Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization
Ahmed Jubair, Mohammed
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimizing Smart Power Grid Stability Based on the Prediction of a Deep Learning Model Hamad Khaleefah, Shihab; A. Mostafa, Salama; Gunasekaran, Saraswathy Shamini; Farooq Khattak, Umar; Ahmed Jubair, Mohammed; Afyenni, Rita
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.8.2.2758

Abstract

A smart grid is an electricity transmission system that uses digital technology to control getting and dispatching electricity from all generation sources to satisfy end users' fluctuating electricity demands. It achieves this through deploying technologies such as technology and smart grids, which are pivotal in increasing the power supply's efficiency, reliability, and sustainability to the public. Decentralized Smart Grid Control (DSGC) is a system where the control and decision-making functions are distributed to different grid points instead of in one central place. This paradigm is critical for the fault resistance and efficiency of the grid because it enables the local regions to carry on by themselves, manage electric power flows, respond to changes, and integrate many kinds of energy sources successfully. The grid frequency is monitored via the DSGC to ensure dynamic grid stability estimation. All parties, from users to energy producers, may take advantage of the price of power tied to grid frequency. The DSGC, a vital component of this research, gathered information about clients' consumption and used several assumptions to predict the behavior of the consumers. It establishes a method to assess against current supply circumstances and the resultant recommended pricing information. This research proposes a long short-term memory (LSTM) model to analyze data gathered regarding smart grid characteristics and predict grid stability. The results show a strong capacity for the LSTM model, achieving an accuracy of 96.73% with a loss of just 7.44%. The model also achieves a precision of 96.70%, recall of 98.18%, and F1-score of 97.43%.
Sample An Improved Lite-Yolov4 Object Detection Model for Mobile Augmented Reality Mansoor Nafea, Mohammed; Siok Yeeb, Tan; Tareq, Mustafa; Ahmed Jubair, Mohammed; Fatikhan Ataalla, Abdalrahman
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3341

Abstract

Augmented reality (AR) enhances user experiences by overlaying digital information on real-world objects or places. Augmented reality makes unprecedentedly immersive experiences possible in marketing, industry, education, entertainment, fashion, and healthcare. While current augmented reality methods can identify 3D items in their environment, the recognition of tiny, complex objects remains a problem for most object detection methods. In addition, object detection is a key in computer vision and AR systems. The Object detection process aims to classify and localize objects in applications like face detection, text detection, and people counting. Many natural features detection models were proposed, like YOLO, YOLO-LITE, and YOLOv4-tiny. However, the detection of objects from natural images remains a challenging task, often compromising accuracy or requiring longer processing times. To overcome these challenges, this article suggests a novel method that combines the strengths of YOLO-LITE and YOLOv4-tiny into a hybrid model. The suggested model name is LITE-YOLOv4, which stands for “LITE-You Only Look Once Version 4. The model design depends on YOLO-LITE as a backbone. LITE-YOLOv4 uses a feature pyramid network to extract feature maps of various sizes. It also utilizes a "shallow and narrow" convolution layer to optimize its object detection capability. The proposed model aims to achieve a speed and accuracy balance, making it suitable for use in AR apps on portable devices and PCs without GPUs. LITE-YOLOv4 achieved a mean average precision (mAP) of 52.6% on the PASCAL VOC dataset and 33.3% on the COCO dataset. The suggested model achieved a respectable speed, which is 20 frames per second (FPS). LITE-YOLOv4 provides better accuracy and reasonable computational time than state-of-the-art non-GPU models.
Co-Authors A. Mostafa, Salama Farooq Khattak, Umar Fatikhan Ataalla, Abdalrahman Gunasekaran, Saraswathy Shamini Hamad Khaleefah, Shihab Mansoor Nafea, Mohammed Rita Afyenni Siok Yeeb, Tan Tareq, Mustafa