The real-time hand and object recognition for virtual interaction.

Recognizing hand-object interactions presents a significant challenge in computer vision. It arises due to the varying nature of hand-object interactions. Moreover, estimating the 3D position of a hand from a single frame can be problematic, especially when the hand obstructs the view of the object from the observer's perspective. In this article, we present a novel approach to recognizing objects and facilitating virtual interactions, using a steering wheel as an illustrative example. We propose a real-time solution for identifying hand-object interactions in eXtended reality (XR) environments. Our approach relies on data captured by a single RGB camera during a manipulation scenario involving a steering wheel. Our model pipeline consists of three key components: (a) a hand landmark detector based on the MediaPipe cross-platform hand tracking solution; (b) a three-spoke steering wheel model tracker implemented using the faster region-based convolutional neural network (Faster R-CNN) architecture; and (c) a gesture recognition module designed to analyze interactions between the hand and the steering wheel. This approach not only offers a realistic experience of interacting with steering-based mechanisms but also contributes to reducing emissions in the real-world environment. Our experimental results demonstrate the natural interaction between physical objects in virtual environments, showcasing precision and stability in our system.

Pneumonia Detection Using Enhanced Convolutional Neural Network Model on Chest X-Ray Images.

Pneumonia, caused by microorganisms, is a severely contagious disease that damages one or both the lungs of the patients. Early detection and treatment are typically favored to recover infected patients since untreated pneumonia can lead to major complications in the elderly (>65 years) and children (<5 years). The objectives of this work are to develop several models to evaluate big X-ray images (XRIs) of the chest, to determine whether the images show/do not show signs of pneumonia, and to compare the models based on their accuracy, precision, recall, loss, and receiver operating characteristic area under the ROC curve scores. Enhanced convolutional neural network (CNN), VGG-19, ResNet-50, and ResNet-50 with fine-tuning are some of the deep learning (DL) algorithms employed in this study. By training the transfer learning model and enhanced CNN model using a big data set, these techniques are used to identify pneumonia. The data set for the study was obtained from Kaggle. It should be noted that the data set has been expanded to include further records. This data set included 5863 chest XRIs, which were categorized into 3 different folders (i.e., train, val, test). These data are produced every day from personnel records and Internet of Medical Things devices. According to the experimental findings, the ResNet-50 model showed the lowest accuracy, that is, 82.8%, while the enhanced CNN model showed the highest accuracy of 92.4%. Owing to its high accuracy, enhanced CNN was regarded as the best model in this study. The techniques developed in this study outperformed the popular ensemble techniques, and the models showed better results than those generated by cutting-edge methods. Our study implication is that a DL models can detect the progression of pneumonia, which improves the general diagnostic accuracy and gives patients new hope for speedy treatment. Since enhanced CNN and ResNet-50 showed the highest accuracy compared with other algorithms, it was concluded that these techniques could be effectively used to identify pneumonia after performing fine-tuning.

Detection of COVID-19 using deep learning on x-ray lung images.

COVID-19 is a widespread deadly virus that directly affects the human lungs. The spread of COVID-19 did not stop at humans but also reached animals, so it was necessary to limit it is spread and diagnose cases quickly by applying a quarantine to the infected people. Recently x-ray lung images are used to determine the infection and from here the idea of this research came to use deep learning techniques to analyze x-ray lung images publicly available on Kaggle to possibly detect COVID-19 infection. In this article, we have proposed a method to possibly detect the COVID-19 by analyzing the X-ray images and applying a number of deep learning pre-trained models such as InceptionV3, DenseNet121, ResNet50, and VGG16, and the results are compared to determine the best performance model and accuracy with the least loss for our dataset. Our evaluation results showed that the best performing model for our dataset is ResNet50 with accuracies of 99.99%, 99.50%, and 99.44% for training, validation, and testing respectively followed by DenseNet121, InceptionV3, and finally VGG16.

The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors.

The spread of COVID-19 poses a threat to humanity, as this pandemic has forced many global activities to close, including educational activities. To reduce the spread of the virus, education institutions have been forced to switch to e-learning using available educational platforms, despite the challenges facing this sudden transformation. In order to further explore the potentials challenges facing learning activities, the focus of this study is on e-learning from students' and instructor's perspectives on using and implementing e-learning systems in a public university during the COVID-19 pandemic. The study targets the society that includes students and teaching staff in the Information Technology (IT) faculty at the University of Benghazi. The descriptive-analytical approach was applied and the results were analyzed by statistical methods. Two types of questionnaires were designed and distributed, i.e., the student questionnaire and the instructor questionnaire. Four dimensions have been highlighted to reach the expected results, i.e., the extent of using e-learning during the COVID-19 pandemic, advantages, disadvantages and obstacles of implementing E-learning in the IT faculty. By analyzing the results, we achieved encouraging results that throw light on some of the issues, challenges and advantages of using e-learning systems instead of traditional education in higher education in general and during emergency periods.

Data science for analyzing and improving educational processes.

In this full review paper, the recent emerging trends in Educational Data Science have been reviewed and explored to address the recent topics and contributions in the era of Smart Education. This includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art, frameworks and techniques research projects in the area of Data Science applied to Education, using different approaches such as Information Fusion, Soft Computing, Machine Learning, and Internet of Things, among others. Based on this systematic review, we have put some recommendations and suggestions for researchers, practitioners and scholars to improve their research quality in this area.

The adoption of an e-learning system using information systems success model: a case study of Jazan University.

BACKGROUND: The e-learning system has gained a phenomenal significance than ever before in the present COVID-19 crisis. The E-learning delivery mechanisms have evolved to enhanced levels facilitating the education delivery with greater penetration and access to mass student population worldwide. Nevertheless, there is still scope to conduct further research in order to innovate and improve higher quality delivery mechanism using the state-of-the-art information and communication technologies (ICT) available today. In the present pandemic crisis all the stakeholders in the higher education system, i.e., the governments, institutions, and the students expect seamless and efficient content delivery via e-learning platforms. This study proposes the adoption of the e-learning system by the integration of the model proposed by Delon and Mcclean "Information System Success Model" in Jazan University, Kingdom of Saudi Arabia (KSA) and further attempts to identify the factors affecting E-learning applications' success among the students. METHODS: The data were gathered from 568 respondents. The Statistical Package for the Social Sciences version 26 (SPSS v.26.0) was used for the data analysis and one-way ANOVA is applied to test the hypothesis. RESULT: The overall results of this study allude to the fact that there is a significant relationship between Information system Success Model factors and the adoption of e-learning systems. The research results indicated that the information system success model has a strong associating cost-benefit value towards the adoption of e-learning systems across the Jazan University that may be further expanded to the other Saudi universities.