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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Hassan, Mohammad
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Application of machine learning in chemical engineering: outlook and perspectives Al Sharah, Ashraf; Abu Owida, Hamza; Alnaimat, Feras; Hassan, Mohammad; Abuowaida, Suhaila; Alhaj, Mohammad; Sharadqeh, Ahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp619-630

Abstract

Chemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component.
An evaluation of multiple classifiers for traffic congestion prediction in Jordan Hassan, Mohammad; Arabiat, Areen
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp461-468

Abstract

This study contributes to the growing body of literature on traffic congestion prediction using machine learning (ML) techniques. By evaluating multiple classifiers and selecting the most appropriate one for predicting traffic congestion, this research provides valuable insights for urban planners and policymakers seeking to optimize traffic flow and reduce jamming and. Traffic jamming is a global issue that wastes time, pollutes the environment, and increases fuel usage. The purpose of this project is to forecast traffic congestion at One of the most congested areas in Amman city using multiple ML classifiers. The Naïve Bayes (NB), stochastic gradient descent (SGD) fuzzy unordered rule induction algorithm (FURIA), logistic regression (LR), decision tree (DT), random forest (RF), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with our study area. These will be assessed by accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments show that FURIA is the classifier that presents the highest predictions of traffic congestion where By 100% achieved Accuracy, Precision, Sensitivity and F-measure. In the future further studies can be used more datasets and variables such as weather conditions; and drivers behavior that could integrated to predict traffic congestion accurately.
WEKA-based machine learning for traffic congestion prediction in Amman City Arabiat, Areen; Hassan, Mohammad; Almomani, Omar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4422-4434

Abstract

Traffic congestion leads to wasted time, pollution, and increased fuel consumption. Traffic congestion prediction has become a developing research topic in recent years, particularly in the field of machine learning (ML). The evaluation of various traffic parameters is used to predict traffic congestion by relying on historical data. In this study, we will predict traffic congestion in Amman City, specifically at the 8th circle, using different ML classifiers. The 8th circle links four main streets: Westbound, Northbound, Eastbound, and Southbound. Datasets were collected from the greater Amman municipality hourly. The logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with the 8th circle. The waikato environment for knowledge analysis (WEKA) data mining tool is used to evaluate chosen classifiers by determining accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments have demonstrated that SVM is the best classifier to predict traffic congestion. The accuracy of SVM to predict traffic congestion at Westbound Street, Northbound Street, Eastbound Street, and Southbound Street was 99.4%, 99.7%, 99.6%, and 99.1%, respectively.
Classifier comparison benchmark for machine learning weather prediction enhancement Arabiat, Areen; Hassan, Mohammad
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.12060

Abstract

Artificial intelligence (AI) and data mining can improve next-generation weather forecasting for urban planning, agriculture, and disaster management. This study investigates how machine learning (ML) classifiers can reduce forecast errors and support decision-making in sectors that require accurate predictions, including agriculture and transportation. We evaluate four classifiers—K-nearest neighbor (KNN), random forest (RF), Naive Bayes (NB), and multilayer perceptron (MLP)—using Waikato environment for knowledge analysis (WEKA) and Orange3 to compare their performance in identifying rain. A 10-fold cross-validation approach is applied to reduce overfitting, and model effectiveness is measured using key performance indicators including accuracy, precision, sensitivity (recall), and F-measure. Results show that classifier performance varies across tools, indicating that the analytical framework can influence outcomes. Among all models, the RF classifier performs best, achieving 99.92% accuracy in WEKA and 99.9% in Orange3. The MLP also shows strong performance with 99.20% accuracy in WEKA and 98.7% in Orange3. KNN and NB exhibit comparable performance, but lower precision and F-measure in WEKA. Overall, the findings suggest that RF is the most effective approach for rain prediction using data mining tools, with practical relevance for agriculture, transportation, and power systems.
Co-Authors Abu Owida, Hamza Abuowaida, Suhaila Al Sharah, Ashraf Alhaj, Mohammad Almomani, Omar Alnaimat, Feras Arabiat, Areen Sharadqeh, Ahmad