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All Journal JOIV : International Journal on Informatics Visualization
Hashim, Noramiza
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Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Optimizing Retail Recommendation via Similarity Measures and Machine Learning Approach Bhattacharijee, Arpita; Ting, Choo Yee; Ghauth, Khairil Imran; Peng, Loh Yuen; Hashim, Noramiza; Mohd Isa, Wan Noorshahida; Suvon, Injamul Haque; Matsah, Wan Razali
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2360

Abstract

Finding a suitable retail business with potential success in a specific location can be challenging for retailers. The process is often lengthy and inconsistent due to the subjective nature of expert opinions. Previous research has demonstrated several techniques that consider numerous influential attributes for location optimization problems. However, while many studies rely on a business's core data for analytical purposes, accessing this information is often a significant constraint. This study aims to address the challenge of extracting valuable location features to enhance the profitability of chosen businesses despite the inaccessibility of core business data. The proposed methodology involves three main steps. First, an analytical dataset must be created by utilizing geographic and demographic information. Second, we conduct similarity measures by applying Manhattan distance to the entire analytical dataset, using an ideal business outlet that contains the footfall information. Through this process, we can identify businesses that share similar characteristics with popular outlets. Finally, several supervised machine learning models are trained to employ the extracted meta-features. Experimental results show that the XGBoost classifier performs best with an 87% accuracy score, outperforming the baseline models. The proposed methodology in this research presents a robust framework that demonstrated remarkable efficiency in achieving the stated objectives and improving the performance of retail business recommendations within a given location. Future work could consider a broader range of features that could potentially enhance model performance by applying ensemble learning. 
Deep Learning-based Utility Pole Safety Assessment from Visual Data Abbas M. Elsayed, Mohamed; Hashim, Noramiza; Abdul Rahman, Abdul Aziz; Alhayek, Mohamed
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3039

Abstract

Utility poles are crucial infrastructure components, and efficiently assessing the safety of these structures and ensuring they adhere to the clearance guidelines, which specify the minimum distance between the pole and any surrounding objects, remains a challenge; the current manual inspection process is time-consuming, costly, and often subjective. This work proposes an automated deep learning-inspired solution to improve utility pole detection and measure the clearance distance. The biggest challenge was the lack of a comprehensive pole dataset; therefore, we collected a dataset containing utility poles in varied backgrounds, environments, and conditions. We compared data augmentation techniques and employed them to address the limited dataset size. The proposed approach consists of two main stages: pole detection and differentiation and pole distance measurement. The first stage is a comparison of multiple object detection models on our utility pole dataset; we used the results from the best-performing model to estimate the distance between the two pole objects. The results show that our pipeline with the YOLOv8 model outperforms SSD and achieves 83% accuracy in classifying pole compliance. The system can accurately detect and estimate clearance violations even with limited data. The success of the pipeline opens opportunities for future research; obtaining depth by using additional sensors or deep learning models could enhance the detection module. Scaling the approach to large utility pole networks while retaining real-time performance could lead to improved autonomous infrastructure maintenance.
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 Abbas M. Elsayed, Mohamed Abdul Rahman, Abdul Aziz Aini Hussain Alhayek, Mohamed Ali, Aziah Bhattacharijee, Arpita Choo Yee Ting Ghauth, Khairil Imran Hossain, Mubdiul Matsah, Wan Razali Mohd Isa, Wan Noorshahida Peng, Loh Yuen Suvon, Injamul Haque Wan Zaki, Wan Mimi Diyana