AIMC Topic: Learning

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One-step Bayesian example-dependent cost classification: The OsC-MLP method.

Neural networks : the official journal of the International Neural Network Society
Example-dependent cost classification problems are those where the decision costs depend not only on the true and the attributed classes but also on the sample features. Discriminative algorithms that carry out such classification tasks must take thi...

A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection.

IEEE transactions on neural networks and learning systems
Single-cell RNA sequencing (scRNA-seq) technology is famous for providing a microscopic view to help capture cellular heterogeneity. This characteristic has advanced the field of genomics by enabling the delicate differentiation of cell types. Howeve...

Snippet Policy Network V2: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification.

IEEE transactions on neural networks and learning systems
Early time series classification predicts the class label of a given time series before it is completely observed. In time-critical applications, such as arrhythmia monitoring in ICU, early treatment contributes to the patient's fast recovery, and ea...

Data-driven learning of chaotic dynamical systems using Discrete-Temporal Sobolev Networks.

Neural networks : the official journal of the International Neural Network Society
We introduce the Discrete-Temporal Sobolev Network (DTSN), a neural network loss function that assists dynamical system forecasting by minimizing variational differences between the network output and the training data via a temporal Sobolev norm. Th...

Robust face recognition using quaternion interval type II fuzzy logic-based feature extraction on colour images.

Medical & biological engineering & computing
In this paper, we propose a new robust and fast learning technique by investigating the effect of integration of quaternion and interval type II fuzzy logic along with non-iterative, parameter free deterministic learning machine (DLM) pertaining to f...

Evaluation of ChatGPT's Real-Life Implementation in Undergraduate Dental Education: Mixed Methods Study.

JMIR medical education
BACKGROUND: The recent artificial intelligence tool ChatGPT seems to offer a range of benefits in academic education while also raising concerns. Relevant literature encompasses issues of plagiarism and academic dishonesty, as well as pedagogy and ed...

Predicting disease-gene associations through self-supervised mutual infomax graph convolution network.

Computers in biology and medicine
Illuminating associations between diseases and genes can help reveal the pathogenesis of syndromes and contribute to treatments, but a large number of associations remained unexplored. To identify novel disease-gene associations, many computational m...

Controlling human causal inference through in silico task design.

Cell reports
Learning causal relationships is crucial for survival. The human brain's functional flexibility allows for effective causal inference, underlying various learning processes. While past studies focused on environmental factors influencing causal infer...

Using artificial intelligence platforms to support student learning in physiology.

Advances in physiology education
The advent of artificial intelligence (AI)-powered language models presents new opportunities and challenges in education. By teaching students how to craft prompts that elicit insightful responses, faculty can scaffold activities where AI acts as a ...

A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data.

JMIR mental health
BACKGROUND: Health care providers and health-related researchers face significant challenges when applying sentiment analysis tools to health-related free-text survey data. Most state-of-the-art applications were developed in domains such as social m...