AI Medical Compendium Topic:
Support Vector Machine

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Coupled Multimodal Emotional Feature Analysis Based on Broad-Deep Fusion Networks in Human-Robot Interaction.

IEEE transactions on neural networks and learning systems
A coupled multimodal emotional feature analysis (CMEFA) method based on broad-deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracte...

Analyzing speed-difference impact on freeway joint injury severities of Leading-Following vehicles using statistical and data-driven models.

Accident; analysis and prevention
Rear-end (RE) crashes are notably prevalent and pose a substantial risk on freeways. This paper explores the correlation between speed difference among the following and leading vehicles (Δν) and RE crash risk. Three joint models, comprising both unc...

Explainable artificial intelligence for LDL cholesterol prediction and classification.

Clinical biochemistry
INTRODUCTION: Monitoring LDL-C levels is essential in clinical practice because there is a direct relation between low-density lipoprotein cholesterol (LDL-C) levels and atherosclerotic heart disease risk. Therefore, measurement or estimate of LDL-C ...

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews.

Scientific reports
This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is i...

Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements.

Environmental research
Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite mate...

Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches.

Medical engineering & physics
Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal...

Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Třeboň fishponds, Czech Republic.

The Science of the total environment
Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This s...

Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.

Journal of assisted reproduction and genetics
PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data.

Machine learning-based Cerebral Venous Thrombosis diagnosis with clinical data.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorit...

ESCCPred: a machine learning model for diagnostic prediction of early esophageal squamous cell carcinoma using autoantibody profiles.

British journal of cancer
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically ideal biomarkers for early diagnosis. The objective of this study was to develop and validate a user-friendly diagnostic tool for early ESCC detection.