Game user perception is of great significance for game developers and network operators for improving service quality and operational efficiency. At present, the most common approach is to use the linear model that considers only the impact of networ...
The prediction of stock price movement is a popular area of research in academic and industrial fields due to the dynamic, highly sensitive, nonlinear and chaotic nature of stock prices. In this paper, we constructed a convolutional neural network mo...
Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negat...
Multi-label classification has been extensively researched and utilized for several decades. However, the performance of these methods is highly susceptible to the presence of noisy data samples, resulting in a significant decrease in accuracy when n...
Extracellular vesicle (EV) transcriptomic data provides a high-dimensional representation of cellular states but remains challenging to interpret due to noise, redundancy, and limited sample sizes. Most existing approaches rely on supervised differen...
Aiming at the difficulty in real-time measuring and the long offline measurement cycle for the content of cement clinker free lime (fCaO), it is very important to build an online prediction model for fCaO content. In this work, on the basis of Choles...
Currently, machine learning (ML) methods provide a practical approach to model complex systems. Unlike purely analytical models, ML methods can describe the uncertainties (e.g., hysteresis, temperature effects) that are difficult to deal with, potent...
Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionar...
Factor implicit thought in factor space theory can be used to solve the classification problem of machine learning. On the basis of factor implicitness, a serial sweeping class classification algorithm is proposed, and a factor implicit model is cons...
The online sequential extreme learning machine with persistent regularization and forgetting factor (OSELM-PRFF) can avoid potential singularities or ill-posed problems of online sequential regularized extreme learning machines with forgetting factor...
Long-term sequelae of coronavirus disease 2019 (COVID-19) infection are common and can have debilitating consequences. There is a need to understand risk factors for Long COVID-19 to give impetus to the development of targeted yet holistic clinical a...
The aim of this article is to develop a machine learning (ML)-based predictive model for industrial accidents in the energy sector. The dataset used in this study was obtained from the Kaggle platform and consists of summaries derived from reports of...
Securing online financial transactions has become a critical concern in an era where financial services are becoming more and more digital. The transition to digital platforms for conducting daily transactions exposed customers to possible risks from...
In this research, we focused on armed conflicts and related violence. The study reviewed the use of machine learning to predict the likelihood of conflict escalation and the role of conditioning factors. The results showed that machine learning and p...
This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studie...
Nowadays, to deal with the increasing data of users and items and better mine the potential relationship between the data, the model used by the recommendation system has become more and more complex. In this case, how to ensure the prediction accura...
The dynamic servo error of heavy-duty vertical machining centres is one of the decisive factors affecting the machining accuracy of large and complex parts. Due to the characteristics of large mass, large load, and the large travel distance of the ma...
College dropout represents a significant challenge for universities, and despite advances in machine learning technologies, predicting dropout remains a complex task. This literature review focuses on investigating the factors that influence college...
Rainfall erosivity factor (R-factor) is one of the Universal Soil Loss Equation (USLE) input parameters that account for impacts of rainfall intensity in estimating soil loss. Although many studies have calculated the R-factor using various empirical...
Scholars and investors have been interested in factor models for a long time. This paper builds models using the monthly data of the A-share market. We construct a seven-factor model by adding the Hurst exponent factor and the momentum factor to a Fa...
Batrachospermaceae is the largest family of freshwater red algae, widely distributed around the world, and plays an important role in maintaining the balance of spring and creek ecosystems. The deterioration of the current global ecological environme...
Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning...
To reduce the disasters caused by slope instability, this paper proposes a new machine learning (ML) model for slope stability prediction. This improved SVR model uses support vector machine regression (SVR) as the basic prediction tool and the grid...
Purpose: Using a data and machine learning approach, from classical to complex, we aim to approximate the relationship between factors such as behavioral, social or comorbidity and the ejection fraction for hospitalized patients. To measure how much...
The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challen...
Background: Caregiver burden represents a multidimensional syndrome influenced by patient-related, relational, and contextual factors in neurological disorders. Although stroke, Parkinson’s disease (PD), and Alzheimer’s disease (AD) diffe...
Gouty arthritis (GA) and its association with kidney failure present significant challenges in healthcare, necessitating effective detection and management strategies. GA, characterized by the deposition of monosodium urate crystals in joints and oth...
Based on forest management inventory data, this study applies machine learning algorithms to explore the relationships between forest water conservation capacity and forest management inventory factors, thus providing more extensive insights into for...
Landslides are one of the most common natural hazards in China, and the efficient screening of important factors is crucial for landslide susceptibility mapping. Taking the Zigui–Badong section of the Three Gorges Reservoir Area (TGRA) as the s...
In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from...
Accurately predicting patient clinical outcomes is a complex task that requires integrating diverse factors, including individual characteristics, treatment histories, and environmental influences. This challenge is further exacerbated by missing dat...
This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a multi-layer neu...
Cities need photovoltaic (PV) systems to meet climate-neutral goals, yet dense urban forms and variable weather limit their output. This review synthesizes how machine learning (ML) models capture both static factors (orientation, roof, and faç...
Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling...
Age-related macular degeneration (AMD) is the leading cause of visual dysfunction and irreversible blindness in developed countries and a rising cause in underdeveloped countries. There is a current debate on whether or not cataracts are significant...
9 January 2024
(1) Background: Parkinson’s disease (PD) is a progressively worsening neurodegenerative disorder affecting movement, mental well-being, sleep, and pain. While no cure exists, treatments like hyperbaric oxygen therapy (HBOT) offer potential reli...
23 June 2021
The COVID-19 pandemic affected the whole world, but not all countries were impacted equally. This opens the question of what factors can explain the initial faster spread in some countries compared to others. Many such factors are overshadowed by the...
As type 2 diabetes becomes more prevalent across the globe, predicting its sources becomes more important. However, there is a big void in predicting the risk factors of this disease. Thus, the purpose of this study is to predict diabetes risk factor...
9 June 2020
Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning...
It is of great theoretical and practical significance to understand the inherent relationship and evolution patterns among various environmental factors in the oceans. In this study, we used scientific data obtained by the Tara Oceans Project to cond...
14 February 2026
Grassroots recyclers play a fundamental role in solid waste management in Ecuador; however, they often work under precarious conditions that may compromise their health. This study aimed to identify factors associated with nutritional status, operati...
This study sought to identify key factors in university students’ physical and mental health by using a combination of methods, including classical statistical analysis and machine learning techniques. Physical and mental health test data were...
Road traffic injuries are a major cause of morbidity and mortality worldwide and currently rank ninth globally among the leading causes of disease burden regarding disability-adjusted life years lost. Nonthaburi and Pathum Thani are parts of the grea...
As plants and animals grow, there are many factors that influence the changes that will affect how plants grow and how botanical experts distinguish them. The use of the Internet of Things (IoT) for data collection is an important part of smart agric...
Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under vario...
The use of machine learning techniques to identify contributing factors in air incidents has grown significantly, helping to identify and prevent accidents and improve air safety. In this paper, classifier models such as LS, KNN, Random Forest, Extra...
Low-temperature co-fired ceramics (LTCCs) have been attracting attention due to rapid advances in wireless telecommunications. Low-dielectric-constant (Dk) and low-dissipation-factor (Df) LTCCs enable a low propagation delay and high signal quality....
The frequency of typhoons in China has gradually increased, resulting in serious damage to low-voltage power grid lines. Therefore, it is of great significance to study the influencing factors and predict the amount of damage, which contributes to en...
The power factor in electrical power systems is of paramount importance because of the influence on the economic cost of energy consumption as well as the power quality requested by the grid. Low power factor affects both electrical consumers and sup...
In the aviation sector, human factors are the primary cause of safety incidents. Intelligent prediction systems, which are capable of evaluating human state and managing risk, have been developed over the years to identify and prevent human factors....
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