AIMC Topic: Neural Networks, Computer

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Distinguishing shadows from surface boundaries using local achromatic cues.

PLoS computational biology
In order to accurately parse the visual scene into distinct surfaces, it is essential to determine whether a local luminance edge is caused by a boundary between two surfaces or a shadow cast across a single surface. Previous studies have demonstrate...

Boundary-Preserved Deep Denoising of Stochastic Resonance Enhanced Multiphoton Images.

IEEE journal of translational engineering in health and medicine
OBJECTIVE: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conv...

An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection.

Computational intelligence and neuroscience
In this research, we proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of various plant leaf diseases. Six blocks of layers were used to develop ResNet197. ResNet197 was trained and tested using...

LawRec: Automatic Recommendation of Legal Provisions Based on Legal Text Analysis.

Computational intelligence and neuroscience
Smart court technologies are making full use of modern science to promote the modernization of the trial system and trial capabilities, for example, artificial intelligence, Internet of things, and cloud computing. The smart court technologies can im...

Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges caused by various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architectures...

A novel model based on CEEMDAN, IWOA, and LSTM for ultra-short-term wind power forecasting.

Environmental science and pollution research international
The randomness and instability of wind power bring challenges to power grid dispatching. Accurate prediction of wind power is significant to ensure the stable development of power grid. In this paper, a new ultra-short-term wind power forecasting mod...

DeeProPre: A promoter predictor based on deep learning.

Computational biology and chemistry
The promoter is a DNA sequence recognized, bound and transcribed by RNA polymerase. It is usually located at the upstream or 5'end of the transcription start site (TSS). Studies have shown that the structure of the promoter affects its affinity for R...

Generative deep learning applied to biomechanics: A new augmentation technique for motion capture datasets.

Journal of biomechanics
Deep learning biomechanical models perform optimally when trained with large datasets, however these can be challenging to collect in gait labs, while limited augmentation techniques are available. This study presents a data augmentation approach bas...

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study.

Medical image analysis
Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, th...

A model for predicting ncRNA-protein interactions based on graph neural networks and community detection.

Methods (San Diego, Calif.)
Non-coding RNA (ncRNA) s play an considerable role in the current biological sciences, such as gene transcription, gene expression, etc. Exploring the ncRNA-protein interactions(NPI) is of great significance, while some experimental techniques are ve...