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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Moath Alsafasfeh
Al-Hussein Bin Talal University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Formalization of the prediction and ranking of software development life cycle models Laiali Almazaydeh; Moath Alsafasfeh; Reyad Alsalameen; Shoroq Alsharari
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp534-540

Abstract

The study of software engineering professional practices includes the use of the formal methodology in a software development. Identifying the appropriate methodology will not only reduce the failure of software but will also help to deliver the software in accordance with the predetermined budget and schedule. In literature, few works have been developed a tool for prediction of the most appropriate methodology for the specific software project. In this paper, a method for selecting an appropriate software development life cycle (SDLC) model based on a ranking manner from the highest to the lowest scoring is presented. The selection and ranking of appropriate SDLC elaborate the related SDLC’s critical factors, these factors are given different weights according to the SDLC, then these weights are used by the proposed mathematical method. The proposed approach has been extensively experimented on a dataset by software practitioners who are working in the software industry. Experimental results show that, the proposed method represents an applicable tool in predicting and ranking suitable SDLC models on various types of projects, such as: life-critical systems, commercial uses systems, and entertainment applications.
Super-linear speedup for real-time condition monitoring using image processing and drones Moath Alsafasfeh; Bradely Bazuin; Ikhlas Abdel-Qader
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1548-1557

Abstract

Real-time inspections for the large-scale solar system may take a long time to get the hazard situations for any failures that may take place in the solar panels normal operations, where prior hazards detection is important. Reducing the execution time and improving the system’s performance are the ultimate goals of multiprocessing or multicore systems. Real-time video processing and analysis from two camcorders, thermal and charge-coupling devices (CCD), mounted on a drone compose the embedded system being proposed for solar panels inspection. The inspection method needs more time for capturing and processing the frames and detecting the faulty panels. The system can determine the longitude and latitude of the defect position information in real-time. In this work, we investigate parallel processing for the image processing operations which reduces the processing time for the inspection systems. The results show a super-linear speedup for real-time condition monitoring in large-scale solar systems. Using the multiprocessing module in Python, we execute fault detection algorithms using streamed frames from both video cameras. The experimental results show a super-linear speedup for thermal and CCD video processing, the execution time is efficiently reduced with an average of 3.1 times and 6.3 times using 2 processes and 4 processes respectively.
Image compression approach for improving deep learning applications Raed Altabeiri; Moath Alsafasfeh; Mohanad Alhasanat
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5607-5616

Abstract

In deep learning, dataset plays a main role in training and getting accurate results of detection and recognition objects in an image. Any training model needs a large size of dataset to be more accurate, where improving the dataset size is one of the most research problems that needs enhancement. In this paper, an image compression approach was developed to reduce the dataset size and improve classification accuracy for the trained model using a convolutional neural network (CNN), and speeds up the machine learning process, while maintaining image quality. The results revealed that the best scenario for deep learning models that provided good and acceptable classification accuracy was one that had the following parameters: 80×80 image size, 10 epochs, 64 batch size, 40 images dataset quality (images compressed 60%), and gray image mode. For this scenario a Dog vs Cat dataset is used, and the training time was 48 minutes, classification accuracy was 86%, and images dataset size was 317 MB on storage device. This size makes up 58% of the size of the original image’s dataset, saves 42% of the storage space and reduces the processing resources consumption.
Benchmarking machine learning models for natural disaster prediction with synthetic IoT data Moath Alsafasfeh; Abdullah Alhasanat; Atheer Bassel; Moahand Alhasanat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp257-268

Abstract

Natural disasters pose severe threats to human life and infrastructure, demanding robust early warning systems (EWS) supported by machine learning (ML) and internet of things (IoT)-based sensing. This study benchmarks ML models for predicting floods and earthquakes using synthetic IoT sensor data. A dataset comprising nine environmental and seismic parameters was generated and labeled into three classes: no disaster, flood, and earthquake, where the feature preprocessing was applied during model training. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) models were trained and evaluated using accuracy, precision, recall, and F1-score. Experimental results on the World-A test set show that ensemble models consistently outperform LR, with XGBoost and RF achieving F1-scores of up to 97%and99%,respectively, compared to79%forLR.Anindependenttestonthe separately generated World-B dataset revealed that ensemble models maintained higher generalization capability with F1-scores of 80% for XGBoost and 78% for RF. In contrast, LR showed substantial degradation with an F1-score of 54%. Stress testing on the World-B dataset under simulated situations, such as sensor failures, noise injection, and extreme weather events, confirms the resilience performance of ensemble models in comparison to LR. These results demonstrate the usefulness of ensemble learning in handling unpredictable IoT data for disaster prediction and highlight their potential integration into intelligent EWS. Future work will focus on expanding the framework to include cross-time prediction, incorporating additional environmental features, and deploying the models in real-time IoT systems for field validation.
Co-Authors Abdullah Alhasanat Atheer Bassel Bradely Bazuin Ikhlas Abdel-Qader Laiali Almazaydeh Moahand Alhasanat Mohanad Alhasanat Raed Altabeiri Reyad Alsalameen Shoroq Alsharari