Autism

Effect Evaluation of Electronic Health PDCA Nursing in Treatment of Childhood Asthma with Artificial Intelligence.

Asthma in children has a long duration and is prone to recurring attacks. Children will feel chest tightness, shortness of breath, cough, and difficulty breathing when they are onset, which has a serious impact on their health. Clinical nursing is of great significance in the treatment of childhood asthma. At present, the electronic health PDCA nursing model is widely used in clinical nursing as a...

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network.

With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to bui...

IoT and AI-Based Application for Automatic Interpretation of the Affective State of Children Diagnosed with Autism.

In the context in which it was demonstrated that humanoid robots are efficient in helping children diagnosed with autism in exploring their affective ...

Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care.

Neonatology has experienced a significant reduction in mortality rates of the preterm population and critically ill infants over the last few decades....

Analysis of Bank Credit Risk Evaluation Model Based on BP Neural Network.

Commercial banks are of great value to social and economic development. Therefore, how to accurately evaluate their credit risk and establish a credit...

Generalized Fast Discharges Along the Genetic Generalized Epilepsy Spectrum: Clinical and Prognostic Significance.

OBJECTIVE: To investigate the electroclinical characteristics and the prognostic impact of generalized fast discharges in a large cohort of genetic ge...

A novel multi-objective medical feature selection compass method for binary classification.

The use of Artificial Intelligence in medical decision support systems has been widely studied. Since a medical decision is frequently the result of a...

Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal.

The COVID-19 has resulted in one of the world's most significant worldwide lock-downs, affecting human mental health. Therefore, emotion recognition i...

Breast cancer detection using artificial intelligence techniques: A systematic literature review.

Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common c...

An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.

Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features suc...

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms.

Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum diso...

GGA-MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron.

The task of designing an Artificial Neural Network (ANN) can be thought of as an optimization problem that involves many parameters whose optimal valu...

Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.

Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relie...

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning.

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging ...

The application of machine learning to predict genetic relatedness using human mtDNA hypervariable region I sequences.

Human identification of unknown samples following disaster and mass casualty events is essential, especially to bring closure to family and friends of...

Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types.

In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histo...

Predictive classification of Alzheimer's disease using brain imaging and genetic data.

For now, Alzheimer's disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the ...

Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods.

BACKGROUND: Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still m...

CVDF DYNAMIC-A Dynamic Fuzzy Testing Sample Generation Framework Based on BI-LSTM and Genetic Algorithm.

As one of the most effective methods of vulnerability mining, fuzzy testing has scalability and complex path detection ability. Fuzzy testing sample g...

Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized...