Measuring College Students' Satisfaction with the Teaching Effectiveness of Online English Courses Based on the IPA Model

The English learning ability and academic performance of pre-service teachers affect the future professional development of preschool and primary education teachers. The English course has been transferred to online due to COVID-19. Whether the practicability of e-learning is consistent with students' expectations primarily affect teaching effectiveness. A paired-sample t-test on the importance and satisfaction of online English learning effectiveness of pre-service teachers from freshmen to juniors at a private university revealed no significant difference in the overall importance and satisfaction. Then the coordinated system is constructed according to the Importance -Performance Analysis (IPA) to identify the critical indicators for improving the teaching effect of online courses. The results imply that network stability and teachers' timely responses to students' questions should be concentrated. In addition, students are pretty satisfied with the e-learning platform, teaching quality and management, which should be further maintained. The suggestions for improving the effectiveness of online English teaching in private universities are proposed accordingly. © 2023 IEEE.

Precision Diagnosis to Discriminate Covid-19 and Pneumonia using Mixed-data Model based on Custom Neural Networks

Classifying Covid-19 and Pneumonia is one of the most important and challenging tasks in the field of the medical sector since manual classification with human assistance can lead to incorrect prediction and diagnosis. Additionally, it is a difficult operation when there is a lot of data that need to be analyzed thoroughly. Due to the similarity in symptoms as well as in chest X-ray images of Covid-19 and Pneumonia diseases, it is difficult to distinguish those. The study presents a technological solution to build a mixed-data model using customized neural networks to discriminate between Covid-19 and Pneumonia. The proposed method is applied to the chest X-ray images and symptoms of patients of Covid-19 and Pneumonia. This helps to perform immediate prediction of Covid-19 and Pneumonia providing fast and specialized treatment to the patients appropriately. This prediction also helps the radiologist or doctors in making quick decisions. In this work, imaging data (such as Chest X-ray images) and text data (such as disease symptoms like cough, body pain, short breathing, fever, etc.) are taken for detecting Covid-19, Pneumonia and Normal patients. Data Synthesis is carried out due to the unavailability of mixed data and it has created dataset of 450 entries of Covid-19, Normal and Pneumonia cases. The goal is to design a system that accurately classifies Covid19, Pneumonia, and Normal patients by utilizing convolutional neural networks (CNN) and multi-layer perceptron (MLP) algorithms. An accuracy of 93.33% is obtained for the mixed-data model using a deep neural network, that is designed by combining custom CNN and MLP architectures. © 2022 IEEE.

What Do We Know? A Bibliometric Analysis of Current Literature on COVID-19 and its Implications

The COVID-19 pandemic has plunged the world into chaos by affecting people's lifestyles and imposing immense pressures on healthcare professionals. Since its outbreak in Wuhan, China, back in December 2019, researchers all across the globe have been working tirelessly to provide reliable insights to understand and combat the virus. As a result, the number of publications related to the novel coronavirus has been increasing rapidly. This study aims to quantify and summarize the progress of SARS-CoV-2 related research from November 2019 onwards to January 2021 by employing a bibliometric analysis and topic modelling approaches. A total of 33,159 research publications, downloaded from the Web of Science (WoS) core collection database, were analyzed. The key aspects of our study include identifying important publications, their distribution across countries and organizations, important journals and central authors who have made a significant contribution to the current literature. We have also delineated the major themes addressed in the academic community. © 2022 IEEE Computer Society. All rights reserved.

Analysis of environmental performance and interactivity of ports and regions

The port is the basic support for regional economic development and the global allocation of resources. With the rapid development of China's economy and growing ecological awareness, the assessment of port and regional efficiency has received unprecedented attention. In the current context of the COVID-19 pandemic, how the port and its region will be coordinated under the common goal of development has become a hot topic. In this study, the port subsystem (P-subsystem) and the regional subsystem (R-subsystem) are unified into the port–region system (PR system), and a new meta-frontier two-stage data envelopment analysis model is constructed to evaluate the P-subsystem efficiency and the environmental efficiency of the PR system. This research also measures the port–regional coordination level using the coordination index and explores the inefficiency of the PR system with the help of management improvement and technology improvement indices. Main results show that the overall efficiency of the Chinese PR system is increasing. The technological level of the PR system in coastal areas is close to the optimal level. The inefficiency of the Chinese PR system is mainly affected by management inefficiency. The coordination of regional and port development in China is also poor. Finally, on the basis of the research findings, this study provides targeted countermeasure suggestions to promote the efficiency enhancement and coordinated development of the PR system. © 2023 Elsevier Ltd

A Digital Twins Model for Analyzing and Simulating Cold Chain Risks

Due to the outbreak of COVID-19, increasing attention has been paid to designing a cold chain logistics mechanism to ensure the quality of vaccine delivery. In this study, a cold chain digital twins-based risk analysis model is constructed to handle and monitor the vaccine delivery process with a high level of reliability and traceability. The model integrates the Internet of Things (IoT) and digital twins to acquire data on environmental conditions and shipment movements and connect physical cold chain logistics to the digital world. Through the simulation of cold chain logistics in a virtual environment, the risk levels relating to physical operations at a certain forecast horizon can be predicted beforehand, to prevent a 'broken' cold chain. The result of this investigation will reshape the cold chain in the digital age, benefit society in terms of sustainability and environmental impact, and hence contribute to the development of cold chain logistics in Hong Kong. © 2023 IEEE.

Research on machine learning-based epidemic impact on Social Electricity Consumption model

This paper uses social electricity consumption data from 2015-2021 in a city in Hubei province, and uses some methods of artificial intelligence, for example, python function fitting and machine learning to construct an impact analysis and prediction model of the COVID-19 epidemic on Electricity Consumption. Through comparison with the effects of general linear regression and polynomial regression, a better model is developed which comprises four independent variables and uses polynomial regression. The model developed in this paper helps to quantify and measure the impact of the epidemic on society's electricity consumption, and ultimately enables users in the electricity industry to make convenient and rapid forecasts, helping them to make reasonable power supply plans, trading plans and dispatch plans, and to ensure safe and economic operation of the Electricity System. © 2022 ACM.

Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

The outbreak of the pandemic, caused by Coronavirus Disease 2019 (COVID-19), has affected the daily activities of people across the globe. During COVID-19 outbreak and the successive lockdowns, Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously. Several studies used Sentiment Analysis (SA) to analyze the emotions expressed through tweets upon COVID-19. Therefore, in current study, a new Artificial Bee Colony (ABC) with Machine Learning-driven SA (ABCML-SA) model is developed for conducting Sentiment Analysis of COVID-19 Twitter data. The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made upon COVID-19. It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors. For identification and classification of the sentiments, the Support Vector Machine (SVM) model is exploited. At last, the ABC algorithm is applied to fine tune the parameters involved in SVM. To demonstrate the improved performance of the proposed ABCML-SA model, a sequence of simulations was conducted. The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches. © 2023 Tech Science Press. All rights reserved.

Sentiment Analysis Model for KlikIndomaret Android App During Pandemic Using Vader and Transformers NLTK Library

COVID-19 has changed the Indonesian people's shopping habits for consumer goods. The online retail application came as a response to social distancing and stay-at-home advice. KlikIndomaret is an online retail application that uses the omnichannel concept. As the number of downloads increased, the number of various comments and sentiments on that application also increased. In this study, the researcher did a sentiment analysis aimed to improve the quality of application experiences and retail services. The result of the analysis reflected the services given to customers thus far. The data included reviews and star ratings derived from 4,066 reviews which went under the process of data pre-processing. The methods used in this study were VADER and NLTK, improved by Transformer, without pre-training data. These methods could filter the users' reviews with sarcasm tone. The results were sentiment labels that were appropriate based on the score comparison of positive and negative sentiments in one user's review. This approach made the review sentiment process of thousands of data faster and more accurate. © 2022 IEEE.

Classifying Customers' Journey from Online Reviews of Amazon Fresh via Sentiment Analysis and Topic Modelling

A positive customer journey experience is necessary to maintain customer loyalty in online retailing. After the outbreak of Covid-19, there has been a significant increase in the number of customers who buy online groceries. Due to the anonymity and convenience throughout the customer journey, E-grocery shopping platforms have become a reliable source for gathering online customer reviews. In the study, we used text mining and machine learning (ML) models to an e-grocery customer review database from the Amazon Fresh website to forecast customer feelings in the data set. To be more specific, this study aimed to determine whether the customers are satisfied with the online purchase of products or not. Further, the study aims to analyze whether the customers would recommend the purchased products or not. For sentiment analysis a sample of 78,619 reviews was used. We used a linguistic approach consisting of ML and dictionary scoring algorithms to forecast customers' sentiment based on their reviews. Topic modeling (TM) on 3,26,120 customer reviews was used to reveal 'themes' from customer reviews to grasp a better knowledge of customers experiences. © 2022 IEEE.

Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling

The global rampancy of COVID-19 has caused profound changes in education sectors. Perhaps the most salient change is the shift of the instructional paradigm from face-to-face instruction to fully online learning. To address the challenges facing the education sector, researchers and educational practitioners have extensively investigated the transition in teaching mode under COVID-19, with a growing contribution to a range of topics in relation to online learning. Against this backdrop, it is necessary to gain a comprehensive understanding of the major hotspots and issues of online learning so as to develop appropriate and effective policies on strategic (re-)allocation of resources to more critical initiatives. This study aims to adopt bibliometrics and topic modeling to identify prominent research topics on online learning under COVID-19 from the large-scale, unstructured text of research publications. Specifically, structural topic modeling will be used to identify predominant topics concerned by scholars working in the field of online learning research. The non-parametrical Mann-Kendell trend test will also be applied to uncover the developmental tendency of each identified topic. In addition, the correlations among the key topics will be revealed and visualized by hierarchical clustering analysis. Based on the analytical results, suggestions will be made to facilitate educational policy formulation to promote the development and effective implementation of technological, scientific, and pedagogical activities of online learning. © 2022 IEEE.

Government as a Brain: How Can Governments Better Understand, Think, Create, and Remember, and Avoid the Traps of Collective Stupidity Both in Emergencies and Normal Times

Governments are evolving new ways to think, combining observation, memory, analysis, models, and creativity. This article describes how they think, how the COVID crisis has accelerated innovation in new ways of thinking, the use of metaphors to understand these processes, the role of democracy and civil society, and the new skills needed. © 2022 Copyright held by the owner/author(s).

Covid-19: Sentiment Analysis of Students Readiness on Open Distance Learning (ODL) via Twitter

The widespread of Corona virus in Malaysia has led to open distance learning (ODL) for every student in continuing their study. However, the student readiness in ODL is unknown for the university. Thus, university institutions must always monitor what the university says about social media and the student readiness on ODL. This project aimed at developing a sentiment classification by means of a Naïve Bayes algorithm in displaying the readiness index of students by extracting Twitter tweets. A standard sentiment analysis performance measurement was used in evaluating the developed sentiment analysis model. About 98.8% of the extracted tweet are negative tweet about the online learning which indicate that the students are not ready for the ODL. The classifier model's precision, recall and f1-measure for each category on online learning readiness are obtained. The best f1-score of the classifier model is in online category with 29.0% for negative tweets and 31.7% for positive tweets compare to the other category with a precision of 27.9% for negative tweets and 30.7% for positive tweets while for the recall value is 29.6% for negative tweets and 32.2% for positive tweets. However, there are room for improvements by implement of the autocorrect function for the sentiment analysis model, implementation of a live update function in dynamically for student readiness index live when new tweets appear, implement a machine classification model, providing dynamic database application and used higher performance computer for data scrapping. © 2022 IEEE.

A Systematic Approach for Contextualizing Focal Structure Analysis in Social Networks

Focal Structures are key sets of individuals who may be responsible for coordinating events, protests, or leading citizen engagement efforts on social media networks. Discovering focal structures that can promote online social campaigns is important but complex. Unlike influential individuals, focal structures can effect large-scale complex social processes. In our prior work, we applied a greedy algorithm and bi-level decomposition optimization solution to identify focal structures in social media networks. However, the outcomes lacked a contextual representation of the focal structures that affected interpretability. In this research, we present a novel Contextual Focal Structure Analysis (CFSA) model to enhance the discovery and the interpretability of the focal structures to provide the context in terms of the content shared by individuals in the focal structures through their communication network. The CFSA model utilizes multiplex networks, where the first layer is the users-users network based on mentions, replies, friends, and followers, and the second layer is the hashtag co-occurrence network. The two layers have interconnections based on the user hashtag relations. The model's performance was evaluated on real-world datasets from Twitter related to domestic extremist groups spreading information about COVID-19 and the Black Lives Matter (BLM) social movement during the 2020–2021 time. The model identified Contextual Focal Structure (CFS) sets revealing the context regarding individuals’ interests. We then evaluated the model's efficacy by measuring the influence of the CFS sets in the network using various network structural measures such as the modularity method, network stability, and average clustering coefficient values. The ranking Correlation Coefficient (RCC) was used to conduct a comparative evaluation with real-world scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

MAISoN'22: 8th International Workshop on Mining Actionable Insights from Social Networks Special Edition on Mental Health and Social Media

The eighth edition of the workshop on Mining Actionable Insights from Social Networks (MAISoN 2022) took place virtually on April 26th, 2022, co-located with the ACM Web Conference 2022 (WWW 2022). This year, we organized a special edition with focus on mental health and social media. The aim of this edition was to bring together researchers from different disciplines to discuss research that goes beyond descriptive analysis of social media data and instead investigate different techniques that use social media data for building diagnostic, predictive and prescriptive analysis models for mental health applications. This topic attracted a lot of interest from the community especially because of all the considerations surrounding the impact of social media during the COVID-19 pandemic which has impacted on people's mental health issues. © 2022 Owner/Author.

A Comparative Analysis and Prediction Model for Diagnosing Covid19 Surge on People Having Anxiety by Using Machine Learning Techniques

Due to COVID-19 and pandemic emergency might affect the safety, health and well-being both as individuals (causes insecurity, stigma, emotional isolation and confusion). All communities (results deficient distribution of necessities, economic loss of jobs and school closures, insufficient resources for medical responses). These will effect to translate into a scale of emotional reaction (distress or psychiatric scenarios), lack of health symptoms (extra substance in use), and with noncompliance with public health regulations (such as home confidence and vaccination) in population who influences with stress, depression and anxiety. Addition to this autism ASD(Autism Spectrum Disorder) affected personalities might undergoes with more panic with these unexpected pandemic situations especially with children. Environmental situations that will affects communications and reciprocal social interactions, repetitive behaviors. To classify and predict these types of effected people. This research initially classifies people based on dictionaries (+ve and ve) as keywords with high variance by bagged trees for training observations. The next level is to filter the people based on the dictionaries and their commonalities with respect to distance vector mechanism with mixed attributed data. Dramatically altered the predictions of people by using these classified results in the prediction of learned tree (bagged tree) from support vector machine (SVM) and Random Forest (RF) approaches. With SVM and RF high regression was found in perspective of predicting the people who are suffering from anxiety, stress and depression during this pandemic time by considering with various other parameters. © 2022 IEEE.

New Ideas and Methods of Coping Mechanism for Infectious Diseases Based on Big Data: A Critical Literature Review

Based on big data analysis, we discuss how to formulate an optimal coping mechanism for infectious diseases, especially major and emerging infectious diseases. First, by combining big data analysis and statistical analysis model and deducing whether the emerging disease is contagious, the strength of the contagion effect and the possible consequences, this study will determine whether the corresponding coping strategies should be implemented for infectious diseases, especially major and emerging infectious diseases. Secondly, according to the inspection results and actual situation, the optimal coping strategy is formulated to minimize the loss of life and property security of the country and the society by using the optimization principle and the objective management in management science. Finally, the statistical analysis method and the six sigma principle are combined to develop a feedback mechanism to evaluate whether the formulated coping strategies can achieve the expected results in practice. Our research has improved the research framework of infectious diseases in theory and provided scientific reference and experience for the major and emerging infectious diseases in practice for the future. © 2022 IEEE.

Using IoT Assistive Technologies for Older People Non-Invasive Monitoring and Living Support in Their Homes.

Many western societies are confronted with issues in planning and adapting their health policies due to an ageing population living alone. The "NOt Alone at Home-NOAH" project aimed to involve older people in the Agile co-creation of services for a collaborative monitoring and awareness notification for remote caregivers. Our research aim was to create a scalable and modern information system that permitted a non-invasive monitorization of the users for keeping their caregivers up to date. This was done via a cloud IoT (Internet of Things), which collects and processes data from its domotic sensors. The notifications generated by the system, via the three applications we developed (NOAH/NOAH Care/Admin Centre), offer caregivers an easy way of detecting changes in the day-to-day behaviour and activities of their patients, giving them time to intervene in case of abnormal activity. Such an approach would lead to a longer and more independent life for the older people. We evaluated our system by conducting a year-long pilot-study, offering caregivers constant information from the end-users while still living independently. For creating our pilot groups, we used the ABAS (Adaptive Behaviour Assessment System) II, which we then matched with the pre-profiled Behavioral Analysis Models of older people familiar with modern communication devices. Our results showed a low association between daily skills and the sensors we used, in contrast with the results from previous studies done in this field. Another result was efficiently capturing the behaviour changes that took place due to the COVID-19 Lockdown measures.

Multi-Modal Sarcasm Detection in Social Networks: A Comparative Review

Sentiment Analysis (SA) has become an extremely sought after area of research especially post COVID-19 when people used to spent a lot of time on the social media to interact with each other. This interaction was done through posts having both textual and visual cues and also by participating in online discussions forums. Some of the inherent challenges encountered in the process of SA include discernment of sarcasm, irony, humor, negation, multi-polarity or Aspect-Level Sentiment Analysis (ASA) etc. Researchers are now gradually shifting their focus to the identification and detection of sarcasm and how it can empower SA. Sarcasm expresses a person's downside feelings by using positive words in an implicit way. It also has an overall impact on increasing the efficiency of the SA models. Eliciting sarcastic statements is tough for humans as well as for machines without the knowledge of the context or background in which it is expressed, body language and/or facial expression of the speaker and his voice modulation. This review paper studies some of the approaches used for sarcasm detection and also guides researchers in exploring the different modalities of data for developing applications like a virtual chat-bot or assistant, depression analysis, stress management system at workplace etc. © 2022 IEEE.

How Does Energy Efficiency Mitigate Carbon Emissions Without Reducing Economic Growth in Post COVID-19 Era

A comprehensive analytical study to assess the performance level of industrial functions in the environment has become necessary at the present time. According to existing research, the COVID-19 pandemic resulted in a significant reduction in carbon emissions in 2020. Policymakers are focusing on the discrepancies and negative environmental effect caused by various industries during their routine operations. This study aims to estimate the performance level of energy in the context of the environment of the countries that are members of the European Union This evaluation is performed through a data envelopment analysis (DEA) model, through which we have applied a non-proportional adjustment, taking into account the input of energy and its undesirable output. The DEA model allows dynamic assessment of sources in the field of measuring energy efficiency and its environmental effects. The score of measurement of efficiency lies between zero and one, which means China and Russia are awarded this score of one (1), which shows the highest level of efficiency in clean energy, while Bangladesh (0.19), Uzbekistan (0.09), Mongolia and Cambodia (0.06), and Kyrgyzstan (0.04) are at the lowest level of performance in clean energy. The results of the study showed that clean energy efficiency levels increased in all countries over the study period. The emission level of greenhouse gases in the first world countries was found to be better in the context of improvement in performance enhancement in the sector of the energy mix. Evasion score is measured as 365 kt of CO2. This score for NO2 is 280 kt and for SO2 is 82 kt, whereas it is 23 kt (0.24 kg/cap) of particulate hazardous matter. The higher performance level of energy yields a negative relationship with emissions of gases, with a significant number of 12% for NO2 in 2000, as compared to 13% for SO2 and 14% for PM2.5. Whereas PM10 has the highest concentration (18%). Public policymakers may enhance the facilitation system for better free trade and a result-oriented corporate environment to enhance the performance level of energy in the electric sector. Copyright © 2022 Li, Yao-Ping Peng, Nazar, Ngozi Adeleye, Shang and Waqas.

Analysis Model of Epidemic Speech based on BiLSTM and MCNN Structure

An analysis model of epidemic speech based on BiLSTM and MCNN structure is proposed in order to know the news and information about COVID-19 in time and the views and focus of citizens on the situation. BERT pre-training model is used to extract word vectors, and then the information of bidirectional long-term and short-term memory network and convolution neural network models at different levels is fused. Finally, the speech is classified into two categories, and whether its emotion is positive or negative is calculated. Experimental results show that this model can better classify the polarity of speech emotion than the previous word vector model and the traditional neural network model. © 2021 IEEE.