Impact of e-Learning in Education Sector: A Sentiment Analysis View

The COVID-19 condition had a substantial impact on the education sector, corporate sector and even the life of individual. With this pandemic situation e-learning/distance learning has become certain in the education sector. In spite of being beneficial to students and teachers, its efficacy in the education domain depends on several factors such as handiness of ICT devices in various socio economic groups of people and accessible internet facility. To analyze the effectiveness of this new system of e learning Sentiment Analysis plays a predominant role in identifying the user's perception. This paper focus on identifying opinions of social media users i.e. Twitter on the most prevailing issue of online learning. To analyze the subjectivity and polarity of the dynamic tweets extracted from Twitter the proposed study adopts TextBlob. As Machine Learning (ML) models and techniques manifests superior accuracy and efficacy in opinion classification, the proposed solution uses, TF-IDF (Term Frequency-Inverse Document Frequency) as feature extraction technique to build and evaluate the model. This manuscript analyses the performance of Multinomial Naive Bayes Classifier, DecisionTreeClassifier, SVC and MLP Classifier with respect to performance measure as Accuracy. © 2022 IEEE.

Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer

This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. In image-based diagnosis of viral pneumonia cases including COVID-19, both local and global image features are important because viral pneumonia cause many ground glass opacities and consolidations in large areas in the lung. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. MLP-Mixer is a recent method of image classification using Vision Transformer-like architecture. It performs classification using both local and global image features. To classify 3D CT volumes, we developed a hybrid classification model that consists of both a 3D convolutional neural network (CNN) and a 3D version of the MLP-Mixer. Classification accuracy of the proposed method was evaluated using a dataset that contains 1205 CT volumes and obtained 79.5% of classification accuracy. The accuracy was higher than that of conventional 3D CNN models consists of 3D CNN layers and simple MLP layers. © 2023 SPIE.

Classification of Covid-19 Vaccine Opinion and Detection of Symptom-Reporting on Twitter Using Neural Networks

This paper describes my work for the Information Retrieval from Microblogs during Disasters.This track is divided into two sub-tasks. Task 1 is to build an effective classifier for 3-class classification on tweets with respect to the stance reflected towards COVID-19 vaccines.Task 2 is to devise an effective classifier for 4-class classification on tweets that can detect tweets that report someone experiencing COVID-19 symptoms.This paper proposes a classification method based on MLP classifier model.The evaluation shows the performance of our approach, which achieved 0.304 on F-Score in Task 1 and 0.239 on F-Score in Task 2. © 2022 Copyright for this paper by its authors.

A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19

Deep learning is increasingly gaining traction in cutting-edge medical sciences such as image classification, and genomics due to the high computational performance and accuracy in evaluating medical data. In this study, we investigate the cardiac properties of ECG Images and predict COVID-19 in a binary classification of patients who tested positive for COVID-19 and Normal Persons who tested negative. We analyzed the electrocardiogram (ECG) images by preprocessing the ECG data and building an ECG- Deep Learning- COVID-19 (ECG-DL-COVID) classifier to predict disease. The deep learning models in our experiments constituted CNN, Multi-Layer Perceptron (MLP), and Transfer Learning. Performance evaluation was done to compare the effectiveness of the proposed methodologies with other COVID-19 deep learning-related works. In the three experiments, we achieved an 87% prediction accuracy for MLP, a 90% prediction for CNN and a 93.8% prediction for Transfer Learning. Experimental results and performance evaluation show that the proposed models outperformed previous deep-learning models in the prediction of COVID-19 by a considerable margin. © 2023 IEEE.

MLP-based classification of COVID-19 and skin diseases.

Recent years have witnessed a growing interest in neural network-based medical image classification methods, which have demonstrated remarkable performance in this field. Typically, convolutional neural network (CNN) architectures have been commonly employed to extract local features. However, the transformer, a newly emerged architecture, has gained popularity due to its ability to explore the relevance of remote elements in an image through a self-attention mechanism. Despite this, it is crucial to establish not only local connectivity but also remote relationships between lesion features and capture the overall image structure to improve image classification accuracy. Therefore, to tackle the aforementioned issues, this paper proposes a network based on multilayer perceptrons (MLPs) that can learn the local features of medical images on the one hand and capture the overall feature information in both spatial and channel dimensions on the other hand, thus utilizing image features effectively. This paper has been extensively validated on COVID19-CT dataset and ISIC 2018 dataset, and the results show that the method in this paper is more competitive and has higher performance in medical image classification compared with existing methods. This shows that the use of MLP to capture image features and establish connections between lesions is expected to provide novel ideas for medical image classification tasks in the future.

The political trajectory of biogas in Brazil from a multilevel perspective (PMN)

This research aims to analyze the political trajectory of biogas in Brazil according to the Multi -Level Perspective (MLP). Political trajectory is understood as the set of political events that constitute the path taken by a given transition towards sustainability. Politics, in turn, is understood as a regime dimension that is influenced by pressures from both the landscape and the socio-technical niche. In this context, policy can be used to create barriers to innovations and maintain the dominant position of regime actors, or it can facilitate their advancement. Therefore, this research is classified as documentary research focusing on the National Biofuels Policy (RenovaBio) enacted in 2017. Its realization involved documentary research in several national and international institutions. The main results point to a policy built on mandatory targets, Decarbonization Credits (CBIOs), and biofuel certification. Thereafter, the regulation of RenovaBio, the pandemic of COVID-19 and the taxation of CBIOs emerge as major developments. It is concluded that RenovaBio emerges as a response to the Paris Agreement that provoked the adaptation of the socio-technical system of fossil fuels in Brazil.

A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module.

During the past three years, the coronavirus disease 2019 (COVID-19) has swept the world. The rapid and accurate recognition of covid-19 pneumonia are ,therefore, of great importance. To handle this problem, we propose a new pipeline of deep learning framework for diagnosing COVID-19 pneumonia via chest X-ray images from normal, COVID-19, and other pneumonia patients. In detail, the self-trained YOLO-v4 network was first used to locate and segment the thoracic region, and the output images were scaled to the same size. Subsequently, the pre-trained convolutional neural network was adopted to extract the features of X-ray images from 13 convolutional layers, which were fused with the original image to form a 14-dimensional image matrix. It was then put into three parallel pyramid multi-layer perceptron (MLP)-Mixer modules for comprehensive feature extraction through spatial fusion and channel fusion based on different scales so as to grasp more extensive feature correlation. Finally, by combining all image features from the 14-channel output, the classification task was achieved using two fully connected layers as well as Softmax classifier for classification. Extensive simulations based on a total of 4099 chest X-ray images were conducted to verify the effectiveness of the proposed method. Experimental results indicated that our proposed method can achieve the best performance in almost all cases, which is good for auxiliary diagnosis of COVID-19 and has great clinical application potential.

Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks.

The success of the supervised learning process for feedforward neural networks, especially multilayer perceptron neural network (MLP), depends on the suitable configuration of its controlling parameters (i.e., weights and biases). Normally, the gradient descent method is used to find the optimal values of weights and biases. The gradient descent method suffers from the local optimal trap and slow convergence. Therefore, stochastic approximation methods such as metaheuristics are invited. Coronavirus herd immunity optimizer (CHIO) is a recent metaheuristic human-based algorithm stemmed from the herd immunity mechanism as a way to treat the spread of the coronavirus pandemic. In this paper, an external archive strategy is proposed and applied to direct the population closer to more promising search regions. The external archive is implemented during the algorithm evolution, and it saves the best solutions to be used later. This enhanced version of CHIO is called ACHIO. The algorithm is utilized in the training process of MLP to find its optimal controlling parameters thus empowering their classification accuracy. The proposed approach is evaluated using 15 classification datasets with classes ranging between 2 to 10. The performance of ACHIO is compared against six well-known swarm intelligence algorithms and the original CHIO in terms of classification accuracy. Interestingly, ACHIO is able to produce accurate results that excel other comparative methods in ten out of the fifteen classification datasets and very competitive results for others.

Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Cough Audio Analysis for COVID-19 Diagnosis.

Humanity has suffered catastrophically due to the COVID-19 pandemic. One of the most reliable diagnoses of COVID-19 is RT-PCR (reverse-transcription polymer chain reaction) testing. This method, however, has its limitations. It is time consuming and requires scalability. This research work carries out a preliminary prognosis of COVID-19, which is scalable and less time consuming. The research carried out a competitive analysis of four machine-learning models namely, Multilayer Perceptron, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory, and VGG-19 with Support Vector Machines. Out of these models, Multilayer Perceptron outperformed with higher specificity of 94.5% and accuracy of 96.8%. The results show that Multilayer Perceptron was able to distinguish between positive and negative COVID-19 coughs by a robust feature embedding technique.

Classification of COVID-19 and Nodule in CT Images using Deep Convolutional Neural Network

Distinguishing between coronavirus disease 2019 (COVID-19) and nodule as an early indicator of lung cancer in Computed Tomography (CT) images has been a challenge that radiologists have faced sinceCOVID-19 was announced as a pandemic. The similarity between these two infections is the main reason that brings dilemmas for them and may lead to a misdiagnosis. As a result, manual classification is not as efficient as automated classification. This paper proposes an automated approach to classify COVID-19 infections from nodules in CT images. Convolutional Neural Networks (CNNs) have significantly meliorated automated image classification tasks, particularly for medical images. Accordingly, we propose a refined CNN-based architecture through modifications in the network layers to reduce complexity. Furthermore, to vanquish the lack of training data, data augmentation approaches are utilized. In our method, Multi Layer Perceptron (MLP) is obligated to categorize the feature vectors extracted from denoised input images by convolutional layers into two main classes of COVID-19 infections and nodules. To the best of our knowledge, other state-of-the-art methods can only classify one of the two classes listed above. Compared to the mentioned counterparts, our proposed method has a promising performance with an accuracy of 97.80%. © 2022 IEEE.

Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach

In today's era, the importance and implementation of blockchain networks have become feasible as it improves the resilience of the supply chain network at all levels by clarifying information and creating security in the network, improving the speed of response, and gaining the trust of customers. This paper aims to investigate the behavior of the blockchain acceptance rate (BAR) in the home appliances flexible supply chain in Iran using. system dynamics (SD), which is used to better define the relationships between the variables of the model that are non-linearly connected. Through simulating the behavior of the BAR in the long term in the supply chain, whilst conducting sensitivity analysis, policy design, and validation, this model will be implemented for the years 2020 to 2030. Additionally, post-simulation, blockchain acceptance behavior will be assessed by having simulated data considered as input for studied Multi-Layer Perceptron (MLP) and Vector Regression (SVR) (data that have the highest correlation with BAR). The acceptance rate behavior is predicted with the help of machine learning methods to have the best behavior and prediction for the data of 2020-2022 since the prediction function is compared to daily real data obtained these years. The results show that in 2030, the BAR will be around 0.6 if the COVID-19 outbreak impact is medium, and if the considered policy designs are implemented, this rate will reach a maximum of 0.8. So paying attention to the creation and design of policies can achieve positive implications for increasing the resilience of the supply chain in the long run. Findings suggest that the SD-MLP method is better than the SD-SVR method as it has less error and can predict the better behavior of the BAR.

Automated Online Exam Proctoring System Using Computer Vision and Hybrid ML Classifier

Importance of online education can be seen especially during the ongoing Covid-19 when going to schools or colleges is not possible. So validity of online exams should also be maintained with respect to traditional pen-paper examinations. However, absence of invigilator makes it easy for the examinees to cheat during the exam. Though there are already many systems for online proctoring, not all educational institutes can afford them as the systems are very expensive. In this paper, we have used eye gaze and head pose estimation as the main features to design our online proctoring system. Therefore, the purpose of this paper is to use these features to create an online proctoring system using computer vision and machine learning and stop cheating attempts in exams. © 2021 IEEE.

[Digital futures of the university: Scenarios of sociotechnical change]. / Digitale Zukünfte der Universität: Szenarien soziotechnischen Wandels.

For the university, digital technologies proved to be a central element of crisis management during the COVID-19 pandemic. This is especially true for teaching. From a "multi-level perspective" (Geels 2004), the disruptive effects of the pandemic open a "window of opportunity" for profound and lasting sociotechnical change. Against this backdrop, this article discusses how members of the university assess the future significance of digital technologies. On the basis of qualitative, empirical research, five scenarios can be distinguished, each of which outlines digital futures in a different way. However, with our analysis of these future scenarios we do not scrutinise the probability of their occurrence, but their desirability. In this way, we identify justifications diploid to argue why further steps towards a digital university should be taken or why not. At this point in time, it is impossible to assess which scenarios of digital universities will ultimately prevail. Not least for this reason, this article is intended as a basis for a broad debate that is yet to be conducted.

Multiclass Classification for Air Quality In Jakarta Using Support Vector Machine and Multi-Layer Perceptron Classifier

Air is one of the necessities of living things. Therefore, it is necessary to have good air quality. Air pollution can cause many negative impacts on life. Therefore, it is important to know the air quality in an area. Jakarta is one of the cities with poor air quality in Indonesia and the world. During the Covid-19 pandemic, the government implemented a large-scale social restriction policy, the impact of this policy was better air quality. But now it has started to back to normal, then it is important to control air quality. There are 5 locations to measure air quality in Jakarta. The results of the independence test between the location of air quality measurements and critical variables on air pollution indicate a relationship between the two variables. Moreover, there are differences in air quality before Covid-19 and during Covid-19 based on the results of the t-test. Air quality classification was carried out in this research using machine learning methods. Because there are several levels of air quality, the classification uses a multiclass classification. Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) Classifier are used in this research. Because based on the results of literature reviews, both methods produce high accuracy. The results of this research showed a comparison of the two methods. The comparison showed that the SVM method is better than the MLP Classifier. © 2022 IEEE.

Using of Box and Jenkins and Artificial Neural Networks for Modeling The Covid-19 Epidemic in Iraq

We aim by this research to use mathematical methods to model the Coved-19 epidemic in Iraq by comparing time series by using box & Jenkins model and artificial neural networks. The infections, cures and deaths data were used for the period from 24/2/2020 to 30/11/2020. The study found a tendency in the numbers of infections and cures using the Box & Jenkins model to rise, while the numbers of deaths tended to stabilize. Artificial neural networks, using the MLP algorithm, have found a tendency to number of infections by decline and cures to rise, while deaths numbers tended to decrease and then to stability. In addition, the study found that the forecasting of the numbers of infections was more accurate using artificial networks, while the forecasting of the numbers of cures was more accurate in the Box Jenkins model and the forecasting of death numbers was at the same level of accuracy in the trade-off between the two methods. The study recommends to sue the artificial networks to forecast the number of infections and deaths and the use of the Box Jenkins model to forecast cures. In addition, the study recommends the use of these mathematical methods to help decision makers respond to the epidemic. And also recommends to conduct another study using other techniques for artificial networks as an algorithm extreme learning machines (ELM). The study also recommends a survey of habits associated with the spread of the epidemic, such as social distancing and other, linking them with the numbers of infections, cures and deaths to reach a protocol specific to Iraq based on accurate mathematical and scientific foundations. © 2022 American Institute of Physics Inc.. All rights reserved.

Head and face shape classification and type prediction of female college students based on MLP neural network

Currently with the changes in living habits and eating habits of China consumers have higher requirements for the wearing comfort and fit of head and face products such as helmets and masks. In addition the outbreak of COVID-19 in 2019 has made suitable masks an important protective equipment for medical staff and the general population. How to improve the safety protection level of masks has also become a hot social issue of concern. The fit of the mask is directly related to the protection effectiveness so it is urgent to measure track and update human head and face data. The research on the characteristics and classification of human head and face is an important basis for the structural design size formulation fit research and plate shape optimization of masks and helmets. Multilayer perceptron is an ANN algorithm. With the development of neural network technology it is gradually applied to prediction and classification. The model with strong nonlinear approximation function simple structure controllable number of input variables and strong operability can be applied to the classification and prediction of human body shape. In order to improve the adaptability of head and face products this paper took 189 female college students aged 18 - 26 as the research subjects and used the Martin measuring instrument to measure the head and face of the subjects. Feature factors affecting head and face shape were extracted by principal component analysis PCA the K-Means method was used to classify the head and face morphology and the index classification method was used to quantify head and face morphology. As a result a head-face shape prediction model based on MLP-ANN was proposed to improve the problem of low production work efficiency caused by too many head and face sizes in classifying or selecting models with too many references. The study found that through the analysis of head and face characteristics of 189 subjects seven important characteristic factors affecting the head and face shape were extracted head contour factor morphological facial factor morphological facial factor eye factor nose factor and mouth & lip factor. The head and face shapes were divided into five sizes according to the clustering center value of each category XS type/morphological index > 93 S type/morphological index 88 93 M type/morphological index 84 88 L type/morphological index 79 84 XL type/morphological index 79 and the M type was the most widely distributed and had a big coverage rate so it can be used as an intermediate type. Then through the MLP neural network seven head-face feature factors were used to predict head-face shape classification. The generated model had a 93. 42% correct prediction result and the research results can provide a reference for the design and production of head and face products. This paper provides an objective method for the study of head and facial features but there are still some limitations. In the future we can continue to improve the classification of head and face shape by expanding the area and age of the experimental subjects for comparative research. We can apply the classification to the head and face product specification system so as to accumulate morphological data for the study of the head and face characteristics of contemporary Chinese people and the design of head and face products such as masks for the Chinese market. © 2022 China Silk Association. All rights reserved.

A Deep learning based Safe Travel Advisory Tool for Covid-19

Looking at the massive spread of SARS CoV2(COVID-19), it not only requires medical solutions at this point but different alternatives must also be examined to prevent its contagious nature getting its hands on a large number of individuals. Getting some prior information before its actual cause can help us to prepare ourselves to fight this pandemic better. It can assist authorities and administration to make better decisions in relatively less time to figure out the most suitable solutions. Since it is difficult to devise a permanent solution to this kind of pandemic, such data analysis can be used to strategize ourselves to cope with it. This study focuses on the forecasting of the number of active cases using deep neural networks. The models used in this approach are Multilayer Perceptron(MLP), Convolution Neural Networks(CNN) and Long Short Term Memory(LSTM). The performance of all three models is analyzed and although all of them are reasonably well, the MLP model outperforms the other two. These models can be used to predict the number of cases on a given day and a potential future outbreak. © 2022 IEEE.

Deep learning for Covid-19 forecasting: State-of-the-art review.

The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning.

PSO-Optimized CoVID-19 MLP-NARX Mortality Prediction Model

Mortality prediction models localized for Malaysia is limited, warranting a research gap to study further. A predictive model for CoVID-19 mortality prediction is presented in this paper. The model utilized the MLP-NARX structure. Parameters for the model were optimized using PSO. Prediction results yielded average MSE value of 8.1141x10x(-7) with acceptable validation results.