AIMC Topic: Machine Learning

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A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing.

IEEE transactions on neural networks and learning systems
Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolution...

Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction.

IEEE transactions on neural networks and learning systems
Sea subsurface temperature, an essential component of aquatic wildlife, underwater dynamics, and heat transfer with the sea surface, is affected by global warming in climate change. Existing research is commonly based on either physics-based numerica...

Deep Reinforcement Learning on Autonomous Driving Policy With Auxiliary Critic Network.

IEEE transactions on neural networks and learning systems
Deep reinforcement learning (DRL) is a machine learning method based on rewards, which can be extended to solve some complex and realistic decision-making problems. Autonomous driving needs to deal with a variety of complex and changeable traffic sce...

An Intelligent Method for Predicting the Pressure Coefficient Curve of Airfoil-Based Conditional Generative Adversarial Networks.

IEEE transactions on neural networks and learning systems
Recently, extensive studies have focused on analyzing aerodynamic performance due to its important impact on aircraft design. Most of these works compute the aerodynamic coefficient of the airfoil through computational fluid dynamics (CFD) simulation...

Experimental drugs in clinical trials for COPD: artificial intelligence via machine learning approach to predict the successful advance from early-stage development to approval.

Expert opinion on investigational drugs
INTRODUCTION: Therapeutic advances in drug therapy of chronic obstructive pulmonary disease (COPD) really effective in suppressing the pathological processes underlying the disease deterioration are still needed. Artificial Intelligence (AI) via Mach...

Count-Based Morgan Fingerprint: A More Efficient and Interpretable Molecular Representation in Developing Machine Learning-Based Predictive Regression Models for Water Contaminants' Activities and Properties.

Environmental science & technology
In this study, we introduce the count-based Morgan fingerprint (C-MF) to represent chemical structures of contaminants and develop machine learning (ML)-based predictive models for their activities and properties. Compared with the binary Morgan fing...

Identifying inpatient mortality in MarketScan claims data using machine learning.

Pharmacoepidemiology and drug safety
PURPOSE: Inpatient mortality is an important variable in epidemiology studies using claims data. In 2016, MarketScan data began obscuring specific hospital discharge status types for patient privacy, including inpatient deaths, by setting the values ...

Deep Learning-Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Neurodegenerative diseases (NDDs) are prevalent among older adults worldwide. Early diagnosis of NDD is challenging yet crucial. Gait status has been identified as an indicator of early-stage NDD changes and can play a significant role in...

HRFSVM: identification of fish disease using hybrid Random Forest and Support Vector Machine.

Environmental monitoring and assessment
Aquaculture fish diseases pose a serious threat to the security of food supplies. Fish species vary widely, and because they resemble one another so much, it is challenging to distinguish between them based solely on appearance. To stop the spread of...

Multi-population Black Hole Algorithm for the problem of data clustering.

PloS one
The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data aroun...