AIMC Topic: Neural Networks, Computer

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The concepts of muscle activity generation driven by upper limb kinematics.

Biomedical engineering online
BACKGROUND: The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the e...

Modeling dissolved oxygen concentration using machine learning techniques with dimensionality reduction approach.

Environmental monitoring and assessment
Oxygen is crucial to keep the life cycle balance in any aspect. Aquatic life is highly influenced by the levels of dissolved oxygen (DO). This calls for not just constant monitoring of the DO in aquatic systems, but to generate an accurate prediction...

Intelligent solution predictive networks for non-linear tumor-immune delayed model.

Computer methods in biomechanics and biomedical engineering
In this article, we analyze the dynamics of the non-linear tumor-immune delayed (TID) model illustrating the interaction among tumor cells and the immune system (cytotoxic T lymphocytes, T helper cells), where the delays portray the times required fo...

Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning.

Journal of biophotonics
Digital holographic microscopy as a non-contacting, non-invasive, and highly accurate measurement technology, is becoming a valuable method for quantitatively investigating cells and tissues. Reconstruction of phases from a digital hologram is a key ...

Using a deep learning neural network for the identification of malignant cells in effusion cytology material.

Cytopathology : official journal of the British Society for Clinical Cytology
AIM: To evaluate the application of an artificial neural network in the detection of malignant cells in effusion samples.

Unveiling the benefits of multitasking in disentangled representation formation.

Trends in cognitive sciences
Johnston and Fusi recently investigated the emergence of disentangled representations when a neural network was trained to perform multiple simultaneous tasks. Such experiments explore the benefits of flexible representations and add to a growing fie...

Flood discharge prediction using improved ANFIS model combined with hybrid particle swarm optimisation and slime mould algorithm.

Environmental science and pollution research international
Due to the disastrous socio-economic impacts of flood hazards and estimated rise of its occurrences in the near future, there has been an increase in the importance of flood prediction worldwide. Artificial intelligence (AI) models have contributed s...

A unified hybrid transformer for joint MRI sequences super-resolution and missing data imputation.

Physics in medicine and biology
High-resolution multi-modal magnetic resonance imaging (MRI) is crucial in clinical practice for accurate diagnosis and treatment. However, challenges such as budget constraints, potential contrast agent deposition, and image corruption often limit t...

Tree-Structured Data Clustering-Driven Neural Network for Intra Prediction in Video Coding.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Intra prediction is a crucial part of video compression, which utilizes local information in images to eliminate spatial redundancy. As the state-of-the-art video coding standard, Versatile Video Coding (H.266/VVC) employs multiple directional predic...

Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions - A comparative study on generalizability.

Zeitschrift fur medizinische Physik
Multimodal image registration is applied in medical image analysis as it allows the integration of complementary data from multiple imaging modalities. In recent years, various neural network-based approaches for medical image registration have been ...