ADHD/ADD

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 scientific breeding. This paper takes peanut as an example to explore a new method for crop variety identification. Peanut is a crucial oil crop and cash crop. The yield and quality of different peanut varieties are different, so it is necessary to identify and classify different peanut varieties. The trad...

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 nowadays with the emergence production of new synthetic ATS drugs with sophisticated design compounds. In the present study, we proposed a one-dimensional convolutional neural network (1DCNN) model to perform ATS drug classification as an alternative method. We investigate as well as explore the classif...

Machine learning for cardiology.

This paper reviews recent cardiology literature and reports how artificial intelligence tools (specifically, machine learning techniques) are being us...

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 development. It functions as a unifying framework allow...

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 hyperactivity disorder (ADHD) is lacking and incomplete....

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 tomography [CT] scans) can have significant value, but a...

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 irregular heartbeat leads to blood clots, heart f...

Clinical Application Study of Polymeric Nanospheres Network in Methylphenidate Extraction from Urine Samples by Dispersive Solid Phase Extraction Adsorbent.

This research introduces a polymeric nanosphere as a new dispersive solid phase extraction (DSPE) adsorbent for the extraction of methylphenidate (MP...

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 introducing "disruptive" technologies persists. Intri...

A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks.

Cyber-physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used t...

Treatment initiation prediction by EHR mapped PPD tensor based convolutional neural networks boosting algorithm.

Electronic health records contain patient's information that can be used for health analytics tasks such as disease detection, disease progression pre...

Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation.

Magnetic resonance fingerprinting (MRF) is a quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters ...

Intelligent Recognition of Hospital Image Based on Deep Learning: The Relationship between Adaptive Behavior and Family Function in Children with ADHD.

Chronic diseases are gradually becoming the main threat to human health. By designing an efficient hospital management platform to quickly identify th...

Robust diagnostic classification via Q-learning.

Machine learning (ML) models have demonstrated the power of utilizing clinical instruments to provide tools for domain experts in gaining additional i...

The use of a UV-C disinfection robot in the routine cleaning process: a field study in an Academic hospital.

BACKGROUND: Environmental surface decontamination is a crucial tool to prevent the spread of infections in hospitals. However, manual cleaning and dis...

VirtualFlow Ants-Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization.

The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them ar...

Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning.

Early diagnosis of attention deficit and hyperactivity disorder (ADHD) by experts is difficult. Some solutions using electroencephalography (EEG) sign...

An artificial neural network-pharmacokinetic model and its interpretation using Shapley additive explanations.

We developed a method to apply artificial neural networks (ANNs) for predicting time-series pharmacokinetics (PKs), and an interpretable the ANN-PK mo...

Evaluating atypical language in autism using automated language measures.

Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using l...

A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy.

In post-operative radiotherapy for prostate cancer, precisely contouring the clinical target volume (CTV) to be irradiated is challenging, because the...