AIMC Topic: Machine Learning

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Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing.

Scientific reports
In this study, the capabilities of classical and novel integrated machine learning models were investigated to predict sediment discharge (Q) in free-flow flushing. Developed models include Multivariate Linear Regression (MLR), Artificial Neural Netw...

Recognition of the Effect of Vocal Exercises by Fuzzy Triangular Naive Bayes, a Machine Learning Classifier: A Preliminary Analysis.

Journal of voice : official journal of the Voice Foundation
OBJECTIVES: Machine learning (ML) methods allow the development of expert systems for pattern recognition and predictive analysis of intervention outcomes. It has been used in Voice Sciences, mainly to discriminate between healthy and dysphonic voice...

Neurology education in the era of artificial intelligence.

Current opinion in neurology
PURPOSE OF REVIEW: The practice of neurology is undergoing a paradigm shift because of advances in the field of data science, artificial intelligence, and machine learning. To ensure a smooth transition, physicians must have the knowledge and compete...

Diagnostic performance of machine learning models using cell population data for the detection of sepsis: a comparative study.

Clinical chemistry and laboratory medicine
OBJECTIVES: To compare the artificial intelligence algorithms as powerful machine learning methods for evaluating patients with suspected sepsis using data from routinely available blood tests performed on arrival at the hospital. Results were compar...

Development of benchmark datasets for text mining and sentiment analysis to accelerate regulatory literature review.

Regulatory toxicology and pharmacology : RTP
In the field of regulatory science, reviewing literature is an essential and important step, which most of the time is conducted by manually reading hundreds of articles. Although this process is highly time-consuming and labor-intensive, most output...

Artificial intelligence for prediction of response to cancer immunotherapy.

Seminars in cancer biology
Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-car...

DeepIDC: A Prediction Framework of Injectable Drug Combination Based on Heterogeneous Information and Deep Learning.

Clinical pharmacokinetics
BACKGROUND AND OBJECTIVE: In clinical practice, injectable drug combination (IDC) usually provides good therapeutic effects for patients. Numerous clinical studies have directly indicated that inappropriate IDC generates adverse drug events (ADEs). T...

Perspective on a chemistry classification system for AI-assisted formulation development.

Journal of controlled release : official journal of the Controlled Release Society
This perspective article draws a distinction between some of the well-known drug classification systems and a "Chemistry Classification System" (CCS). Rather than have drug classification based on some simple properties like solubility and permeabili...

Charge Recombination Dynamics in a Metal Halide Perovskite Simulated by Nonadiabatic Molecular Dynamics Combined with Machine Learning.

The journal of physical chemistry letters
Nonadiabatic coupling (NAC) plays a central role in driving nonadiabatic dynamics in various photophysical and photochemical processes. However, the high computational cost of NAC limits the time scale and system size of quantum dynamics simulation. ...

Harnessing interpretable machine learning for holistic inverse design of origami.

Scientific reports
This work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems. We established a work flow based on decision tree-random forest method to fit origami databases, containing both...