Dr. Nidal Al Said obtained his Ph.D from the National Technical University of Athens, Greece. Prior to his tenure at Ajman University, he held various academic positions at different institutions. Notably, he served as the Chairperson of the Electrical and Computer Engineering department and acted as the coordinator for the Graphic Design major in the Mass Communication College. Currently, Dr. Al Said serves as the Institutional Effectiveness Coordinator for the Mass Communication College and is the Head of the Academic Accreditation Committee. He has an impressive publication record that spans several areas, including IT in Education and Media, Human-Computer Interaction and Smart Applications, Artificial Intelligence and Machine Learning, Data and Web Mining
The problem under the study of this work is investigating data mining algorithms for intelligent analysis of data written in Arabic. The study comprised instead involves several stages, including Data Collection and Pre-Processing; Data Mining Algorithms (Multinomial Naïve Bayes Classifier, Naïve Bayes Classifier, Support Vector Machine and Modified K-Means); Study Results Processing and Software Implementation. A total of 16,732 Facebook posts written exclusively in Arabic were downloaded. Almost two-thirds of them (namely 11,155 items) were used to train algorithms, while the rest (5577 items) were subject to research. The training data were categorized into five groups based on subjects (politics, entertainment, medicine, science, and religion) with five keywords used for testing in each group. Most posts (4736 items) were related to politics. The most accurate algorithm proved to be the multinomial Naïve Bayesian classifier for the maximum number of test data, while the minimum values of this feature were recorded for the Support vector machine. The effectiveness of the multinomial Naïve Bayesian classifier algorithm was most remarkable for the maximum amount of data, while the Support Vector Machine was most effective for the minimum amount. The argument’s fit score is maximum at 5577 data points for the multinomial Naïve Bayesian classifier and 1394 data points for K-means. To improve and refine the results of data mining, the sample must be expanded, adding more data classes and keywords. Other machine learning models, such as deep learning algorithms, could also be used. The significance of investigation is very important because it expands our knowledge about the use of Machine Learning Algorithms to mine Arabic texts on social media platforms.
ML models and neural networks for analyzing 3D data spatial planning tasks: Example of Khasansky urban district of the Russian Federation
New information and computer technologies transform the social interaction and impose new demands for skills and thinking upon media specialists. The aim of this study is to determine the most effective set of information technologies, which can help media specialists develop competencies and thus stay competitive in the labor market. The research methodology is based on the overview of case studies concerning issues such as technology trends, human capital, and talent competitiveness. The qualitative analysis was performed in three phases – overviewing case studies, distinguishing trends and problem-solving. Analyzing data on skill supply and demand, the key skills needed to succeed in the workplace were identified. The results of the three-phase research revealed that the most important competencies needed to be in demand today are technology literacy, stress tolerance, and big data skills. The major finding of this study is that a media specialist needs to focus on learning throughout his life and gain hard and soft skills in the process.