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

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Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach Owida, Hamza Abu; AlMahadin, Ghayth; Al-Nabulsi, Jamal I.; Turab, Nidal; Abuowaida, Suhaila; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3150-3158

Abstract

The treatment of brain tumors poses significant challenges and contributes to a significant number of deaths on a global scale. The process of identifying brain tumors in medical practice involves the visual analysis of photographs by healthcare experts, who manually delineate the tumor locations. However, this approach is characterized by its time-consuming nature and susceptibility to errors. In recent years, scholars have put forth automated approaches to early detection of brain tumors. However, these techniques face challenges attributed to their limited precision and significant false-positive rates. There is a need for an effective methodology to identify and classify tumors, which involves extracting reliable features and achieving precise disease classification. This work presents a novel model architecture that is derived from the EfficientNetB3. The suggested framework has been trained and assessed on a dataset consisting of 7,023 magnetic resonance images. The findings of this study indicate that the fused feature vector exhibits superior performance compared to the individual vectors. Furthermore, the technique that was provided showed superior performance compared to the currently available systems and attained a 100% accuracy rate. As a result, it is viable to employ this technique within a clinical environment for the purpose of categorizing brain tumors based on magnetic resonance images scans.
Towards fostering the role of 5G networks in the field of digital health Turab, Nidal M.; Al-Nabulsi, Jamal Ibrahim; Abu-Alhaija, Mwaffaq; Owida, Hamza Abu; Alsharaiah, Mohammad; Abuthawabeh, Ala
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6595-6608

Abstract

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future.
Bio-engineered strategies for osteochondral defect repair Alnaimat, Feras; Owida, Hamza Abu; Turab, Nidal M.; Al-Nabulsi, Jamal I.
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.v13i5.7316

Abstract

Due to the absence of blood vessels and nerves, the regenerative potential of articular cartilage is significantly constrained. This implies that the impact of a ruptured cartilage extends to the entire joint. Osteoarthritis, a health condition, may arise due to injury and the progressive breakdown of joint tissues. The progression of osteoarthritis can be accelerated by the extensive degradation of articular cartilage, which is ranked as the third most prevalent musculoskeletal disorder necessitating rehabilitation, following low back pain and fractures. The existing therapeutic interventions for cartilage repair exhibit limited efficacy and seldom achieve complete restoration of both functional capacity and tissue homeostasis. Emerging technological advancements in the field of tissue engineering hold significant promise for the development of viable substitutes for cartilage tissue, capable of exhibiting functional properties. The overarching strategy involves ensuring that the cell source is enriched with bioactive molecules that facilitate cellular differentiation and/or maturation. This review provides a comprehensive summary of recent advancements in the field of cartilage tissue engineering. Additionally, it offers an overview of recent clinical trials that have been conducted to examine the latest research developments and clinical applications pertaining to weakened articular cartilage and osteoarthritis.
Application of smart hydrogels scaffolds for bone tissue engineering Owida, Hamza Abu; Alnaimat, Feras; Al-Nabulsi, Jamal I.; Al-Ayyad, Muhammad; Turab, Nidal M.
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7608

Abstract

Recent attention in the biomedical and orthopedic sectors has been drawn towards bone defects, emerging as a prominent focus within orthopedic clinics. Hydrogels, due to their biocompatibility, elevated water content, softness, and flexibility, are increasingly acknowledged in tissue regeneration research. Advanced biomaterials offer numerous advantages over traditional materials, notably the capacity to respond to diverse physical, chemical, and biological stimuli. Their responsiveness to environmental cues, such as three-dimensional (3D) morphology and phase conditions, holds promise for enhancing the efficacy of localized bone lesion repairs. This paper aims to revolutionize the treatment of severe bone abnormalities by providing a comprehensive examination of hydrogels capable of morphological adaptation to environmental changes. It delineates their classification, manufacturing principles, and current research status within the field of bone defect regeneration.
A perspective on smart universities as being downsized smart cities: a technological view of internet of thing and big data Abdul Jawwad, Abdul Kareem; Turab, Nidal; Al-Mahadin, Ghayth; Owida, Hamza Abu; Al-Nabulsi, Jamal
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1162-1170

Abstract

The integration of internet of things (IoT) and big data technologies is transforming the overall perspective of managing various sectors of modern life; with higher educational sectors being no exception of this transformation. This paper explores the idea of a “smart university” as an extension of the overarching “smart city” framework, emphasizing the blending of IoT and big data technologies within higher education institutions. The study investigates the incorporation of IoT technologies throughout university campuses, including intelligent classrooms, smart infrastructure, and device networking. Moreover, the paper delves into the substantial role played by big data analytics in processing and extracting meaningful insights from extensive data generated by IoT devices in a Smart University. The use of predictive analytics, machine learning algorithms, and data-driven decision-making contributes to personalized learning experiences, adaptive campus management, and proactive maintenance of university facilities. Furthermore, this paper not only emphasizes the potential benefits of deploying IoT and big data in a university setting but also addresses challenges related to security, privacy, and ethical considerations. By embracing a comprehensive approach to technology integration, universities can leverage the capabilities of IoT and big data to establish intelligent, interconnected, and flexible educational environments that align with the broader vision of a smart city.
Automated blood cancer detection models based on EfficientNet-B3 architecture and transfer learning Alshdaifat, Nawaf; Owida, Hamza Abu; Mustafa, Zaid; Aburomman, Ahmad; Abuowaida, Suhaila; Ibrahim, Abdullah; Alsharafat, Wafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1731-1738

Abstract

In blood smear images, there are difficulties in diagnosing blood cancer diseases like leukemia and lymphoma because of their various forms that appear in the human body. In this paper, a method for automatic detection of blood cancer is suggested that uses the EfficientNet-B3 architecture along with transfer learning techniques to improve accuracy and efficiency. We first fine-tuned the EfficientNet-B3 model, which was pre-trained on a large dataset consisting of annotated blood smear images, to capture pertinent features linked with blood malignant cells. To expedite the training process and adapt the model to our task, we use transfer learning. The proposed approach’s results from our experiments show that it outperforms traditional deep learning models and state-of-the-art methods in blood cancer detection. Additionally, with high precision and recall rates, this model also detects different types of blood cancers with robustness in its performance since its accuracy is over 99%. This means that when used together with the EfficientNet-B3 architecture, transfer learning can help the developed methods generalize among different types of blood cancers and conditions.
Improved deep learning architecture for skin cancer classification Owida, Hamza Abu; Alshdaifat, Nawaf; Almaghthawi, Ahmed; Abuowaida, Suhaila; Aburomman, Ahmad; Al-Momani, Adai; Arabiat, Mohammad; Chan, Huah Yong
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp501-508

Abstract

A leading cause of mortality globally, skin cancer is deadly. Early skin cancer diagnosis reduces mortality. Visual inspection is the main skin cancer diagnosis tool; however, it is imprecise. Researchers propose deep-learning techniques to assist physicians identify skin tumors fast and correctly. Deep convolutional neural networks (CNNs) can identify distinct objects in complex tasks. We train a CNN on photos with merely pixels and illness labels to classify skin lesions. We train on HAM-10000 using a CNN. On the HAM10000 dataset, the suggested model scored 95.23% efficiency, 95.30% sensitivity, and 95.91% specificity.
Perspective on the applications of terahertz imaging in skin cancer diagnosis Owida, Hamza Abu; Al-Nabulsi, Jamal I.; Al-Ayyad, Muhammad; Turab, Nidal; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1242-1250

Abstract

Applications of terahertz (THz) imaging technologies have advanced significantly in the disciplines of biology, medical diagnostics, and non- destructive testing in the past several decades. Significant progress has been made in THz biomedical imaging, allowing for the label-free diagnosis of malignant tumors. Terahertz frequencies, which lie between those of the microwave and infrared, are highly sensitive to water concentration and are significantly muted by water. Terahertz radiation does not cause ionization of biological tissues because of its low photon energy. Recently, terahertz spectra, including spectroscopic investigations of cancer, have been reported at an increasing rate due to the growing interest in their biological applications sparked by these unique features. To improve cancer diagnosis with terahertz imaging, an appropriate differentiation technique is required to increased blood supply and localized rise in tissue water content that commonly accompany the presence of malignancy. Terahertz imaging has been found to benefit from structural alterations in afflicted tissues. This study provides an overview of terahertz technology and briefly discusses the use of terahertz imaging techniques in the detection of skin cancer. Research into the promise and perils of terahertz imaging will also be discussed.
Narrative review of the literature: application of mechanical self powered sensors for continuous surveillance of heart functions Owida, Hamza Abu; Al-Nabulsi, Jamal I.; Turab, Nidal; Al-Ayyad, Muhammad; Al Hawamdeh, Nour; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp243-251

Abstract

Cardiovascular disease consistently occupies a prominent position among the leading global causes of mortality. Continuous and real-time monitoring of cardiovascular signs over an extended duration is necessary to identify irregularities and prompt timely intervention. Due to this reason, researchers have invested heavily in developing adaptive sensors that may be worn or implanted and continuously monitor numerous vital physiological characteristics. Mechanical sensors represent a category of devices capable of precisely capturing the temporal variations in pressure within the heart and arteries. Mechanical sensors possess inherent advantages such as exceptional precision and a wide range of adaptability. This article examines four distinct mechanical sensor technologies that rely on capacitive, piezoresistive, piezoelectric, and triboelectric principles. These technologies show great potential as novel approaches for monitoring the cardiovascular system. The subsequent section provides a comprehensive analysis of the biomechanical components of the cardiovascular system, accompanied by an in-depth examination of the methods employed to monitor these intricate systems. These systems measure blood and endocardial pressure, pulse wave, and heart rhythm. Finally, we discuss the potential benefits of continuing health monitoring in vascular disease treatment and the challenges of integrating it into clinical settings.
Harnessing the power of blockchain to strengthen cybersecurity measures: a review Turab, Nidal; Owida, Hamza Abu; Al-Nabulsi, Jamal I.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp593-600

Abstract

As the digital environment continues to evolve with the increasing frequency and complexity of cybersecurity threats, there is growing interest in using blockchain (BC) technology. BC is a technology with desirable properties such as decentralization, integrity, and transparency. The decentralized nature of BC eliminates single points of failure, reducing the vulnerability of critical systems to targeted attacks. The complex and rapidly evolving nature of cyber threats requires an earlier and adaptive approach. This review paper examined several papers collected from official websites. Focusing on using BC technology to improve cybersecurity, the main keywords of the review paper were BC technology, supply chain management, proof of work, and proof of stake. This review paper aims to investigate the security components through a threat assessment that compares the security of BC in different classes and real attack environments. It highlights the potential of BC to strengthen cybersecurity measures, citing unique features. The review paper also points out that there is a lack of focus on addressing security challenges related to computer data and digital systems and calling for a deeper discussion on problem-solving.
Co-Authors Abdul Jawwad, Abdul Kareem Abu-Alhaija, Mwaffaq Abuowaida, Suhaila Aburomman, Ahmad Abushattal, Abdelrahman Abuthawabeh, Ala Al Hawamdeh, Nour Al-Ayyad, Muhammad Al-Mahadin, Ghayth Al-Momani, Adai Al-Nabulsi, Jamal Al-Nabulsi, Jamal I. Al-Nabulsi, Jamal Ibrahim Alawneh, Tareq A. Alkasassbeh, Jawdat S. Almaghthawi, Ahmed AlMahadin, Ghayth Alnaimat, Feras Alsharafat, Wafa Alsharaiah, Mohammad Alshdaifat, Nawaf Arabiat, Mohammad Chan, Huah Yong Ibrahim, Abdullah Iqbal, Zahid Izneid, Basem Abu Mustafa, Zaid Sharah, Ashraf Al Turab, Nidal Turab, Nidal M.