Research

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21 pages, 2485 KiB

Open AccessArticle

Investigating the Impact of COVID-19 on E-Learning: Country Development and COVID-19 Response

by Mirjana Pejić Bach, Božidar Jaković, Ivan Jajić and Maja Meško

Mathematics 2023, 11(6), 1520; https://0-doi-org.brum.beds.ac.uk/10.3390/math11061520 - 21 Mar 2023

Cited by 3 | Viewed by 1287

Due to its severity, the outbreak of COVID-19 led to unprecedented levels of social isolation that affected educational institutions, among others. Digital technologies such as cloud computing and video broadcasting helped the adoption of e-learning during the crisis. However, the speed and efficiency [...] Read more.

Due to its severity, the outbreak of COVID-19 led to unprecedented levels of social isolation that affected educational institutions, among others. Digital technologies such as cloud computing and video broadcasting helped the adoption of e-learning during the crisis. However, the speed and efficiency of e-learning adoption during the COVID-19 period varied across countries. This paper compares the adoption of e-learning in European countries before and during the COVID-19 pandemic and the relationship between the pandemic, e-learning, and economic development. First, the adoption of e-learning in European countries before and during the pandemic is compared. Second, using fuzzy C-means clustering, homogeneous groups of European countries are formed based on e-learning indicators for the periods before and during the pandemic. Third, GDP per capita is used as an indicator of economic development and severity indices are used as an indicator of the severity of the response to the pandemic to compare the different clusters. The research results show that economically and digitally advanced countries led the adoption of e-learning in both the period before and the period during the pandemic. However, they also responded less strictly to the pandemic. Less-advanced countries responded more strictly to the pandemic, likely due to a lack of healthcare resources, and also fell behind in the adoption of e-learning. Full article

(This article belongs to the Special Issue Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech)

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13 pages, 791 KiB

Open AccessArticle

NFTs and Cryptocurrencies—The Metamorphosis of the Economy under the Sign of Blockchain: A Time Series Approach

by Simona Andreea Apostu, Mirela Panait, Làszló Vasa, Constanta Mihaescu and Zbyslaw Dobrowolski

Mathematics 2022, 10(17), 3218; https://0-doi-org.brum.beds.ac.uk/10.3390/math10173218 - 05 Sep 2022

Cited by 12 | Viewed by 4951

Abstract

Although NFTs (non-fungible tokens) and cryptocurrencies are active on the same market, their prices are not so closely related over time. The objective of this paper is to identify the relationship between the two types of assets (NFTs and the cryptocurrencies Ethereum, Crypto [...] Read more.

Although NFTs (non-fungible tokens) and cryptocurrencies are active on the same market, their prices are not so closely related over time. The objective of this paper is to identify the relationship between the two types of assets (NFTs and the cryptocurrencies Ethereum, Crypto Coin, and Bitcoin), using data for the period between September 2020 until February 2022. The conclusions of the study are useful for cryptocurrency and NFT issuers, but also for investors on the financial market who are reconfiguring their portfolios with increasing frequency, and use these new assets for speculative or hedging purposes based on blockchain technology. The results highlighted relationships between NFTs and Ethereum, between Ethereum and Crypto Coin, and between Bitcoin and Ethereum, Ethereum being a bridge between all four. Therefore, NFTs present a relationship with Ethereum, the NFTs price had a causal effect on the price of Ethereum. Full article

(This article belongs to the Special Issue Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech)

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15 pages, 2065 KiB

Open AccessArticle

Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models

by Yunsun Kim and Sahm Kim

Mathematics 2021, 9(18), 2347; https://0-doi-org.brum.beds.ac.uk/10.3390/math9182347 - 21 Sep 2021

Cited by 1 | Viewed by 1828

Abstract

This study was conducted to investigate the applicability of measuring internet traffic as an input of short-term electricity demand forecasts. We believe our study makes a significant contribution to the literature, especially in short-term load prediction techniques, as we found that Internet traffic [...] Read more.

This study was conducted to investigate the applicability of measuring internet traffic as an input of short-term electricity demand forecasts. We believe our study makes a significant contribution to the literature, especially in short-term load prediction techniques, as we found that Internet traffic can be a useful variable in certain models and can increase prediction accuracy when compared to models in which it is not a variable. In addition, we found that the prediction error could be further reduced by applying a new multivariate model called VARX, which added exogenous variables to the univariate model called VAR. The VAR model showed excellent forecasting performance in the univariate model, rather than using the artificial neural network model, which had high prediction accuracy in the previous study. Full article

(This article belongs to the Special Issue Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech)

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16 pages, 7595 KiB

Open AccessArticle

A Study on Facial Expression Change Detection Using Machine Learning Methods with Feature Selection Technique

by Sang-Ha Sung, Sangjin Kim, Byung-Kwon Park, Do-Young Kang, Sunhae Sul, Jaehyun Jeong and Sung-Phil Kim

Mathematics 2021, 9(17), 2062; https://0-doi-org.brum.beds.ac.uk/10.3390/math9172062 - 26 Aug 2021

Cited by 1 | Viewed by 2644

Abstract

Along with the fourth industrial revolution, research in the biomedical engineering field is being actively conducted. Among these research fields, the brain–computer interface (BCI) research, which studies the direct interaction between the brain and external devices, is in the spotlight. However, in the [...] Read more.

Along with the fourth industrial revolution, research in the biomedical engineering field is being actively conducted. Among these research fields, the brain–computer interface (BCI) research, which studies the direct interaction between the brain and external devices, is in the spotlight. However, in the case of electroencephalograph (EEG) data measured through BCI, there are a huge number of features, which can lead to many difficulties in analysis because of complex relationships between features. For this reason, research on BCIs using EEG data is often insufficient. Therefore, in this study, we develop the methodology for selecting features for a specific type of BCI that predicts whether a person correctly detects facial expression changes or not by classifying EEG-based features. We also investigate whether specific EEG features affect expression change detection. Various feature selection methods were used to check the influence of each feature on expression change detection, and the best combination was selected using several machine learning classification techniques. As a best result of the classification accuracy, 71% of accuracy was obtained with XGBoost using 52 features. EEG topography was confirmed using the selected major features, showing that the detection of changes in facial expression largely engages brain activity in the frontal regions. Full article

(This article belongs to the Special Issue Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech)

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26 pages, 1705 KiB

Open AccessArticle

Blockchain Technology for Secure Accounting Management: Research Trends Analysis

by Emilio Abad-Segura, Alfonso Infante-Moro, Mariana-Daniela González-Zamar and Eloy López-Meneses

Mathematics 2021, 9(14), 1631; https://0-doi-org.brum.beds.ac.uk/10.3390/math9141631 - 10 Jul 2021

Cited by 36 | Viewed by 6792

Abstract

The scope of blockchain technology, initially associated with the cryptocurrency Bitcoin, is greater due to the multiple applications in various disciplines. Its use in accounting lies mainly in the fact that it reduces risks and the eventuality of fraud, eliminates human error, promotes [...] Read more.

The scope of blockchain technology, initially associated with the cryptocurrency Bitcoin, is greater due to the multiple applications in various disciplines. Its use in accounting lies mainly in the fact that it reduces risks and the eventuality of fraud, eliminates human error, promotes efficiency, and increases transparency and reliability. This means that different economic sectors assume it as a recording and management instrument. The aim is to examine current and emerging research lines at a global level on blockchain technology for secure accounting management. The evolution of the publication of the number of articles between 2016 and 2020 was analyzed. Statistical and mathematical techniques were applied to a sample of 1130 records from the Scopus database. The data uncovered a polynomial trend in this period. The seven main lines of work were identified: blockchain, network security, information management, digital storage, edge computing, commerce, and the Internet of Things. The ten most outstanding emerging research lines are detected. This study provides the past and future thematic axes on this incipient field of knowledge, which is a tool for decision-making by academics, researchers, and directors of research investment programs. Full article

(This article belongs to the Special Issue Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech)

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16 pages, 1132 KiB

Open AccessArticle

Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility

by Jong-Min Kim, Chulhee Jun and Junyoup Lee

Mathematics 2021, 9(14), 1614; https://0-doi-org.brum.beds.ac.uk/10.3390/math9141614 - 08 Jul 2021

Cited by 21 | Viewed by 5676

Abstract

This study examines the volatility of nine leading cryptocurrencies by market capitalization—Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models. We find that when we deal with extremely volatile financial [...] Read more.

This study examines the volatility of nine leading cryptocurrencies by market capitalization—Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models. We find that when we deal with extremely volatile financial data, such as cryptocurrencies, the SV model performs better than the GARCH family models. Moreover, the forecasting errors of the SV model, compared with the GARCH models, tend to be more accurate as forecast time horizons are longer. This deepens our insight into volatility forecast models in the complex market of cryptocurrencies. Full article

(This article belongs to the Special Issue Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech)

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18 pages, 459 KiB

Open AccessArticle

Deep Learning-Based Survival Analysis for High-Dimensional Survival Data

by Lin Hao, Juncheol Kim, Sookhee Kwon and Il Do Ha

Mathematics 2021, 9(11), 1244; https://0-doi-org.brum.beds.ac.uk/10.3390/math9111244 - 28 May 2021

Cited by 18 | Viewed by 6856

Abstract

With the development of high-throughput technologies, more and more high-dimensional or ultra-high-dimensional genomic data are being generated. Therefore, effectively analyzing such data has become a significant challenge. Machine learning (ML) algorithms have been widely applied for modeling nonlinear and complicated interactions in a [...] Read more.

With the development of high-throughput technologies, more and more high-dimensional or ultra-high-dimensional genomic data are being generated. Therefore, effectively analyzing such data has become a significant challenge. Machine learning (ML) algorithms have been widely applied for modeling nonlinear and complicated interactions in a variety of practical fields such as high-dimensional survival data. Recently, multilayer deep neural network (DNN) models have made remarkable achievements. Thus, a Cox-based DNN prediction survival model (DNNSurv model), which was built with Keras and TensorFlow, was developed. However, its results were only evaluated on the survival datasets with high-dimensional or large sample sizes. In this paper, we evaluated the prediction performance of the DNNSurv model using ultra-high-dimensional and high-dimensional survival datasets and compared it with three popular ML survival prediction models (i.e., random survival forest and the Cox-based LASSO and Ridge models). For this purpose, we also present the optimal setting of several hyperparameters, including the selection of a tuning parameter. The proposed method demonstrated via data analysis that the DNNSurv model performed well overall as compared with the ML models, in terms of the three main evaluation measures (i.e., concordance index, time-dependent Brier score, and the time-dependent AUC) for survival prediction performance. Full article

(This article belongs to the Special Issue Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech)

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Review

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20 pages, 384 KiB

Open AccessReview

Design and Experience of Mobile Applications: A Pilot Survey

by Mudita Sandesara, Umesh Bodkhe, Sudeep Tanwar, Mohammad Dahman Alshehri, Ravi Sharma, Bogdan-Constantin Neagu, Gheorghe Grigoras and Maria Simona Raboaca

Mathematics 2022, 10(14), 2380; https://0-doi-org.brum.beds.ac.uk/10.3390/math10142380 - 06 Jul 2022

Cited by 11 | Viewed by 6735

Abstract

With the tremendous growth in mobile phones, mobile application development is an important emerging arena. Moreover, various applications fail to serve the purpose of getting the attention of the intended users, which is determined by their User Interface (UI) and User Experience (UX). [...] Read more.

With the tremendous growth in mobile phones, mobile application development is an important emerging arena. Moreover, various applications fail to serve the purpose of getting the attention of the intended users, which is determined by their User Interface (UI) and User Experience (UX). As a result, developers often find it challenging to meet the users’ expectations. To date, several reviews have been carried out which explored various aspects of design and the experience of mobile applications using UX/UI. However, many of these existing surveys primarily focused on only some of the issues in isolation but did not consider all the major parameters such as visualisation/graphics, context, user behaviour/emotions/control, usability, adaptability/flexibility, language, and feedback. In our pilot survey, we gathered the preferences and perceptions of a heterogeneous group of concerned people and considered all the aforementioned parameters. These preferences would serve as a reference to mobile application developers, giving them useful insights. Our proposed approach would help mobile application developers and designers focus on the particular UI/UX problems of mobile applications as per their relevant context. A comparative analysis of the various UI and UX factors that determine a mobile application interface is presented in this paper. Full article

(This article belongs to the Special Issue Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech)

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