Autism

An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images.

Cerebellum measures taken from routinely obtained ultrasound (US) images have been frequently employed to determine gestational age and identify developing central nervous system's anatomical abnormalities. Standardized cerebellar assessments from large-scale clinical datasets are required to investigate correlations between the growing cerebellum and postnatal neurodevelopmental results. These st...

Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site.

Optimization of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved by engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, the design of specific genetic parts remains challenging because of the lack of reliable design methods. To address this problem, we have crea...

Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network.

We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a mu...

Big data, machine learning, and population health: predicting cognitive outcomes in childhood.

The application of machine learning (ML) to address population health challenges has received much less attention than its application in the clinical...

Research on Image Segmentation Algorithm Based on Multimodal Hierarchical Attention Mechanism and Genetic Neural Network.

Multimodal tasks based on attention mechanism and language face numerous problems. Based on multimodal hierarchical attention mechanism and genetic ne...

An Animation Model Generation Method Based on Gaussian Mutation Genetic Algorithm to Optimize Neural Network.

With the rapid development of computer graphics, 3D animation has been applied to all fields of people's lives, especially in the industries of film a...

Contrastive machine learning reveals the structure of neuroanatomical variation within autism.

Autism spectrum disorder (ASD) is highly heterogeneous. Identifying systematic individual differences in neuroanatomy could inform diagnosis and perso...

Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review.

BACKGROUND: Machine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application...

Innovating Pedagogical Practices for Handmade Courses in Preschool Education Using Artificial Intelligence.

Handmade is an important part of preschool education, which was aimed at improving children's ability to work with their hands. Preschool education is...

A Novel Genetic Neural Network Algorithm with Link Switches and Its Application in University Professional Course Evaluation.

This study exploits a novel enhanced genetic neural network algorithm with link switches (EGA-NNLS) to model the professional university course evalua...

Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired so...

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computa...

Classification of antiseizure drugs in cultured neuronal networks using multielectrode arrays and unsupervised learning.

OBJECTIVE: Antiseizure drugs (ASDs) modulate synaptic and ion channel function to prevent abnormal hypersynchronous or excitatory activity arising in ...

Optimization of Reservoir Flood Control Operation Based on Multialgorithm Deep Learning.

With the rapid development of China's social economy, it is the most important task for the water conservancy industry to make use of the existing wat...

An improved genetic algorithm and its application in neural network adversarial attack.

The choice of crossover and mutation strategies plays a crucial role in the searchability, convergence efficiency and precision of genetic algorithms....

Artificial intelligence to identify genetic alterations in conventional histopathology.

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular ...

Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework.

Atypical visual attention is a hallmark of autism spectrum disorder (ASD). Identifying the attention features accurately discerning between people wit...

Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of...

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...

Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA).

In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO) fixation of microalgae which is based on the hybrid a...