AIMC Topic: Machine Learning

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Construction of English and American Literature Corpus Based on Machine Learning Algorithm.

Computational intelligence and neuroscience
In China, the application of corpus in language teaching, especially in English and American literature teaching, is still in the preliminary research stage, and there are various shortcomings, which have not been paid due attention by front-line edu...

Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification.

Computational intelligence and neuroscience
The systems of sensing technology along with machine learning techniques provide a robust solution in a smart home due to which health monitoring, elderly care, and independent living take advantage. This study addresses the overlapping problem in ac...

Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.

Computational and mathematical methods in medicine
Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study. Although tradit...

A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks.

Behavior research methods
Early detection of risk of failure on interactive tasks comes with great potential for better understanding how examinees differ in their initial behavior as well as for adaptively tailoring interactive tasks to examinees' competence levels. Drawing ...

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation.

Annual review of phytopathology
Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in art...

Novel Pediatric Height Outlier Detection Methodology for Electronic Health Records via Machine Learning With Monotonic Bayesian Additive Regression Trees.

Journal of pediatric gastroenterology and nutrition
OBJECTIVE: To create a new methodology that has a single simple rule to identify height outliers in the electronic health records (EHR) of children.

Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward.

Neurocritical care
Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the "readiness" of this data for robust artificial intelligence (AI) and machine learning (ML) applications is lo...

TocoDecoy: A New Approach to Design Unbiased Datasets for Training and Benchmarking Machine-Learning Scoring Functions.

Journal of medicinal chemistry
Development of accurate machine-learning-based scoring functions (MLSFs) for structure-based virtual screening against a given target requires a large unbiased dataset with structurally diverse actives and decoys. However, most datasets for the devel...

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants.

Physical chemistry chemical physics : PCCP
In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on -phenyl-1-naphthylamine and -phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, includ...

Neural Schrödinger Equation: Physical Law as Deep Neural Network.

IEEE transactions on neural networks and learning systems
We show a new family of neural networks based on the Schrödinger equation (SE-NET). In this analogy, the trainable weights of the neural networks correspond to the physical quantities of the Schrödinger equation. These physical quantities can be trai...