AIMC Topic: Machine Learning

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Convolutional Neural Network-Based Discriminator for Outlier Detection.

Computational intelligence and neuroscience
Noise in training data increases the tendency of many machine learning methods to overfit the training data, which undermines the performance. Outliers occur in big data as a result of various factors, including human errors. In this work, we present...

Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease.

Frontiers in endocrinology
BACKGROUND: There are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transsphenoidal surgery (TSS) in patients diagnosed with histology-positive Cushing's disease (CD).

Prediction of Sepsis in COVID-19 Using Laboratory Indicators.

Frontiers in cellular and infection microbiology
BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients' condition. We...

Analysis of protein determinants of host-specific infection properties of polyomaviruses using machine learning.

Genes & genomics
BACKGROUND: The large tumor antigen (LT-Ag) and major capsid protein VP1 are known to play important roles in determining the host-specific infection properties of polyomaviruses (PyVs).

A Review: Point Cloud-Based 3D Human Joints Estimation.

Sensors (Basel, Switzerland)
Joint estimation of the human body is suitable for many fields such as human-computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or...

A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.

Sensors (Basel, Switzerland)
Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods ...

Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors.

Sensors (Basel, Switzerland)
Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data from activity classes that are not represented in the training data of a Machine Learning (ML) algorithm. OOD data are a challenge to classify accurately for ...

Development of a Machine Learning Model Using Electronic Health Record Data to Identify Antibiotic Use Among Hospitalized Patients.

JAMA network open
IMPORTANCE: Comparisons of antimicrobial use among hospitals are difficult to interpret owing to variations in patient case mix. Risk-adjustment strategies incorporating larger numbers of variables haves been proposed as a method to improve compariso...