AIMC Topic: Neural Networks, Computer

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QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network.

BioMed research international
Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many c...

The Generation and Modulation of Distinct Gamma Oscillations with Local, Horizontal, and Feedback Connections in the Primary Visual Cortex: A Model Study on Large-Scale Networks.

Neural plasticity
Gamma oscillation (GAMMA) in the local field potential (LFP) is a synchronized activity commonly found in many brain regions, and it has been thought as a functional signature of network connectivity in the brain, which plays important roles in infor...

Sonar Objective Detection Based on Dilated Separable Densely Connected CNNs and Quantum-Behaved PSO Algorithm.

Computational intelligence and neuroscience
Underwater sonar objective detection plays an important role in the field of ocean exploration. In order to solve the problem of sonar objective detection under the complex environment, a sonar objective detection method is proposed based on dilated ...

Is Context-Aware CNN Ready for the Surroundings? Panoramic Semantic Segmentation in the Wild.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Semantic segmentation, unifying most navigational perception tasks at the pixel level has catalyzed striking progress in the field of autonomous transportation. Modern Convolution Neural Networks (CNNs) are able to perform semantic segmentation both ...

Application of artificial intelligence and machine learning for prediction of oral cancer risk.

Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
BACKGROUND: Oral cancer requires early diagnosis and treatment to increase the chances of survival. This study aimed to develop an artificial neural network model that helps to predict the individuals' risk of developing oral cancer based on data on ...

High performance in risk stratification of intraductal papillary mucinous neoplasms by confocal laser endomicroscopy image analysis with convolutional neural networks (with video).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differentiate high-grade dysplasia/adenocarcinoma (HGD-Ca) in intraductal papillary mucinous neoplasms (IPMNs) but requires manual interpretation. We sought to ...

Performance evaluation of artificial intelligence paradigms-artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction.

Environmental science and pollution research international
Flood prediction has gained prominence world over due to the calamitous socio-economic impacts this hazard has and the anticipated increase of its incidence in the near future. Artificial intelligence (AI) models have contributed significantly over t...

Deep Learning of Biological Models from Data: Applications to ODE Models.

Bulletin of mathematical biology
Mathematical equations are often used to model biological processes. However, for many systems, determining analytically the underlying equations is highly challenging due to the complexity and unknown factors involved in the biological processes. In...

Simultaneous spectrophotometric quantitative analysis of velpatasvir and sofosbuvir in recently approved FDA pharmaceutical preparation using artificial neural networks and genetic algorithm artificial neural networks.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Two chemometric assisted spectrophotometric models were applied for the quantitative analysis of velpatasvir and sofosbuvir in their newly FDA approved pharmaceutical dosage form. The UV absorption spectra of velpatasvir and sofosbuvir showed certain...

Empirically constrained network models for contrast-dependent modulation of gamma rhythm in V1.

NeuroImage
Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simp...