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Alqudah, Mashal Kasem
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Deep Learning Incorporated with Augmented Reality Application for Watch Try-On Andri, Andri; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Alqudah, Mashal Kasem; Alqudah, Musab Kasim; Zakaria, Mohd Zaki; Hisham, Putri Aisha Athira binti
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.529

Abstract

In evaluating the dynamic landscape of online shopping, the integration of Augmented Reality (AR) technologies has emerged as a transformative force, redefining the way consumers engage with products in virtual environments. This research project investigates the intersection of deep learning and AR in the context of online shopping, with a particular focus on a Watch Try-On application. The experimentation involves the use of SSD MobileNet's models for real-time object detection aimed at enhancing the user experience during online watch shopping. Training both SSD MobileNet's V1 and V2 models through 50,000 iterations, the results reveal intriguing insights into their performance. SSD MobileNet's V1 demonstrated superior results, boasting a mean average precision (mAP) of 0.9725 and a significant reduction in total loss from 0.774 to 0.5405. However, the longer training time of 7 hours and 42 minutes prompted the selection of SSD MobileNet's V2 for real-time applications due to its faster inference capabilities. Extending beyond traditional online shopping experiences, the research explores the potential of AR technologies to revolutionize product visualization and interaction. The choice of the Vuforia model target for the Watch Try-On application showcases the synergy between deep learning and AR, allowing users to virtually try on watches and visualize them in their real-world environment. The application successfully detects users' hands with high accuracy, creating an immersive and visually enriching experience. In conclusion, this project contributes to the ongoing discourse on the fusion of deep learning and AR for online shopping. The exploration of SSD MobileNet's models, coupled with the integration of AR technologies, underscores the potential to elevate the online shopping experience by providing users with dynamic, interactive, and personalized ways to engage with products.
Convolutional Neural Network Based Deep Learning Model for Accurate Classification of Durian Types Diana, Diana; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Alqudah, Mashal Kasem; Alqudah, Musab Kasim; Zakari, Mohd Zaki; Fuad, Eyna Fahera Binti Eddie
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.480

Abstract

Durian recognition is significant among fans of the durian community since many people tend to get confused, especially if they are not familiar with durian species, which can lead them to be involved in durian fraud. The development of this prototype can detect and classify durian fruits into three categories, including Musang King, Black Thorn, and D24, which can significantly benefit consumers. The prototype in this research involves training using a dataset of durian images, specifically in Musang King, Black Thorn, and D24 varieties. Preprocessing techniques such as resizing and scaling data are applied to enhance the quality and consistency of the dataset. The models chosen to develop this prototype include VGG-16 and Xception, and each model is compared according to its accuracy percentage. The accuracy outcomes of VGG-16 and Xception models are 56.64% and 92%, respectively. The models used a total of 1,372 images of durian with three classifications. Based on the findings, further enhancement of the CNN models for durian classification can be done by implementing different architectures, techniques, and methods. Moreover, future models can consider real-time image capture and processing capabilities to enhance the practicality of the system for durian consumers. The prototype developed in this study demonstrates the feasibility of using deep learning techniques for accurate and efficient durian classification, paving the way for future advancements in automated fruit grading and quality control systems in the durian industry.
Co-Authors Alqudah, Musab Kasim Andri Andri Dewi, Deshinta Arrova Diana Diana Fuad, Eyna Fahera Binti Eddie Hisham, Putri Aisha Athira binti Kurniawan, Tri Basuki Zakari, Mohd Zaki Zakaria, Mohd Zaki