AIMC Topic: Machine Learning

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AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden.

Sensors (Basel, Switzerland)
The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, t...

Sliding Window-Based Machine Learning for Environmental Inspection Resource Allocation.

Environmental science & technology
Environmental regulation is pivotal in mitigating environmental risks and promoting sustainable development, yet regulators frequently encounter resource constraints when inspecting enterprises. To address this limitation, we employed four sliding wi...

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types.

PeerJ
Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been imp...

Artificial intelligence and its clinical application in Anesthesiology: a systematic review.

Journal of clinical monitoring and computing
PURPOSE: Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, o...

Integrating Pre-Trained protein language model and multiple window scanning deep learning networks for accurate identification of secondary active transporters in membrane proteins.

Methods (San Diego, Calif.)
Secondary active transporters play pivotal roles in regulating ion and molecule transport across cell membranes, with implications in diseases like cancer. However, studying transporters via biochemical experiments poses challenges. We propose an eff...

Machine learning based assessment of preclinical health questionnaires.

International journal of medical informatics
BACKGROUND: Within modern health systems, the possibility of accessing a large amount and a variety of data related to patients' health has increased significantly over the years. The source of this data could be mobile and wearable electronic system...

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.

Journal of robotic surgery
The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11...

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review.

The Annals of otology, rhinology, and laryngology
BACKGROUND: Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease...

Radiomics-based Machine Learning to Predict the Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.

Academic radiology
RATIONALE AND OBJECTIVES: Recurrence of hepatocellular carcinoma (HCC) is a major concern in its management. Accurately predicting the risk of recurrence is crucial for determining appropriate treatment strategies and improving patient outcomes. A ce...

Few-shot image generation with reverse contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Generative models, such as Generative Adversarial Networks (GANs), have recently shown remarkable capabilities in various generation tasks. However, the success of these models heavily depends on the availability of a large-scale training dataset. Wh...