Latest AI and machine learning research in adhd/add for healthcare professionals.
Recently, generative adversarial network (GAN) has shown its strong ability on modeling data distribution via adversarial learning. Cross-modal GAN, which attempts to utilize the power of GAN to model the cross-modal joint distribution and to learn compatible cross-modal features, is becoming the research hotspot. However, the existing cross-modal GAN approaches typically 1) require labeled multim...
Deep hashing method is widely applied in the field of image retrieval because of its advantages of low storage consumption and fast retrieval speed. There is a defect of insufficiency feature extraction when existing deep hashing method uses the convolutional neural network (CNN) to extract images semantic features. Some studies propose to add channel-based or spatial-based attention modules. Howe...
Cross-media visual communication is an extremely complex task. In order to solve the problem of segmentation of visual foreground and background, impr...
Clinically, physicians collect the benchmark medical data to establish archives for a stroke patient and then add the follow up data regularly. It has...
Imbalanced datasets greatly affect the analysis capability of intrusion detection models, biasing their classification results toward normal behavior ...
Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children. At the same time, ADHD is prone to coexist with o...
There is broad consensus that to improve the treatment of adult Attention-Deficit/Hyperactivity Disorder (ADHD), the various therapy options need to b...
Post-analytical reflexive (automated) and/or reflective (patient tailored and thought driven) interventions (PARRI), have played a subsidiary role in ...
Chronic kidney disease (CKD) has become a widespread disease among people. It is related to various serious risks like cardiovascular disease, heighte...
We present machine learning models for predicting the chemical context for Buchwald-Hartwig coupling reactions, i. e., what chemicals to add to the re...
Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection mode...
The use of artificial intelligence methods in the image-based diagnostic assessment of hematological diseases is a growing trend in recent years. In t...
The lack of interest of children at school is one of the biggest problems that Mexican education faces. Two important factors causing this lack of int...
Weakly supervised learning has emerged as an appealing alternative to alleviate the need for large labeled datasets in semantic segmentation. Most cur...
Nowadays, activity prediction is key to understanding the mechanism-of-action of active structures discovered from phenotypic screening or found in na...
Although cellular elastic property (CEP, also known as cellular elastic modulus) has been frequently reported as a biomarker to distinguish some cance...
Interest in Machine Learning applications to tackle clinical and biological problems is increasing. This is driven by promising results reported in ma...
Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patient...
The first ever insurance reimbursement for an artificial intelligence (AI) system, which expedites triage of acute stroke, occurred in 2020 when the C...
This paper analyzes and collates the research on traditional homeschooling attention mechanism and homeschooling attention mechanism based on two-way ...