AIMC Topic: Neural Networks, Computer

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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...

Medical image identification methods: A review.

Computers in biology and medicine
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging pr...

AttCON: With better MSAs and attention mechanism for accurate protein contact map prediction.

Computers in biology and medicine
Protein contact map prediction is a critical and vital step in protein structure prediction, and its accuracy is highly contingent upon the feature representations of protein sequence information and the efficacy of deep learning models. In this pape...

Grounding neuroscience in behavioral changes using artificial neural networks.

Current opinion in neurobiology
Connecting neural activity to function is a common aim in neuroscience. How to define and conceptualize function, however, can vary. Here I focus on grounding this goal in the specific question of how a given change in behavior is produced by a chang...

Computational drug discovery on human immunodeficiency virus with a customized long short-term memory variational autoencoder deep-learning architecture.

CPT: pharmacometrics & systems pharmacology
Despite attempts to control the spread of human immunodeficiency virus (HIV) through the use of anti-HIV medications, the absence of an effective vaccine continues to present a significant obstacle. In addition, the development of drug resistance by ...

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation.

Nature methods
Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convoluti...

Prediction of Spheroid Cell Death Using Fluorescence Staining and Convolutional Neural Networks.

Chemical research in toxicology
Three-dimensional (3D) cell culture is emerging for drug design and drug screening. Skin toxicity is one of the most important assays for determining the toxicity of a compound before being used in skin application. Much work has been done to find an...

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.

Tomography (Ann Arbor, Mich.)
Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy...

Stress Monitoring in Free-Living Environments.

IEEE journal of biomedical and health informatics
Stress monitoring is an important area of research with significant implications for individuals' physical and mental health. We present a data-driven approach for stress detection based on convolutional neural networks while addressing the problems ...

Fusion-Based Deep Learning Architecture for Detecting Drug-Target Binding Affinity Using Target and Drug Sequence and Structure.

IEEE journal of biomedical and health informatics
Accurately predicting drug-target binding affinity plays a vital role in accelerating drug discovery. Many computational approaches have been proposed due to costly and time-consuming of wet laboratory experiments. In the input representation, most m...