This review aims to present the latest advancements in prediction models for diabetes mellitus, with a particular focus on children and adolescents. It highlights models for predicting both type 1 and type 2 diabetes in this population, emphasizing t...
Appropriate asphalt binder selection is dependent on the correct determination of maximum and minimum pavement temperatures. Temperature prediction models have been developed to determine pavement design temperatures. Accordingly, accurate temperatur...
Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gro...
Building energy usage has been an important issue in recent decades, and energy prediction models are important tools for analysing this problem. This study provides a comprehensive review of building energy prediction models and uncertainties in the...
Machine learning research focuses on the improvement of prediction performance. Progress was made with black-box models that flexibly adapt to the given data. However, due to their increased complexity, black-box models are more difficult to interpre...
Accurate and concise temperature prediction models have important applications in meteorological science, agriculture, energy, and electricity. This study aims to compare the performance of simple models and deep learning models in temperature predic...
This article provides a comprehensive overview of the development and application of statistical methods, process-based models, machine learning, and deep learning techniques in potato yield forecasting. It emphasizes the importance of integrating di...
Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches...
Time series analysis of daily stock data and building predictive models are complicated. This paper presents a comparative study for stock price prediction using three different methods, namely autoregressive integrated moving average, artificial neu...
The prediction of out-of-sample values is an interesting problem in any regression model. In the context of penalized smoothing using a mixed-model reparameterization, a general framework has been proposed for predicting in additive models but withou...
The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By dia...
Adequate forecasting and preparation for heavy rain can minimize life and property damage. Some studies have been conducted on the heavy rain damage prediction model (HDPM), however, most of their models are limited to the linear regression model tha...
We analyze return predictability for U.S. sectors based on fundamental, macroeconomic, and technical indicators and analyze whether return predictions improve tactical asset allocation decisions. We study the out-of-sample predictive power of individ...
In recent years, the population growth rate has been gradually declining in China. As the population problem becomes increasingly significant, the accurate prediction of population development trends has become a top priority, used to facilitate nati...
The performance of bituminous materials is mainly affected by the prevailing maximum and minimum temperatures, and their mechanical properties can vary significantly with the magnitude of the temperature changes. The given effect can be observed from...
Combustion of energy fuels or organic waste is associated with the emission of harmful gases and aerosols into the atmosphere, which strongly affects air quality. Air quality monitoring devices are unreliable and measurement gaps appear quite often....
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduc...
The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicl...
The presence of tinnitus does not necessarily imply associated suffering. Prediction models on the impact of tinnitus on daily life could aid medical professionals to direct specific medical resources to those (groups of) tinnitus patients with speci...
As an industry that consumes a quarter of social energy and emits a third of greenhouse gases, the construction industry has an important responsibility to achieve carbon peaking and carbon neutrality. Based on Web of Science, Science-Direct, and CNK...
Investigating forest phenology prediction is a key parameter for assessing the relationship between climate and environmental changes. Traditional machine learning models are not good at capturing long-term dependencies due to the problem of vanishin...
Among the critical global crises curbing world development and sustainability, air quality degradation has been a long-lasting and increasingly urgent one and it has been sufficiently proven to pose severe threats to human health and social welfare....
Heating, ventilation, and air-conditioning systems (HVAC) have significant potential to support demand response programs within power grids. Model Predictive Control (MPC) is an effective technique for utilizing the flexibility of HVAC systems to ach...
Traffic prediction is a critical task for intelligent transportation systems (ITS). Prediction at intersections is challenging as it involves various participants, such as vehicles, cyclists, and pedestrians. In this paper, we propose a novel approac...
Background: Acute pancreatitis is a common condition with a variable prognosis. While the overall mortality rate of acute pancreatitis is relatively low, ranging between 3 and 5% in most cases, severe forms can result in significantly higher morbidit...
The coronavirus disease 2019 (COVID-19) pandemic has placed a great burden on healthcare systems worldwide. COVID-19 clinical prediction models are needed to relieve the burden of the pandemic on healthcare systems. In the absence of COVID-19 clinica...
Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: T...
Large-scale rock formations, referred to as “rockmasses”, consist of intact rock separated by pre-existing discontinuities (i.e., joints). The mechanical behavior of rockmasses is difficult to directly test in the laboratory due to the re...
This article represents a further step in the continuously developing process of improving the prediction capabilities using databases. Its aim is to compare and evaluate the operation, performance and validity of knowledge extraction techniques rela...
The quantitative effect of seasonality on the prevalence of infectious diseases has been widely studied in epidemiological models. However, its influence in clinical prediction models has not been analyzed in great depth. In this work, we study the d...
As a rule, the economy regularly undergoes various phases, from a recession up to expansion. This paper is focused on models predicting corporate financial distress. Its aim is to analyze impact of individual phases of the economic cycle on final sco...
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlyin...
In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening...
The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications...
The performance degradation of concrete structures directly impacts their safety. As such, accurately predicting their remaining service life is critical for effective operation and maintenance management. This paper reviews the key factors influenci...
5 March 2021
Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machin...
The hydraulic conductivity of saturated soil is a crucial parameter in the study of any engineering problem concerning groundwater. Hydraulic conductivity mainly depends on particle size distribution, soil compaction, and properties that influence ag...
Chloride ion concentration significantly impacts the durability of reinforced concrete, particularly regarding corrosion. Accurately assessing how this concentration varies with the age of structures is crucial for ensuring their safety and longevity...
The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods for predicting this hazard is...
The enteric methane (CH4) emission from dairy cattle is a significant factor contributing to anthropogenic climate change and the energy loss of animals. The objective of this study was to evaluate the prediction accuracy of the existing CH4 estimati...
Wastewater infrastructure systems deteriorate over time due to a combination of aging, physical, and chemical factors, among others. Failure of these critical structures cause social, environmental, and economic impacts. To avoid such problems, infra...
Atmospheric drag is an important influencing factor in precise orbit determination and the prediction of low-orbit space debris. It has received widespread attention. Currently, calculating atmospheric drag mainly relies on different atmospheric dens...
This study utilized Chlorophyll-a, sea surface temperature (SST), and sea surface height (SSH) as the environmental variables to identify skipjack tuna catch hotspots. This study conducted statistical methods (decision tree, DT, and generalized linea...
The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learni...
To predict the corrosion failure of carbon steel oil and gas pipelines more accurately, a new corrosion failure prediction model for submarine oil and gas transport pipelines was constructed. A corrosion failure prediction management system was also...
Runoff prediction plays an extremely important role in flood prevention, mitigation, and the efficient use of water resources. Machine learning runoff prediction models have become popular due to their high computational efficiency. To select a model...
Increasing internal state security requires an understanding of the factors that influence the commission of repetitive crimes (recidivism) since the crime is not caused by public danger but by the criminal person. Against the background of informati...
This contribution exposes the relative uncertainties associated with prediction patterns of landslide susceptibility. The patterns are based on relationships between direct and indirect spatial evidence of landslide occurrences. In a spatial database...
The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total...
This study undertook to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy using serum biomarkers and clinical features. Three radical prostatectomy cohorts were used to build and validate a model of clinical...
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