AIMC Topic: Neural Networks, Computer

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Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on Chest CT.

Sensors (Basel, Switzerland)
Convolutional Neural Networks (CNNs) have been successfully applied in the medical diagnosis of different types of diseases. However, selecting the architecture and the best set of hyperparameters among the possible combinations can be a significant ...

Prediction of activity cliffs on the basis of images using convolutional neural networks.

Journal of computer-aided molecular design
An activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference in potency. Accordingly, ACs reveal structure-activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein...

Loss odyssey in medical image segmentation.

Medical image analysis
The loss function is an important component in deep learning-based segmentation methods. Over the past five years, many loss functions have been proposed for various segmentation tasks. However, a systematic study of the utility of these loss functio...

Using a deep convolutional network to predict the longitudinal dispersion coefficient.

Journal of contaminant hydrology
Given the interest in accurately predicting the Longitudinal Dispersion Coefficient (D) within the fields of hydraulic and water quality modeling, a wide range of methods have been used to estimate this parameter. In order to improve the accuracy of ...

DRER: Deep Learning-Based Driver's Real Emotion Recognizer.

Sensors (Basel, Switzerland)
In intelligent vehicles, it is essential to monitor the driver's condition; however, recognizing the driver's emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monito...

Deep generative neural network for accurate drug response imputation.

Nature communications
Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outc...

SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models.

BMC research notes
OBJECTIVE: To address the challenge of computational identification of cell type-specific regulatory elements on a genome-wide scale.

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis.

BMC infectious diseases
BACKGROUND: Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of gr...

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction.

BMC bioinformatics
BACKGROUND: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Ther...

Influence of the enterprise's intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period.

PloS one
The purpose is to study the performance compensation of the bid purchased during the mergers and acquisitions (M&A) process. An intelligent model of enterprise performance appraisal is built to analyze the performances of the acquired enterprises. Fi...