Mazen Salem Alzyoud
@aabu.edu.jo
Al al-Bayt University
Scopus Publications
Scholar Citations
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Scopus Publications
Mazen Alzyoud, Raed Alazaidah, Mohammad Aljaidi, Ghassan Samara, Mais Haj Qasem, Muhammad Khalid, and Najah Al-Shanableh
Growing Science
Diabetes Mellitus (DM) is a frequent condition in which the body's sugar levels are abnormally high for an extended length of time. It is a major cause of death with high mortality rates and the second leading cause of total years lived with disability worldwide. Its seriousness comes from its long-term complications, including nephropathy, retinopathy, and neuropathy leading to kidney failure, poor vision and blindness, and peripheral sensory loss, respectively. Such conditions are life-threatening and affect patients’ quality of life. Therefore, this paper aims to identify the most relevant features in the diagnosis of DM and identify the best classifier that can efficiently diagnose DM based on a set of relevant features. To achieve this, four different feature selection methods have been utilized. Moreover, twelve different classifiers that belong to six learning strategies have been evaluated using two datasets and several evaluation metrics such as Accuracy, Precision, Recall, F1-measure, and ROC area. The obtained results revealed that the correlation attribute evaluation method would be the best choice to handle the task of feature selection and ranking for the considered datasets, especially when considering the Accuracy metric. Furthermore, MultiClassClassifier would be the best classifier to handle Diabetes datasets, especially when considering True Positive, precision, and Recall metrics.
Iman Akour, Mazen Alzyoud, Enass Khalil Alquqa, Emad Tariq, Nidal Alzboun, Sulieman Ibraheem Shelash Al-Hawary, and Muhammad Turki Alshurideh
Growing Science
Recent technological advancements are endless and have had a profound influence on everyone in every part of life throughout the preceding decades. Artificial intelligence is one such invention that has the potential to change the world. Now, artificial intelligence is being used in almost all commercial operations. Hence, this research attempted to investigate the impact of artificial intelligence dimensions, including natural language processing, machine learning, expert systems, and computer vision on the financial decisions of pharmaceutical companies in Jordan. A cross-sectional approach was used through a comprehensive survey to collect research data from 148 accountants and financial managers in pharmaceutical companies listed on the Amman Stock Exchange with a response rate of 81.3%. The research hypotheses were examined using structural equation modeling of the collected quantitative data. The results indicated that the dimensions of artificial intelligence positively impact financial decisions. Accordingly, companies should spend on building strong artificial intelligence infrastructure and skills. Access to modern artificial intelligence technology, data analysis tools and cloud computing resources are also essential to rationalizing financial decision-making. Besides, Jordan's pharmaceutical sector can overcome these limitations and realize the full potential of artificial intelligence in financial decision-making by solving data privacy issues, encouraging ethical AI re-search, investing in artificial intelligence expertise, and enhancing collaboration.
Mazen Alzyoud, Najah Al-Shanableh, Saleh Alomar, As’ad Mahmoud As’adAlnaser, Akram Mustafad, Ala’a Al-Momani, and Sulieman Ibraheem Shelash Al-Hawary
Growing Science
The growing significance of Artificial Intelligence (AI) across different fields highlights the essential role of user acceptance, as the success of this technology largely depends on its adoption and practical use by individuals. This research aims to examine how perceived cybersecurity, novelty value, and perceived trust affect students' willingness to accept AI in educational settings. The study's theoretical basis is the AI Device Use Acceptance (AIDUA) model. Using structural equation modeling, the study tested hypothesized relationships using data from 526 students at Jordanian universities. The results showed that social influence is positively associated with performance expectancy, while perceived cybersecurity is positively related to both performance and effort expectancy. Novelty value is positively associated with performance expectancy but a negative one with effort expectancy. Additionally, effort and performance expectancy significantly influence perceived trust and the willingness to accept AI. Moreover, perceived trust has a notable positive effect on the willingness to accept AI in education. These findings provide valuable guidance for the creation and improvement of AI-driven educational systems in universities, contributing to the broader understanding of AI technology acceptance in the educational field.
Najah Al-shanableh, Mazen Alzyoud, Saleh Alomar, Yousef Kilani, Eman Nashnush, Sulieman Al-Hawary, and Ala’a Al-Momani
Growing Science
While many small and medium enterprises (SMEs)recognize the benefits of Big Data Analytics (BDA) for digital transformation, they face challenges in implementing this technology, highlighting the need for more research on its adoption by SMEs. The objective of this study is to amalgamate the Technology Organization Environment (TOE) framework with the Diffusion of Innovation (DOI) theory, aiming to dissect the factors that sway BDA adoption in Jordanian SMEs. Additionally, the study delves into how perceived usefulness impacts this adoption process. Utilizing structural equation modeling, the study examined data from 388 managers in Jordan. The study validates all its hypotheses, revealing that variables like relative advantage, compatibility, complexity, top management support, competitive pressure, and security influence perceived usefulness, which subsequently has a positive impact on BDA adoption. This research presents a range of theoretical and practical insights.
R. Alazaidah, A. Al-Shaikh, M. Al-Mousa, H. Khafajah, G. Samara, M. Alzyoud, N. Al-Shanableh and S. Almatarneh
Natural Sciences Publishing
: Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods.
Raed Alazaidah, Mazen Alzyoud, Najah Al-Shanableh, and Haneen Alzoubi
AIP Publishing
Mazen Alzyoud, Raed Alazaidah, Haneen Alzoubi, Najah Al-shanableh, Mohammad Aljaidi, and Sattam Almatarneh
IEEE
Classification is a significant task in data mining, machine learning and data science. It aims to predict the class label for a new case accurately. Classification is of two types: Single Label Classification (SLC) and Multi-label Classification (MLC). In SLC, cases and instances are linked to one class label only, while in MLC, cases and instances could be linked to more than once class labels. Several SLC algorithms have been adapted and upgraded to handle MLC and showed different predictive performance. Hence, this paper attempts to identify and investigate the best SLC that could handle the problem of MLC with respect to three datasets and using three evaluation metrics. Moreover, the paper also aims to identify the best Problem Transformation Method (PTM) among five well-known methods. The results revealed that RandomForest and DecisionTable showed the best performance among the fifteen classifier. Also, Most Frequent Label (MFL) is the best transformation method among the five considered PTMs in term of Accuracy, Precision, and Recall.
Mohammad Batah, Mazen Alzyoud, Raed Alazaidah, Malek Toubat, Haneen AlZoubi, and Areej Olaiyat
ScopeMed
According to recent studies and statistics, Cervical Cancer (CC) is one of the most common causes of death worldwide, and mainly in the developing countries. CC has a mortality rate around 60%, in less developing countries and the percentages could go even higher, due to poor screening processes, lack of sensitization, and several other reasons. Therefore, this paper aims to utilize the high capabilities of machine learning techniques in the early prediction of CC. In specific, three well-known feature selection and ranking methods have been used to identify the most significant features that help in the diagnosis process. Also, eighteen different classifiers that belong to six learning strategies have been trained and extensively evaluated against a primary data which consists of five hundred images. Moreover, an investigation regarding the problem of imbalance class distribution which is common in medical dataset is conducted. The results revealed that LWNB and RandomForest classifiers showed the best performance in general, and considering four different evaluation metrics. Also, LWNB and Logistic classifiers were the best choices to handle the problem of imbalance class distribution which is common in medical diagnosis task. The final conclusion could be made is that using an ensemble model which consists of several classifiers such as LWNB, RandomForest, and Logistic is the best solution to handle this type of problems.