AIMC Topic: Neural Networks, Computer

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Combining Sound and Deep Neural Networks for the Measurement of Jump Height in Sports Science.

Sensors (Basel, Switzerland)
Jump height tests are employed to measure lower-limb muscle power of athletic and non-athletic populations. The most popular instruments for this purpose are jump mats and, in recent years, smartphone apps, which compute jump height through the manua...

Investigation of Motor Learning Effects Using a Hybrid Rehabilitation System Based on Motion Estimation.

Sensors (Basel, Switzerland)
Upper-limb paralysis requires extensive rehabilitation to recover functionality for everyday living, and such assistance can be supported with robot technology. Against such a background, we have proposed an electromyography (EMG)-driven hybrid rehab...

A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik.

Environmental monitoring and assessment
Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosys...

Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm.

Network (Bristol, England)
Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the le...

Study on breast cancerization and isolated diagnosis in situ by HOF-ATR-MIR spectroscopy with deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Mid-infrared (MIR) spectroscopy can characterize the content and structural changes of macromolecular components in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classifi...

Adaptive sampling artificial-actual control for non-zero-sum games of constrained systems.

Neural networks : the official journal of the International Neural Network Society
Considering physical constraints encountered by actuators, this paper addresses the non-zero-sum game of continuous nonlinear systems with symmetric and asymmetric input constraints through aperiodic sampling artificial-actual control. Initially, the...

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.

Medical image analysis
Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and c...

Light&fast generative adversarial network for high-fidelity CT image synthesis of liver tumor.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma is a common disease with high mortality. Through deep learning methods to analyze HCC CT, the screening classification and prognosis model of HCC can be established, which further promotes the develo...

On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Evaluating the interpretability of Deep Learning models is crucial for building trust and gaining insights into their decision-making processes. In this work, we employ class activation map based attribution methods in a set...

Clinical Application of Artificial Intelligence in the Ultrasound Classification of Hepatic Cystic Echinococcosis.

The American journal of tropical medicine and hygiene
Hepatic cystic echinococcosis (HCE) is a zoonotic disease that occurs when the larvae of Echinococcus granulosus parasitize the livers of humans and mammals. HCE has five subtypes, and accurate subtype classification is critical for choosing a treatm...