ADHD/ADD

RA V-Net: deep learning network for automated liver segmentation.

Segmenting liver from CT images is the first step for doctors to diagnose a patient's disease. Processing medical images with deep learning models has become a current research trend. Although it can automate segmenting region of interest of medical images, the inability to achieve the required segmentation accuracy is an urgent problem to be solved.Residual Attention V-Net (RA V-Net) based on U-N...

Automatic Cobb angle measurement method based on vertebra segmentation by deep learning.

The accuracy of the Cobb measurement is essential for the diagnosis and treatment of scoliosis. Manual measurement is however influenced by the observer variability hence affecting progression evaluation. In this paper, we propose a fully automatic Cobb measurement method to address the accuracy issue of manual measurement. We improve the U-shaped network based on the multi-scale feature fusion to...

Automatic Image Processing Algorithm for Light Environment Optimization Based on Multimodal Neural Network Model.

In this paper, we conduct an in-depth study and analysis of the automatic image processing algorithm based on a multimodal Recurrent Neural Network (m...

The road to change: Broadband China strategy and enterprise digitization.

The digitization of a company necessitates not only the effort of the company but also state backing of network infrastructure. In this study, we appl...

Submillimeter-scale multimaterial terrestrial robots.

Robots with submillimeter dimensions are of interest for applications that range from tools for minimally invasive surgical procedures in clinical med...

Uncertainty-aware selecting for an ensemble of deep food recognition models.

Deep learning is a machine learning technique that has revolutionized the research community due to its impressive results on various real-life proble...

Novel Multitask Conditional Neural-Network Surrogate Models for Expensive Optimization.

Multiple-related tasks can be learned simultaneously by sharing information among tasks to avoid tabula rasa learning and to improve performance in th...

Predictive models for human-AI nexus in group decision making.

Machine learning (ML) and artificial intelligence (AI) have had a profound impact on our lives. Domains like health and learning are naturally helped ...

HearCough: Enabling continuous cough event detection on edge computing hearables.

Cough event detection is the foundation of any measurement associated with cough, one of the primary symptoms of pulmonary illnesses. This paper propo...

Sparse Adversarial Video Attacks via Superpixel-Based Jacobian Computation.

Adversarial examples have aroused great attention during the past years owing to their threat to the deep neural networks (DNNs). Recently, they have ...

Small-scale soft grippers with environmentally responsive logic gates.

Small-scale soft grippers are adaptive and deformable, and can be utilized for confined environments (, the human body). Small-scale soft grippers req...

Joint Feature Synthesis and Embedding: Adversarial Cross-Modal Retrieval Revisited.

Recently, generative adversarial network (GAN) has shown its strong ability on modeling data distribution via adversarial learning. Cross-modal GAN, w...

Deep parameter-free attention hashing for image retrieval.

Deep hashing method is widely applied in the field of image retrieval because of its advantages of low storage consumption and fast retrieval speed. T...

Improved Deep Neural Network for Cross-Media Visual Communication.

Cross-media visual communication is an extremely complex task. In order to solve the problem of segmentation of visual foreground and background, impr...

Interpretability Analysis of One-Year Mortality Prediction for Stroke Patients Based on Deep Neural Network.

Clinically, physicians collect the benchmark medical data to establish archives for a stroke patient and then add the follow up data regularly. It has...

Network Intrusion Detection Method Based on FCWGAN and BiLSTM.

Imbalanced datasets greatly affect the analysis capability of intrusion detection models, biasing their classification results toward normal behavior ...

Deep Learning Enabled Diagnosis of Children's ADHD Based on the Big Data of Video Screen Long-Range EEG.

Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children. At the same time, ADHD is prone to coexist with o...

Multimodal treatment efficacy differs in dependence of core symptom profiles in adult Attention-Deficit/Hyperactivity Disorder: An analysis of the randomized controlled COMPAS trial.

There is broad consensus that to improve the treatment of adult Attention-Deficit/Hyperactivity Disorder (ADHD), the various therapy options need to b...

Reflex and reflective laboratory interventions for adding value to test results; an integral part of laboratory stewardship.

Post-analytical reflexive (automated) and/or reflective (patient tailored and thought driven) interventions (PARRI), have played a subsidiary role in ...

Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark.

Chronic kidney disease (CKD) has become a widespread disease among people. It is related to various serious risks like cardiovascular disease, heighte...