AIMC Topic: Neural Networks, Computer

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Enhancing urban blue-green landscape quality assessment through hybrid genetic algorithm-back propagation (GA-BP) neural network approach: a case study in Fucheng, China.

Environmental monitoring and assessment
This study employs an artificial neural network optimization algorithm, enhanced with a Genetic Algorithm-Back Propagation (GA-BP) network, to assess the service quality of urban water bodies and green spaces, aiming to promote healthy urban environm...

DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.

PLoS computational biology
Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wid...

Evaluation of water resources security in Anhui Province based on GA-BP model.

Environmental science and pollution research international
Water resources security is an important cornerstone of regional sustainable development, but the current evaluation system of water resources security is not scientific, and the measurement of safety level has not been optimized by combining algorit...

TNCB: Tri-Net With Cross-Balanced Pseudo Supervision for Class Imbalanced Medical Image Classification.

IEEE journal of biomedical and health informatics
In clinical settings, the implementation of deep neural networks is impeded by the prevalent problems of label scarcity and class imbalance in medical images. To mitigate the need for labeled data, semi-supervised learning (SSL) has gained traction. ...

Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust.

IEEE transactions on neural networks and learning systems
The first step toward investigating the effectiveness of a treatment via a randomized trial is to split the population into control and treatment groups then compare the average response of the treatment group receiving the treatment to the control g...

Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic.

IEEE journal of biomedical and health informatics
People across the globe have felt and are still going through the impact of COVID-19. Some of them share their feelings and suffering online via different online social media networks such as Twitter. Due to strict restrictions to reduce the spread o...

SwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
The precise segmentation of medical images is one of the key challenges in pathology research and clinical practice. However, many medical image segmentation tasks have problems such as large differences between different types of lesions and similar...

Graph Representation Learning for Large-Scale Neuronal Morphological Analysis.

IEEE transactions on neural networks and learning systems
The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphologic...

A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network.

IEEE transactions on neural networks and learning systems
The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. T...

Holistic-Guided Disentangled Learning With Cross-Video Semantics Mining for Concurrent First-Person and Third-Person Activity Recognition.

IEEE transactions on neural networks and learning systems
The popularity of wearable devices has increased the demands for the research on first-person activity recognition. However, most of the current first-person activity datasets are built based on the assumption that only the human-object interaction (...