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Prediction of building occupancy is a very important element of structural health monitoring (SHM), smart building systems, and energy management. In this study, a number of visualization tools are used to analyze the pe...

Abstract The integration of large-scale distributed power sources complicates grid topology and intensifies spatiotemporal coupling in power data. This complicates the analysis of intrinsic correlations and makes predict...

Abstract Accurate estimation of the shear capacity of steel fiber reinforced concrete (SFRC) beams without stirrups remains challenging because the response is governed by strongly coupled geometric, material, and reinfo...

Viscoplastic deformation of mantle rocks produces crystal preferred orientations (CPO or texture) of olivine and, by consequence, an anisotropic mechanical response for subsequent deformation events. Olivine polycrystals...

Medical image prediction plays a very significant role in clinical decision-making and early detection and diagnosis of different diseases. However, the quality of medical images has a huge impact on the predictive model...

IntroductionWe evaluated whether a generative adversarial network (GAN)-based super-resolution (SR) radiomics pipeline could improve the pre-fine-needle aspiration (FNA) prediction of nondiagnostic (Bethesda I) outcomes ...

The adoption of artificial intelligence (AI) in cardiovascular disease prediction has significantly improved risk stratification, offering new avenues for early diagnosis and preventive care. With the growing availabilit...

The management and operation of the power system would be significantly enhanced if accurate prior knowledge of power exchanges were provided, which could provide valuable insights into the system and help predict the re...

Abstract Background Surgical site infections (SSIs) continue to exert a substantial burden on healthcare systems, particularly in resource-limited settings where they contribute to prolonged hospitalizations, escalated c...

The growth of variable renewable energy sources has increased the occurrence of curtailment in power systems, particularly in regions affected by transmission constraints and limited operational flexibility. Reliable for...

INTRODUCTION: With the deepening of China's "Dual Carbon" goals, the scale of photovoltaic (PV) installations continues to expand, placing stringent requirements on the accuracy, scenario adaptability, and robustness of ...

ObjectiveTo evaluate a novel multichannel deep learning (DL) model using contrast-enhanced ultrasound (CEUS) data with multiple regions of interest (ROIs) and time-intensity curve (TIC)-derived key frames for predicting ...

Abstract This study presents a novel hybrid methodology combining machine learning (ML) with conventional reservoir simulation to optimize waterflooding in the geologically complex Bahariya Formation, Western Desert, Egy...

Abstract Background Interstitial lung disease (ILD) is characterized by marked heterogeneity and an overall poor prognosis, with many patients experiencing rapid progression and high short-term mortality. Accurate early ...

Abstract Accurate prediction of compressive strength is important for the efficient design of sustainable concrete incorporating palm oil fuel ash (POFA). This study developed a hybrid artificial neural network model opt...

Nowadays, ship safety is a significant concern, with over 50% of ship accidents occurring due to collisions. Therefore, accurately predicting the future position of a ship is essential to avoid such incidents. This is pa...

BackgroundThere is limited understanding regarding the factors that predict recreational therapy engagement among veterans in long-term care facilities. We aimed to develop and validate machine learning models to predict...

Recent advances in protein structure prediction, such as AlphaFold, have demonstrated the power of deep neural architectures like the Evoformer for capturing complex spatial and evolutionary constraints on protein confor...

This study provides a quantitative framework for wellbeing outcome prediction through intentional cognitive pattern alteration. We demonstrated 81.67% accurate prediction of wellbeing states, in a three-level classificat...

Sevgi Ünal,1 Remzi Gürfidan2 1Department of Radiology, Izmir Katip Celebi University Ataturk Training and Research Hospital, Izmir, Türkiye; 2Database, Network Design and Management, Isparta University of Applied Scie...

The foremost task in agriculture is the decisive identification of citrus plants and the timely identification of diseases in the plants with the aim of improving the quality of crops and the yield. In this work, a machi...

Abstract This study investigates whether machine learning effectively processes high-dimensional data, a challenging task for traditional predictive models, to optimize portfolio strategies. Using daily data from Decembe...

BackgroundTo explore the changes in various laboratory parameters in children with Kawasaki disease, analyze their correlations with complete and incomplete Kawasaki disease as well as coronary artery lesions and non-cor...

Key Points. Lower maternal midterm eGFR was independently associated with increased risks of low birth weight and small for gestational age. Adding eGFR to conventional risk models improved prediction of adverse birth ou...