Noor Aldeen Alawad, Ph.D

SIGIR 2016 – 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy (July 2016) – “Network-Aware Recommendations of Novel Tweets”.
ICICS 2023 – The 14th International Conference on Information and Communication Systems (IEEE Jordan Section, Technical Co-Sponsor) – Served as Program Committee Member for the tracks of Data Science and Big Data and Natural Language Processing and Applications.

Teaching Courses

Artificial Intelligence

Data structures and Algorithms

Computer Organization

Discrete Mathematics

Programming Languages:Python, Java, C++

Object-Oriented Programming

Problem Solving

Evolutionary Algorithms

Human-Computer Interaction

Graduate Courses: Artificial Intelligence Models, Knowledge Based Systems

Memberships

Editorial Board Member, Humanities & Social Sciences Communications (Springer Nature), Artificial Intelligence.

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all.

This person’s work contributes towards the following SDG(s):

Sustainable Finance

Enhanced chimp optimization algorithm using crossover and mutation techniques with machine learning for IoT intrusion detection system

Published in: Cluster Computing

Jul 31, 2025

Ahmad Nasayreh Noor Aldeen Alawad Ameera Jaradat

One of the most prevalent challenges nowadays is detecting intrusions into the Internet of Things (IoT) systems, which pose a variety of wide-ranging cyber threats. These devices encompass smart cities, industries, and homes, all integral to modern living. Their widespread adoption increases the urgency of addressing security vulnerabilities. Ensuring secure user interactions is of particular importance. This study proposes an intrusion detection approach that combines K-Nearest Niebuhr (KNN) and the Chimp Optimization Algorithm (ChOA) for detecting various and advanced cyber threats. Integrating ChOA with KNN aims to enhance classification accuracy by selecting the optimal subset of features from the dataset. To accomplish this objective, we devised the ChOA algorithm, employing the S-shaped and V-shaped models for conversion to the binary system. The algorithm incorporates three crossover operators and mutation techniques: average crossover, discrete crossover, flat crossover, and boundary mutation. These techniques significantly contribute to the population’s overall diversity and lead to the optimal solution through continued exploration and exploitation, resulting in high accuracy in identifying cyber threats. We evaluated the proposed approach on nine IoT-related datasets. We analyzed detailed performance metrics such as accuracy, precision, recall, and F1-score. IBChOA1 demonstrated superior results, outperforming the original ChOA and six other algorithms. We also compared our developed algorithm with six metaheuristic algorithms, demonstrating significant superiority in accuracy and fitness, and with four machine learning algorithms, demonstrating a notable edge over them. The enhanced ChOA outperforms standard methods due to its refined balance between exploration and exploitation, which is facilitated by S-shaped and V-shaped transfer functions, as well as robust feature space navigation enabled by diverse crossover methods and boundary mutation. These improvements ensure comprehensive feature selection, optimizing performance by effectively using the most predictive features. We evaluated it using several measures such as accuracy, recall, precision, F1 score, fitness function, and feature selection. We conducted a statistical analysis using the Fredman test and Wilcoxson test to statistically verify the results, highlighting the significance of the proposed approach compared to other algorithms based on accuracy. As a result of this study, we conclude that the proposed IBChOA1 greatly improves the security of IoT systems, providing a powerful way to find complex cyber threats. It is highly efficient in detecting diverse cyber-attacks, which has broader implications for the deployment of secure IoT networks in various fields, such as smart cities and industrial IoT.

Sustainable Finance

EBAO: An intrusion detection framework for wireless sensor networks using an enhanced binary Aquila Optimizer

Published in: Knowledge-Based Systems

Mar 15, 2025

Noor Aldeen Alawad Bilal H. Abed-alguni Ala Mohammad Shakhatreh

Traditionally, Wireless Sensor Networks (WSNs) lack built-in algorithms to identify and react to intrusions and threats, necessitating the establishment of Intrusion Detection Systems (IDSs). In practice, it is difficult to design an IDS in WSNs due to the large scale, mobility, and limited memory of the sensors in WSNs. Feature Selection (FS) responds to these challenges by reducing data dimensionality and improving IDS accuracy, thereby enhancing intrusion detection classification by selecting the most expressive features in intrusion detection datasets. This paper presents a new intrusion detection framework called the Enhanced Binary Aquila Optimizer (EBAO) model. EBAO aims to efficiently improve the feature space to enhance detection accuracy while minimizing computational complexity in IDSs. EBAO integrates four improvements in the original Aquila Optimizer (AO) to address the FS problem. First, it uses a hybrid initialization approach that combines the Lévy flight generation function and the random uniform generation function to generate suitable solutions for the FS problem. Second, it employs the β-hill climbing algorithm as a local search method to enable the AO method to search efficiently in the FS solution space. Third, it employs the mutation equations of the Harris Hawks optimization method in the optimization process of AO based on a probabilistic function to explore the FS solution space. Lastly, it models the FS solution space in AO using two categories of binarization techniques (S-shaped and V-shaped). The performance of EBOA was evaluated using ten WSN datasets and eight transfer functions and then compared with eight metaheuristic-based IDSs and six machine learning algorithms. The Wilcoxon pair signed rank and Friedman tests were used to determine the statistical differences and rankings of the evaluated algorithms based on classification accuracy and fitness value. The experimental and statistical analysis strongly indicated that EBAO demonstrates superior effectiveness compared to the eight popular optimization wrapper algorithms for all ten datasets, which highlights its robustness and reliability. EBAO is https://github.com/drnooraldeen/EBOA.git

Sustainable Finance

BOC-PDO: An intrusion detection model using binary opposition cellular prairie dog optimization algorithm

Published in: Cluster Computing

Jul 20, 2024

Bilal H. Abed-alguni Basil M. Alzboun Noor Aldeen Alawad

Intrusion detection datasets are highly likely to contain numerous redundant, irrelevant, and noisy features that slow the performance of the machine learning techniques and classifiers that may be applied to them. The feature selection approach is used for reducing the number of features in intrusion detection datasets and eliminating those that are not important. One of the most powerful structured population approaches is the Cellular Automata approach, which is used to enhance the diversity and convergence of population-based optimization algorithms. In this work, the Cellular Automata approach, Mixed opposition-based learning, and the K-Nearest Neighbor classifier are incorporated with the Prairie dog optimization algorithm (PDO) in a new intrusion detection framework called Binary Opposition Cellular Prairie dog optimization algorithm (BOC-PDO). The proposed framework contains four key features. First, the Cellular Automata model is utilized to enhance the population of feasible solutions in the PDO. Second, four S-shaped and four V-shaped Binary Transfer Functions are used to convert the continuous solutions in BOC-PDO to binary ones. Third, the Mixed opposition-based learning approach is used at the end of the optimization loop of BOC-PDO to improve capacity for exploration. Fourth, the K-Nearest Neighbor classifier is used as the main learning model in BOC-PDO. Eleven famous intrusion detection datasets were employed in the evaluation of the effectiveness of BOC-PDO compared to eight popular binary optimization algorithms and four machine learning approaches. According to the overall simulation results, BOC-PDO scored the highest accuracy, best objective value, and fewest selected features for each of the eleven intrusion detection datasets. Besides, the reliability and consistency of the simulation results of BOC-PDO compared to the other tested algorithms were established using Friedman and Wilcoxon statistical tests.

Sustainable Finance

Hybrid Snake Optimizer Algorithm for Solving Economic Load Dispatch Problem with Valve Point Effect

Published in: The Journal of Supercomputing

May 26, 2024

Noor Aldeen Alawad Bilal H. Abed-alguni Misaa El-ibini

Snake optimizer (SO) is an optimization algorithm drawn from the reproductive habits of serpents. It exhibits outstanding effectiveness in solving continuous optimization problems. However, SO may face some performance challenges related to its population diversity and early convergence behavior. In this paper, we address the challenges of SO by introducing the Hybrid snake optimizer algorithm (HSOA). HSOA is a novel approach to optimization that incorporates two new optimization techniques into the SO algorithm. First, it incorporates a new opposition-based learning technique called Oppositional-mutual learning into the initialization stage of the SO algorithm. Second, it integrates dynamic polynomial mutation, which is an intelligent mutation method, into the initialization and optimization stages of the SO algorithm. These integrated approaches aim to increase the population’s diversity of SO, while improving its searchability during its optimization stage. In power systems, the economic load dispatch (ELD) is an intricate optimization problem that becomes more challenging when the restrictions of the valve point effect (VPE) are incorporated into it. ELD with VPE is non-convex, lacking smoothness, and exhibiting nonlinearity that considers operational limitations expressed as both equality and inequality constraints to generate electricity. The suggested HSOA algorithm underwent evaluation and was compared with 47 renowned optimization algorithms across five real-world ELD problems with different specifications: generators with different unit capacities, transmission losses, prohibited operation zones, and Ramp Rate restrictions. The experimental results demonstrate that HSOA produces competitive solutions for the five real-world ELD problems. In detail, HSOA achieves the top rank in three cases of ELD problems with a 3-unit generator, and it secures the second and third positions in high-dimensional ELD problems with 40-unit and 80-unit generators, respectively. The statistical tests confirm the reliability and efficiency of HSOA. In addition, the effectiveness of HSOA was evaluated using the single-objective IEEE-CEC 2014 functions and compared to the results of eight popular metaheuristic algorithms. The results demonstrate that HSOA is a competitive optimization algorithm capable of solving the functions of IEEE-CEC 2014.

Sustainable Finance

Improved arithmetic optimization algorithm for patient admission scheduling problem

Published in: Soft Computing

Dec 16, 2023

Noor Aldeen Alawad Bilal H. Abed-alguni Israa I. Saleh

The patient admission scheduling problem (PASP) has been studied for many years as one of the most important scheduling problems in the health sector. The primary goal of PASP is to assign patients to appropriate hospital beds while considering some hard and soft constraints. PASP is an NP-hard problem, which implies that the optimization approach is one of the best approaches that can be used to solve it. The arithmetic optimization algorithm (AOA) is a new optimization algorithm that can effectively solve both continuous and discrete optimization problems. However, it may suffer from poor exploration and premature convergence to sub-optimal solutions due to some problems with its exploration operators. In this paper, we propose a new method for the PASP using an improved AOA algorithm called the island-based AOA (iAOA). The new algorithm is based on a structured population model called the island model. This model distributes the population of candidate solutions among islands that periodically exchange some of the candidate solutions among each other based on a migration protocol. In iAOA, some modifications were applied to AOA’s parameters to make it capable of handling discrete space. We evaluated iAOA using a public benchmark for the PASP and compared our results with those of other baseline algorithms (DFPA, SA, HS, GD-NLGD, HTS, TS, I-BBO, and MBBO). The simulation results revealed that iAOA achieved the minimum average execution time on 5 out of 6 instances of the public benchmark. It also showed that iAOA achieved the second minimum average violation of the objective function over 10 independent runs. Finally, the statistical examination of the experimental results using Friedman and Wilcoxon tests confirms the significance of the results of iAOA compared with the other baseline algorithms.

Sustainable Finance

Improving multilayer perceptron neural network using two enhanced moth-flame optimizers to forecast iron ore prices

Published in: Journal of Intelligent Systems

Oct 04, 2023

Iyad Abu Doush Basem Ahmed Mohammed A. Awadallah Mohammed Azmi Al-Betar Noor Aldeen Alawad

The quality of the output produced by the multi-layer perceptron neural network depends on the careful selection of its weights and biases. The gradient descent technique is commonly used for choosing MLP’s optimal configuration, but it can suffer from being stuck in local optima and slow convergence toward promising regions in the search space. In this article, we propose two new optimization algorithms based on the moth-flame optimization algorithm (MFO), which mimics moths’ special navigation methods at night. We use these algorithms to enhance the performance of the training process of the MLP neural network. To demonstrate the effectiveness of our approach, we apply it to the problem of predicting iron ore prices, which plays an important role in the continuous development of the steel industry. We use a large number of features to predict the iron ore price, and we select a promising set of features using two feature reduction methods: Pearson’s correlation and a newly proposed categorized correlation. Surprisingly, new features not mentioned in the literature are discovered, and some are discarded. The time series dataset used has been extracted from several sources and pre-processed to fit the proposed model. We compare our two proposed MFO algorithms, the roulette wheel moth-flame optimization algorithm and the global best moth-flame optimization algorithm, against four swarm intelligence algorithms and five classical machine learning techniques when predicting the iron ore price. The results acquired indicate the superior performance of the suggested algorithms concerning prediction accuracy, root-mean-square error, mean-square error, average absolute relative deviation, and mean absolute error. Overall, our work presents a promising approach for improving the performance of MLP neural networks, and it demonstrates its effectiveness in the challenging problem of predicting iron ore prices

College of Engineering

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