Evaluation of E-learning Experience in the Light of the Covid-19 in Higher Education

Covid-19 has been stated as a worldwide outbreak of pandemic disease and crisis. The Covid-19 pandemic has dramatically affected the teaching and learning experience at universities and schools. In response, governments and higher education institutions around the world put significant efforts to ensure that students continue to obtain the best possible level of education and learning outcomes. As such effective evaluation of e-learning is essential in order to ensure that students get proper learning and education especially during the current circumstances of Covid-19. Our study was carried out to determine the main elements and factors related to students\u27 satisfaction and quality of e-learning during the Covid-19 pandemic era based on various aspects and dimensions of e-learning. The main findings of the study indicated that students satisfaction and evaluation of the e-learning experience during the pandemic were not promising. Therefore, higher education institutions should reconsider their efforts and approaches to improve the quality of e-learning and the learning outcomes achieved. For example, IT infrastructure, Internet access, and particularly network connectivity could be improved to support fully online courses. Such elements need to be addressed because of the prevalence of the current Covid-19 pandemic which perhaps will lead to e-learning occurring for a long time. With the move to e-learning, the size of the class (the number of students in each class) has been increased leading to other significant challenges related to communication and participation in the class and reducing the possible interactivity for each student. Furthermore, it has been also observed that new students need relevant training on IT and e-learning applications to ensure sufficient use and utilization of these applications in their e-learning journey

SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which we include a global average pooling layer, flattening, and two dense layers that are fully connected. The model’s effectiveness is evaluated using balanced and unbalanced COVID-19 radiography datasets. After that, our model’s performance is analyzed using six evaluation measures: accuracy, sensitivity, specificity, precision, F1-score, and Matthew’s correlation coefficient (MCC). Experiments demonstrated that the proposed SEL-COVIDNET with tuned DenseNet121, InceptionResNetV2, and MobileNetV3Large models outperformed the results of comparative SOTA for multi-class classification (COVID-19 vs. No-finding vs. Pneumonia) in terms of accuracy (98.52%), specificity (98.5%), sensitivity (98.5%), precision (98.7%), F1-score (98.7%), and MCC (97.5%). For the COVID-19 vs. No-finding classification, our method had an accuracy of 99.77%, a specificity of 99.85%, a sensitivity of 99.85%, a precision of 99.55%, an F1-score of 99.7%, and an MCC of 99.4%. The proposed model offers an accurate approach for detecting COVID-19 patients, which aids in the containment of the COVID-19 pandemic

Deep convolutional neural network-based system for fish classification

In computer vision, image classification is one of the potential image processing tasks. Nowadays, fish classification is a wide considered issue within the areas of machine learning and image segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective fish classification method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous fish species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 different state-of-the-art gradient descent-based optimizers, namely adaptive delta (AdaDelta), stochastic gradient descent (SGD), adaptive momentum (Adam), adaptive max pooling (Adamax), Root mean square propagation (Rmsprop), for CNN. Overall, the obtained experimental results show that Rmsprop, Adam, Adamax performed well compared to the other optimization techniques used, while AdaDelta and SGD performed the worst. Furthermore, the experimental results demonstrated that Adam optimizer attained the best results in performance measures for 70-30% and 80-20% splitting experiments, while the Rmsprop optimizer attained the best results in terms of performance measures of 70-25% splitting experiments. Finally, the proposed model is then compared with state-of-the-art deep CNNs models. Therefore, the proposed model attained the best accuracy of 98.46% in enhancing the CNN ability in classification, among others

Intelligent information systems and image processing: A novel pan-sharpening technique based on Multiscale decomposition

© 2018 Association for Computing Machinery. Pansharpening is a technique belong to the image fusion field, the main goal of pansharpening is to enhance the Spatial resolution of multi-spectral image while preserving the spectral resolution. In this paper a simple pan-sharpening technique using saliency detection based on a guided filter is presented. In this technique the guided filter is used to build saliency detection algorithm. The saliency can be identified by making a saliency map that can be an outstanding part of the salient information of the image. we evaluate our technique by using some real and degraded data-sets then compare our technique with some existing methods in both subjective and objective aspects. The experimental results show that the proposed strategy can accomplish magnificent execution in both subjective and objective aspects

The Effect of an Online Extensive Reading Instructional Program on Jordanian Eleventh Grade Students' Proficiency in English

This study aimed at investigating the effect of an Online Extensive Reading (hereafter, OER) instructional program on Jordanian secondary stage students' proficiency in English. It also aimed at detecting the students' opinions towards the OER program in terms of its effect on their proficiency in English. A sample of two intact Eleventh grade sections from King Abdullah the Second School for Excellence was selected; one section was assigned as an experimental group, the other as a control group. The quantitative findings of the test revealed that the mean scores of the experimental group's English proficiency were significantly higher than the mean scores of the control group, particularly in writing, speaking, vocabulary, reading comprehension and listening due to the teaching methodology. The findings of the test did not reveal any significant difference in the students' grammar proficiency ascribed to the intervention variable. Further, the results of the questionnaire revealed that the respondents were appreciative and pleased with the efficacy of the OER program. This result was also supported by the qualitative findings of the interview. Keywords: Online Extensive Reading (OER), English proficiency, Jordanian secondary stage student

Files cryptography based on one-time pad algorithm

The Vernam-cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the use of the AES algorithm; thus, the protection record becomes very high. The Huffman algorithm is then used for data compression to reduce the size of the output file. A set of files are encrypted and decrypted using our methodology. The experiments demonstrate the flexibility of our method, and it’s successful without losing any information

Perception of Strategic Human Resource Architecture and Strategic Direction Among Employees of Jordanian Private Companies

This paper represent an attempt to investigate the perception of employees at Jordanian private companies with regards to their companies strategic human resource architecture, strategic direction and impediments to the implementation of strategic human resource. A total of 103 employees participated in the study. The respondents are employees of Jordanian private companies located in Irbid, north Jordan. The results of the study suggest that the level of perception among the employees on the three variables are quite high. In addition, there exist a relationship between strategic human resource architecture and strategic direction

Smart Health Care System for Early Detection of COVID-19 Using X-ray Scans

The novel Coronavirus spread in the world in December 2019. Millions of people are infected due to this disease. Due to viral illness, daily life routines and the economy are affected in many countries. According to a clinical study, the disease directly attacks the lungs and disturbs the respiratory system. X-ray and CT scans are the main imaging techniques to discover that disease. However, X-ray scans cost is low as comparatively CT scans. In the limited resources, deep learning plays a key role in diagnosing the COVID-19 with the help of X-ray scans. This study proposed a new transfer learning approach based on the convolutional neural network (CNN). We used the four different classes during the experimental process: COVID-19, pneumonia, lung opacity, and viral pneumonia. We also compared our proposed model with other transfer learning-based techniques. Our proposed COVID-TL model attained the best results in terms of classification. The proposed model is a beneficial tool for radiologists to get the early diagnosis results and help the patients in their early stages

Smart RFID application in health care: Using RFID technology for smart inventory and logistic systems in hospitals

In today\u27s hospital environments, many medical devices and tools are used. While some of these will be stationary due to size and bulk, many devices can also be moved from room to room. To facilitate an efficiently running hospital environment and protect expensive devices from being lost, it is important to keep track of the whereabouts of every medical device or utensil. We propose an RFID based system with a smartphone application based frontend for tracking the locations of medical devices and utensils in a hospital environment, both enabling medical professionals to quickly locate required devices as well as allowing hospital administration to keep track of when and where devices leave hospital premises, optionally alerting security after a configurable grace period. In addition to this, our proposed application allows doctors and other medical personnal to reserve equipment and rooms such as examination or operating rooms and to easily find which rooms or pieces of equipment are available at a given time. This reduces administrative overhead and allows a smoother operation of the hospital, where efficiency is needed not only for the sake of profits but also to ensure the continued well-being of patients

RFID in Health care: A review of the real-world application in hospitals

Radio frequency identification (RFID) has been considered one of the most promising technologies in healthcare and has been recognized as a smart tool with the potential to overcome many challenges that health care encounters such as inaccurate pharmaceutical stock, inability to track medical equipment, difficulty in tracking patient locations, patient safety incidents, administration of incorrect drugs, medical errors including mislabeled blood samples, and drug quantities. This study builds on work of the author and looks at the real-world experience of adoption in hospitals via a systematic literature review. The findings uncover only a limited number of cases of RFID use in hospitals mainly in the form of pilot studies. Benefits are reported primarily in the area of improved safety, better management of equipment and better efficiency from improved patient flow prediction