Dr. Riyadh holds a PhD degree in computer Science in the area of artificial intelligence and pattern recognition and an MSc degree in Software Engineering both from Liverpool University. He has an extensive academic, industrial as well as administrative experience. He was the dean of the College of Information Technology and the chairperson of the Computer Science Department before that at Ajman University. Currently, he is chairperson of Department of the Information Technology. His research interests are in the area of soft computing paradigms: Neural Networks, Fuzzy Logic, and Genetic Algorithms and their applications in software engineering, pattern recognition, and in providing solutions to other business and information technology problems. On the teaching side, he has taught many courses at the undergraduate as well as postgraduate level in the computing field. Supervised students’ theses at the MSc as well as PhD levels. I also have industrial experience working as a system analyst. He also has an extensive experience in the assessment and accreditation processes of academic programs as well as the development of curricula for undergraduate as well as postgraduate degree programs. He is also an active ABET program evaluator with the Computing Accreditation Commission for accrediting computing programs.
The study aims to predict the value of intellectual capital (IC) based on the performance contribution approach. Theoretically, through the development of a derivative model (DM) that explains the relationship between IC and the firms performance. The study develops a DM of 16 equations to construct the relationship between the investment in IC and its effectiveness in generating income. This is one of the few studies in predicting IC and the only empirical study applied to UAE listed companies. The study applies the neural network system of 47 firms listed in DFM over five years. The study ranks return on assets, p/e ratio, market value of assets, and return on equity as predictors of IC. It helps managers in predicting and determining investment in IC and its impact on the performance. In future, other than financial factors need to be included, increase the sample, and conduct research on predicting the components of IC.
Predicting student performance in computing majors and the factors affecting his success can have a substantial effect on improving student academic performance and his on-time graduation with all the financial benefits that come with that. There is a limited amount of time an academic advisor may allocate to each student to identify problem areas in the curriculum and take appropriate actions and advise the student based on informed judgement. Thus, there is a need to predict which students are at risk early on in the program. In this work, we have built a prediction model based on particle swarm optimization to estimate the final graduation grade point average (GPA) of students enrolled in the information technology program at Ajman University. Input predictors used in this work were Students' final GPA scores in core courses and high school average grade. Based on records of 74 students who have graduated from the program so far, we have found that the most influential predictor of graduation GPA is high school grade average. Our results showed that the Data Structures and Discrete Mathematics have no role to play in the prediction of GPA while networking and security courses have the most significant prediction contribution. Forty per cent of predicted values fall within 0.25 of the real GPA, which has a maximum upper bound of four. However, the accuracy of the model significantly improved when applied to a much larger publically available dataset with 88% of GPA scores falling within 0.25 of the actual GPA.
Abstract: The COVID-19 pandemic introduced unprecedented challenges for people and governments. Vaccines are an available solution to this pandemic. Recipients of the vaccines are of different ages, gender, and religion. Muslims follow specific Islamic guidelines that prohibit them from taking a vaccine with certain ingredients. This study aims at analyzing Facebook and Twitter data to understand the discourse related to halal vaccines using aspect-based sentiment analysis and text emotion analysis. We searched for the term “halal vaccine” and limited the timeline to the period between 1 January 2020, and 30 April 2021, and collected 6037 tweets and 3918 Facebook posts. We performed data preprocessing on tweets and Facebook posts and built the Latent Dirichlet Allocation (LDA) model to identify topics. Calculating the sentiment analysis for each topic was the next step. Finally, this study further investigates emotions in the data using the National Research Council of Canada Emotion Lexicon. Our analysis identified four topics in each of the Twitter dataset and Facebook dataset. Two topics of “COVID-19 vaccine” and “halal vaccine” are shared between the two datasets. The other two topics in tweets are “halal certificate” and “must halal”, while “sinovac vaccine” and “ulema council” are two other topics in the Facebook dataset. The sentiment analysis shows that the sentiment toward halal vaccine is mostly neutral in Twitter data, whereas it is positive in Facebook data. The emotion analysis indicates that trust is the most present emotion among the top three emotions in both datasets, followed by anticipation and fear.Abstract: The COVID-19 pandemic introduced unprecedented challenges for people and governments. Vaccines are an available solution to this pandemic. Recipients of the vaccines are of different ages, gender, and religion. Muslims follow specific Islamic guidelines that prohibit them from taking a vaccine with certain ingredients. This study aims at analyzing Facebook and Twitter data to understand the discourse related to halal vaccines using aspect-based sentiment analysis and text emotion analysis. We searched for the term “halal vaccine” and limited the timeline to the period between 1 January 2020, and 30 April 2021, and collected 6037 tweets and 3918 Facebook posts. We performed data preprocessing on tweets and Facebook posts and built the Latent Dirichlet Allocation (LDA) model to identify topics. Calculating the sentiment analysis for each topic was the next step. Finally, this study further investigates emotions in the data using the National Research Council of Canada Emotion Lexicon. Our analysis identified four topics in each of the Twitter dataset and Facebook dataset. Two topics of “COVID-19 vaccine” and “halal vaccine” are shared between the two datasets. The other two topics in tweets are “halal certificate” and “must halal”, while “sinovac vaccine” and “ulema council” are two other topics in the Facebook dataset. The sentiment analysis shows that the sentiment toward halal vaccine is mostly neutral in Twitter data, whereas it is positive in Facebook data. The emotion analysis indicates that trust is the most present emotion among the top three emotions in both datasets, followed by anticipation and fear.
Predicting student’s successful completion of academic programs and the features that influence their performance can have a significant effect on improving students’ completion, and graduation rates and reduce attrition rates. Therefore, identifying students are at risk, and the courses where improvements in content, delivery mode, pedagogy, and assessment activities can improve students’ learning experience and completion rates. In this work, we have developed a prediction and explanatory model using adaptive neuro-fuzzy inference system (ANFIS) methodology to predict the grade point average (GPA), at graduation time, of students enrolled in the information technology program at Ajman University. The approach adopted uses students’ grades in introductory and fundamental IT courses and high school grade point average (HSGPA) as predictors. Sensitivity analysis was performed on the model to quantify the relative significance of each predictor in explaining variations in graduation GPA. Our findings indicate HSGPA is the most influential factor in predicting graduation GPA, with data structures, operating systems, and software engineering coming closely in second place. On the explanatory side, we have found that discrete mathematics was the most influential course causing variations in graduation GPA, followed by software engineering, information security, and HSGPA. When we ran the model on the testing data, 77% of the predicted values fell within one root mean square error (0.29) of the actual GPA, which has a maximum of four. We have also shown that the ANFIS approach has better predictive accuracy than commonly used techniques such as multilinear regression. We recommend that IT programs at other institutions conduct comparable studies and shed some light on our findings.