ADHD/ADD

Physics-informed kernel function neural networks for solving partial differential equations.

This paper proposes an improved version of physics-informed neural networks (PINNs), the physics-informed kernel function neural networks (PIKFNNs), to solve various linear and some specific nonlinear partial differential equations (PDEs). It can also be considered as a novel radial basis function neural network (RBFNN). In the proposed PIKFNNs, it employs one-hidden-layer shallow neural network w...

Deep learning for transesophageal echocardiography view classification.

Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and intraprocedural TEE data is the complexity and unstructured nature of these images. In the present study, we developed a deep learning-based, multi-cat...

Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.

The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecting youngsters, is characterized by persistent patt...

Improved bioimpedance spectroscopy tissue classification through data augmentation from generative adversarial networks.

Bioimpedance spectroscopy is a tissue classification technique with many clinical applications. Similarly to other data-driven methods, it requires la...

Detection and classification of brain tumor using hybrid deep learning models.

Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely u...

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification.

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with thi...

Rapid screening of ternary amorphous formulations by a spray drying robot.

Spray drying is commonly used for producing amorphous solid dispersions to improve drug solubility. The development of such formulations typically rel...

Postbiotic Activities of : Impacts on Viability, Structural Integrity, and Cell Death Markers in Human Intestinal C2BBe1 Cells.

Acute diarrheal disease (ADD) caused by rotavirus (RV) contributes significantly to morbidity and mortality in children under five years of age. Curre...

Cortico-cortical stimulation and robot-assisted therapy (CCS and RAT) for upper limb recovery after stroke: study protocol for a randomised controlled trial.

BACKGROUND: Since birth, during the exploration of the environment to interact with objects, we exploit both the motor and sensory components of the u...

Psychological Alterations in Youths with Type I Diabetes: Associations with Salivary Cortisol Concentration.

Type 1 diabetes mellitus (T1DM) is one of the most common chronic diseases in children and adolescents, and is associated with stress and other psych...

Comparing clinical decision-making of AI technology to a multi-professional care team in an electronic cognitive behavioural therapy program for depression: protocol.

INTRODUCTION: Depression is a leading cause of disability worldwide, affecting up to 300 million people globally. Despite its high prevalence and debi...

Exploring the biofunctionalities of lactic fermented cactus pear ( Mill.) fruit beverage: an exotic superfood.

Cactus pear fruit is known with many health benefits in ethnomedicine of countries like Mexico, Portugal, Chine, India etc. The study was aimed to dev...

Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy.

INTRODUCTION: The externalizing disorders of attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorde...

Toward explainable AI (XAI) for mental health detection based on language behavior.

Advances in artificial intelligence (AI) in general and Natural Language Processing (NLP) in particular are paving the new way forward for the automat...

Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision.

In the target article, Bowers et al. dispute deep artificial neural network (ANN) models as the currently leading models of human vision without produ...

ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.

UNLABELLED: In this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its co...

Enhancing robustness in video recognition models: Sparse adversarial attacks and beyond.

Recent years have witnessed increasing interest in adversarial attacks on images, while adversarial video attacks have seldom been explored. In this p...

Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence.

Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% of all new lung cancer diagnoses and a 30-55% of recurrence rate aft...

Using deep neural networks as a guide for modeling human planning.

When developing models in cognitive science, researchers typically start with their own intuitions about human behavior in a given task and then build...

Utilizing artificial intelligence-based eye tracking technology for screening ADHD symptoms in children.

OBJECTIVE: To explore the potential of using artificial intelligence (AI)-based eye tracking technology on a tablet for screening Attention-deficit/hy...