AIMC Topic: Neural Networks, Computer

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Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks.

Computational intelligence and neuroscience
At present, diabetes is one of the most important chronic noncommunicable diseases, that have threatened human health. By 2020, the number of diabetic patients worldwide has reached 425 million. This amazing number has attracted the great attention o...

Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based Feature Extraction and Classification.

Computational intelligence and neuroscience
Microscopy image analysis gives quantitative support for enhancing the characterizations of various diseases, including breast cancer, lung cancer, and brain tumors. As a result, it is crucial in computer-assisted diagnosis and prognosis. Understandi...

A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level.

Journal of dentistry
OBJECTIVES: Bone level as measured by clinical attachment levels (CAL) are critical findings that determine the diagnosis of periodontal disease. Deep learning algorithms are being used to determine CAL which aid in the diagnosis of periodontal disea...

Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks.

Computational intelligence and neuroscience
Digitalization brings challenges and new opportunities to the development of landscape gardening, "smart gardening," which is a product of landscape gardening in response to the development of the digital era. Based on the multimodal intelligent comp...

Diagnosis of Alternaria disease and leafminer pest on tomato leaves using image processing techniques.

Journal of the science of food and agriculture
BACKGROUND: Diseases such as Alternaria and pests such as leafminer threaten tomato as one of the most widely used agricultural products. These pests and diseases first damage the leaves of tomatoes, then the flowers, and finally the fruit. Therefore...

A deep learning model designed for Raman spectroscopy with a novel hyperparameter optimization method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Raman spectroscopy is a spectroscopic technique typically used to determine vibrational modes of molecules and is commonly used in chemistry to provide a structural fingerprint by which molecules can be identified. With the help of deep learning, Ram...

Reconstruction of missing spring discharge by using deep learning models with ensemble empirical mode decomposition of precipitation.

Environmental science and pollution research international
A continuous and complete spring discharge record is critical in understanding the hydrodynamic behavior of karst aquifers and the variability of freshwater resources. However, due to equipment errors, failure of observation and other reasons, missin...

UAT: Universal Attention Transformer for Video Captioning.

Sensors (Basel, Switzerland)
Video captioning via encoder-decoder structures is a successful sentence generation method. In addition, using various feature extraction networks for extracting multiple features to obtain multiple kinds of visual features in the encoding process is...

Low-Complexity Adaptive Sampling of Block Compressed Sensing Based on Distortion Minimization.

Sensors (Basel, Switzerland)
Block compressed sensing (BCS) is suitable for image sampling and compression in resource-constrained applications. Adaptive sampling methods can effectively improve the rate-distortion performance of BCS. However, adaptive sampling methods bring hig...

Research on Virtual Interactive Animation Design System Based on Deep Learning.

Computational intelligence and neuroscience
With the rapid development of computer network technology, the advantages of virtual reality technology in the field of instant messaging are becoming more and more significant. Virtual reality technology plays an important role in communication netw...