AIMC Topic: Neural Networks, Computer

Clear Filters Showing 8451 to 8460 of 31376 articles

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting.

Scientific reports
COVID-19 has resulted in significant morbidity and mortality globally. We develop a model that uses data from thirty days before a fixed time point to forecast the daily number of new COVID-19 cases fourteen days later in the early stages of the pand...

An event-triggered collaborative neurodynamic approach to distributed global optimization.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose an event-triggered collaborative neurodynamic approach to distributed global optimization in the presence of nonconvexity. We design a projection neural network group consisting of multiple projection neural networks coupled...

Before-after safety evaluation of part-time protected right-turn signals: An extreme value theory approach by applying artificial intelligence-based video analytics.

Accident; analysis and prevention
Extreme value theory models have opened doors for before-after safety evaluation of engineering treatments using traffic conflict techniques. Recent advancements in automated conflict extraction technologies have further expedited conflict-based safe...

Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules.

Journal of chemical information and modeling
We introduce an exploratory active learning (AL) algorithm using Gaussian process regression and marginalized graph kernel (GPR-MGK) to sample chemical compound space (CCS) at minimal cost. Targeting 251,728 enumerated alkane molecules with 4-19 carb...

Novel Solution for Using Neural Networks for Kidney Boundary Extraction in 2D Ultrasound Data.

Biomolecules
: Kidney ultrasound (US) imaging is a significant imaging modality for evaluating kidney health and is essential for diagnosis, treatment, surgical intervention planning, and follow-up assessments. Kidney US image segmentation consists of extracting ...

Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning.

BMC medical imaging
BACKGROUND: The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the model pre...

Rapid prediction of lab-grown tissue properties using deep learning.

Physical biology
The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of...

Neural network execution using nicked DNA and microfluidics.

PloS one
DNA has been discussed as a potential medium for data storage. Potentially it could be denser, could consume less energy, and could be more durable than conventional storage media such as hard drives, solid-state storage, and optical media. However, ...

Voice pathology detection using optimized convolutional neural networks and explainable artificial intelligence-based analysis.

Computer methods in biomechanics and biomedical engineering
This article proposes a noninvasive computer-aided assessment approach based on optimized convolutional neural network for healthy and pathological voice detection. Firstly, the input voice samples are first transformed into mel-spectrogram time-freq...