AIMC Topic: Machine Learning

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Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.

Journal of neural engineering
. Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal charact...

Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.

International journal of medical sciences
The clinical manifestation of aortic dissection (AD) is complex and varied, making early diagnosis crucial for patient survival. This study aimed to identify immune-related markers to establish a nomogram model for AD diagnosis. Three datasets from G...

Genomic and algorithm-based predictive risk assessment models for benzene exposure.

Frontiers in public health
AIM: In this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.

Development and validation of machine learning models for MASLD: based on multiple potential screening indicators.

Frontiers in endocrinology
BACKGROUND: Multifaceted factors play a crucial role in the prevention and treatment of metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine l...

A comparison of the response-pattern-based faking detection methods.

The Journal of applied psychology
The covariance index method, the idiosyncratic item response method, and the machine learning method are the three primary response-pattern-based (RPB) approaches to detect faking on personality tests. However, less is known about how their performan...

General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.

Molecular diversity
Adenosine receptors (A, A, A, A) play critical roles in cellular signaling and are implicated in various physiological and pathological processes, including inflammations and cancer. The main aim of this research was to investigate structure-activity...

A Machine Learning Model for Predicting the HER2 Positive Expression of Breast Cancer Based on Clinicopathological and Imaging Features.

Academic radiology
RATIONALE AND OBJECTIVES: To develop a machine learning (ML) model based on clinicopathological and imaging features to predict the Human Epidermal Growth Factor Receptor 2 (HER2) positive expression (HER2-p) of breast cancer (BC), and to compare its...

Adaptive hybrid ANFIS-PSO and ANFIS-GA approach for occupational risk prediction.

International journal of occupational safety and ergonomics : JOSE
This study attempted to optimize the adaptive neuro-fuzzy inference system (ANFIS) using particle swarm optimization (PSO) and a genetic algorithm (GA) for calculating occupational risk. Numerous studies have shown that the ANFIS is a good approach f...

Machine Learning-Based Diagnosis of Chronic Subjective Tinnitus With Altered Cognitive Function: An Event-Related Potential Study.

Ear and hearing
OBJECTIVES: Due to the absence of objective diagnostic criteria, tinnitus diagnosis primarily relies on subjective assessments. However, its neuropathological features can be objectively quantified using electroencephalography (EEG). Despite the exis...