AIMC Topic: Neural Networks, Computer

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Application of Digital Image Based on Machine Learning in Media Art Design.

Computational intelligence and neuroscience
In digital media art, expressive force is an important art form of media. This paper studies digital images that have the same effect when applied to media art. The research object is media art images, and the application effect of the proposed algor...

FWNNet: Presentation of a New Classifier of Brain Tumor Diagnosis Based on Fuzzy Logic and the Wavelet-Based Neural Network Using Machine-Learning Methods.

Computational intelligence and neuroscience
In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. This classifier includes a layer to predict the numerical feature corresponded to labels or classes. The presented classifie...

Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA).

Computational intelligence and neuroscience
The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the paramete...

Classification of renal biopsy direct immunofluorescence image using multiple attention convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Direct immunofluorescence (DIF) is an important medical evaluation tool for renal pathology. In the DIF images, the deposition appearances and locations of immunoglobulin on glomeruli involve immunological characteristics o...

Deep generative neural networks for spectral image processing.

Analytica chimica acta
An artificial intelligence approach based on deep generative neural networks for spectral imaging processing was proposed. The key idea was to treat different spectral image processing operations such as segmentation, regression, and classification a...

A novel complementation method of an acoustic shadow region utilizing a convolutional neural network for ultrasound-guided therapy.

International journal of computer assisted radiology and surgery
PURPOSE: Noise-free ultrasound images are essential for organ monitoring during regional ultrasound-guided therapy. When the affected area is located under the ribs, however, acoustic shadow is caused by the reflection of sound from hard tissues such...

Sensor-Based Human Activity Recognition Using Adaptive Class Hierarchy.

Sensors (Basel, Switzerland)
In sensor-based human activity recognition, many methods based on convolutional neural networks (CNNs) have been proposed. In the typical CNN-based activity recognition model, each class is treated independently of others. However, actual activity cl...

Cervical cell multi-classification algorithm using global context information and attention mechanism.

Tissue & cell
Cervical cancer is the second biggest killer of female cancer, second only to breast cancer. The cure rate of precancerous lesions found early is relatively high. Therefore, cervical cell classification has very important clinical value in the early ...

Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation.

Sensors (Basel, Switzerland)
Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia. Still, correct localization of the nerve's structure is needed to avoid adverse effects; thereby, ultrasound images are used as an aid approach. In addition, imag...

A Hierarchical Feature Extraction Network for Fast Scene Segmentation.

Sensors (Basel, Switzerland)
Semantic segmentation is one of the most active research topics in computer vision with the goal to assign dense semantic labels for all pixels in a given image. In this paper, we introduce HFEN (Hierarchical Feature Extraction Network), a lightweigh...