AIMC Topic: Machine Learning

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Damage identification using wave damage interaction coefficients predicted by deep neural networks.

Ultrasonics
The ever-increasing demand for efficiency and cost improvements in lightweight structures with guaranteed safety and reliability is leading to the application of a damage-tolerant design philosophy. Here, accurate knowledge of structural health is cr...

A Layered Spiking Neural System for Classification Problems.

International journal of neural systems
Biological brains have a natural capacity for resolving certain classification tasks. Studies on biologically plausible spiking neurons, architectures and mechanisms of artificial neural systems that closely match biological observations while giving...

Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities.

Journal of proteome research
Machine learning has been an integral part of interpreting data from mass spectrometry (MS)-based proteomics for a long time. Relatively recently, a machine-learning structure appeared successful in other areas of bioinformatics, Transformers. Furthe...

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.

Molecular pharmaceutics
Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in anim...

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries.

Advanced materials (Deerfield Beach, Fla.)
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of a...

automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning.

Analytical chemistry
Preprocessing of liquid chromatography-mass spectrometry (LC-MS) raw data facilitates downstream statistical and biological data analyses. In the case of targeted LC-MS data, consistent recognition of chromatographic peaks is a main challenge, in par...

A Hidden Markov Ensemble Algorithm Design for Time Series Analysis.

Sensors (Basel, Switzerland)
With the exponential growth of data, solving classification or regression tasks by mining time series data has become a research hotspot. Commonly used methods include machine learning, artificial neural networks, and so on. However, these methods on...

Clustering at the Disposal of Industry 4.0: Automatic Extraction of Plant Behaviors.

Sensors (Basel, Switzerland)
For two centuries, the industrial sector has never stopped evolving. Since the dawn of the Fourth Industrial Revolution, commonly known as Industry 4.0, deep and accurate understandings of systems have become essential for real-time monitoring, predi...

Large-Scale Textual Datasets and Deep Learning for the Prediction of Depressed Symptoms.

Computational intelligence and neuroscience
Millions of people worldwide suffer from depression. Assessing, treating, and preventing recurrence requires early detection of depressive symptoms as depression-related datasets expand and machine learning improves, intelligent approaches to detect ...

Online Disease Identification and Diagnosis and Treatment Based on Machine Learning Technology.

Journal of healthcare engineering
The article uses machine learning algorithms to extract disease symptom keyword vectors. At the same time, we used deep learning technology to design a disease symptom classification model. We apply this model to an online disease consultation recomm...