AIMC Topic: Neural Networks, Computer

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CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach.

PLoS computational biology
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role ...

Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems.

PloS one
The increasing energy demand and the target to reduce environmental pollution make it essential to use efficient and environment-friendly renewable energy systems. One of these systems is the Photovoltaic (PV) system which generates energy subject to...

A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor.

Brain imaging and behavior
A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approach. This proposed method includes preprocessing, classification, and segmentation of the primary occu...

Deep learning-based convolutional neural network for intramodality brain MRI synthesis.

Journal of applied clinical medical physics
PURPOSE: The existence of multicontrast magnetic resonance (MR) images increases the level of clinical information available for the diagnosis and treatment of brain cancer patients. However, acquiring the complete set of multicontrast MR images is n...

Computing within bacteria: Programming of bacterial behavior by means of a plasmid encoding a perceptron neural network.

Bio Systems
In nature, bacteria exhibit a limited repertoire of behaviors in response to environmental changes. Synthetic biology has now opened up the possibility of programming cells or unicellular organisms in order to enable them to perform certain tasks, wh...

Colon tissue image segmentation with MWSI-NET.

Medical & biological engineering & computing
Developments in deep learning have resulted in computer-aided diagnosis for many types of cancer. Previously, pathologists manually performed the labeling work in the analysis of colon tissues, which is both time-consuming and labor-intensive. Result...

Quantification of uptake in pelvis F-18 FLT PET-CT images using a 3D localization and segmentation CNN.

Medical physics
PURPOSE: The purpose of this work was to develop and validate a deep convolutional neural network (CNN) approach for the automated pelvis segmentation in computed tomography (CT) scans to enable the quantification of active pelvic bone marrow by mean...

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy.

Journal of chemical information and modeling
We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed gr...

Performance of a deep learning-based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness.

Medical physics
PURPOSE: Deep learning (DL) is rapidly finding applications in low-dose CT image denoising. While having the potential to improve the image quality (IQ) over the filtered back projection method (FBP) and produce images quickly, performance generaliza...

Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning.

Sensors (Basel, Switzerland)
The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patte...