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As a mathematical instrument, the fractional order grey system model can be employed to characterize uncertain real-world data. This paper presents new definitions for the generalized conformable fractional accumulation (GCFA) and the generalized ...

The hybrid vehicle to everything (V2X) network based on visible light communication (VLC) and radio frequency (RF) is an excellent choice for future vehicle communication. In this paper, the (user behavior)-based longitudinal-separation (LS) ...

Type 2 diabetes mellitus(T2DM) has become a major social problem threatening the health of the population; the ability to predict its prevalence can help in prevention and early treatment. Existing prediction methods face difficult discovery of ...

Over the years, machine learning (ML) algorithms have proven their ability to make reliable predictions of the toxicity of metal oxide nanoparticles. This paper proposed a predictive ML model of the potential toxicity of metal oxide nanoparticles. ...

Recessive GJB2 variants, the most common genetic cause of hearing loss, may contribute to progressive sensorineural hearing loss (SNHL). The aim of this study is to build a realistic predictive model for GJB2-related SNHL using machine ...

• GJB2 mutations are a leading cause of hearing loss worldwide.
• Slow progression is a hallmark of GJB2-related hearing impairment.
• Machine learning allows accurate prediction hearing outcome of GJB2-related hearing impairment.
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• An multi-strategy sparrow search algorithm with excellent search ability is proposed.
• A novel light gradient boosting machine is presented to establish the model.
• SHAP is used to identify significant features affecting high arch ...

Dam stress is an important physical quantity to assess high arch dam safety during the operation period. To address the issues of low prediction capability and poor interpretability for high arch dam stress, a prediction model for high arch dam ...

Air pollution has been a significant issue in recent years due to rising industrialization and maritime activity around the globe, making air pollution forecasting a crucial concept in environmental study. This prompted the deployment of principal ...

Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches ...

The emergence of 6G networks initiates significant transformations in the communication technology landscape. Yet, the melding of quantum computing (QC) with 6G networks although promising an array of benefits, particularly in secure ...

This paper presents a plan for preparing for changes in the business environment by analyzing and predicting business district data in Seoul. The COVID-19 pandemic and economic crisis caused by inflation have led to an increase in store closures ...

Regression modelling often presents a trade-off between predictiveness and interpretability. Highly predictive and popular tree-based algorithms such as Random Forest and boosted trees predict very well the outcome of new observations, but the ...

Constructing a deformation prediction model for dams that can accurately capture deformation trends is crucial to ensure their operational safety. The accurate monitoring data are critical to the presentation of the variation patterns of the ...

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients ...

• The detection of coronary heart disease in diabetes patients and taking appropriate preventive measures will reduce the disease burden of diabetes mellitus.
• Through the analysis of more than 300,000 diabetes patients in southwest China,...

• The ROI quality can be assessed irrespective of categories under poor light exposure.

Lighting conditions can affect the performance of vision-based robots in manufacturing. This paper presents a predictive exposure control method that allows the acquisition of high-quality images in real time under poor lighting ...

Cardiometabolic multimorbidity (CMM) is increasing globally as a result of lifestyle changes and the aging population. Even though previous studies have examined risk factors associated with CMM, there is a shortage of prediction ...

• Lifestyle factors, metabolic health and psychological trait can predict cardiometabolic multimorbidity risk.
• Prediction models that include multiple factors have shown good discrimination.
• The sex-specific nomograms of ...

Amyotrophic lateral sclerosis (ALS) is a progressive and lethal neurodegenerative disease. Several studies have suggested the involvement of cuproptosis in its pathogenesis. In this research, we intend to explore the cuproptosis-...

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• Cuproptosis-related genes are involved in the pathogenesis of ALS.
• Two distinct clusters were identified in the molecular characteristics.
• The model accurately predicts the risk of ALS.
• This study provides the first integrated ...

Machine learning (ML) has been suggested as a promising tool in the design of telecommunication systems to overcome the challenges of traditional methods and meet the requirements regarding future cellular networks. As an enabler of 5G ...

Quantum computing (QC) holds the potential to revolutionize computing by solving complex problems exponentially faster than classical computers, transforming fields such as cryptography, optimization, and scientific simulations. To unlock ...

This paper establishes a BP neural network model based on the VS database to predict the sales volume of the six samples requested in the coming week, and introduces a genetic algorithm to optimize the shortcomings of the original model, which ...

Aiming at the problems that the current artificial intelligence (AI) large model can not accurately carry out small-sample time series data prediction and the sample incremental method was not applicable to small-sample time series data prediction, we ...