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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization
Ali, Aziah
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Computer Science & IT Engineering

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Transforming campus mobility: the DigiSticker system in digital parking solutions Md. Mojnur, Rahman; Sarker, Md. Tanjil; Mohd-Isa, Wan-Noorshahida; Al Farid, Fahmid; Sheam, Md. Rakibul Hassan; Abdul Karim, Hezerul; Ramasamy, Gobbi; Ali, Aziah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3667-3680

Abstract

University digital parking systems have several benefits and solve many problems with traditional parking. Universities without a digital parking system face restricted parking, traffic congestion, inefficient space utilization, security issues, limited decision-making data, and diminished sustainability initiatives. This study paper discusses the benefits of digital parking systems and the drawbacks of traditional methods. By using technology to streamline university parking administration, the DigiSticker system offers an innovative solution. The DigiSticker system improves parking efficiency, convenience, and security for students, professors, and staff by delivering real-time parking information, assistance, and automated payments. This system has a user-friendly website and mobile app, fast registration, gate access management, security, user experience enhancement, and sustainability. Universities can improve student and staff parking experiences while improving parking management efficiency, security, and sustainability by meeting these requirements.
Mobile Implementation of Retinal Image Analysis for Efficient Vessel, Optic Disc, and Lesion Detection Hossain, Mubdiul; Ali, Aziah; Hashim, Noramiza; Mohd Isa, Wan Noorshahida; Wan Zaki, Wan Mimi Diyana; Hussain, Aini
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2363

Abstract

Smartphone-based mobile fundus photography is gaining popularity due to the rise of handheld fundus lenses, allowing a portable solution for a mobile-based computer-assisted diagnostic system (CADS). With such a system, professionals can monitor and diagnose numerous retinal diseases, including diabetic retinopathy (DR), glaucoma, age-related macular degeneration, etc. on their smartphone devices. In this study, we proposed a unified CADS tool for smartphone devices that can detect and identify six crucial retinal features utilizing both a filtering approach and a deep learning (DL) approach. These features are retinal blood vessels (RBV), optic discs (OD), hemorrhages (HM), microaneurysm (MA), hard exudates (HE), and soft exudates (SE). Traditional filtering is applied for RBV segmentation using B-COSFIRE and Frangi filter, whereas vessel inpainting and automatic canny edge-based Hough transform are used to localize OD center and radius. The DR lesions (HM, MA, HE, OD segmentation, and SE) are detected using a transfer learning-based Resnet50 backbone and multiclass DL U-net architecture. RBV segmentation achieved 94.94% and 94.44% accuracy in the DRIVE and STARE datasets. OD localization achieved an accuracy of 99.60% in the MESSIDOR dataset. Lastly, the IDRiD dataset is used to train and validate the DR lesions with an overall accuracy of 99.7%, F1-score of 77.4, and mean IoU of 59.2. The smartphone application can perform all the segmentation tasks at once in an average of 30 seconds. Given the availability, it is possible to improve the accuracy of the DL method further by training with more mobile fundus datasets.
Co-Authors Abdul Karim, Hezerul Aini Hussain Al Farid, Fahmid Hashim, Noramiza Hossain, Mubdiul Md. Mojnur, Rahman Mohd Isa, Wan Noorshahida Mohd-Isa, Wan-Noorshahida Ramasamy, Gobbi Sarker, Md. Tanjil Sheam, Md. Rakibul Hassan Wan Zaki, Wan Mimi Diyana