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

Oil spill monitoring using satellite imagery in the Sharm El-Maya Bay of Sharm El-Sheikh, Egypt

Sharm el-Sheikh, in Egypt, is a prominent tourist destination. The city attracts millions of visitors annually due to its exceptional location and pleasant climate. Owing to its natural ecosystem and marine diversity, Sharm El-Maya Bay in Sharm el-Sheikh attracts beachgoers and vacationers. In 1999, however, an oil spill occurred at the site. Previous investigations detected a network of buried steel pipelines and a number of buried reinforced concrete tanks, both of which may have contributed to the contamination problem. Although the problem is so detrimental to health and the environment, no follow-up studies were conducted after 2013. Therefore, the author chose to monitor oil leaks over the headland using frequent, high-resolution Google Earth Pro remote sensing data for the years 2017 to 2022. To disclose whether any corrective measures were taken to address the contamination problem. Moreover, to demonstrate if any unanticipated variations have occurred over many years due to climatic factors. The elucidation of the aforementioned issues demonstrates Google Earth Pro's effectiveness in monitoring pollution problems. The results revealed that the area and perimeter of four oil spots had changed slightly over time. During the specified time period, the standard deviations of the four monitored locations fluctuated between 111.1 m2, 71.6 m2, 83.7 m2, and 254.3 m2. The research proved that the pollution problem has not improved over time because stakeholders have not reacted. In addition, it highlighted the uniqueness of Google Earth Pro in tracking the changes in oil spot size over a time series

Integrating Motion Priors For End-To-End Attention-Based Multi-Object Tracking

Recent advancements in multi-object tracking (MOT) have heavily relied on object detection models, with attention-based models like DEtection TRansformer (DETR) demonstrating state-of-the-art capabilities. However, the utilization of attention-based detection models in tracking poses a limitation due to their large parameter count, necessitating substantial training data and powerful hardware for parameter estimation. Ignoring this limitation can lead to a loss of valuable temporal information, resulting in decreased tracking performance and increased identity (ID) switches. To address this challenge, we propose a novel framework that directly incorporates motion priors into the tracking attention layer, enabling an end-to-end solution. Our contributions include: I) a novel approach for integrating motion priors into attention-based multi-object tracking models, and II) a specific realisation of this approach using a Kalman filter with a constant velocity assumption as motion prior. Our method was evaluated on the Multi-Object Tracking dataset MOT17, initial results are reported in the paper. Compared to a baseline model without motion prior, we achieve a reduction in the number of ID switches with the new method

Preface: Workshop “Semantics3D - Semantic Scene Analysis and 3D Reconstruction from Images and Image Sequences”

[no abstract available

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

Enhancing The Quality Of CNN-Based Burned Area Detection In Satellite Imagery Through Data Augmentation

This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes the impact of image flipping, rotation, and noise addition on the overall accuracy for different classes of burned areas in a forest: fire, burned, smoke and background. Results demonstrate that while single augmentation techniques such as flipping and rotation alone did not result in significant improvements, a combined approach and the addition of noise resulted in an enhancement of the classification accuracy. Moreover, the study shows that augmenting the dataset through the use of multiple augmentation methods concurrently, resulting in a fivefold increase in input data, also enhanced the recognition accuracy. The study also highlights the need for further research in developing more efficient CNN models and in experimenting with additional augmentation methods to improve the accuracy of burned area detection, which would benefit environmental protection and emergency response services