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All Journal International Journal of Electrical and Computer Engineering
Mutasem Khalil Alsmadi
Imam Abdulrahman Bin Faisal University
Computer Science & IT Electrical & Electronics Engineering

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Modified SEIR and machine learning prediction of the trend of the epidemic of COVID-19 in Jordan under lockdowns impact Mutasem Khalil Alsmadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5455-5466

Abstract

Susceptible exposed infectious recovered (SEIR) is a fitting model for coronavirus disease (COVID-19) spread prediction. Hence, to examine the effect of different levels of social distancing on the spreading of the disease, a variable was introduced in the SEIR equations system used in this work. We also used an artificial intelligence approach using a machine learning (ML) method known as deep neural network. This modified SEIR model was applied on the available initial spread data until June 25th, 2021 for the Hashemite Kingdom of Jordan. Without lockdown in Jordan, the analysis demonstrates potential infection to roughly 3.1 million people during the peak of spread approximately 3 months, starting from the date of lockdown (March 21st). Conversely, the present partial lockdowns strategy by the Kingdom was expected to reduce the predicted number of infections to 0.5 million in 9 months period. The analysis also demonstrates the ability of stricter lockdowns to effectively flatten the graph curve of COVID-19 in Jordan. Our modified SEIR and deep neural network (DNN) model were efficient in the prediction of COVID-19 epidemic sizes and peaks. The measures taken to control the epidemic by the government decreased the size of the COVID-19 epidemic.
Appling tracking game system to measure user behavior toward cybersecurity policies Khalid Adnan Alissa; Bashar Abedalmohdi Aldeeb; Hanan Abdullah Alshehri; Shahad Abdulaziz Dahdouh; Basstaa Mohammad Alsubaie; Afnan Mohammad Alghamdi; Moath Khaireddin Alkenani; Mutasem Khalil Alsmadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5164-5175

Abstract

Institutions wrestle to protect their information from threats and cybercrime. Therefore, it is dedicating a great deal of their concern to improving the information security infrastructure. Users’ behaviors were explored by applying traditional questionnaire as a research instrument in data collocate process. But researchers usually suffer from a lack of respondents' credibility when asking someone to fill out a questionnaire, and the credibility may decline further if the research topic relates to aspects of the use and implementation of information security policies. Therefore, there is insufficient reliability of the respondent's answers to the questionnaire’s questions, and the responses might not reflect the actual behavior based on the human bias when facing the problems theoretically. The current study creates a new idea to track and study the behavior of the respondents by building a tracking game system aligned with the questionnaire whose results are required to be known. The system will allow the respondent to answer the survey questions related to the compliance with the information security policies by tracking their behavior while using the system.
Learning trends in customer churn with rule-based and kernel methods Nahier Aldhafferi; Abdullah Alqahtani; Fatema Sabeen Shaikh; Sunday Olusanya Olatunji; Abdullah Almurayh; Fahad A. Alghamdi; Ghalib H. Alshammri; Amani K. Samha; Mutasem Khalil Alsmadi; Hayat Alfagham; Abderrazak Ben Salah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5364-5374

Abstract

In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program.
A comprehensive study of machine learning for predicting cardiovascular disease using Weka and SPSS tools Belal Abuhaija; Aladeen Alloubani; Mohammad Almatari; Ghaith M. Jaradat; Hemn Barzan Abdallah; Abdallah Mohd Abualkishik; Mutasem Khalil Alsmadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1891-1902

Abstract

Artificial intelligence (AI) is simulating human intelligence processes by machines and software simulators to help humans in making accurate, informed, and fast decisions based on data analysis. The medical field can make use of such AI simulators because medical data records are enormous with many overlapping parameters. Using in-depth classification techniques and data analysis can be the first step in identifying and reducing the risk factors. In this research, we are evaluating a dataset of cardiovascular abnormalities affecting a group of potential patients. We aim to employ the help of AI simulators such as Weka to understand the effect of each parameter on the risk of suffering from cardiovascular disease (CVD). We are utilizing seven classes, such as baseline accuracy, naïve Bayes, k-nearest neighbor, decision tree, support vector machine, linear regression, and artificial neural network multilayer perceptron. The classifiers are assisted by a correlation-based filter to select the most influential attributes that may have an impact on obtaining a higher classification accuracy. Analysis of the results based on sensitivity, specificity, accuracy, and precision results from Weka and Statistical Package for Social Sciences (SPSS) is illustrated. A decision tree method (J48) demonstrated its ability to classify CVD cases with high accuracy 95.76%.
Co-Authors Abdallah Mohd Abualkishik Abderrazak Ben Salah Abdullah Almurayh Abdullah Alqahtani Afnan Mohammad Alghamdi Aladeen Alloubani Amani K. Samha Bashar Abedalmohdi Aldeeb Basstaa Mohammad Alsubaie Belal Abuhaija Fahad A. Alghamdi Fatema Sabeen Shaikh Ghaith M. Jaradat Ghalib H. Alshammri Hanan Abdullah Alshehri Hayat Alfagham Hemn Barzan Abdallah Khalid Adnan Alissa Moath Khaireddin Alkenani Mohammad Almatari Nahier Aldhafferi Shahad Abdulaziz Dahdouh Sunday Olusanya Olatunji