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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Abu Owida, Hamza
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Computer Science & IT Electrical & Electronics Engineering Engineering

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The performance of artificial intelligence in prostate magnetic resonance imaging screening Abu Owida, Hamza; R. Hassan, Mohammad; Ali, Ali Mohd; Alnaimat, Feras; Al Sharah, Ashraf; Abuowaida, Suhaila; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2234-2241

Abstract

Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Automated detection of kidney masses lesions using a deep learning approach ALMahadin, Ghayth; Abu Owida, Hamza; Al Nabulsi, Jamal; Turab, Nidal; Al Hawamdeh, Nour
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2862-2869

Abstract

Deep learning has emerged as a potent tool for various tasks, such as image classification. However, in the medical domain, there exists a scarcity of data, which poses a challenge in obtaining a well-balanced and high-quality dataset. Commonly seen issues in the realm of renal health include conditions such as kidney stones, cysts, and tumors. This study is centered on the examination of deep learning models for the purpose of classifying renal computed tomography (CT)-scan pictures. State-of-the-art classification models, such as convolutional neural network (CNN) approaches, are employed to boost model performance and improve accuracy. The algorithm is comprised of six convolutional layers that progressively increase in complexity. Every layer in the network utilizes a uniform 3x3 kernel size and applies the rectified linear unit (ReLU) activation function. This is followed by a max-pooling layer that downsamples the feature maps using a 2x2 pool size. Following this, a flatten layer was implemented in order to preprocess the data for the fully linked layers. The consistent utilization of uniform kernel sizes and activation functions throughout all layers of the model facilitated the smooth extraction of complex features, thereby enhancing the model’s ability to accurately identify different kidney conditions. As a result, we achieved a high accuracy rate of 99.8%, precision is 99.8%, and F1 score of approximately 99.7%.
Application of machine learning in chemical engineering: outlook and perspectives Al Sharah, Ashraf; Abu Owida, Hamza; Alnaimat, Feras; Hassan, Mohammad; Abuowaida, Suhaila; Alhaj, Mohammad; Sharadqeh, Ahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp619-630

Abstract

Chemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component.
Applications of nanostructured materials for severe acute respiratory syndrome-CoV-2 diagnostic Turab, Nidal M.; Abu Owida, Hamza; Al-Nabulsi, Jamal I.; Alnaimat, Feras
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.v13i4.7325

Abstract

There is a growing concern that severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infections will continue to rise, and there is now no safe and effective vaccination available to prevent a pandemic. This has increased the need for rapid, sensitive, and highly selective diagnostic techniques for coronavirus disease (COVID-19) detection to levels never seen before. Researchers are now looking at other biosensing techniques that may be able to detect the COVID-19 infection and stop its spread. According to high sensitivity, and selectivity that could provide real-time results at a reasonable cost, nanomaterial show great promise for quick coronavirus detection. In order to better comprehend the rapid course of the infection and administer more effective treatments, these diagnostic methods can be used for widespread COVID-19 identification. This article summarises the current state of research into nanomaterial-based biosensors for quick SARS‑CoV‑2 diagnosis as well as the prospects for future advancement in this field. This research will be very useful during the COVID-19 epidemic in terms of establishing rules for designing nanostructure materials to deal with the outbreak. In order to predict the spread of the SARS-CoV-2 virus, we investigate the advantages of using nano-structure material and its biosensing applications.
Progress in self-powered medical devices for breathing recording Abu Owida, Hamza; Turab, Nidal; Al-Nabulsi, Jamal I.; Al-Ayyad, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.5253

Abstract

Wearable and implantable medical technologies are increasingly being used for the diagnosis, treatment, and prevention of illnesses and other health concerns. One's respiration can be monitored using any number of different biosensors and tracking devices. Self-powered sensors, for example, have a reduced total cost, are easy to prepare, have a high degree of design-ability, and are available in a number of different forms when compared to other types of sensors. The mechanical energy stored in the respiratory system could be converted into electrical energy by using airflow to operate self-powered sensors. Self-recharging sensors and systems are now in development to make home health monitoring and diagnosis more practical. There has not been a lot of study devoted to the models of respiratory sickness or the output signals that connect with them. Thus, investigating the character of their bond is not only difficult but also crucial. This article examined the theory behind self-powered breathing sensors and systems, as well as their output characteristics, detection indices, and other cutting-edge developments. To help communicate knowledge to other academics working in this field and interested in this topic, we also explored the challenges and potential benefits of autonomous sensors.
Progression of polymeric nanostructured fibres for pharmaceutical applications Abu Owida, Hamza; I. Al-Nabulsi, Jamal; M. Turab, Nidal; Al-Ayyad, Muhammad; Alazaidah, Raed; Alshdaifat, Nawaf
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7315

Abstract

Electrospinning has emerged as a simple and cost-effective technique for producing polymer nanofibers, offering a versatile approach for creating nanostructured fibers from a wide range of polymer materials. The pharmaceutical field has particularly welcomed the advent of electrospun nanofibers, as they hold immense potential for revolutionizing drug delivery systems. The recent surge of interest in electrospun nanofibers can be attributed to their unique characteristics, including elasticity and biocompatibility, which make them highly suitable for various biomedical applications. By incorporating functional ingredients into blends of nanostructured fibers, the capabilities and reliability of drug delivery devices have been significantly enhanced. This review aims to provide a comprehensive summary of recent research endeavors focusing on the concept of nanofibrous mesh and its multifaceted applications. With an emphasis on the simplicity of fabrication and the virtually limitless combinations of materials achievable through this approach, nanofibrous meshes hold the promise of transforming specific treatment modalities. By streamlining the delivery of therapeutic agents and offering enhanced control over drug release kinetics, nanofibrous meshes may herald a new era in targeted and personalized medicine.
Advancement in self-powered implantable medical systems Abu Owida, Hamza; Al-Nabulsi, Jamal; Turab, Nidal; Al-Ayyad, Muhammad; Al Hawamdeh, Nour; Alshdaifat, Nawaf
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.5881

Abstract

Many different elements of patient monitoring and treatment can be supported by implantable devices, which have proven to be extremely reliable and efficient in the medical profession. Medical professionals can use the data they collect to better diagnose and treat patients as a result. The devices’ power sources, on the other hand, are battery-based, which introduces a slew of issues. As part of this review, we explore the use of harvesters in implanted devices and evaluate various materials and procedures and look at how new and improved circuits can enable the harvesters to sustain medical devices.
Advancements in machine learning techniques for precise detection and classification of lung cancer Abu Owida, Hamza; Arabiat, Areen; Al-Ayyad, Muhammad; Altayeb, Muneera
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10527

Abstract

Lung cancer remains one of the most prevalent and lethal malignancies worldwide, necessitating early detection and accurate classification for effective treatment. In this work, we present a unique machine learning (ML) model that uses medical imaging data to detect and classify lung cancer. Utilizing a dataset of 613 images which obtained from Kaggle, our model combines sophisticated feature extraction methods with three essential algorithms: AdaBoost, stochastic gradient descent (SGD), and random forest (RF). Orange3 data mining software was used to classify the model after it was preprocessed and features were extracted using MATLAB. Nonetheless, the model showed good performance in identifying lung cancer lesions in four different categories: squamous cell carcinoma, big cell carcinoma, adenocarcinoma, and normal. With an accuracy of 0.998 and an AUC range of 1.000, AdaBoost notably produced the best results. Overall, ensemble ML techniques demonstrated notable benefits over single classifiers, indicating its potential to aid in the creation of accurate instruments for the diagnosis of lung cancer in its early stages.
Co-Authors Abuowaida, Suhaila Al Hawamdeh, Nour Al Nabulsi, Jamal Al Sharah, Ashraf Al-Ayyad, Muhammad Al-Nabulsi, Jamal Al-Nabulsi, Jamal I. Alazaidah, Raed Alhaj, Mohammad Ali, Ali Mohd AlMahadin, Ghayth Alnaimat, Feras Alshdaifat, Nawaf Altayeb, Muneera Arabiat, Areen Hassan, Mohammad I. Al-Nabulsi, Jamal M. Turab, Nidal R. Hassan, Mohammad Sharadqeh, Ahmad Turab, Nidal Turab, Nidal M.