Educational Resilience: Conceptual Discussions

Education is a formative process of every subject in a society, it is a right recognized in a tangible way. In the school environment, it infers a comprehensive process for all the actors of the student community. One aspect that has developed over time and that has had greater employability since the COVID-19 pandemic was resilience as a means of management and prompt attention to the process of adaptation and impact in the educational field. This document describes, based on legal criteria, the importance of the resilient process, also approached from the conceptual construction framework of various actors, considering educational resilience as an agent of transformative change in academic performance, as well as the importance of continuous training in and for all the actors of the educational community.

Federated Learning for Detecting COVID-19 in Chest CT Images: A Lightweight Federated Learning Approach

The novel coronavirus is spreading rapidly worldwide, and finding an effective and rapid diagnostic method is apriority. Medical data involves patient privacy, and the centralized collection of large amounts of medical data is impossible. Federated learning is a privacy-preserving machine learning paradigm that can be well applied to smart healthcare by coordinating multiple hospitals to perform deep learning training without transmitting data. This paper demonstrates the feasibility of a federated learning approach for detecting COVID-19 through chest CT images. We propose a lightweight federated learning method that normalizes the local training process by globally averaged feature vectors. In the federated training process, the models' parameters do not need to be transmitted, and the local client only uploads the average of the feature vectors of each class. Clients can choose different local models according to their computing capabilities. We performed a comprehensive evaluation using various deep-learning models on COVID-19 chest CT images. The results show that our approach can effectively reduce the communication load of federated learning while having high accuracy for detecting COVID-19 on chest CT images. © 2022 IEEE.

VIRTUAL REALITY TRAINING OF EMPLOYEES IN FOREST-BASED SMES AS A PART OF A CRISIS MANAGEMENT

Science and manufacturing have always been a generator and conduit of innovations in every field of human life. The innovations are of both fundamental and purely applied nature. The first environment for testing these innovations is the internal firm's educational system. In this regard, the last two years circumstances around the pandemic of COVID-19 served as a catalyst for the training in companies to adopt contemporary, interactive and attractive methods of training processes. Of course, some of these methods have been used in the pre-pandemic environment, but they have not been widespread. This confirms the rule related to a crisis management, namely that any crisis must be seen not only as a threat, but also as an opportunity to master new approaches and to show their effectiveness in practice. The aim of this paper is to focus on the possibilities of using virtual reality in training employees in forest-based SMEs such as specific manufacturing procedures, healthy work condition, organization of manufacturing etc. A number of research methods will be used. These will include: literature research, retrospective analysis, method of comparison etc. © 2022 15th International Scientific Conference WoodEMA 2022 - Crisis Management and Safety Foresight in Forest-Based Sector and SMES Operating in the Global Environment. All rights reserved.

Devices with Fuzzy Logic Control of Artificial Lung Ventilation

This article presents the development of a ventilator and its control algorithm. The main feature of the developed ventilator is compressed by a pneumatic drive. The control algorithm is based on the adaptive fuzzy inference system (ANFIS), which integrates the principles of fuzzy logic. The paper also presents a simulation model to test the designed control approach. The results of the experiment provide verification of the developed control system. The novelty of the article is, on the one hand, the implementation of the ANFIS controller, pressure control, with a description of the training process. On the other hand, in the article presented a draft ventilator with a detailed description of the hardware and control system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Video-based Physiotherapy Exercise Dataset

A patient recovering from a stroke, injury, or physical pain needs continuous physiotherapy and rehabilitation to achieve a quick and complete recovery. It is often difficult for elderly people to visit clinics to undertake exercises. Finding physiotherapists and relevant treatments becomes more difficult, particularly in an epidemic condition like covid-19. AI-driven at-home physiotherapy exercise monitoring and assessment systems can be the straightforward feasible solution in this regard. Accurate recognition of particular exercises, exercise assessments, providing feedback, etc. are parts of the whole system, which a machine typically learns through a data-heavy training process. A key issue in this regard is the lack of specific training data for physiotherapy exercises. There exist only a few datasets in the literature that are designed for physiotherapy exercises;most of them however are based on multiple body sensors or Kinect device. Sensor devices are quite costly, and their availability is not guaranteed everywhere. In contrast, video data can be a better alternative, where video can be acquired easily from an available smartphone camera or desktop/laptop webcam. Addressing this issue, we present a new video-based physiotherapy exercise database containing 1237 video clips of 14 physiotherapy exercises that were carefully elicited from an extensively conducted survey from multiple physiotherapists. Exercises were recorded with 28 male and female subjects within various lighting conditions, camera angles, and camera jitters to simulate the real-world setting. Several machine learning algorithms were utilized to carry out an experimental study on the dataset, and the results are provided for future reference. © 2022 IEEE.

Innovative and efficient CNN structure works for chest X-ray images classification

In the era of global transmission of COVID-19, it is a challenge for physicians to efficiently and accurately use chest Xray images to diagnose whether a patient is positive or not. The application of deep learning and computer vision in medical image processing solves this problem, but a highly accurate method is still needed. In this research, we proposed an innovative CNN structure used for chest X-ray classification. Based on deep learning and CNN, this new architecture has an efficient training process and the performance of accuracy is better than other classic nets. The best accuracy on the test dataset is 97.68%. It has competitive advantages over AlexNet, LeNet-5, and Vgg-16. Dropout, Data augmentation, and Grad-CAM technique are added to improve performance. © 2022 IEEE.

Andragogy reception in school education during the COVID-19 pandemic in Peru

The article addresses how the application of the principles of andragogy is perceived in basic education during the COVID-19 pandemic in Peru. This study has a naturist view, follows a research methodology with a phenomenological design and a quality approach. The analysis is engaged from an interview application semi structured as an instrument applied to twelve teachers in public and private institutions of basic regular education in Peru. Among the results it was found in the teachers training process changing from formal education to online had many issues in its execution due to lack of strategies and technological resources. Besides, most teachers expressed their unconformity with the loaded work process and training they received although they had improvements within their media capabilities. In this sense, teachers manifested the lack of the horizontal principle from the andragogy principle, opposite to this they developed the participation and synergy between professors to face challenges. Furthermore, its manifested in this study that due to the training and transitioning's process complexities some teachers decided to drop out of their educational facilities. The answers obtained offer information to forecast the teachers training in events of worldwide disaster such as COVID-19. According to the results obtained in this study we can have the teachers training process general view highlighting social inequity, educational and digital that has been installed for a long time in these educational systems, both public and private sectors. © 2022 IEEE.

The use of online teaching tools in the Tourism Master of the University of Huelva after the COVID-19 pandemic

This study points out the factors to be taken into account so that the professors of the Master's Degree in Tourism at the University of Huelva do not stop using online teaching tools in their training process after the COVID-19 pandemic, since some of which can enrich face-to-face teaching. A causal analysis was carried out through fuzzy cognitive maps and the results obtained indicated that if it is intended that these teachers continue using these tools, from the direction of the master's degree they must be allowed to use them and facilitate their use, sending them training courses on these tools, giving them accessibility to this type of tools and allowing them to use them if they require it. © 2022 IEEE.

CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis

This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/Spina1Net © 2022 IEEE.

A Simplified Convolutional Neural Network Design for COVID-19 Classification on Chest X-ray Images

COVID-19 is a respiratory virus that causes the spread of infection and has affected human around the world. The infection frequently results in pneumonia in human which can be detected using lung imaging, chest X-ray images. Deep learning models have been demonstrated to an effective COVID-19 interpretation on chest radiography. In this paper, we have proposed a simplified convolutional neural network model for COVID-19 screening that can classify the appearance of COVID-19 lesion into two classes. The proposed model;despite using fewer layers and the utilization of data augmentation approach in training process, can achieve the greater outcome. To evaluate the proposed model, we have used a partial of the public dataset, COVID-19 Radiography Database which is a collection of 13,808 chest X-ray images. At the final stage, the Grad-CAM visualization method has been used to enhance the important region of chest X-ray images in order to provide the explanations of COVID-19 predictions. © 2022 IEEE.

Gamification: A Motivation Metric Based in a Markov Model

The current situation in the world with the COVID-19 pandemic has reinforced a pre-existing trend based on increasing the use of gamification tools in education to motivate students. In this work, a study based on a Markov model is proposed to assess motivation during the training process in higher education. The evolution of Faculty of Business Administration graduates when using a gamified smartphone application (HEgameApp) has been measured. The behavior of graduates is assessed through collaboration in fora created by HegameApp, and the recognition given by their classmates. A utility function is defined to obtain a statistical estimator used in the assignment of motivational states of the study participants. In addition, a decrement function is assigned to the value of the components of the utility function to estimate the time variation of motivation during the process of knowledge assimilation. The proposed solution shows that when graduates are involved in using the app, they significantly increase their academic outcomes and satisfaction while receiving the lectures. In addition, the positive feedback perceived through the application fora has a measurable effect on their motivation. © 2022. International Journal of Emerging Technologies in Learning. All Rights Reserved.

Classification of Proper or Improper Masks during a Pandemic Using a Custom Convolutional Neural Network with Data Augmentation

Classification of the use of masks today is very necessary regarding the pandemic period that is not over yet. This is mainly to break the chain of transmission of the COVID-19 virus from one person to another. From the literature that has been studied, the Convolutional neural network method can be used to distinguish the types of masks used in society. The advantage of the Convolutional Neural Network is that it can recognize objects with a fairly high level of accuracy, but it has a weakness, namely that the training process time is still quite high. This is the author's concern by doing a custom layer on the convolutional neural network. In addition, the addition of data augmentation is done to increase the number of data variations. The result used 18-34 custom layers in an average of around 97.93%, with an average computation time for the training process of about 1 minute 83 seconds. The resulting classification errors using Mean Absolute Error is 0,0163 © 2021 IEEE.

The Socioconstructivist Approach for the Teaching Process in the Training Conditions of COVID-19

Introduction: The current condition marked by the Covid 19 require graduating professionals who resolve with integrity, Independence and in a creative way, the problems that arise in the exercise of their work activity. Objective: To show, organically and in the context of the pandemic, the arguments regarding the importance of the socioconstructivist didactic approach focused on the development of logical-intellectual and professional skills. Development: A qualitative study was carried out on the socioconstructivist approach and the teaching process in the training conditions of COVID-19. A bibliographic review was carried out based on theoretical inquiries such as logical-historical analysis and documental analysis, with vast information that served as a source of knowledge about the professional training process demanded nowadays. A challenge is imposed on the training process and its main actors (professors and students), because the assimilation of contents by anybody who learns must be based on the development of the logical-professional skills included in the professional profile. This requires a teaching leadership for encouraging the development of theoretical-systemic, critical and creative thinking, closely related to values and to work ethics. Conclusions: The socioconstructivist didactic approach focused on the development of logical-intellectual and professional skills imposes a challenge to the training process and its main actors (teachers and students), due to the assimilation of contents by learners based on the development of logical-intellectual and professional skills, included in the professional profile. © 2021, Editorial Ciencias Medicas. All rights reserved.

The Use of Computer Training Systems in Distance Learning

Information and communication technologies in the context of the Industrial Revolution 4.0 are used in all industries. In the context of COVID-19 pandemic and the shift to distance learning, computer hardware and software systems are an effective tool for the successful implementation of the training process in the universities. Virtual laboratories, simulators, test computer systems are the components of the virtual educational environment of the universities. Today these tools contain a specific multimedia element - a virtual robot assistant. Student's interaction with a virtual assistant should be comfortable and provide the successful educational process. The authors examined several aspects of effective student's interaction with the multimedia components of the interface of the computerized learning environment. The results of the conducted experiment can be used by developers of computer virtual laboratories, simulators, experimental research stands, test computer systems, a component of which is a virtual robot assistant, to optimize the processes of educational activities distance learning included. © 2022 IEEE.

Face Mask Detection in the Covid-19 Pandemic Era by Implementing Convolutional Neural Network and Pre-Trained CNN Models

The Coronavirus or Covid-19 has spread widely throughout the world since the beginning of 2020. WHO provides basic guidance in preventing the spread of the virus that can be done by the community. One of them is the use of masks when doing activities outside the home. Lack of awareness in mask usage become the obstacle in the process of efforts to prevent the spread of covid 19. The aim of this research is to develop a face mask detection model by implementing the convolutional neural network and pre trained CNN algorithm. The accuracy of the proposed models in training process, the accuracy of CNN, VGG16, and VGG19 are 97.79%, 99.87% and 100%, respectively. The proposed models evaluated using confusion matrix using testing datasets given. © 2021 IEEE.

Sentiment Analysis of the Coronavirus Vaccine on Social Media

The rapid spread of Internet use has allowed people to access social media more easily. Especially after the Coronavirus epidemic, some restrictions and people's working from home caused them to use social media intensively. While the Coronavirus epidemic continues its affect all over the world, developments related to vaccines are followed with interest by people. Conversations and shares about vaccines and vaccine brands continue to increase. It's a fact that social media has attracted the attention of society even to any ordinary subject and has almost become a decision-making mechanism. The thoughts and feelings of many people about the vaccine can be extremely decisive for society. People's attitudes towards vaccines can change throughout the pandemic process. Therefore, this study focuses to learn and monitor people's feelings about the vaccine over a period of time. On the Reddit platform, which has no character limit for posts, the data is pre-processed to classify comments into emotional tags. The data was subjected to a series of training processes to classify with higher accuracy using the Natural Language Understanding (NLU) library. Then people's emotional changes related to the vaccine monitored on a monthly basis. © 2021 IEEE.