AIMC Topic: Humans

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Deep-learning-based seizure detection and prediction from electroencephalography signals.

International journal for numerical methods in biomedical engineering
Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively lo...

Carrying Position-Independent Ensemble Machine Learning Step-Counting Algorithm for Smartphones.

Sensors (Basel, Switzerland)
Current step-count estimation techniques use either an accelerometer or gyroscope sensors to calculate the number of steps. However, because of smartphones unfixed placement and direction, their accuracy is insufficient. It is necessary to consider t...

Sparking the Interest of Girls in Computer Science via Chemical Experimentation and Robotics: The Qui-Bot HO Case Study.

Sensors (Basel, Switzerland)
We report a new learning approach in science and technology through the Qui-Bot HO project: a multidisciplinary and interdisciplinary project developed with the main objective of inclusively increasing interest in computer science engineering among c...

A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster.

International journal of environmental research and public health
The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of me...

Development and validation of an ensemble artificial intelligence model for comprehensive imaging quality check to classify body parts and contrast enhancement.

BMC medical imaging
BACKGROUND: Despite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, the first step of image quality check (image QC), which aims to check whether images are uploaded appropriately according to the...

Cross-tissue immune cell analysis reveals tissue-specific features in humans.

Science (New York, N.Y.)
Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a ...

A geometry-guided multi-beamlet deep learning technique for CT reconstruction.

Biomedical physics & engineering express
. Previous studies have proposed deep-learning techniques to reconstruct CT images from sinograms. However, these techniques employ large fully-connected (FC) layers for projection-to-image domain transformation, producing large models requiring subs...

Feature blindness: A challenge for understanding and modelling visual object recognition.

PLoS computational biology
Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal tha...

Construction of Knowledge Graph English Online Homework Evaluation System Based on Multimodal Neural Network Feature Extraction.

Computational intelligence and neuroscience
This paper defines the data schema of the multimodal knowledge graph, that is, the definition of entity types and relationships between entities. The knowledge point entities are defined as three types of structures, algorithms, and related terms, sp...

Deep neural networks to recover unknown physical parameters from oscillating time series.

PloS one
Deep neural networks are widely used in pattern-recognition tasks for which a human-comprehensible, quantitative description of the data-generating process, cannot be obtained. While doing so, neural networks often produce an abstract (entangled and ...