AIMC Topic: Neural Networks, Computer

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autoBioSeqpy: A Deep Learning Tool for the Classification of Biological Sequences.

Journal of chemical information and modeling
Deep learning has proven to be a powerful method with applications in various fields including image, language, and biomedical data. Thanks to the libraries and toolkits such as TensorFlow, PyTorch, and Keras, researchers can use different deep learn...

Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models.

Sensors (Basel, Switzerland)
Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usual...

Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging.

Physics in medicine and biology
The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, m...

Memristor-based LSTM network with in situ training and its applications.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks (ANNs), such as the convolutional neural network (CNN) and long short-term memory (LSTM), have high complexity and contain large numbers of parameters. Memristor-based neural networks, which have the ability of in-memory an...

Exponential synchronization of stochastic delayed memristive neural networks via a novel hybrid control.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the exponential synchronization issue of stochastic delayed memristive neural networks (SDMNNs) via a novel hybrid control (HC), where impulsive instants are determined by the state-dependent trigger condition. The switching a...

Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Breast cancer is a frequently diagnosed cancer in women, contributing to significant mortality rates. Death rates are relatively higher in developing nations due to the shortage of early detection amenities and constraints o...

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction.

Journal of chemical information and modeling
Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and r...

Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data.

Genes
With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy o...

Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surgery. The previous criteria such as systemic inflammatory response syndrome or Sepsis-3 are not availab...

Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images.

Journal of biomedical informatics
Pathologists are responsible for cancer type diagnoses from histopathological cancer tissues. However, it is known that microscopic examination is tedious and time-consuming. In recent years, a long list of machine learning approaches to image classi...