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Air travel demand is a crucial part of planning for airlines and airports. It helps in elaborating decisions and recognizing risks and opportunities. Forecasting air passenger demand is an interesting research study that deserves investigation. This problem requires prediction techniques such that Linear Regression and Neural Network. These techniques are efficient, but they have several parameters that necessitate appropriate values to provide the least error rate of prediction. Some recent air travel demand studies investigated Genetic Algorithms to provide optimal values for these parameters. In this article, we propose to explore the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) to find the optimal values for Linear Regression (LR) coefficients. This study presents two new hybrid prediction techniques (PSO based LR and FA based LR) to handle airline demand forecasting, which researchers have not previously covered. The results of PSO based LR, FA-based LR and LR are compared to find the best model with the lowest prediction error rate. The results showed that PSO based LR achieved the best prediction results with a lower error rate compared to FA based LR and LR alone. This study is performed using the data of Los Angeles International airport.

Arabic natural language processing (ANLP) consists of developing techniques and tools that can utilize and analyze the Arabic language in both written and spoken contexts. ANLP makes an important contribution to many existing developed systems. It provides Arabic and non-Arabic speakers with helpful and convenient tools that can be used in different domains. Modern ANLP tools are developed using machine learning (ML) techniques. ML algorithms are widely used in NLP because of their high accuracy rate regardless of the robustness of the data that is used and because of the ease with which they can be implemented. On the other hand, the methodology of ANLP applications based on ML involves several distinct phases. It is, therefore, crucial to recognize and understand these phases in detail as well as the most widely used ML algorithms. This survey discusses this concept in detail, shows the involvement of ML techniques in developing such tools, and identifies well-known techniques used in ANLP. Moreover, this survey discusses the characteristics and complexity of the Arabic language in addition to the importance and needs of ANLP.

This paper presents a project that explores the possibility of assessing the readability level of Arabic medicine information leaflets using machine learning techniques. There are a number of popular readability formulas and tools that have been successfully used to assess the readability of health-related information in several languages. However, there is limited work on the readability assessment of health-related information, specifically medicine information leaflets in Arabic. We describe the design of a tool that uses machine learning to assess the readability of medicine information leaflets. We utilize a corpus comprising 1112 medicine information leaflets annotated with three difficulty levels. Based on a study of existing literature, we selected a number of features influencing text difficulty. The tool will help specialized organizations in medicine information leaflets production to produce the leaflets at appropriate level of reading for the majority of leaflets consumers.