AIMC Topic: Machine Learning

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Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements.

Nature communications
Advances in geospatial and Machine Learning techniques for large datasets of georeferenced observations have made it possible to produce model-based global maps of ecological and environmental variables. However, the implementation of existing scient...

Ultrasonic image denoising using machine learning in point contact excitation and detection method.

Ultrasonics
A point contact/Coulomb coupling technique is generally used for visualizing the ultrasonic waves in Lead Zirconate Titanate (PZT) ceramics. The point contact and delta pulse excitation produce a broadband frequency spectrum and wide directional wave...

Machine learning and deep learning modeling and simulation for predicting PM2.5 concentrations.

Chemosphere
Particulate matter (PM) pollution greatly endanger human physical and mental health, and it is of great practical significance to predict PM concentrations accurately. This study measured one-year monitoring data of six main meteorological parameters...

Tf-GCZSL: Task-free generalized continual zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
Learning continually from a stream of training data or tasks with an ability to learn the unseen classes using a zero-shot learning framework is gaining attention in the literature. It is referred to as continual zero-shot learning (CZSL). Existing C...

Low precision decentralized distributed training over IID and non-IID data.

Neural networks : the official journal of the International Neural Network Society
Decentralized distributed learning is the key to enabling large-scale machine learning (training) on the edge devices utilizing private user-generated local data, without relying on the cloud. However, practical realization of such on-device training...

Imbalanced low-rank tensor completion via latent matrix factorization.

Neural networks : the official journal of the International Neural Network Society
Tensor completion has been widely used in computer vision and machine learning. Most existing tensor completion methods empirically assume the intrinsic tensor is simultaneous low-rank in all over modes. However, tensor data recorded from real-world ...

Plant-scale biogas production prediction based on multiple hybrid machine learning technique.

Bioresource technology
The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to im...

Suicidal behaviour prediction models using machine learning techniques: A systematic review.

Artificial intelligence in medicine
BACKGROUND: Early detection and prediction of suicidal behaviour are key factors in suicide control. In conjunction with recent advances in the field of artificial intelligence, there is increasing research into how machine learning can assist in the...

Algorithmic fairness in computational medicine.

EBioMedicine
Machine learning models are increasingly adopted for facilitating clinical decision-making. However, recent research has shown that machine learning techniques may result in potential biases when making decisions for people in different subgroups, wh...

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X.

ACS nano
Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on labor...