AIMC Topic: Machine Learning

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Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review.

Environmental science and pollution research international
Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biog...

A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model.

Scientific reports
In light of the prevalent issues concerning the mechanical grading of fresh tea leaves, characterized by high damage rates and poor accuracy, as well as the limited grading precision through the integration of machine vision and machine learning (ML)...

An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators.

Journal of ovarian research
OBJECTIVE: To construct a machine learning diagnostic model integrating feature dimensionality reduction techniques and artificial neural network classifiers to develop the value of clinical routine blood indexes for the auxiliary diagnosis of ovaria...

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges.

Circulation. Cardiovascular imaging
Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to...

Inference of annealed protein fitness landscapes with AnnealDCA.

PLoS computational biology
The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a...

Prediction of postoperative gait speed change after bilateral primary total knee arthroplasty in female patients using a machine learning algorithm.

Orthopaedics & traumatology, surgery & research : OTSR
BACKGROUND: An important aim of total knee arthroplasty is to achieve functional recovery, which includes post-operative increase in walking speed. Therefore, predicting whether a patient will walk faster or slower after surgery is important in TKA, ...

Investigation of bacterial DNA gyrase Inhibitor classification models and structural requirements utilizing multiple machine learning methods.

Molecular diversity
Infections from multidrug-resistant (MDR) bacteria have emerged as a paramount global health concern, and the therapeutic effectiveness of current treatments is swiftly diminishing. An urgent need exists to explore innovative strategies for counterin...

Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies.

Brain pathology (Zurich, Switzerland)
Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neur...

A machine-learning exploration of the exposome from preconception in early childhood atopic eczema, rhinitis and wheeze development.

Environmental research
BACKGROUND: Most previous research on the environmental epidemiology of childhood atopic eczema, rhinitis and wheeze is limited in the scope of risk factors studied. Our study adopted a machine learning approach to explore the role of the exposome st...

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.

Nature methods
Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets chal...