Research Title: Numerical Investigation on the Efficiency of Hemp Fiber Composites for Repairing Heat-Damaged Cantilever Beams Using Machine Learning

Author: Sawsan Mohammad Alkhawaldeh, Published Year: 2025

Journal of Soft Computing in Civil Engineering, 10-2 (2026) 1906

Faculty: Engineering and Technology

Abstract: The research examines the use of hemp fiber composites in restoring heat-damaged cantilever beams, applying numerical simulations and machine learning methods. This study investigates the efficacy of ecologically sustainable hemp fiber composites as a repair material using finite element models that have been verified using experimental data. The study emphasizes the capacity of hemp composites to enhance structural efficacy and sustainability in the building field. Using a meticulous technique, this study evaluates the influence of composites on mechanical parameters, including load-bearing capacity and deflection, across different temperature conditions. Machine learning improves predicted accuracy for structural behavior, showcasing an innovative method in structural engineering analysis. This study presents a quantitative approach for assessing the restoration of heat-damaged beams using hemp fiber composites, revealing substantial improvements in structural integrity. The research used finite element modeling to provide precise temperature and load-deflection predictions, eliminating the need for lengthy fire testing. The failure loads of hemp fiber repairs were enhanced by 32.2% and 30.7% at temperatures of 400 °C and 500 °C, respectively. This provides a cost-effective and sustainable option for repairs. In addition, machine learning models, particularly Gradient Boosting and Random Forest demonstrated high predicted accuracy in analyzing structural behavior. This represents a significant and hopeful development in structural engineering and sustainable repair methods.

Keywords: Hemp fiber composites; Heat-damaged beams; Finite element modeling; Structural performance; Machine learning predictions.

Research Title: A Review of CNN-Based Techniques for Accurate Plant Disease Detection

Author: Mahmoud Mohammed Mahmoud Hussein, Published Year: 2023

IJCI. International Journal of Computers and Information , 10

Faculty: Information Technology

Abstract: Abstract— Various techniques have revolutionized the field of plant disease detection, offering accurate approaches for timely detection and recognition of crop diseases. This comprehensive review explores the current utilization of diverse techniques for plant disease detection and classification. It analyzes recent publications, considering aspects such as disease detection methods and dataset characteristics. These techniques have significantly advanced object detection and recognition in agriculture, facilitating efficient crop management and higher yields. However, the complexity of identifying and detecting plant diseases from images necessitates species-specific detection for customized control strategies. This study discusses the challenges and proposed solutions associated with the use of different techniques in early disease detection concentrated on deep learning methods. Overall, the review demonstrates the considerable potential of these techniques in disease detection and emphasizes the ongoing need for research and development to address current challenges and optimize their benefits in agriculture. and also underscores the importance of incorporating emerging technologies and data-driven approaches to further enhance the precision and scalability of plant disease detection systems.

Keywords: Deep learning; CNN; plant disease datasets ; pre-trained models

Research Title: Reuse-Based Agile Development Process for Drone Software Systems

Author: Mahmoud Mohammed Mahmoud Hussein, Published Year: 2022

International Journal of Software Engineering and Knowledge Engineering, 32

Faculty: Information Technology

Abstract: Drones can perform air operations that are hard to be executed using manned aircrafts. The usage of drones in different domains brings significant environmental benefits and economic savings while decreasing risks to human life. Recently, a number of approaches have been introduced to support the development of drone software systems. However, developing customized drone software based on end-user needs is still a time consuming process. Such delay in software production does not match end-users expectations. Therefore, in the COMP4DRONES project (C4D, for short), we propose an agile-development process that is based on reuse to shorten the drone software development. In this process, based on the user requirements, a number of reusable components are selected from a repository that matches the user requirements. These components are then integrated to have a fully functioning drone system. This repository will be filled with reusable components that are being developed during the C4D project (i.e. the key enabling technologies for drones).

Keywords: Drones, software development, agile methodology, DO-178C, software reuse

Research Title: Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder

Author: Mahmoud Mohammed Mahmoud Hussein, Published Year: 2022

Future Internet, 14

Faculty: Information Technology

Abstract: Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybrid technique to recognize denial-of-service (DDoS) attacks that combine deep learning and feedforward neural networks as autoencoders. Two datasets were analyzed for the training and testing model, first statically and then iteratively. The auto-encoding model is constructed by stacking the input layer and hidden layer of self-encoding models’ layer by layer, with each self-encoding model using a hidden layer. To evaluate our model, we use a three-part data split (train, test, and validate) rather than the common two-part split (train and test). The resulting proposed model achieved a higher accuracy for the static dataset, where for ISCX-IDS-2012 dataset, accuracy reached a high of 99.35% in training, 99.3% in validation and 99.99% in precision, recall, and F1-score. for the UNSW2018 dataset, the accuracy reached a high of 99.95% in training, 0.99.94% in validation, and 99.99% in precision, recall, and F1-score. In addition, the model achieved great results with a dynamic dataset (using an emulator), reaching a high of 97.68% in accuracy.

Keywords: autoencoder; denial-of-service (DDoS); deep neural network; DDoS detection; software-defined network (SDN)

Research Title: Reference Architecture Specification for Drone Systems

Author: Mahmoud Mohammed Mahmoud Hussein, Published Year: 2022

Microprocessors and Microsystems, 95

Faculty: Information Technology

Abstract: Unmanned Aerial Systems (UASs) are useful for performing missions ranging from simple to tedious/dangerous ones. In recent years, UAS architectures have been introduced by different groups with different viewpoints. However, these architectures do not take into account all UAS features and their interactions. Thus, the task of developing a new UAS following one of the existing architectures is very difficult. Therefore, to ease the UAS development task, in this paper, we provide a reference architecture specification for future drone systems. It includes the different blocks (functions) of the UAS and their interactions. The blocks are divided into four main groups: flight navigation, flight control, flight management, and mission management.

Keywords: Unmanned Aerial Systems, flight management, mission management

Research Title: Context-aware Recommender System for Multi-User Smart Home

Author: Mahmoud Mohammed Mahmoud Hussein, Published Year: 2023

International Journal of Electrical and Computer Engineering, 13

Faculty: Information Technology

Abstract: Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user’s behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system’s efficiency is reduced when datasets increase. Therefore, in this paper, we firstly build a context-aware recommender system that considers multi-user’s preferences and solves their conflicts by using unsupervised algorithms to deliver useful recommendation services. Secondly, we improve smart home’s responsive using parallel computing. The results reveal that the proposed method is better than existing approaches.

Keywords: data mining; internet of things; parallel computing; recommender system; smart homes; unsupervised algorithms;

Research Title: Plant Disease Detection Using Deep Learning

Author: Mahmoud Mohammed Mahmoud Hussein, Published Year: 2023

Journal of Intelligent Systems and Applications, 15

Faculty: Information Technology

Abstract: Agricultural development is a critical strategy for promoting prosperity and addressing the challenge of feeding nearly 10 billion people by 2050. Plant diseases can significantly impact food production, reducing both quantity and diversity. Therefore, early detection of plant diseases through automatic detection methods based on deep learning can improve food production quality and reduce economic losses. While previous models have been implemented for a single type of plant to ensure high accuracy, they require high-quality images for proper classification and are not effective with low-resolution images. To address these limitations, this paper proposes the use of pre-trained model based on convolutional neural networks (CNN) for plant disease detection. The focus is on fine-tuning the hyperparameters of popular pre-trained model such as EfficientNetV2S, to achieve higher accuracy in detecting plant diseases in lower resolution images, crowded and misleading backgrounds, shadows on leaves, different textures, and changes in brightness. The study utilized the Plant Diseases Dataset, which includes infected and uninfected crop leaves comprising 38 classes. In pursuit of improving the adaptability and robustness of our neural networks, we intentionally exposed them to a deliberately noisy training dataset. This strategic move followed the modification of the Plant Diseases Dataset, tailored to better suit the demands of our training process. Our objective was to enhance the network's ability to generalize effectively and perform robustly in real-world scenarios. This approach represents a critical step in our study's overarching goal of advancing plant disease detection, especially in challenging conditions, and underscores the importance of dataset optimization in deep learning applications.

Keywords: CNN, Deep Learning, EfficientNetV2S, Classification, Plant Diseases

Research Title: A Framework for Predicting Breast Cancer Recurrence

Author: Mahmoud Mohammed Mahmoud Hussein, Published Year: 2024

Expert Systems with Applications, 240

Faculty: Information Technology

Abstract: Breast cancer is one of the serious diseases that threaten the life of many women worldwide. The seriousness of this disease is that it is often discovered in late stages after a period of its occurrence. This causes a wide spread of the disease and difficulty in its treatment. Another important characteristic of this disease is that it can return again after a period of its treatment. Therefore, predicting the occurrence or recurrence of such disease early is the best solution to have a high cure rate. The main objective of this paper is to improve the prediction performance of the breast cancer recurrence. Many methods have been proposed to predict breast cancer recurrence. However, these methods did not achieve the desired results on one of the most famous datasets in the field of breast cancer recurrence’s prediction (i.e., Wisconsin Prognosis Breast Cancer (WPBC) dataset). The highest accuracy achieved using the previous methods is 89.89%. Therefore, this paper provides a framework for improving the prediction of breast cancer recurrence. The proposed framework has the ability to overcome many of the challenges in the existing dataset such as: (a) the problem of imbalance between classes using a data over-sampling technique; and (b) the large number of data dimensions using Principal Component Analysis (PCA), and a wrapper dimensionality reduction technique based on Genetic Algorithm (GA). It also uses the neural network algorithm to fuse the results of two individual classifiers (i.e., Random Forest (RF) and Support Vector Machine (SVM)). Our proposed framework evaluation showed a significant improvement in the predication performance. It achieved an accuracy of 98.3%, area under the curve of 99%, and precision, recall, and f1-measure of 98%.

Keywords: Breast cance, prediction, imbalance data

Research Title: Secure Context-Aware Traffic Light Scheduling System: Integrity of Vehicles’ Identities

Author: Said Ahmad Ammar Ghoul, Published Year: 2025

world electric vehicle Journal, 16

Faculty: Information Technology

Abstract: Autonomous vehicles and intelligent traffic transportation are widely investigated for road networks. Context-aware traffic light scheduling algorithms determine signal phases by analyzing the real-time characteristics and contextual information of competing traffic flows. The context of traffic flows mainly considers the existence of regular, emergency, or heavy vehicles. This is an important factor in setting the phases of the traffic light schedule and assigning a high priority for emergency vehicles to pass through the signalized intersection first. VANET technology, through its communication capabilities and the exchange of data packets among moving vehicles, is utilized to collect real-time traffic information for the analyzed road scenarios. This introduces an attractive environment for hackers, intruders, and criminals to deceive drivers and intelligent infrastructure by manipulating the transmitted packets. This consequently leads to the deployment of less efficient traffic light scheduling algorithms. Therefore, ensuring secure communications between traveling vehicles and verifying the integrity of transmitted data are crucial. In this work, we investigate the possible attacks on the integrity of transferred messages and vehicles’ identities and their effects on the traffic light schedules. Then, a new secure context-aware traffic light scheduling system is proposed that guarantees the integrity of transmitted messages and verifies the vehicles’ identities. Finally, a comprehensive series of experiments were performed to assess the proposed secure system in comparison to the absence of security mechanisms within a simulated road intersection. We can infer from the experimental study that attacks on the integrity of vehicles have different effects on the efficiency of the scheduling algorithm. The throughput of the signalized intersection and the waiting delay time of traveling vehicles are highly affected parameters

Keywords: intelligent transport system; context-aware algorithm; traffic light; security; integrity; vehicle identity

Research Title: Optimized National Grid Harmonics Using Low Switching PSO Algorithm of Multilevel Inverter

Author: Akram Mohammed Al-mahrouk, Published Year: 2025

International Conference on Engineering and Computing Technologies (EngiTek 2025), Irbid/Jordan

Faculty: Engineering and Technology

Abstract: This paper presents an optimized technique for mitigating low-order harmonics in the national grid (NG) using an on-grid DC–AC seven-level (7L) multilevel inverter (MLI) operated at low switching frequency. The switching sequences are determined by particle swarm optimization (PSO) based on the NG harmonic spectrum, with 315 sampling points per period of the 50 Hz waveform. Case studies are conducted, including pure NG waveform and targeted harmonic reduction. The proposed technique effectively reduces selected harmonics of the NG, such as the fifth, seventh, and ninth orders, to approximately 1%. The proposed design is based on a lookup table to reduce processing time, making it suitable for practical implementation while enhancing harmonic reduction performance.

Keywords: Multilevel Inverter, Total Harmonics Distortion, Practical SWARM Objective, Smart Grid.