AIMC Topic: Neural Networks, Computer

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CNN and LSTM-Based Emotion Charting Using Physiological Signals.

Sensors (Basel, Switzerland)
Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance impr...

Bubbly flow prediction with randomized neural cells artificial learning and fuzzy systems based on k-ε turbulence and Eulerian model data set.

Scientific reports
Computing gas and liquid interactions based on interfacial force models require a proper turbulence model that accurately resolve the turbulent scales such as turbulence kinetic energy and turbulence dissipation rate with cheap computational resource...

Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits.

PLoS computational biology
Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the re...

Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.

PloS one
Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding the application of machin...

Automated diatom searching in the digital scanning electron microscopy images of drowning cases using the deep neural networks.

International journal of legal medicine
Forensic diatom test has been widely accepted as a way of providing supportive evidences in the diagnosis of drowning. The current workflow is primarily based on the observation of diatoms by forensic pathologists under a microscopy, and this process...

Adaptive balancing of exploration and exploitation around the edge of chaos in internal-chaos-based learning.

Neural networks : the official journal of the International Neural Network Society
This paper addresses learning with exploration driven by chaotic internal dynamics of a neural network. Hoerzer et al. showed that a chaotic reservoir network (RN) can learn with exploration driven by external random noise and a sequential reward. In...

Variability and reproducibility in deep learning for medical image segmentation.

Scientific reports
Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep le...

Deep neural networks enable quantitative movement analysis using single-camera videos.

Nature communications
Many neurological and musculoskeletal diseases impair movement, which limits people's function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion cap...

Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model.

Journal of integrative bioinformatics
As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disea...

EBM+: Advancing Evidence-Based Medicine via two level automatic identification of Populations, Interventions, Outcomes in medical literature.

Artificial intelligence in medicine
Evidence-Based Medicine (EBM) has been an important practice for medical practitioners. However, as the number of medical publications increases dramatically, it is becoming extremely difficult for medical experts to review all the contents available...