AIMC Topic: Machine Learning

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Predicting Low Cognitive Ability at Age 5-Feature Selection Using Machine Learning Methods and Birth Cohort Data.

International journal of public health
In this study, we applied the random forest (RF) algorithm to birth-cohort data to train a model to predict low cognitive ability at 5 years of age and to identify the important predictive features. Data was from 1,070 participants in the Irish pop...

A TastePeptides-Meta system including an umami/bitter classification model Umami_YYDS, a TastePeptidesDB database and an open-source package Auto_Taste_ML.

Food chemistry
Taste peptides with umami/bitterness play a role in food attributes. However, the taste mechanisms of peptides are not fully understood, and the identification of these peptides is time-consuming. Here, we created a taste peptide database by collecti...

Small UAS Online Audio DOA Estimation and Real-Time Identification Using Machine Learning.

Sensors (Basel, Switzerland)
The wide range of unmanned aerial system (UAS) applications has led to a substantial increase in their numbers, giving rise to a whole new area of systems aiming at detecting and/or mitigating their potentially unauthorized activities. The majority o...

Experiments on Adversarial Examples for Deep Learning Model Using Multimodal Sensors.

Sensors (Basel, Switzerland)
Recently, artificial intelligence (AI) based on IoT sensors has been widely used, which has increased the risk of attacks targeting AI. Adversarial examples are among the most serious types of attacks in which the attacker designs inputs that can cau...

Machine Learning for Industry 4.0: A Systematic Review Using Deep Learning-Based Topic Modelling.

Sensors (Basel, Switzerland)
Machine learning (ML) has a well-established reputation for successfully enabling automation through its scalable predictive power. Industry 4.0 encapsulates a new stage of industrial processes and value chains driven by smart connection and automati...

Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.

BMC medical informatics and decision making
BACKGROUND: Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have uncontrolled seizures yet incur 80% of the cost. Anti-epileptic drugs commonly result in resistance...

Deep learning-based framework for slide-based histopathological image analysis.

Scientific reports
Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images (WSIs) analysis. Major applications in digital pathology using machine learning include automatic ca...

Machine learning for morbid glomerular hypertrophy.

Scientific reports
A practical research method integrating data-driven machine learning with conventional model-driven statistics is sought after in medicine. Although glomerular hypertrophy (or a large renal corpuscle) on renal biopsy has pathophysiological implicatio...

Establishment of a model for predicting the outcome of induced labor in full-term pregnancy based on machine learning algorithm.

Scientific reports
To evaluate and establish a prediction model of the outcome of induced labor based on machine learning algorithm. This was a cross-sectional design. The subjects were divided into primipara and multipara, and the risk factors for the outcomes of indu...

Prediction of anemia using facial images and deep learning technology in the emergency department.

Frontiers in public health
BACKGROUND: According to the WHO, anemia is a highly prevalent disease, especially for patients in the emergency department. The pathophysiological mechanism by which anemia can affect facial characteristics, such as membrane pallor, has been proven ...