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A Hybrid Deep Learning Emotion Classification System Using Multimodal Data.

Sensors (Basel, Switzerland)
This paper proposes a hybrid deep learning emotion classification system (HDECS), a hybrid multimodal deep learning system designed for emotion classification in a specific national language. Emotion classification is important in diverse fields, inc...

Memristor-induced hyperchaos, multiscroll and extreme multistability in fractional-order HNN: Image encryption and FPGA implementation.

Neural networks : the official journal of the International Neural Network Society
Fractional-order differentiation (FOD) can record information from the past, present, and future. Compared with integer-order systems, FOD systems have higher complexity and more accurate ability to describe the real world. In this paper, two types o...

CCL-DTI: contributing the contrastive loss in drug-target interaction prediction.

BMC bioinformatics
BACKGROUND: The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modul...

Contrastive learning of graphs under label noise.

Neural networks : the official journal of the International Neural Network Society
In the domain of graph-structured data learning, semi-supervised node classification serves as a critical task, relying mainly on the information from unlabeled nodes and a minor fraction of labeled nodes for training. However, real-world graph-struc...

ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks.

Computers in biology and medicine
The prediction of multi-label protein subcellular localization (SCL) is a pivotal area in bioinformatics research. Recent advancements in protein structure research have facilitated the application of graph neural networks. This paper introduces a no...

SEA-NET: medical image segmentation network based on spiral squeeze-and-excitation and attention modules.

BMC medical imaging
BACKGROUND: Medical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are required for them. The current deep neural network based medical segmentation methods have good effect on...

Essential genes identification model based on sequence feature map and graph convolutional neural network.

BMC genomics
BACKGROUND: Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying...

Analyzing Molecular Dynamics Trajectories Thermodynamically through Artificial Intelligence.

Journal of chemical theory and computation
Molecular dynamics simulations produce trajectories that correspond to vast amounts of structure when exploring biochemical processes. Extracting valuable information, e.g., important intermediate states and collective variables (CVs) that describe t...

A validation of an entropy-based artificial intelligence for ultrasound data in breast tumors.

BMC medical informatics and decision making
BACKGROUND: The application of artificial intelligence (AI) in the ultrasound (US) diagnosis of breast cancer (BCa) is increasingly prevalent. However, the impact of US-probe frequencies on the diagnostic efficacy of AI models has not been clearly es...

Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery.

Computers in biology and medicine
COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of ...