AIMC Topic: Machine Learning

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Antistroke Network Pharmacological Prediction of Xiaoshuan Tongluo Recipe Based on Drug-Target Interaction Based on Deep Learning.

Computational and mathematical methods in medicine
Stroke is a common cerebrovascular disease that threatens human health, and the search for therapeutic drugs is the key to treatment. New drug discovery was driven by many accidental factors in the early stage. With the deepening of research, disease...

A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teams.

PloS one
Algorithmic agents, popularly known as bots, have been accused of spreading misinformation online and supporting fringe views. Collectives are vulnerable to hidden-profile environments, where task-relevant information is unevenly distributed across i...

Federated learning with workload-aware client scheduling in heterogeneous systems.

Neural networks : the official journal of the International Neural Network Society
Federated Learning (FL) is a novel distributed machine learning, which allows thousands of edge devices to train models locally without uploading data to the central server. Since devices in real federated settings are resource-constrained, FL encoun...

Machine learning-based inverse design for electrochemically controlled microscopic gradients of O and HO.

Proceedings of the National Academy of Sciences of the United States of America
A fundamental understanding of extracellular microenvironments of O and reactive oxygen species (ROS) such as HO, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O and HO at microscop...

UV-Visible Absorption Spectra of Solvated Molecules by Quantum Chemical Machine Learning.

Journal of chemical theory and computation
Predicting UV-visible absorption spectra is essential to understand photochemical processes and design energy materials. Quantum chemical methods can deliver accurate calculations of UV-visible absorption spectra, but they are computationally expensi...

Long Short-Term Memory Neural Network with Transfer Learning and Ensemble Learning for Remaining Useful Life Prediction.

Sensors (Basel, Switzerland)
Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain s...

Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.

BMC medical informatics and decision making
BACKGROUND: Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been ...

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques.

Biomedical engineering online
BACKGROUND: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and te...

An interpretable neural network for outcome prediction in traumatic brain injury.

BMC medical informatics and decision making
BACKGROUND: Traumatic Brain Injury (TBI) is a common condition with potentially severe long-term complications, the prediction of which remains challenging. Machine learning (ML) methods have been used previously to help physicians predict long-term ...

Transfer learning based generalized framework for state of health estimation of Li-ion cells.

Scientific reports
Estimating the state of health (SOH) of batteries powering electronic devices in real-time while in use is a necessity. The applicability of most of the existing methods is limited to the datasets that are used to train the models. In this work, we p...