AIMC Topic: Neural Networks, Computer

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A New Steel Defect Detection Algorithm Based on Deep Learning.

Computational intelligence and neuroscience
In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a proble...

A Fast Spatial Pool Learning Algorithm of Hierarchical Temporal Memory Based on Minicolumn's Self-Nomination.

Computational intelligence and neuroscience
As a new type of artificial neural network model, HTM has become the focus of current research and application. The sparse distributed representation is the basis of the HTM model, but the existing spatial pool learning algorithms have high training ...

Blind Image Quality Assessment With Active Inference.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Blind image quality assessment (BIQA) is a useful but challenging task. It is a promising idea to design BIQA methods by mimicking the working mechanism of human visual system (HVS). The internal generative mechanism (IGM) indicates that the HVS acti...

Candidate region aware nested named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Named entity recognition (NER) is crucial in various natural language processing (NLP) tasks. However, the nested entities which are common in practical corpus are often ignored in most of current NER models. To extract the nested entities, two categ...

Fast and efficient retinal blood vessel segmentation method based on deep learning network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The segmentation of the retinal vascular tree presents a major step for detecting ocular pathologies. The clinical context expects higher segmentation performance with a reduced processing time. For higher accurate segmentation, several automated met...

Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology.

Plant reproduction
Advances in deep learning are providing a powerful set of image analysis tools that are readily accessible for high-throughput phenotyping applications in plant reproductive biology. High-throughput phenotyping systems are becoming critical for answe...

Radiation dose calculation in 3D heterogeneous media using artificial neural networks.

Medical physics
PURPOSE: External beam radiotherapy (EBRT) treatment planning requires a fast and accurate method of calculating the dose delivered by a clinical treatment plan. However, existing methods of calculating dose distributions have limitations. Monte Carl...

Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging.

Journal of digital imaging
PURPOSE: The objective of this paper was to develop a computer-aided diagnostic (CAD) tools for automated analysis of capsule endoscopic (CE) images, more precisely, detect small intestinal abnormalities like bleeding.

Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19.

International journal of environmental research and public health
COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseas...

Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves.

Sensors (Basel, Switzerland)
The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and mo...