Abdulfattah S. Mashat

Virtual Rehabilitation for Multiple Sclerosis Using a Kinect-Based System: Randomized Controlled Trial

Background The methods used for the motor rehabilitation of patients with neurological disorders include a number of different rehabilitation exercises. For patients who have been diagnosed with multiple sclerosis (MS), the performance of motor rehabilitation exercises is essential. Nevertheless, this rehabilitation may be tedious, negatively influencing patients’ motivation and adherence to treatment. Objective We present RemoviEM, a system based on Kinect that uses virtual reality (VR) and natural user interfaces (NUI) to offer patients with MS an intuitive and motivating way to perform several motor rehabilitation exercises. It offers therapists a new motor rehabilitation tool for the rehabilitation process, providing feedback on the patient’s progress. Moreover, it is a low-cost system, a feature that can facilitate its integration in clinical rehabilitation centers. Methods A randomized and controlled single blinded study was carried out to assess the influence of a Kinect-based virtual rehabilitation system on the balance rehabilitation of patients with MS. This study describes RemoviEM and evaluates its effectiveness compared to standard rehabilitation. To achieve this objective, a clinical trial was carried out. Eleven patients from a MS association participated in the clinical trial. The mean age was 44.82 (SD 10.44) and the mean time from diagnosis (years) was 9.77 (SD 10.40). Clinical effectiveness was evaluated using clinical balance scales. Results Significant group-by-time interaction was detected in the scores of the Berg Balance Scale (P=.011) and the Anterior Reach Test in standing position (P=.011). Post-hoc analysis showed greater improvement in the experimental group for these variables than in the control group for these variables. The Suitability Evaluation Questionnaire (SEQ) showed good results in usability, acceptance, security, and safety for the evaluated system. Conclusions The results obtained suggest that RemoviEM represents a motivational and effective alternative to traditional motor rehabilitation for MS patients. These results have encouraged us to improve the system with new exercises, which are currently being developed.

Saudi-KAU Coupled Global Climate Model: Description and Performance

BackgroundA new coupled global climate model (CGCM) has been developed at the Center of Excellence for Climate Change Research (CECCR), King Abdulaziz University (KAU), known as Saudi-KAU CGCM.PurposeThe main aim of the model development is to generate seasonal to subseasonal forecasting and long-term climate simulations.MethodsThe Saudi-KAU CGCM currently includes two atmospheric dynamical cores, two land components, three ocean components, and multiple physical parameterization options. The component modules and parameterization schemes have been adopted from different sources, and some have undergone modifications at CECCR. The model is characterized by its versatility, ease of use, and the physical fidelity of its climate simulations, in both idealized and realistic configurations. A description of the model, its component packages, and parameterizations is provided.ResultsResults from selected configurations demonstrate the model’s ability to reasonably simulate the climate on different time scales. The coupled model simulates El Niño-Southern Oscillation (ENSO) variability, which is fundamental for seasonal forecasting. It also simulates Madden-Julian Oscillation (MJO)-like disturbances with features similar to observations, although slightly weaker.ConclusionsThe Saudi-KAU CGCM ability to simulate the ENSO and the MJO suggests that it is capable of making useful predictions on subseasonal to seasonal timescales.

A Nonlinear Goal Programming Model for University Admission Capacity Planning with Modified Differential Evolution Algorithm

This paper proposes a nonlinear Goal Programming Model (GPM) for solving the problem of admission capacity planning in academic universities. Many factors of university admission capacity planning have been taken into consideration among which are number of admitted students in the past years, total population in the country, number of graduates from secondary schools, desired ratios of specific specialties, faculty-to-students ratio, and the past number of graduates. The proposed model is general and has been tested at King Abdulaziz University (KAU) in the Kingdom of Saudi Arabia, where the work aims to achieve the key objectives of a five-year development plan in addition to a 25-year future plan (AAFAQ) for universities education in the Kingdom. Based on the results of this test, the proposed GPM with a modified differential evolution algorithm has approved an ability to solve general admission capacity planning problem in terms of high quality, rapid convergence speed, efficiency, and robustness.

Mecca access and security control system

Mecca (Makka al-Mukarrama) is a city in Saudi Arabia. For Muslims, the pilgrimage to Mecca is part of one of the fundamentals of their faith, so-called pillars of Islam. Each year, nearly five millions of pilgrims go to the holy city, to perform the Hajj during the Muslim month of Dhu al-Hijjah. The existent of this big number of people in such a small enclosure make the occurrence of incidences happened so often. For this, in this paper we present an access and security control system, to prevent the suffering of these incidences. This is done thanks to the use of ICT, this system will perform image tracking and pilgrims monitoring, to help these pilgrims, and provide them all the needed attention in an effective and technological way.

Modeling and predicting execution time of scientific workflows in the Grid using radial basis function neural network

With the maturity of electronic science (e-science) the scientific applications are growing to be more complex composed of a set of coordinating tasks with complex dependencies among them referred to as workflows. For optimized execution of workflows in the Grid, the high level middleware services (like task scheduler, resource broker, performance steering service etc.) need in-advance estimates of workflow execution times. However, modeling and predicting workflow execution time in the Grid is complex due to several tasks in a workflow, their distributed execution on multiple heterogeneous Grid-sites, and dynamic behaviour of the shared Grid resources. In this paper, we describe a novel method based on radial basis function neural network to model and predict workflow execution time in the Grid. We model workflows execution time in terms of attributes describing workflow structure and execution runtime information. To further refine our models, we employ principle component analysis to eliminate attributes of lesser importance. We recommend a set of only 14 attributes (as compared with total 21) to effectively model workflow execution time. Our reduced set of attributes improves the prediction accuracy by

IMIDB: An Algorithm for Indexed Mining of Incremental Databases

16\%

Virtual reality system for multiple sclerosis rehabilitation using KINECT

16%. Results of our prediction experiments for three real-world scientific workflows are presented to show that our predictions are more accurate than the two best methods from related work so far.

Cataloging teaching units: Resources, evaluation and collaboration

Sentiment analysis from Twitter is one of the interesting research fields recently. It combines natural language processing techniques with the data mining approaches for building such systems. In this paper, we introduced an efficient system for Twitter sentiment analysis. The proposed system built a machine learning model for detecting positive and negative tweets. This model used different techniques to represent the input labeled tweets in the training phase using different features sets. In the classification phase, the classifier ensemble is presented with different base classifiers for more accurate results. The proposed system can be used for measuring users’ opinion from their tweets which is very useful in many applications such as marketing, political polarity detection and reviewing products.