AIMC Topic: Entropy

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Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a dual encoder variational autoencoder-generative adversarial...

Modified CPT-TODIM method for evaluating the development level of digital inclusive finance under probabilistic hesitant fuzzy environment.

PloS one
Unlike traditional finance, digital inclusive finance is committed to integrating digital technology with the financial industry to bring groups originally excluded from traditional finance back into formal financial services and provide financial se...

Research on the evaluation method of agricultural intelligent robot design solutions.

PloS one
BACKGROUND: At present, agricultural robots are produced in large quantities and used in agricultural planting, and the traditional agricultural model is gradually shifting to rely on the Internet of Things and sensors to accurately detect crop growt...

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties.

Physical chemistry chemical physics : PCCP
The prediction of the free energy (Δ) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its ce...

Novel Information Measures for Fermatean Fuzzy Sets and Their Applications to Pattern Recognition and Medical Diagnosis.

Computational intelligence and neuroscience
Fermatean fuzzy sets (FFSs) have piqued the interest of researchers in a wide range of domains. The striking framework of the FFS is keen to provide the larger preference domain for the modeling of ambiguous information deploying the degrees of membe...

Securing Multimedia Using a Deep Learning Based Chaotic Logistic Map.

IEEE journal of biomedical and health informatics
Telemedicine and online consultations with doctors has become very popular during the pandemic and involves the transmission of medical data through the internet. Thus this raises concern about the security of the medical data of the patient as the r...

Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition.

Sensors (Basel, Switzerland)
This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing br...

Self-supervised learning-based Multi-Scale feature Fusion Network for survival analysis from whole slide images.

Computers in biology and medicine
Understanding prognosis and mortality is critical for evaluating the treatment plan of patients. Advances in digital pathology and deep learning techniques have made it practical to perform survival analysis in whole slide images (WSIs). Current meth...

Computer-aided diagnosis of autism spectrum disorder from EEG signals using deep learning with FAWT and multiscale permutation entropy features.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Autism spectrum disorder (ASD), a neurodevelopment disorder, is characterized by significant difficulties in social interaction and emerges as a major threat to children. Its computer-aided diagnosis used by neurologists improves the detection proces...

Research on the Fault Diagnosis Method of an Internal Gear Pump Based on a Convolutional Auto-Encoder and PSO-LSSVM.

Sensors (Basel, Switzerland)
The raw signals produced by internal gear pumps are susceptible to noises brought on by mechanical vibrations and the surrounding environment, and the sample count collected during the various operating periods is not distributed evenly. Accurately d...