MLCNN-COV: A multilabel convolutional neural network-based framework to identify negative COVID medicine responses from the chemical three-dimensional conformer

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer-learning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses. 1225-6463/$ © 2023 ETRI.

Methodology for Virtual Education of Clinical Engineering through the Implementation of Digital Tools for Simulated Scenarios and International Collaboration

The Covid-19 global pandemic has represented a challenge for education, which had to migrate to virtual environments. Universities adopted different teaching methods to keep contributing to the growth of the professionals in various fields. In this context, the Biomedical Engineering program of the Pontificia Universidad Catolica del Peru and the Universidad Peruana Cayetano Heredia had to change or adapt the methodology of the courses included in its curriculum in order to reach the learning objectives. This paper presents a methodology for an innovative approach of simulated scenarios using digital tools for the virtual teaching of Clinical Engineering. The learning results achieved in two semesters of implementation of the methodology, during 2020 and 2021, were measured by means of a survey applied to the students at the end of the course. Obtaining achievement results above 76 % and improvement opportunities that would be useful for the next version of this course and for the replication of the methodology in other universities. © 2023 IEEE.

Instructional Usability and Learner-User eXperience Assessment in a Virtual Reality Educational Milieu: A Deductive Tech-Instructionality Model from EdTech

Educational Technology (EdTech) lacks a foundational, formal, scientific, epistemic theory. Therefore, it lacks native constructs/variables and an epistemological object of study for scientifically deploying its work. This study determines the existence (ontology) of the theorized constructs Instructional Usability (UsI) and Learner-User eXperience (LUX) and defines their characterization (epistemology). Both constructs were modeled and instrumented. Furthermore, a Tech-Instructionality Model (TIM) was theorized and developed in this paper, both analytically and empirically. The model integrates UsI and LUX as two pairs of constructs linked with two EdTech epistemological objects of study, the instructional interface and the instructional interaction in two assessment modalities, testing mode (user-learner view) and inspection mode (expert/designer view). Two instruments were developed and validated in this study for testing mode, the Instructional Usability Scale (SUsI) and the Learner-User eXperience Questionnaire (QLUX). Both instruments were tested in a non-immersive virtual reality educational milieu during the academic lockdown of the Covid19 pandemic. The results show that both SUsI and QLUX consistently measured UsI and LUX, thus, providing a valid assessment for tech-instructionality and a foundation for constructing a scientific theory of EdTech. © 2023 IEEE.

Augmented, Virtual and Immersive Reality as Learning Support in TEC21 Educational Model

Virtual, augmented, and immersive reality opens a world of possibilities in education by allowing students to recreate authentic situations, such as operating machinery, assembling a product, or training tool handling, to mention a few. In the TEC21 educational model, the core is the challenge: A project with a real-world challenge assigned by the training partner results in students offering solution proposals.The trigger that accelerated the development of virtual, augmented, and immersive reality activities in distance learning was COVID-19 confinement. During this, these technologies recreated the laboratory and its facilities' learning through augmented reality (AR) and virtual reality (VR) experiences.Using these technologies in the classroom allows students to achieve a great learning experience and develop skills for postgraduate studies and professional futures.Furthermore, now that we have returned to our physical facilities and laboratories, we can accelerate the learning obtained at the training partners' facilities, recreating processes and machinery through these immersive technologies and a hybrid experience for our students.The present research shows the activity learning design process and the statistical treatment of the data to provide continuous feedback during the activity;we examine the three transcendental variables in the educational process: The learning (academic rigor), the development of competencies, and the involvement or immersion of the students in the classroom. © 2023 IEEE.

The State of Accessibility in Blackboard: Survey and User Reviews Case Study

Context: Nowadays, mobile applications (or apps) have become vital in our daily life, particularly within education. Many institutions increasingly rely on mobile apps to provide access to all their students. However, many education mobile apps remain inaccessible to users with disabilities who need to utilize accessibility features like talkback or screen reader features. Accessibility features have to be considered in mobile apps to foster equity and inclusion in the educational environment allowing to use of such apps without limitations. Gaps in the accessibility to educational systems persist. Objective: In this paper, we focus on the accessibility of the Blackboard mobile app, which is one of the most common Learning Management Systems (LMS) used by many universities, especially during the current COVID-19 pandemic. Method: This study is divided into two-fold. First, we conduct a survey using questionnaires and interviews to explore the extent to which students consider the Blackboard mobile app usability. A Total of 1,308 hearing students and 65 deaf and hard-of-hearing students participated in the study. Second, we collected 15,478 user reviews from the Google Play Store and analyzed the reviews to extract accessibility issues. Result: We observed that most deaf and hard-of-hearing students found difficulty in the Blackboard mobile app, compared to hearing students. Also, our app store analysis showed that only 31% of the reviews reported violations of accessibility principles that apps like Blackboard must comply with. This study highlights these violations and their corresponding implications to support LMS frameworks in becoming more inclusive for all users. © 2023 ACM.

Social Media Acceptance and e-Learning Post-Covid-19: New factors determine the extension of TAM

The key objective of our study involves devising a conceptual model for estimation of social media acceptance by students for effectively accomplishing their educational and academic goals. Factors e.g., perceived social capital, social influence, and perceived mobility that associated with student acceptance of social media were investigated, and integrated into the TAM model using the PLS-SEM. Data were collected through online survey (461 students) at UAE universities. The findings revealed that mentioned factors positively affected students' intention to use social media during their learning process. Respondents' behavioral intention were also linked to both the core and external constructs of the TAM. Important practical insights on technology acceptance in education were provided. © 2022 IEEE.

Context Aware Automatic Subjective and Objective Question Generation using Fast Text to Text Transfer Learning

Online learning has gained a tremendous popularity in the last decade due to the facility to learn anytime, anything, anywhere from the ocean of web resources available. Especially the lockdown all over the world due to the Covid-19 pandemic has brought an enormous attention towards the online learning for value addition and skills development not only for the school/college students, but also to the working professionals. This massive growth in online learning has made the task of assessment very tedious and demands training, experience and resources. Automatic Question generation (AQG) techniques have been introduced to resolve this problem by deriving a question bank from the text documents. However, the performance of conventional AQG techniques is subject to the availability of large labelled training dataset. The requirement of deep linguistic knowledge for the generation of heuristic and hand-crafted rules to transform declarative sentence into interrogative sentence makes the problem further complicated. This paper presents a transfer learning-based text to text transformation model to generate the subjective and objective questions automatically from the text document. The proposed AQG model utilizes the Text-to-Text-Transfer-Transformer (T5) which reframes natural language processing tasks into a unified text-to-text-format and augments it with word sense disambiguation (WSD), ConceptNet and domain adaptation framework to improve the meaningfulness of the questions. Fast T5 library with beam-search decoding algorithm has been used here to reduce the model size and increase the speed of the model through quantization of the whole model by Open Neural Network Exchange (ONNX) framework. The keywords extraction in the proposed framework is performed using the Multipartite graphs to enhance the context awareness. The qualitative and quantitative performance of the proposed AQG model is evaluated through a comprehensive experimental analysis over the publicly available Squad dataset. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

DenseNet201 Model for Robust Detection on Incorrect Use of Mask

In 2022, the COVID-19 pandemic is still occurring. One of the optimal prevention efforts is to wear a mask properly. Several previous studies have classified the use of masks incorrectly. However, the accuracy resulting from the classification process is not optimal. This research aims to use the transfer learning method to achieve optimal accuracy. In this research, we used three classes, namely without a mask, incorrect mask, and with a mask. The use of these three classes is expected to be more detailed in detecting violations of the use of masks on the face. The classification method used in this research uses transfer learning as feature extraction and Global Average Pooling and Dense layers as classification layers. The transfer learning models used in this research are MobileNetV2, InceptionV3, and DenseNet201. We evaluate the three models' accuracy and processing time when using video data. The experimental results show that the DenseNet201 model achieves an accuracy of 93%, but the processing time per video frame is 0.291 s. In contrast to the MobileNetV2 model, which produces an accuracy of 89% and the processing speed of each video frame is 0.106 s. This result is inversely proportional to accuracy and speed. The DenseNet201 model produces high accuracy but slow processing time, while the MobileNetV2 model is less accurate but has faster processing. This research can be applied in the crowd center to monitor health protocols in the use of masks in the hope of inhibiting the transmission of the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Construction of Intelligent Homework System in Schools under the Concept of Wisdom Education

The mega-scale online education conducted nationwide during the COVID-19 epidemic has enabled online learning to move from individualized participation to full participation, practicing and advancing the development of wisdom education to a large extent. In the post-epidemic era, a new educational order that integrates online and offline learning is gradually taking shape, and online learning has become a new norm from emergency. The popularization and promotion of online education has been the general trend. The "double reduction"policy has led to a trust dilemma, a communication dilemma, a cooperation dilemma and an organizational dilemma in the practice of home-school-society collaborative parenting, and an unprecedented challenge for school education and teachers teaching. This study proposes an intelligent operating system based on big data and adaptive learning traction model, rooted in rich pedagogical theories, to solve the above-mentioned challenges in online education by virtue of "wisdom". © 2023 IEEE.

Design of People Count System Based on Object Detection Algorithm

At present, the Covid-19 epidemic is still spreading globally. Although the domestic epidemic has been well controlled, the prevention and control of the epidemic must not be taken lightly. Being able to count the number of people in public places in real time has played a vital role in the prevention and control of the epidemic. Deep learning networks usually cannot be directly deployed on embedded devices with low computing power due to the huge amount of parameters of convolutional neural networks. This article is based on the YOLOv5 object detection algorithm and Jetson Nano embedded platform with TensorRT and C++ accelerating, it can realize the function of counting the number of people in the classroom, on the elevator entrance, and other scenes. © 2022 SPIE.

Promoting College Student's Systems Thinking

This study examined how college students in a medical school in China engaged in learning in asynchronous online learning environments during the COVID-19 health crisis. A quasi-experimental design approach was employed to compare if a class of students had better learning outcomes and developed systems thinking when asynchronous discussion forums incorporated an inquiry-based pedagogical approach in one unit, whereas the other unit followed a traditional instructor-led approach. In sum, 25 junior students participated in this study. Quantitative results show that the students had statistically significant higher assessment scores and improved systems thinking when the unit incorporated the inquiry-based pedagogical approach. Qualitative findings also demonstrated how students engaged in learning and how the instructor scaffolded students' inquiries and learning. Practical implications for instructors' teaching online courses are also discussed. © The author(s) 2023.

COVID-19 X-RAY IMAGE DETECTION ALGORITHM BASED ON DEEP LEARNING

In order to enhance the ability to diagnose and distinguish COVID-19 from ordinary pneumonia, and to assist medical staff in chest X-ray detection of pneumonia patients, this paper proposes a COVID-19 X-ray image detection algorithm based on deep learning network. First of all, a model of deep learning network is set up based on VGG - 16, and then, the network structure and parameter optimization is adjusted, which makes the network model can be applied to COVID - 19 x ray imaging detection task. In the end, through adjusting the image size of the original data set, the input data meets the requirements of the deep learning network. Experimental results show that the proposed algorithm can effectively learn the characteristics of the COVID-19 X-ray image data set and accurately detect three states of COVID-19, common viral pneumonia and non-pneumonia, with a very high detection accuracy of 95.8%. © 2023 SPIE.

Preliminary Diagnosis of COVID-19 using Speech Processing Techniques

This project aims to devise an alternative for Coronavirus detection using various audio signals. The aim is to create a machine-learning model assisted by speech processing techniques that can be trained to distinguish symptomatic and asymptomatic Coronavirus cases. Here the features exclusive to the vocal cord of a person is used for covid detection. The procedure is to train the classifier using a data set containing data of people of various ages both infected and disease-free, including patients with comorbidities. We presented a machine learning-based Coronavirus classifier model that can separate Coronavirus positive or negative patients from cough, breathing, and speech recordings. The model was trained and evaluated using several machine learning classifiers such as Random Forest Classifier, Logistic Regression (LR), Decision Tree Classifier, k-nearest Neighbour (KNN), Naive Bayes Classifier, Linear Discriminant Analysis, and a neural network. This project helps track COVID-19 patients at a low cost using a non-contactable procedure and reduces the workload on testing centers. © 2023 IEEE.

Comparing the Use of Virtual Models vs. Fieldwork in Developing Geomatics Skills in Undergraduate Engineering Education during the COVID-19 Pandemic

Undergraduate students enrolled in Civil Engineering, Architecture, and Urban Planning (CAU) must develop competencies in Geomatics and Topography (G&T) as part of their learning process. During this time, theoretical concepts are traditionally taught with field practice using specialized tools such as a theodolite, laser level, and total station. Due to the environmental restrictions of the COVID-19 pandemic, traditional field practice (TFP) was suspended, preventing access to equipment and study areas. The use of Information and Communication Technologies (ICT), such as Building Information Modeling (BIM) and Virtual Reality (VR), have been explored in the last decade for educational purposes. This paper studies the benefits of using these tools for developing G&T skills. This research aimed to assess students' learning outcomes using a traditional G&T teaching method and a new methodology based on Virtual Field Practice (VFP) for CAU students. The methodology provides a virtual study area for the CAU student by integrating point clouds derived from photogrammetry and terrestrial laser scanning. It also assesses their learning results and compares them against a control group using a validated instrument. Findings suggest continuing with fieldwork for a greater understanding and correct application of G&T concepts by students, and using virtual models as an efficient way to complement the acquisition of spatial information in the teaching-learning process. Until the publication of this article, we found no evidence in the literature at the undergraduate level of applying exercises like those proposed. © 2023 IEEE.

Managing students' attitude, learning engagement, and stickiness towards e-learning post-COVID-19 in Australian universities: a perceived qualities perspective

Studies have been conducted on university students' acceptance of e-learning systems during COVID-19. However, less attention has been paid to students' use of e-learning post-pandemic. This research provides a more comprehensive framework to investigate the effects of e-learning students' various quality perceptions on attitude, learning engagement, and stickiness toward e-learning platforms. A survey-based quantitative method is adopted by this study in which sample data are collected from students in Australian universities. A total of 403 valid samples were analysed using covariance-based structural equation modelling. This study found that students' perceived educational quality, service quality, information quality, and technical system quality play different roles in their attitudes and behaviours towards e-learning. It expands the information system success model by comparing the effects of students' various perceived qualities on their ongoing commitment to e-learning. It provides insights to e-learning providers in pursuing better designs and more sustainable development of educational information systems.

COVID-LiteNet: A lightweight CNN based network for COVID-19 detection using X-ray images

To restrict the virus's transmission in the pandemic and lessen the strain on the healthcare industry, computer-assisted diagnostics for the accurate and speedy diagnosis of coronavirus illness (COVID-19) has become a prerequisite. Compared to other types of imaging and detection, chest X-ray imaging (CXR) provides several advantages. Healthcare practitioners may profit from any technology instrument providing quick and accurate COVID-19 infection detection. COVID-LiteNet is a technique suggested in this paper that combines white balance with Contrast Limited Adaptive Histogram Equalization (CLAHE) and a convolutional neural network (CNN). White balance is employed as an image pre-processing step in this approach, followed by CLAHE, to improve the visibility of CXR images, and CNN is trained using sparse categorical cross-entropy for image classification tasks and gives the smaller parameters file size, i.e., 2.24 MB. The suggested COVID-LiteNet technique produced better results than vanilla CNN with no pre-processing. The proposed approach outperformed several state-of-the-art methods with a binary classification accuracy of 98.44 percent and a multi-class classification accuracy of 97.50 percent. COVID-LiteNet, the suggested technique, outperformed the competition on various performance parameters. COVID-LiteNet may help radiologists discover COVID-19 patients from CXR pictures by providing thorough model interpretations, cutting diagnostic time significantly. © 2023 IEEE.

Identification and Localization of COVID-19Abnormalities on Chest Radiographs using Computer Vision

The Corona Virus (COVID 19) pandemic is quickly becoming the world's most deadly disease. The spreading rate is higher and the early detection helps in faster recovery. The existence of COVID 19 in individuals shall be detected using molecular analysis or through radiographs of lungs. As time and test kit are limited RT- PCR is not suitable to test all. The RT- PCR being a time-consuming process, diagnosis using chest radiographs needs no transportation as the modern X-ray systems are digitized. Deep learning takes an edge over other techniques as it deduces the features automatically and performs massively parallel computations. Multiple feature maps will help in accurate prediction. The objective of the proposed work is to develop a Computer Aided Deep Learning System identify and localize COVID-19 virus from other viruses and pneumonia. It helps to detect COVID-19 within a short period of time thereby improving the lifetime of the individuals. SIIM-FISABIO-RSNA benchmark datasets are used to examine the proposed system. Recall, Precision, Accuracy-rate, and F-Measure are the metrics used to prove the integrity of the system. © 2023 IEEE.

A Deep Learning Approach for Sentiment Classification of COVID-19 Vaccination Tweets

Now-a-days, social media platforms enable people to continuously express their opinions and thoughts about different topics. Monitoring and analyzing the sentiments of people is essential for governments and business organizations to better understand people's feelings and thoughts. The Coronavirus disease 2019 (COVID-19) has been one of the most trending topics on social media over the last two years. Consequently, one of the preventative measures to control and prevent the spread of the virus was vaccination. A dataset was formed by collecting tweets from Twitter for over a month from November 13th to December 31st, 2021. After data cleaning, the tweets were assigned a positive, negative, or neutral label using a natural language processing (NLP) sentiment analysis tool. This study aims to analyze people's public opinion towards the vaccination process against COVID-19. To fulfil this goal, an ensemble model based on deep learning (LSTM-2BiGRU) is proposed that combines long short-term memory (LSTM) and bidirectional gated recurrent unit (BiGRU). The performance of the proposed model is compared to five traditional machine learning models, two deep learning models in addition to state-of-the-art models. By comparing the results of the models used in this study, the results reveal that the proposed model outperforms all the machine and deep learning models employed in this work with a 92.46% accuracy score. This study also shows that the number of tweets that involve neutral, positive, and negative sentiments is 517496 (37%) tweets, 484258 (34%) tweets, and 409570 (29%) tweets, respectively. The findings indicate that the number of people carrying neutral sentiments towards COVID-19 immunization through vaccines is the highest among others. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

A DEEP LEARNING APPROACH LUNG SEGMENTATION AND PNEUMONIA DETECTION FROM X-RAYS

Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of image processing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagnosis (CAD) system for detecting or recognizing broad objects like acute respiratory distress syndrome (ARDS), Tuberculosis, Pneumonia, Lung cancer, Covid, and several other respiratory diseases. This paper presents pneumonia detection from lung segmentation using deep learning methods on chest radiography. Chest X-ray is the most useful technique among other existing techniques, due to its lesser cost. The main drawback of a chest x-ray is that it cannot detect all problems in the chest. Thus, implementing convolutional neural networks (CNN) to perform lung segmentation and to obtain correct results. The 'lost' regions of the lungs are reconstructed by an automatic segmentation method from raw images of chest X-ray. © 2022 IEEE.

Covid Analysis using Recurrent Neural Network

The examination of medical images has benefited greatly from the use of artificial intelligence. In contrast to deep learning systems, which do feature extraction automatically and without human interaction, traditional computer vision methods rely on manually produced features that are particular to a certain domain. Having access to medical information for automated analysis is another major factor driving the trend towards deep learning. Chest x-ray pictures are processed in order to segment the lungs and identify diseases in this thesis. Due to its cheap cost, ease of capture, and non-invasive nature, chest x-ray is the most often used medical imaging technology. However, automatic diagnosis in chest x-rays is difficult due to (1) the presence of the rib-cage and clavicle bones, which can obscure abnormalities that are located beneath them, and (2) the fuzzy intensity transitions near the lung and heart, dense abnormalities, rib-cages, and clavicle bones, which make the identification of lung contours subtle. In x-ray image processing, the Convolutional Neural Network (CNN) is the most often used deep learning architecture. Because to the enormous number of parameters in deep CNN architectures, intensive computing resources are required to train these models. Additionally, chest x-ray datasets are often rather tiny, and there is always the risk of overfitting when developing a model. In this dissertation, we propose five convolutional neural networks (CNNs) to identify illness and segment the lungs in chest x-rays. New Line, New Line In the first research paper, an adaptive lightweight convolutional neural network (ALCNN) is created to detect pneumothorax with few parameters. The model readjusts the feature calibration channel-wise using the convolutional layer and attention mechanism. The suggested model outperformed state-of-the-art deep models trained using three different transfer learning methods. One notable aspect of the suggested model is that it requires ten times less parameters than the best deep models currently available. The second paper suggests the FocusCovid methodology for identifying COVID-19. © 2023 IEEE.