This analysis aims to provide an overview of potential machine learning algorithms that may aid the aviation industry in predicting future air passenger traffic flow, which can help increase stakeholder value as well as improve customer experiences. A review and discussion of the aviation industry’s past, current, and future challenges is provided, as well as an overview of machine learning algorithms, neural networks, and learning methods. Further, an overview and discussion of the architecture of the Long Short-Term Memory (LSTM) network, Support Vector Regression Machine (SVRM), and Random Forest (RF) algorithms is provided. The comparative analysis provides an overview and comparison of the performance of the LSTM, SVRM, and RF models based on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The dataset used includes the hourly number of passengers from scheduled flights at Oslo Airport Gardermoen for the period of January 1, 2009, to December 31, 2019, including the datetime features such as Time (hour), day, month, and year, as well as the weather features of air temperature and mean wind speed, with a total of 96185 samples. The Long Short-Term Memory model exhibited the highest generalization ability, with a performance evaluation on the testing dataset of 0.00445/0.06667 MSE/RMSE. Additionally, the performance of the SVRM and RF models on the testing dataset is 0.00511/0.07147 and 0.00543/0.07368 MSE/RMSE respectively. In addition to the performance, each of the models’ complexity, stability, and ability to predict the hourly and daily fluctuations of passengers are discussed.

Assessment of course learning outcomes is a fundamental measure of student learning success in a course. This assessment is core measure for educational effectiveness required by any specialized accreditation body. Learning analytics is a powerful mean to inform learners, instructors and educational institutions about the student's performance and the progress of the learning process and to support the student success. Many useful LA tools are available in different LMS platforms such as Moodle. They are used to report student performance in specific activity. However there is a lack of integrated LA tool that can assess course learning outcomes and predict the student performance and achievement against specific outcomes. In this paper, we propose a new framework for developing learning analytics tool to assess these outcomes in the well-known open source LMS Moodle. Our framework is based on direct measure of the course learning outcomes through embedded examination of each outcome and its associated LMS activities, then to analyze the result in order to evaluate the achievement of program outcomes and to propose improvement for the coming semester where the studied course will be offer.

Older people experience different Age-Associated Diseases (AAD), and appropriate diagnosis and treatment will help them to lead a peaceful life. Parkinson's disease (PD) is one of the ADD generally found in people aged >60 years. The clinical-level screening of PD is performed using different procedures, including the hand sketches, such as wave/spiral patterns. The proposed research implements Pretrained Lightweight Deep-Learning (PLDL) methods with two-fold training to detect the patterns accurately from hand sketches belonging to the healthy/PD class. The developed system consists of the following stages; (i) Image preprocessing, (ii) Data augmentation, (iii) Two-fold training to improve the detection accuracy, and (iv) Deep features selection with 50% drop-out and binary classification. This work considered the fused features of MobileNets to achieve better detection accuracy. The outcome of this research confirms that this procedure offers a satisfactory result in predicting PD from the considered test images. The K-Nearest Neighbor (KNN) classifier offered a detection accuracy of 100% with the chosen database.

Students’ feedback is pertinent in measuring the quality of the educational process. For example, by applying lexicon-based sentiment analysis to students’ open-ended course feedback, we can detect not only their sentiment orientation (positive, negative, or neutral) but also their emotional valences, such as anger, anticipation, disgust, fear, joy, sadness, surprise, or trust. However, most currently used assessment tools cannot effectively measure emotional engagement, such as interest level, enjoyment, support, curiosity, and sense of belonging. Moreover, none of those tools utilize Bloom’s taxonomy for students’ learning-level assessment. In this work, we develop a user-friendly application based on NLP to help the teachers understand the students’ perception of their learning by analyzing their open-ended feedback. This allows us to examine the sentiment and the embedded emotions using a customized dictionary of emotions related to education. The application can also classify the students’ emotions according to Bloom’s taxonomy. We believe our application will help teachers improve their course delivery.