AIMC Topic: Neural Networks, Computer

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Bayesian reconstruction of memories stored in neural networks from their connectivity.

PLoS computational biology
The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stor...

A practical guide to deep-learning light-field microscopy for 3D imaging of biological dynamics.

STAR protocols
Here, we present a step-by-step protocol for the implementation of deep-learning-enhanced light-field microscopy enabling 3D imaging of instantaneous biological processes. We first provide the instructions to build a light-field microscope (LFM) capa...

Molecular Computation for Molecular Classification.

Advanced biology
DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or ins...

Malaria Detection Using Advanced Deep Learning Architecture.

Sensors (Basel, Switzerland)
Malaria is a life-threatening disease caused by parasites that are transmitted to humans through the bites of infected mosquitoes. The early diagnosis and treatment of malaria are crucial for reducing morbidity and mortality rates, particularly in de...

Y-Net: Identification of Typical Diseases of Corn Leaves Using a 3D-2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module.

Sensors (Basel, Switzerland)
Corn diseases are one of the significant constraints to high-quality corn production, and accurate identification of corn diseases is of great importance for precise disease control. Corn anthracnose and brown spot are typical diseases of corn, and t...

Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation.

Sensors (Basel, Switzerland)
The segmentation of the left ventricle endocardium (LV) and the left ventricle epicardium (LV) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have been the most commonly used approach for echocardiog...

Application of Feedforward and Recurrent Neural Networks for Fusion of Data from Radar and Depth Sensors Applied for Healthcare-Oriented Characterisation of Persons' Gait.

Sensors (Basel, Switzerland)
In this paper, the useability of feedforward and recurrent neural networks for fusion of data from impulse-radar sensors and depth sensors, in the context of healthcare-oriented monitoring of elderly persons, is investigated. Two methods of data fusi...

sEMG-Based Hand Gesture Recognition Using Binarized Neural Network.

Sensors (Basel, Switzerland)
Recently, human-machine interfaces (HMI) that make life convenient have been studied in many fields. In particular, a hand gesture recognition (HGR) system, which can be implemented as a wearable system, has the advantage that users can easily and in...

Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues using deep learning and multispectral imaging.

Journal of biomedical optics
SIGNIFICANCE: Accurate identification of tissues is critical for performing safe surgery. Combining multispectral imaging (MSI) with deep learning is a promising approach to increasing tissue discrimination and classification. Evaluating the contribu...

Applications of Bayesian Neural Networks in Outlier Detection.

Big data
Anomaly detection is crucial in a variety of domains, such as fraud detection, disease diagnosis, and equipment defect detection. With the development of deep learning, anomaly detection with Bayesian neural networks (BNNs) becomes a novel research t...