AIMC Topic: Learning

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EGeRepDR: An enhanced genetic-based representation learning for drug repurposing using multiple biomedical sources.

Journal of biomedical informatics
MOTIVATION: Drug repurposing (DR) is an imminent approach for identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical in...

Development of immersive learning framework (ILF) in achieving the goals of higher education: measuring the impact using a pre-post design.

Scientific reports
Emerging technological tools like Artificial Intelligence-based Chatbots, digital educational alternatives and market-driven educational systems pose a challenge to the fundamental aim of the higher education system; comprehensive education for well-...

Model metamers reveal divergent invariances between biological and artificial neural networks.

Nature neuroscience
Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated 'model metamers', stimuli whose activations within a model ...

A Learning-Free Method for Locomotion Mode Prediction by Terrain Reconstruction and Visual-Inertial Odometry.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to...

Validating instructional design and predicting student performance in histology education: Using machine learning via virtual microscopy.

Anatomical sciences education
As a part of modern technological environments, virtual microscopy enriches histological learning, with support from large institutional investments. However, existing literature does not supply empirical evidence of its role in improving pedagogy. V...

Perceptual discrimination in the face perception of robots is attenuated compared to humans.

Scientific reports
When interacting with groups of robots, we tend to perceive them as a homogenous group where all group members have similar capabilities. This overgeneralization of capabilities is potentially due to a lack of perceptual experience with robots or a l...

Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a practical guide.

Diagnosis (Berlin, Germany)
OBJECTIVES: This study aimed to elucidate effective methodologies for utilizing the generative artificial intelligence (AI) system, namely the Chat Generative Pre-trained Transformer (ChatGPT), in improving clinical reasoning abilities among clinicia...

STTRE: A Spatio-Temporal Transformer with Relative Embeddings for multivariate time series forecasting.

Neural networks : the official journal of the International Neural Network Society
The prevalence of multivariate time series data across several disciplines fosters a demand and, subsequently, significant growth in the research and advancement of multivariate time series analysis. Drawing inspiration from a popular natural languag...

Tell me your position: Distantly supervised biomedical entity relation extraction using entity position marker.

Neural networks : the official journal of the International Neural Network Society
A significant amount of textual data has been produced in the biomedical area recently as a result of the advancement of biomedical technologies. Large-scale biomedical data can be automatically obtained with the help of distant supervision. However,...

Dynamic surface reconstruction in robot-assisted minimally invasive surgery based on neural radiance fields.

International journal of computer assisted radiology and surgery
PURPOSE: The purpose of this study was to improve surgical scene perception by addressing the challenge of reconstructing highly dynamic surgical scenes. We proposed a novel depth estimation network and a reconstruction framework that combines neural...