AIMC Topic: Neural Networks, Computer

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Deep Convolutional Generative Adversarial Networks to Enhance Artificial Intelligence in Healthcare: A Skin Cancer Application.

Sensors (Basel, Switzerland)
In recent years, researchers designed several artificial intelligence solutions for healthcare applications, which usually evolved into functional solutions for clinical practice. Furthermore, deep learning (DL) methods are well-suited to process the...

Hierarchical dynamic convolutional neural network for laryngeal disease classification.

Scientific reports
Laryngeal disease classification is a relatively hard task in medical image processing resulting from its complex structures and varying viewpoints in data collection. Some existing methods try to tackle this task via the convolutional neural network...

A pretraining domain decomposition method using artificial neural networks to solve elliptic PDE boundary value problems.

Scientific reports
Developing methods of domain decomposition (DDM) has been widely studied in the field of numerical computation to estimate solutions of partial differential equations (PDEs). Several case studies have also reported that it is feasible to use the doma...

Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images.

Scientific reports
The detection of maxillary sinus wall is important in dental fields such as implant surgery, tooth extraction, and odontogenic disease diagnosis. The accurate segmentation of the maxillary sinus is required as a cornerstone for diagnosis and treatmen...

Convolution neural network with batch normalization and inception-residual modules for Android malware classification.

Scientific reports
Deep learning technology is changing the landscape of cybersecurity research, especially the study of large amounts of data. With the rapid growth in the number of malware, developing of an efficient and reliable method for classifying malware has be...

Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms.

Contrast media & molecular imaging
Intelligent machines have grown in importance in recent years in object recognition in terms of their ability to envision, comprehend, and reach decisions. There are a lot of complicated algorithms that accomplish AI utilities. In addition to their u...

Insulators' Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models.

Computational intelligence and neuroscience
Insulators identification and their missing defect detection are of paramount importance for the intelligent inspection of high-voltage transmission lines. As the backgrounds are complex, some insulators may be occluded, and the missing defect of the...

Green Finance Evaluation Based on Neural Network Model.

Computational intelligence and neuroscience
The weights of green finance indicators are established in accordance with the AHP in order to suggest an evaluation system that is more thorough and reasonable and to construct an evaluation index system. The findings indicate that the growth of urb...

A CTR prediction model based on session interest.

PloS one
Click-through rate prediction has become a hot research direction in the field of advertising. It is important to build an effective CTR prediction model. However, most existing models ignore the factor that the sequence is composed of sessions, and ...

Deep learning can predict survival directly from histology in clear cell renal cell carcinoma.

PloS one
For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby ...