AIMC Topic: Neural Networks, Computer

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Pretraining to Recognize PICO Elements from Randomized Controlled Trial Literature.

Studies in health technology and informatics
PICO (Population/problem, Intervention, Comparison, and Outcome) is widely adopted for formulating clinical questions to retrieve evidence from the literature. It plays a crucial role in Evidence-Based Medicine (EBM). This paper contributes a scalabl...

The MeSH-Gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for Semantic Similarity.

Studies in health technology and informatics
Eliciting semantic similarity between concepts remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they have risen to efficiently capture semantic relationships. The underlying idea is that two word...

Performance evaluation of neural network assisted motion detection schemes implemented within indoor optical camera based communications.

Optics express
This paper investigates the performance of the neural network (NN) assisted motion detection (MD) over an indoor optical camera communication (OCC) link. The proposed study is based on the performance evaluation of various NN training algorithms, whi...

Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries inf...

DeepPhos: prediction of protein phosphorylation sites with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but m...

Practical method of cell segmentation in electron microscope image stack using deep convolutional neural network☆.

Microscopy (Oxford, England)
Segmentation of three-dimensional (3D) electron microscopy (EM) image stacks is an arduous and tedious task. Deep convolutional neural networks (CNNs) work well to automate the segmentation; however, they require a large training dataset, which is a ...

Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey.

Water science and technology : a journal of the International Association on Water Pollution Research
Wetlands are among the most productive ecosystems that provide services ranging from flood control to climate change mitigation. Wetlands are also critical habitats for the survival of numerous plant and animal species. In this study, we used satelli...

Artificial neural network-assisted Fourier transform infrared spectroscopy for differentiation of Salmonella serogroups and its application on epidemiological tracing of Salmonella Bovismorbificans outbreak isolates from fresh sprouts.

FEMS microbiology letters
Salmonellae represent one of the most common bacterial infection reagents in both humans and animals. For detection and epidemiological elucidation of Salmonella infections, determination of Salmonella serotypes and differentiation between different ...

Artificial neural networks-based classification of emotions using wristband heart rate monitor data.

Medicine
Heart rate variability (HRV) is an objective measure of emotional regulation. This study aimed to estimate the accuracy with which an artificial neural network (ANN) algorithm could classify emotions using HRV data that were obtained using wristband ...