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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Abu-Shareha, Ahmad Adel
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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An innovative approach for enhancing capacity utilization in point-to-point voice over internet protocol calls M. Abualhaj, Mosleh; Abu-Shareha, Ahmad Adel; Al-Khatib, Sumaya Nabil; O. Hiari, Mohammad; Al-Mahadeen, Layth
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp488-496

Abstract

Voice over internet protocol (VoIP) calls are increasingly transported over computer-based networking due to several factors, such as low call rates. However, point-to-point (P-P) calls, as a division of VoIP, are encountering a capacity utilization issue. The main reason for that is the giant packet header, especially when compared to the runt P-P calls packet payload. Therefore, this research article introduced a method to solve the liability of the giant packet header of the P-P calls. The introduced method is named voice segment compaction (VSC). The VSC method employs the unneeded P-P calls packet header elements to carry the voice packet payload. This, in turn, reduces the size of the voice payload and improves network capacity utilization. The preliminary results demonstrated the importance of the introduced VSC method, while network capacity improved by up to 38.33%.
Tuning the K value in K-nearest neighbors for malware detection M. Abualhaj, Mosleh; Abu-Shareha, Ahmad Adel; Shambour, Qusai Y.; Al-Khatib, Sumaya N.; Hiari, Mohammad O.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2275-2282

Abstract

Malicious software, also referred to as malware, poses a serious threat to computer networks, user privacy, and user systems. Effective cybersecurity depends on the correct detection and classification of malware. In order to improve its effectiveness, the K-nearest neighbors (KNN) method is applied systematically in this study to the task of malware detection. The study investigates the effect of the number of neighbors (K) parameter on the KNN's performance. MalMem-2022 malware datasets and relevant evaluation criteria like accuracy, precision, recall, and F1-score will be used to assess the efficacy of the suggested technique. The experiments evaluate how parameter tuning affects the accuracy of malware detection by comparing the performance of various parameter setups. The study findings show that careful parameter adjustment considerably boosts the KNN method's malware detection capability. The research also highlights the potential of KNN with parameter adjustment as a useful tool for malware detection in real-world circumstances, allowing for prompt and precise identification of malware.
A novel approach for e-health recommender systems Alsaaidah, Adeeb M.; Shambour, Qusai Y.; Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7749

Abstract

The increasing use of the internet for health information brings challenges due to the complexity and abundance of data, leading to information overload. This highlights the necessity of implementing recommender systems (RSs) within the healthcare domain, with the aim of facilitating more effective and precise healthcare-related decisions for both healthcare providers and users. Health recommendation systems can suggest suitable healthcare items or services based on users' health conditions and needs, including medications, diagnoses, hospitals, doctors, and healthcare services. Despite their potential benefits, RSs encounter significant limitations, including data sparsity, which can lead to recommendations that are unreliable and misleading. Considering the increasing significance of health recommendation systems and the challenge of sparse data, we propose an effective approach to improve precision and coverage in recommending healthcare items or services. This aims to assist users and healthcare practitioners in making informed decisions tailored to their unique needs and health conditions. Empirical testing on two healthcare rating datasets, including sparse datasets, illustrate that our proposed approach outperforms baseline recommendation methods. It excels in improving both the precision and coverage of health-related recommendations, demonstrating effective handling of extremely sparse datasets.
Early Detection of Female Type-2 Diabetes using Machine Learning and Oversampling Techniques Al-Dabbas, Lana; Abu-Shareha, Ahmad Adel
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.298

Abstract

Early diabetes prediction is crucial as it can save numerous lives and prevent diabetes-related complications. The experiments conducted on diabetes prediction are keen on the limited samples of diabetes and non-diabetes cases provided in the available dataset. Various techniques have been implemented, focusing on the classification technique to improve the accuracy of prediction results. As a significant technique, oversampling has been implemented using SMOTE, which improved the results yet posed limitations due to its naïve technique. In this paper, a framework for diabetes prediction is developed, integrating an advanced oversampling technique using SVMSMOTE with various machine-learning algorithms to achieve the best performance. The proposed framework aims to overcome the problem of inaccurate data and limited samples using preprocessing and oversampling techniques. Besides, these techniques are integrated with other data mining and machine learning algorithms to improve the performance of diabetes prediction. The framework consists of four main stages: data exploration, data preprocessing, data oversampling, and classification. The experiments were conducted on the Pima Indian diabetes dataset, which comprises 768 samples and 9 columns. The results showed that the proposed framework achieved an accuracy of 91%, which improved the accuracy compared to using classification without oversampling, which achieved an accuracy of 90%. In comparison, the best results addressed in the literature were an accuracy of 85.5%. As such, the proposed framework improves the results by approximately 6.4% compared to the existing frameworks. Besides, the proposed framework achieved the best f-measure using the XGBoost classifier and SVMSMOTE, equal to 0.879. The best recall was achieved using RF and SVMSMOTE, which was 0.931. Finally, the best precision was achieved using FR without oversampling, with a value of 0.918.
Enhancing malware detection through self-union feature selection using gray wolf optimizer Abualhaj, Mosleh M.; Shambour, Qusai Y.; Abu-Shareha, Ahmad Adel; Al-Khatib, Sumaya N.; Amer, Amal
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp197-205

Abstract

This research explores the impact of malware on the digital world and presents an innovative system to detect and classify malware instances. The suggested system combines a random forest (RF) classifier and gray wolf optimizer (GWO) to identify and detect malware effectively. Therefore, the suggested system is called RFGWO-Mal. The RFGWO-Mal system employs the GWO for feature selection in binary and multiclass classification scenarios. Then, the RFGWO-Mal system uses a novel self-union feature selection approach, combining features from different subsets of binary and multiclass classification extracted using the GWO optimizer. The RF classifier is then applied for classifying malware and benign data. The comprehensive Obfuscated-MalMem2022 dataset was utilized to evaluate the suggested RFGWO-Mal system, which has been implanted using Python. The suggested RFGWO-Mal system achieves significantly improved results using the novel self-union feature selection approach. Specifically, the RFGWO-Mal system achieves an outstanding accuracy of 99.95% in binary classification and maintains a high accuracy of 86.57% with multiclass classification. The findings underscore the achievement of a self-union feature selection approach in enhancing the performance of malware detection systems, providing a valuable contribution to cybersecurity.
A Framework for Diabetes Detection Using Machine Learning and Data Preprocessing Abu-Shareha, Ahmad Adel; Qutaishat, Haneen; Al-Khayat, Asma
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.363

Abstract

People with diabetes are at an increased risk of developing other complications, such as heart disease and nerve damage. Therefore, diabetes prediction is crucial to reduce the severe consequences of this disease. This study proposed a comprehensive framework for diabetes prediction to maximize the information from available diabetes datasets, which include historical records, laboratory tests, and demographic data. The proposed framework implements a data imputation technique for filling in missing values and adopts feature selection methods to remove less important features for better diabetes classification. An oversampling technique and a parameter tuning approach were used to increase the samples and fine-tune the parameters for training the machine learning algorithms. Various machine learning algorithms, including Neural Networks, Logistic Regression, Support Vector Machines, and Random Forest, were used for the prediction. These algorithms were evaluated using both train-test split and cross-validation techniques. The experiments were conducted on the Pima Indian Diabetes dataset using various evaluation metrics, including accuracy, precision, recall, and F-measure. The results showed that the Random Forest algorithm, particularly when fine-tuned with Grid Search Cross Validation, outperformed other algorithms, achieving an impressive accuracy of 0.99. This demonstrates the robustness and effectiveness of the proposed framework, which outperformed the accuracy of state-of-the-art approaches.
Enhancing intrusion detection systems with hybrid HHO-WOA optimization and gradient boosting machine classifier Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Rateb, Roqia
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp518-526

Abstract

In this paper, we propose a hybrid intrusion detection system (IDS) that leverages Harris Hawks optimization (HHO) and whale optimization algorithm (WOA) for feature selection to enhance the detection of cyberattacks. The hybrid approach reduces the dimensionality of the NSL KDD dataset, allowing the IDS to operate more efficiently. The reduced feature set is then classified using logistic regression (LR) and gradient boosting machine (GBM) classifiers. Performance evaluation demonstrates that the GBM-HHO/WOA combination outperforms the LR-HHO/WOA approach, achieving an accuracy of 97.68%. These results indicate that integrating HHO and WOA significantly improves the IDS's ability to identify intrusions while maintaining high computational efficiency. This research highlights the potential of advanced optimization techniques to strengthen network security against evolving threats.
Detecting spam using Harris Hawks optimizer as a feature selection algorithm Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Nabil Alkhatib, Sumaya; Shambour, Qusai Y.; Alsaaidah, Adeeb M.
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9198

Abstract

The Harris Hawks optimization (HHO) was used in this study to enhance spam identification. Only the features with a high influence on spam detection have been selected using the HHO metaheuristic technique. The HHO technique's assessment of the selected features was conducted using the ISCX-URL2016 dataset. The ISCX-URL2016 dataset has 72 features, but the HHO technique reduces that to just 10 features. Extra tree (ET), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques are used to complete the classification assignment. 99.81% accuracy is attained by the ET, 99.60% by XGBoost, and 98.74% by SVM. As we can see, with the ET, XGBoost, and k-nearest neighbor (KNN) techniques, the HHO technique achieves accuracy above 98%. Nonetheless, the ET technique outperforms the XGBoost and KNN techniques. ET outperforms other methods due to its robust ensemble approach, which benefits from the diverse and relevant feature subset selected by HHO. HHO's effective reduction of noisy or redundant features enhances ET's ability to generalize and avoid overfitting, making it a highly efficient combination for spam detection. Thus, it looks promising to combat spam emails by combining the ET technique for classification with the HHO technique for feature selection.
Improving firewall performance using hybrid of optimization algorithms and decision trees classifier Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Al-Khatib, Sumaya Nabil; Alsaaidah, Adeeb M.; Anbar, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2839-2848

Abstract

One of the primary concerns of governments, corporations, and even individual users is their level of online protection. This is because a large number of attacks target their primary assets. A firewall is a critical tool that almost every organization uses to protect its assets. However, firewalls become less reliable when they deal with large amounts of data. One method for reducing the amount of data and enhancing firewall performance is feature selection. The main aim of this study is to enhance the firewall's performance by proposing a new feature selection method. The proposed feature selection method combines the strengths of Harris Hawks optimization (HHO) and whale optimization algorithm (WOA). Experiments were performed utilizing the NSL-KDD dataset to measure the effectiveness of the proposed method. The experiments employed the decision trees (DTs) as a machine classifier. The experimental results show that the achieved accuracy is 98.46% when using HHO/WOA for feature selection and DT for classification, outperforming the HHO and WOA when used separately for feature selection. The study's findings offer insightful information for researchers and practitioners looking to improve firewall effectiveness and efficiency in defending internet connections against changing threats.
Co-Authors Abualhaj, Mosleh M. Al-Dabbas, Lana Al-Khatib, Sumaya N. Al-Khatib, Sumaya Nabil Al-Khayat, Asma Al-Mahadeen, Layth Alsaaidah, Adeeb M. Amer, Amal Anbar, Mohammed Hiari, Mohammad O. M. Abualhaj, Mosleh Nabil Alkhatib, Sumaya O. Hiari, Mohammad Qutaishat, Haneen Rateb, Roqia Shambour, Qusai Y.