Browsing by Author "Abdulgaleel, Fuad"

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    LoRa-enabled GPU-based CubeSat Yolo Object Detection with Hyperparameter Optimization
    (Institute of Electrical and Electronics Engineers Inc., 2022) Khatib, Ziad El; Mnaouer, Adel Ben; Moussa, Sherif; Abas, Mohd Azman Bin; Ismail, Nor Azman; Abdulgaleel, Fuad; Elmasri, Ibrahim; Ashraf, Loay
    This paper presents a Lora-enabled GPU-based CubeSat Neural-Network Real-Time Object Detection with hyperparameter optimization is presented. When number of epochs increased from 10 to 50 and learning rate tuned to 0.00104, the validation loss improved to 0.018 and training object loss improved to 0.042. Model mean average precision mAP_0.5 is improved to 0.986 and the precision is improved to 98.9%. Epoch and learning rate are traded-off to optimize model accuracy performance. The Lora-enabled CubeSat onboard transceiver provides long range onboard sensors readings providing spaced-based IoT application capability. © 2022 IEEE.
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    Neural network-based parking system object detection and predictive modeling
    (Institute of Advanced Engineering and Science, 2023-03) El Khatib, Ziad; Ben Mnaouer, Adel; Moussa, Sherif; Mashaal, Omar; Ismail, Nor Azman; Abas, Mohd Azman Bin; Abdulgaleel, Fuad
    A neural network-based parking system with real-time license plate detection and vacant space detection using hyper parameter optimization is presented. When number of epochs increased from 30, 50 to 80 and learning rate tuned to 0.001, the validation loss improved to 0.017 and training object loss improved to 0.040. The model means average precision mAP_0.5 is improved to 0.988 and the precision is improved to 99%. The proposed neural network-based parking system also uses a regularization technique for effective predictive modeling. The proposed modified lasso ridge elastic (LRE) regularization technique provides a 5.21 root mean square error (RMSE) and an R-square of 0.71 with a 4.22 mean absolute error (MAE) indicative of higher accuracy performance compared to other regularization regression models. The advantage of the proposed modified LRE is that it enables effective regularization via modified penalty with the feature selection characteristics of both lasso and ridge. © 2023, Institute of Advanced Engineering and Science. All rights reserved.