AIMC Topic: Neural Networks, Computer

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Elderly and visually impaired indoor activity monitoring based on Wi-Fi and Deep Hybrid convolutional neural network.

Scientific reports
A drop in physical activity and a deterioration in the capacity to undertake daily life activities are both connected with ageing and have negative effects on physical and mental health. An Elderly and Visually Impaired Human Activity Monitoring (EV-...

Guided principal component analysis (GPCA): a simple method for improving detection of a known analyte.

The Analyst
There is increasing interest in the application of Raman spectroscopy in a medical setting, ranging from supporting real-time clinical decisions surgical margins to assisting pathologists with disease classification. However, there remain a number o...

Quality assessment of colour fundus and fluorescein angiography images using deep learning.

The British journal of ophthalmology
BACKGROUND/AIMS: Image quality assessment (IQA) is crucial for both reading centres in clinical studies and routine practice, as only adequate quality allows clinicians to correctly identify diseases and treat patients accordingly. Here we aim to dev...

CRESPR: Modular sparsification of DNNs to improve pruning performance and model interpretability.

Neural networks : the official journal of the International Neural Network Society
Modern DNNs often include a huge number of parameters that are expensive for both computation and memory. Pruning can significantly reduce model complexity and lessen resource demands, and less complex models can also be easier to explain and interpr...

Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data.

Sensors (Basel, Switzerland)
Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocar...

GPDRP: a multimodal framework for drug response prediction with graph transformer.

BMC bioinformatics
BACKGROUND: In the field of computational personalized medicine, drug response prediction (DRP) is a critical issue. However, existing studies often characterize drugs as strings, a representation that does not align with the natural description of m...

Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity.

Interdisciplinary sciences, computational life sciences
Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug-disease heterogeneous networks to extract drug-disease features. However, these methods faced difficulty when we are using structurally si...

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Journal of chemical information and modeling
Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influe...

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms.

Scientific reports
The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural i...

Identification of active compounds as novel dipeptidyl peptidase-4 inhibitors through machine learning and structure-based molecular docking simulations.

Journal of biomolecular structure & dynamics
The enzyme dipeptidyl peptidase 4 (DPP4) is a potential therapeutic target for type 2 diabetes (T2DM). Many synthetic anti-DPP4 medications are available to treat T2DM. The need for secure and efficient medicines has been unmet due to the adverse sid...