AIMC Topic: Neural Networks, Computer

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An adaptive ensemble deep learning framework for reliable detection of pandemic patients.

Computers in biology and medicine
Nurses, often considered the backbone of global health services, are disproportionately vulnerable to COVID-19 due to their front-line roles. They conduct essential patient tests, including blood pressure, temperature, and complete blood counts. The ...

An artificial neural network for full-body posture prediction in dynamic lifting activities and effects of its prediction errors on model-estimated spinal loads.

Journal of biomechanics
Musculoskeletal models have indispensable applications in occupational risk assessment/management and clinical treatment/rehabilitation programs. To estimate muscle forces and joint loads, these models require body posture during the activity under c...

Selective peripheral nerve recording using simulated human median nerve activity and convolutional neural networks.

Biomedical engineering online
BACKGROUND: It is difficult to create intuitive methods of controlling prosthetic limbs, often resulting in abandonment. Peripheral nerve interfaces can be used to convert motor intent into commands to a prosthesis. The Extraneural Spatiotemporal Com...

Attention-based neural networks for clinical prediction modelling on electronic health records.

BMC medical research methodology
BACKGROUND: Deep learning models have had a lot of success in various fields. However, on structured data they have struggled. Here we apply four state-of-the-art supervised deep learning models using the attention mechanism and compare against logis...

Fully automated deep learning models with smartphone applicability for prediction of pain using the Feline Grimace Scale.

Scientific reports
This study used deep neural networks and machine learning models to predict facial landmark positions and pain scores using the Feline Grimace Scale (FGS). A total of 3447 face images of cats were annotated with 37 landmarks. Convolutional neural net...

Deep learning-based segmentation of multisite disease in ovarian cancer.

European radiology experimental
PURPOSE: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.

SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.

PLoS computational biology
Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional ...

Increased interpretation of deep learning models using hierarchical cluster-based modelling.

PloS one
Linear prediction models based on data with large inhomogeneity or abrupt non-linearities often perform poorly because relationships between groups in the data dominate the model. Given that the data is locally linear, this can be overcome by splitti...

Accurate COP Trajectory Estimation in Healthy and Pathological Gait Using Multimodal Instrumented Insoles and Deep Learning Models.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Measuring center-of-pressure (COP) trajectories in out-of-the-lab environments may provide valuable information about changes in gait and balance function related to natural disease progression or treatment in neurological disorders. Traditional equi...