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ADHD/ADD

Latest AI and machine learning research in adhd/add for healthcare professionals.

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A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human-Robot Interaction.

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add sa...

Human-Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human-Robot Collaboration.

Human-robot collaborative applications have been receiving increasing attention in industrial applic...

Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm.

Nowadays, with the constant change of public aesthetic standards, a large number of new types and th...

Attention-Based Joint Training of Noise Suppression and Sound Event Detection for Noise-Robust Classification.

Sound event detection (SED) recognizes the corresponding sound event of an incoming signal and estim...

A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images.

Glioma is a relatively common brain tumor disease with high mortality rate. Humans have been seeking...

Research on Information Visualization Graphic Design Teaching Based on DBN Algorithm.

With the advent of the era of big data, how to quickly obtain effective information and efficiently ...

A Novel Handwritten Digit Classification System Based on Convolutional Neural Network Approach.

An enormous number of CNN classification algorithms have been proposed in the literature. Neverthele...

Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification.

Similarity learning using deep convolutional neural networks has been applied extensively in solving...

Deep neural network-estimated electrocardiographic age as a mortality predictor.

The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular dise...

Interpretable and explainable AI (XAI) model for spatial drought prediction.

Accurate prediction of any type of natural hazard is a challenging task. Of all the various hazards,...

Design of Deep Learning Model for Task-Evoked fMRI Data Classification.

Machine learning methods have been successfully applied to neuroimaging signals, one of which is to ...

A novel method for peanut variety identification and classification by Improved VGG16.

Crop variety identification is an essential link in seed detection, phenotype collection and scienti...

Amphetamine-type stimulants (ATS) drug classification using shallow one-dimensional convolutional neural network.

Amphetamine-type stimulants (ATS) drug analysis and identification are challenging and critical nowa...

Machine learning for cardiology.

This paper reviews recent cardiology literature and reports how artificial intelligence tools (speci...

PHOTONAI-A Python API for rapid machine learning model development.

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model devel...

Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.

Body composition measures derived from already available electronic medical records (computed tomogr...

Deep learning prediction of attention-deficit hyperactivity disorder in African Americans by copy number variation.

Current understanding of the underlying molecular network and mechanism for attention-deficit hypera...

An automated method for detecting atrial fat using convolutional neural network.

Atrial Fibrillation (A-fib) is a common cardiac rhythm problem in the population these days in which...

Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach.

Although Artificial Intelligence (AI) is being increasingly applied, considerable distrust about int...

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