic | Object detection Holographic visualization Consumer product Hippopotamus optimization algorithm Binary amplitude hologram IoT |
| url | https://doi.org/10.1038/s41598-025-13224-8 |
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