Mohamad Nassereddine

Dr. Mohamad Nassereddine

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Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 February 2025 in Computers
Verified
Journal Article
Open access
PDF file
GNGhalia Nassreddine
Ghalia Nassreddine
MNMohamad Nassereddine
Mohamad Nassereddine
OAObada Al-Khatib
Obada Al-Khatib
Recent advancements across various sectors have resulted in a significant increase in the utilization of smart gadgets. This augmentation has resulted in an expansion of the network and the devices linked to it. Nevertheless, the development of the network has concurrently resulted in a rise in policy infractions impacting information security. Finding intruders immediately is a critical component of maintaining network security. The intrusion detection system is useful for network security because it can quickly identify threats and give alarms. In this paper, a new approach for network intrusion detection was proposed. Combining the results of machine learning models like the random forest, decision tree, k-nearest neighbors, and XGBoost with logistic regression as a meta-model is what this method is based on. For the feature selection technique, the proposed approach creates an advanced method that combines the correlation-based feature selection with an embedded technique based on XGBoost. For handling the challenge of an imbalanced dataset, a SMOTE-TOMEK technique is used. The suggested algorithm is tested on the NSL-KDD and CIC-IDS datasets. It shows a high performance with an accuracy of 99.99% for both datasets. These results prove the effectiveness of the proposed approach.
5 March 2025 in Applied AI Letters
Verified
Journal Article
Open access
GNGhalia Nassreddine
Ghalia Nassreddine
AAAmal El Arid
Amal El Arid
MNMohamad Nassereddine
Mohamad Nassereddine
OAObada Al-Khatib
Obada Al-Khatib
The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is imperative to implement efficient methods for the accurate detection and diagnosis of PV system faults to prevent unexpected power disruptions. This paper introduces a potential strategy for fault identification and classification through the utilization of machine learning (ML) techniques. The study aimed to use ML algorithms to identify and classify normal operations, seven different types of faults, in two operational modes (maximum power point tracking and intermediate power point tracking). Four machine learning algorithms and ensemble methods (decision trees, k‐nearest neighbors, random forest, and extreme gradient boosting) were employed, followed by hyperparameter tuning and cross‐validation to determine the best configuration. The results indicated that ensemble methods, particularly XGBoost, excelled in detecting and classifying faults in PV systems, achieving a 99% accuracy rate after hyperparameter adjustments. The TPR values show a high sensitivity of 0.999, with some achieving a perfect score of 1.000. The FPR shows very low values, with the majority of metrics indicating FPRs at or close to 0%. This performance is crucial in the solar energy context, as failing to detect faults can result in significant energy loss and increased maintenance costs.
9 August 2024 in Next Energy
Verified
Journal Article
Open access
MNMohamad Nassereddine
Mohamad Nassereddine
GNGhalia Nassreddine
Ghalia Nassreddine
AAAmal El Arid
Amal El Arid
1 April 2014 in IET Generation, Transmission & Distribution
Verified
Journal Article
Open access
PDF file
MNMohamad Nassereddine
Mohamad Nassereddine
JRJamal Rizk
Jamal Rizk
AHAli Hellany
Ali Hellany
MNMahmood Nagrial
Mahmood Nagrial
High voltage (HV) transmission mains form important assets in electrical networks. The existence of this infrastructure within the community necessitates effective earthing design to ensure safety compliance. Overhead earth wire (OHEW) forms parts of the HV transmission mains, as such the OHEW plays an important role when it comes to fault current distributions. The existence of the OHEW introduces the coupling factor between the faulted phase and the OHEW. These coupling factor aides in further reducing the fault current percentage into the substation earth grid. If the OHEW is continuous and infinite between substations, 100% of the coupling factor applies. Under the condition where the OHEW is non‐continuous and finite, the coupling factor will not be to the full effect. This study endeavours to provide information in regards to the coupling factor under substation faults for a continuous and non‐continuous OHEW. Furthermore, the relation between the coupling factor and the length of the OHEW is established. For the finite condition, the impact on the pole grid current and the length of the OHEW is discussed. The study shows the advanced accuracy of the proposed new method with respect to the existing research methods. A case study is also included.
10 April 2025 in IEEE Access
Verified
Journal Article
Open access
GNGhalia Nassreddine
Ghalia Nassreddine
AAAmal El Arid
Amal El Arid
MNMohamad Nassereddine
Mohamad Nassereddine
OAObada Al-Khatib
Obada Al-Khatib
AAanas wasef arram
anas wasef arram
AAAbir El Abed
Abir El Abed
The increasing energy demand, driven by population growth and technological advancements, necessitates a shift from traditional fossil fuel-based power generation to renewable energy systems. Solar photovoltaic panels offer a promising alternative; however, their energy output fluctuations present challenges in maintaining a reliable power source. Accurate estimation of solar energy generation is critical for enhancing the competitiveness of solar power plants and fostering social and economic progress. This paper aims to develop a machine learning model that utilizes meteorological and time-related data to predict solar photovoltaic power generation. It investigates how various factors, such as humidity and seasonal variations, impact prediction accuracy. Three different ML models were used and compared: Support vector regression, XGBoost, and HistGradient Boost. To improve the performance of these models hyperparameter tuning techniques is used. The findings reveal that radiation, humidity levels, and seasonal variations are the most influential factors in predicting photovoltaic solar power generation. Additionally, incorporating seasonal and humidity data can enhance the machine-learning model’s accuracy by 17.9%. XGBoost model outperforms existing methods, achieving the highest accuracy of 95.76% when incorporating humidity and seasonal factors. Its lowest MAPE of 4.23 and highest $R^{2}$ of 99.1. to assess model precision, an uncertainty analysis was performed using p-factor and q-factors. The results confirm model robustness and reliability in predictive performance.
1 July 2009
Verified
Conference Paper
Closed access
MNMohamad Nassereddine
Mohamad Nassereddine
JRJ. Rizk
J. Rizk
AHA. Hellany
A. Hellany
Performing High Voltage (HV) maintenance with a multicraft work force creates a special set of safety circumstances. The safety of people including workers will be discussed in this paper. In addition, this paper will present an effective approach to design an earthing system. This approach explains the theoretical background necessary to perform the calculation of the required safety voltages. Finally, this paper provides a verification of the proposed approach via simulation with current distribution, electromagnetic fields, grounding and soil structure analysis CDEGS software.