AIMC Topic: Neural Networks, Computer

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Dental bitewing radiographs segmentation using deep learning-based convolutional neural network algorithms.

Oral radiology
OBJECTIVES: Dental radiographs, particularly bitewing radiographs, are widely used in dental diagnosis and treatment Dental image segmentation is difficult for various reasons, such as intricate structures, low contrast, noise, roughness, and unclear...

Comparison of hybrid RNA-based models to predict the degradation and mineralization of the microcontaminant hormone 17β-estradiol.

Chemosphere
New alternatives for effluent decontamination, such as electrochemical oxidation, are being developed to provide adequate removal of endocrine disruptors such as 17β-estradiol in wastewater. In this study, data-driven models of response surface metho...

Optimization of operational parameters using RSM, ANN, and SVM in membrane integrated with rotating biological contactor.

Chemosphere
Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameter...

Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis.

Scientific reports
Rapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality and time-intensive process of the prevalent frozen section procedure, various intraoperative diagnosti...

Neural networks memorise personal information from one sample.

Scientific reports
Deep neural networks (DNNs) have achieved high accuracy in diagnosing multiple diseases/conditions at a large scale. However, a number of concerns have been raised about safeguarding data privacy and algorithmic bias of the neural network models. We ...

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.

Self-supervised Learning for DNA sequences with circular dilated convolutional networks.

Neural networks : the official journal of the International Neural Network Society
DNA molecules commonly exhibit wide interactions between the nucleobases. Modeling the interactions is important for obtaining accurate sequence-based inference. Although many deep learning methods have recently been developed for modeling DNA sequen...

The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients.

Chemosphere
A novel approach is introduced for the reliable prediction of PUF-air partition coefficients of organic compounds, which can determine the environmental fate of organic compounds during interactions with air, soil, and water. The biggest accessible m...

m5UMCB: Prediction of RNA 5-methyluridine sites using multi-scale convolutional neural network with BiLSTM.

Computers in biology and medicine
As a prevalent RNA modification, 5-methyluridine (mU) plays a critical role in diverse biological processes and disease pathogenesis. High-throughput identification of mU typically relies on labor-intensive biochemical experiments using various seque...

Ensemble machine learning model trained on a new synthesized dataset generalizes well for stress prediction using wearable devices.

Journal of biomedical informatics
INTRODUCTION: Advances in wearable sensor technology have enabled the collection of biomarkers that may correlate with levels of elevated stress. While significant research has been done in this domain, specifically in using machine learning to detec...