LEARNING STYLES OF EGYPTIAN BUSINESS STUDENTS

The Index of Learning Styles (ILS) instrument based on the Felder-Silverman Learning Style Model was used to determine distribution of learning styles of eighty Egyptian business students enrolled in an Egyptian institution of higher education. Results show that Egyptian business students surveyed in this study prefer sensing, visual, active, and sequential learning styles over intuitive, verbal, reflective, and global learning styles respectively. The majority of business students have a balanced learning style in all four dimensions of the Felder-Silverman model. Gender difference in learning style preference was statistically significant for only two of the four dimensions. The small gender difference was deemed inconsequential for designing teaching and learning methods. More than 85 percent of Egyptian business students are likely to benefit from teaching methods geared toward sensing, visual, active, and sequential learners

Does Culture Influence Learning Styles of Business Students? A Comparative Study of Two Cultures

This paper presents the usage of the Index of Learning Styles (ILS) instrument based on the Felder-Silverman Learning Style Model to investigate the influence of culture on learning style distribution of business students. Western culture was represented by the United States and was compared with middle-eastern culture represented by Egypt. Results of this study show that majority of business students have a balanced learning style in each of the four learning style dimensions of the Felder-Silverman model both in the U. S. and in Egypt. Difference in learning style distribution of business students between the U. S. and Egypt was statistically significant only for the sensing-intuitive and visual-verbal dimensions of the Felder-Silverman model. The difference was not statistically significant for the active-reflective and the sequential-global dimensions

An Assistive Object Recognition System for Enhancing Seniors Quality of Life

AbstractThis paper presents an indoor object recognition system based on the histogram of oriented gradient and Machine Learning (ML) algorithms; such as Support Vector Machines (SVMs), Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms, for classifying different indoor objects to improve quality of elderly people's life. The proposed approach consists of three phases; namely segmentation, feature extraction, and classification phases. Datasets used for these experiments, are totally consisted of 347 images with different eight indoor objects used for both training and testing datasets. Training dataset is divided into eight classes representing the different eight indoor objects. Experimental results showed that RF classification algorithm outperformed both SVMs and LDA algorithms, where RF achieved 80.12%, SVMs and LDA achieved 77.81% and 78.76% respectively

ARIAS: Automated Retinal Image Analysis System

In this paper, a system for automated analysis of retinal images is proposed. This system segments blood vessels in retinal images and recognizes the main features of the fundus on digital color images. The recognized features were defined as blood vessels, optic disc, and fovea. An algorithm called 2D matched filters response has been proposed for the detection of blood vessels. Also, automatic recognition and localization methods for optic disc and fovea have been introduced and discussed. Moreover, a method for detecting left and right retinal fundus images has been presented

Scheduling and Communication Schemes for Decentralized Federated Learning

Federated learning (FL) is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. One central server is not enough, due to problems of connectivity with clients. In this paper, a decentralized federated learning (DFL) model with the stochastic gradient descent (SGD) algorithm has been introduced, as a more scalable approach to improve the learning performance in a network of agents with arbitrary topology. Three scheduling policies for DFL have been proposed for communications between the clients and the parallel servers, and the convergence, accuracy, and loss have been tested in a totally decentralized mplementation of SGD. The experimental results show that the proposed scheduling polices have an impact both on the speed of convergence and in the final global model.Comment: 32nd International Conference on Computer Theory and Applications (ICCTA), Alexandria, Egypt, 202

Deep feature learning for FoG episodes prediction in patients with PD

A common symptom of Parkinson\u27s Disease is Freezing of Gait (FoG) that causes an interrupt of the forward progression of the patient’s feet while walking. Therefore, Freezing of Gait episodes is always engaged to the patient\u27s falls. This paper proposes a model for Freezing of Gait episodes detection and prediction in patients with Parkinson\u27s Disease. Predicting Freezing of Gait in this paper considers as a multi-class classification problem with 3 classes namely, FoG, pre-FoG, and walking episodes. In this paper, the extracted feature scheme applied for the detection and the prediction of FoG is Convolutional Neural Network (CNN) spectrogram time-frequency features. The dataset is collected from three tri-axial accelerometer sensors for PD patients with FoG. The performance of the suggested approach has been distinguished by different machine learning classifiers and accelerometer axes

Intelligent detection and control for environmental noise pollution

Noise pollution is a common environmental problem that directly or indirectly affects people health, productivity, behavior, and sometimes leading to death. Harmful noises are extensively found within specific environments such as airports, power stations, railway lines, road works, factories, construction, demolition sites, etc. Countries around the world initiated regulations in terms of monitoring as well as controlling and treating such pollution. These regulations raised the issue of finding a suitable, cost-effective, and reliable technology to encounter the noise pollution effects. Traditional manual noise detection and treatment solutions are not scalable to the high demand in time and space. So, Wireless Sensor Networks (WSNs) can provide an effective, inexpensive, and flexible real-time acquisition platform to support the detection and control process of noise pollution sources. This paper brings the concept of noise pollution to the light. Also, a model is proposed for automatically monitoring, detecting, and controlling abnormal sound pressure levels representing noise pollution in the ambient environments. The proposed approach utilizes the integration of wireless sensing and Smartphone technologies for dealing with that type of pollution