182 Results Found

The existing spatial panel structural vector auto-regressive model can effectively capture the time and spatial dynamic dependence of endogenous variables. However, the hypothesis that the common factors have the same effect for all spatial units is...

The increasing popularity of online job vacancies and machine learning methods has raised questions about their combination to enhance our understanding of labour markets and algorithms. However, the lack of comparable studies necessitates further in...

Background/Objectives. This study aimed to improve machine learning models for diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) by comparing classical machine learning methods with newer AutoML approaches, utilizing biomarker data and...

This paper presents long- and short-term analyses and predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, and persiste...

In order to predict aeroengine wear accurately and automatically, as a predictor, support vector regression (SVR) was optimized by means of particle swarm optimization (PSO). The guided mutation strategy of PSO (GMPSO) is presented herein to determin...

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the proces...

This paper proposes a novel hybrid forecasting model with three main parts to accurately forecast daily electricity prices. In the first part, where data are divided into high- and low-frequency data using the fractional wavelet transform, the best d...

The study reveals that there is a relationship between foreign direct investments, trade, and growth rate of per capita GDP for Bangladesh with the help of annual time series data for 1973 to 2014. The Vector Error Correction Model (VECM) analysis sh...

One expanding area of bioinformatics is medical diagnosis through the categorization of biomedical characteristics. Automatic medical strategies to boost the diagnostic through machine learning (ML) methods are challenging. They require a formal exam...

For data-driven intelligent manufacturing, many important in-process parameters should be estimated simultaneously to control the machining precision of the parts. However, as two of the most important in-process parameters, there is a lack of multi-...

This paper presents an automatic piecewise (Auto-PW) extreme learning machine (ELM) method for S-parameters modeling radio-frequency (RF) power amplifiers (PAs). A strategy based on splitting regions at the changing points of concave-convex character...

The COVID 19 pandemic has had wide-ranging and severe effects on global economies. Stock markets as usual were the first to react, with drop rates as much as the global financial crises of 2008. This study uses daily data to model the dynamic impact...

Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design....

This study examines the causal relationship between economic growth, financial development, and national governance. To analyze the three-way link of the stated variables, the Panel Vector Auto-Regressive (PVAR) model was applied to 115 economies bet...

Chlorophyll content reflects plants’ photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based mode...

Based on the theoretical analysis of the relationship between China’s higher education input, technological innovation, and economic growth, this paper chooses the 1997–2015 sample data of China, and uses a vector auto regression (VAR) mo...

In response to global climate change, China has voluntarily assumed responsibility and has pledged to reach its peak in carbon emissions by 2030. Industrial structure and urbanization have important impacts on energy consumption. This paper empirical...

This research investigates the responsiveness of climate and low-carbon (green) stock returns to oil prices and conventional energy stock returns, focusing on both contemporaneous and causal relationships, during an oil crisis. Two methodologies are...

(1) Background: Although numerous artificial intelligence (AI)-based air pollution prediction models have been proposed, research that links key pollution drivers, such as regional industrial facilities, to actionable policy recommendations is requir...

This study addresses the accessibility challenges faced by individuals with visual impairments due to limited access to graphic information, which significantly impacts their educational and social integration. Traditional methods for producing tacti...

Neural auto-regressive sequence-to-sequence models have been dominant in text generation tasks, especially the question generation task. However, neural generation models suffer from the global and local semantic semantic drift problems. Hence, we pr...

Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various gree...

This research observes a time varying relationship between stock returns, volatilities and the online search volume in regard to selected CESEE (Central, Eastern and South-Eastern European) stock markets. The main hypothesis of the research assumes t...

This study aims to evaluate the effect of electricity production on industrial development and sustainable economic growth. In this context, Brazil, Russia, India, China, and South Africa (BRICS), countries which have the highest increase in electric...

This study analyzes the relationship between governmental financial support and university performance to determine whether the former enhances the latter or if universities enhance performance to attain government financial support. The types of fin...

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which pr...

This survey paper discusses the Cointegrated Vector AutoRegressive (CVAR) methodology and how it has evolved over the past 30 years. It describes major steps in the econometric development, discusses problems to be solved when confronting theory with...

Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we a...

Energy prices (EPs) play an imperative role in South Asian Country (SAC) Gross Domestic Product (GDP). This research empirically examines the influence of sustainable energy price shocks (EPSs) on macroeconomic indicators. The study is to forecast th...

The Kingdom of Saudi Arabia is one of the countries that seek to achieve sustainable development through Vision 2030. The objective of this research is to study the impact of digitization to ensure the competitiveness of the Ha’il region to ach...

Joint time series of wave height, period and direction are essential input data to computational models which are used to simulate diachronic beach evolution in coastal engineering. However, it is often impractical to collect a large amount of the re...

Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics dat...

The modeling and forecasting of BBS (Bulletin Board System) posts time series is crucial for government agencies, corporations and website operators to monitor public opinion. Accurate prediction of the number of BBS posts will assist government agen...

Forecasting stock market prices is an exciting knowledge area for investors and traders. Successful predictions lead to high financial revenues and prevent investors from market risks. This paper proposes a novel hybrid stock prediction model that im...

Capturing long-term statistics of signals and time series is important for modeling recurrent phenomena, especially when such recurrences are a-periodic and can be characterized by the approximate repetition of variable length motifs, such as pattern...

Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failur...

Through an investigation into seven major industries in China’s stock market from 2002 to 2013, this study focuses on two main external determinants: market condition and policy change on intra-industry information diffusion. We employ both tim...

In this paper, we tried to contribute to the previous literature by analyzing the relationship between renewable energy consumption, socio-economic factors and health in the presence of a stringent environmental policy and lobbying power. Using a Pan...

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse...

With the global economic situation still uncertain and various businesses interconnected within the finance system, financial hazards exhibit characteristics such as rapid propagation and wide scope. Therefore, it is of great significance to analyze...

Venture capital investment and hedge fund investment are two asset classes of alternative investment fund portfolios. The purpose of this study was to determine whether the digital currency named bitcoin truly adds to diversification in an alternativ...

A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight...

The main purpose of the current study is to investigate if tourism affects economic growth of China. The data set has been acquired from the Beijing Municipal Bureau of Statistics, and the time span of the data set takes into account a 20-year time p...

The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and STOXX50...

The SARS-CoV-2 global pandemic prompted governments, institutions, and researchers to investigate its impact, developing strategies based on general indicators to make the most precise predictions possible. Approaches based on epidemiological models...

In the present paper, we focus on the macroeconomic implications of the populist public policies and in particular on the effects of wage led growth strategy, which started to be applied in Romania from January 2017. Such a study is important as a pr...

Among China’s five major industries, the logistics industry is the only one in which carbon emission intensity is continuing to increase, so it is of great importance in developing a low-carbon economy for China. Thus, some scholars have learne...

Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for...

This paper investigates the impact of recent financial crisis on six major stock markets during the three periods. To measure the impact of the crisis on different stock markets, we have applied a vector auto-regression (VAR) model and conducted Gran...

The study aims at examining how fiscal deficits affect the performance of the stock market in India by using annual data from 1988–2012. The study makes use of Ng-Perron unit root tests to check the non-stationarity property of the series; the Auto...