Osama M. Dorgham

• PhD in Computer Science (Al-Balqa Applied University)
• Professor (Associate) at Al-Balqa Applied University

Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed fo...

This study presents a novel method that utilizes Harris Hawks Optimization (HHO) combined with dynamic colormap visualization to enhance the quality of lung CT scan segmentation. The Harris Hawks optimization algorithm is a swarm-based method used to enhance multi-level thresholding for image segmentation, hence facilitating the identification of r...

This paper introduces a novel similarity function that evaluates both the quantitative and qualitative similarities between data instances, named QQ-Means (Qualitative and Quantitative-Means). The values are naturally scaled to fall within the range of − 1 to 1. The magnitude signifies the extent of quantitative similarity, while the sign denotes q...

In our study, we introduce a novel hybrid ensemble model that synergistically combines LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations in cancer. This model was rigorously tested using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset, demonstrating exceptional performance across all evaluatio...

Abstract— This study examines various machine learning models to predict customer responses in the auto insurance industry. We focus on metrics like accuracy, precision, recall, and F1-score, carefully selecting threshold values to balance model performance with practical business applications. Our analysis reveals the XGB Classifier's superiority,...

Abstract— Image stability is very important in a time when digital image communication is essential to many fields. Modern online dangers are often too complicated for old security methods to keep up with. To solve these problems, this study presents a new system that combines Hu moments, digital watermarking, and cryptography hashing. Hu moments c...

Natural Language Processing (NLP) has emerged as a critical technology for understanding and generating human language, with applications including machine translation, sentiment analysis, and, most importantly, question classification. As a subfield of NLP, question classification focuses on determining the type of information being sought, which...

An intrusion attack on the Internet of Things (IoT) is any malicious activity or unauthorized access that jeopardizes the integrity and security of IoT systems, networks, or devices. Regarding IoT, intrusions can result in severe problems, including service disruption, data theft, privacy violations, and even bodily injury. One of the intrusion att...

The prevalence of plant diseases presents a substantial challenge to global agriculture, significantly impacting both production levels and economic stability in numerous countries. This study focuses on the early detection of two prevalent diseases affecting barley leaves: net blotch and spot blotch. We introduce a novel model designed for the acc...

This paper introduces an innovative targeted advertising framework that capitalizes on user-generated images to deliver personalized product recommendations. The framework consists of five main steps: collecting user-generated images, employing deep learning models like Faster R-CNN and Inception v2 for object detection, assigning durability scores...

Vision Transformers (ViTs) have emerged as a promising approach for visual recognition tasks, revolutionizing the field by leveraging the power of transformer-based architectures. Among the various ViT models, Swin Transformers have gained considerable attention due to their hierarchical design and ability to capture both local and global visual fe...

Pandemic-causing pathogens as COVID-19 can lead to a range of symptoms in humans, which may include fever, breathing difficulties, fatigue, cough, and severe respiratory distress. In more serious cases, these pathogens can be fatal. This paper presents the outcomes of a cohort study of 467 confirmed cases of COVID-19 as a specific pandemic-causing...

The purpose of this study is to utilize a Machine Learning-based methodology for predicting the key parameters contributing to severe COVID-19 cases among patients in Oman. To carry out the investigation, a comprehensive dataset of patient information, encompassing a range of blood parameters, was acquired from major government hospitals in Oman. D...

This paper introduces a novel ensemble approach for question classification using state-of-the-art models -- Electra, GloVe, and LSTM. The proposed model is trained and evaluated on the TREC dataset, a well-established benchmark for question classification tasks. The ensemble model combines the strengths of Electra, a transformer-based model for la...

Vision Transformers (ViTs) have emerged as a promising approach for visual recognition tasks, revolutionizing the field by leveraging the power of transformer-based architectures. Among the various ViT models, Swin Transformers have gained considerable attention due to their hierarchical design and ability to capture both local and global visual fe...

This study presents an ensemble model combining LSTM, BiLSTM, CNN, GRU, and GloVe to classify gene mutations using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset. The results were compared against well-known transformers like as BERT, Electra, Roberta, XLNet, Distilbert, and their LSTM ensembles. Our model outperformed all othe...

The accurate segmentation of computed tomography (CT) scan volume is an essential step in radiomic analysis as well as in developing advanced surgical planning techniques with numerous medical applications. When this process is performed manually by a clinician, it is laborious, time consuming, prone to error, and its success depends to a large ext...

In the medical field, image segmentation provides important information for surgical planning and registration, and thus demands accurate segmentation. In order to improve the effectiveness and the threshold accuracy of segmentation, researchers have tended to use a metaheuristic algorithm as the operational algorithm to achieve better exploitation...

Feature selection (FS) is the process of finding the least possible number of features that are able to describe a dataset in the same way as the original features. Feature selection is a crucial preprocessing step for data mining techniques as it improves the performance of the prediction process in terms of speed and accuracy and also provides a...

Classification is achieved through the categorisation of objects into predefined categories or classes, where the categories or classes are created based on a similar set of attributes of the object. This is referred to as supervised learning. Numerous methodologies have been formulated by researchers in order to solve classification problems effec...

The ultimate goal of modern cryptography is to protect the information resource and make it absolutely unbreakable and beyond compromise. However, throughout the history of cryptography, thousands of cryptosystems emerged and believed to be invincible and yet attackers were able to break and compromise their security. The main objective of this pap...

This paper consists of two parts. The first presents a review of the literature on the use of augmented reality (AR) in the diagnosis and treatment of autistic children with a particular focus on the efficacy of AR in assisting autistic children who have communicative, social, sentiment, and attention deficit disorders. The review also investigated...

The rendering of digitally reconstructed radiograph (DRR) images involves creating a digital reconstruction of an image made by a three-dimensional (3D) imaging system, such as a computed tomography (CT) scan, to produce a new two-dimensional (2D) image that emulates a medical X-ray scan. Acceleration of DRR generation has been the focus of much re...

This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance intrusion detection system. This article addresse...

Spiking neural networks (SNN) represents the third generation of neural network models, it differs significantly from the early neural network generation. The time is becoming the most important input. The presence and precise timing of spikes encapsulate have a meaning such as human brain behavior. However, deferent techniques are therefore requir...

Automatic segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This paper proposed an unsupervised and automatic estimation of the required parameter...

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such researc...

This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance intrusion detection system. This article addresse...

Medical image information can be exchanged remotely through cloud-based medical imaging services. Digital Imaging and Communication in Medicine (DICOM) is considered to be the most commonly used medical image format among hospitals. The objective of this article is to enhance the secure transfer and storage of medical images on the cloud by using h...

Medical imaging segmentation provides vital information for surgical diagnosis, and usually demands an accurate segmentation. A fully automated computed tomography image segmentation method is proposed. This method is unsupervised and automatic estimation of the required parameters for identifying the human body as a region of interest. The propose...

Abstract: Statistical studies show that around 28-35% of older people aged 65 and over fall each year. This percentage increases to 32-42% among those over 70 years of age. These figures explain the dramatic increase in the number of systems that have been developed in recent years with aim of detecting falls. In this study, we propose, implement a...

Numerous fast-search block motion estimation algorithms have been developed to circumvent the high computational cost required by the full-search algorithm. These techniques however often converge to a local minimum, which makes them subject to noise and matching errors. Hence, many spatial domain block matching algorithms have been developed in li...

A number of processor allocation strategies have been proposed in literature. A key performance factor that can highlight the difference between these strategies is the amount of communication conducted between the parallel jobs to be allocated. This paper aims to identifying how the density and pattern of communication can affect the performance o...

This paper aims to diagnose Pneumonia infection using image processing algorithms and artificial neural network. A group of infected and normal x-ray images are prepared using segmentation and feature extraction using many processes then using Self Organizing Map algorithm to classified them. Also, artificial neural network is used to build a datab...

Cloud computing technology extended data centers to the cloud; adding new possibilities for devices to access information in anytime and anywhere with reduced costs, faster deployment and maximized flexibility. Although, ensuring high levels of performance that would raise a new security challenges and concerns to be handled. However, most of the c...

Purpose: Simulated 2D X-ray images called digitally reconstructed radiographs (DRRs) have important applications within medical image registration frameworks where they are compared with reference X-rays or used in implementations of digital tomosynthesis (DTS). However, rendering DRRs from a CT volume is computationally demanding and relatively s...

Recent advances in programming languages for graphics processing units (GPUs) provide developers with a convenient way of implementing applications which can be executed on the CPU and GPU interchangeably. GPUs are becoming relatively cheap, powerful, and widely available hardware components, which can be used to perform intensive calculations. The...

In 2D-3D Medical Image Registration (MIR), preoperative 3D CT images are registered with intraoperative 2D X-rays images obtained by fluoroscopy or Electronic Portal Imag- ing Devices (EPID). 2D-3D MIR is established by computing Digitally Reconstructed Ra- diograph (DRR) images rendered from the volumetric data and is useful in many med- ical proc...

In this paper we present an approach for speeding-up the generation of Digitally Reconstructed Radiographs (DRRs). DRRs are needed to confirm patient setup before preplanned clinical procedures such as robotic surgery or radiation therapy in a process known as 2D/3D medical image registration. Rendering DRR images is a computationally intensive pro...

Digitally Reconstructed Radiographs (DRRs) are used in radiation therapy to confirm patient setup prior to treatment. This is often done manually (interactively) but some radiation therapy treatment manufacturers have automated the process. Modern radiation therapy methods place a greater emphasis on accurate patient alignment and there is a need t...

In this paper we present an approach for speeding up the pro-cess of 2D/3D image registration by accelerating the generation of Digitally Reconstructed Radiograph (DRR) images by using entropy to automatically selecting small regions of interest. The implementation exploits low cost multi-core parallel processing architectures currently available o...