AIMC Topic: Neural Networks, Computer

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Exponential Stability of Mixed Time-Delay Neural Networks Based on Switching Approaches.

IEEE transactions on cybernetics
Neural networks (NNs) have been deeply studied due to their wide applicability. Since time delays are unavoidable in reality, it is basic and crucial for all applications based on NNs to guarantee system stability under the influence of mixed time de...

Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model.

Journal of healthcare engineering
Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are presently living with glaucoma (2020). It is expected that 111.8 million people will have glaucoma b...

Recognition of Bookmark Aging Degree Based on Probabilistic Neural Network.

Computational intelligence and neuroscience
Bookmarks are the basis for librarians to get books on and off shelves and borrowers to borrow books. In order to solve the problem of time-consuming and labor-consuming manual checking of bookmark aging, this paper proposes a method of bookmark agin...

Regional Economic Prediction Model Using Backpropagation Integrated with Bayesian Vector Neural Network in Big Data Analytics.

Computational intelligence and neuroscience
Forecasting economic growth is critical for formulating national economic development policies. Neural Networks are a type of artificial intelligence that may be used to model complex target functions. ANN (Artificial Neural Networks) are one of the ...

A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may ...

Intra- and Inter-Slice Contrastive Learning for Point Supervised OCT Fluid Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks achieve great success in OCT fluid segmentation. However, requiring pixel-w...

Compound computer vision workflow for efficient and automated immunohistochemical analysis of whole slide images.

Journal of clinical pathology
AIMS: Immunohistochemistry (IHC) assessment of tissue is a central component of the modern pathology workflow, but quantification is challenged by subjective estimates by pathologists or manual steps in semi-automated digital tools. This study integr...

Hybrid model of a cement rotary kiln using an improved attention-based recurrent neural network.

ISA transactions
A rotary kiln is core equipment in cement calcination. Significant time delay, time-varying, and nonlinear characteristics cause challenges in the advance process control and operational optimization of the rotary kiln. However, the traditional mecha...

DREAM: Drug-drug interaction extraction with enhanced dependency graph and attention mechanism.

Methods (San Diego, Calif.)
Drug-drug interactions (DDIs) aim at describing the effect relations produced by a combination of two or more drugs. It is an important semantic processing task in the field of bioinformatics such as pharmacovigilance and clinical research. Recently,...

ISSMF: Integrated semantic and spatial information of multi-level features for automatic segmentation in prenatal ultrasound images.

Artificial intelligence in medicine
As an effective way of routine prenatal diagnosis, ultrasound (US) imaging has been widely used recently. Biometrics obtained from the fetal segmentation shed light on fetal health monitoring. However, the segmentation in US images has strict require...