AIMC Topic: Neural Networks, Computer

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A Comparative Analysis of Data Synthesis Techniques to Improve Classification Accuracy of Raman Spectroscopy Data.

Journal of chemical information and modeling
Raman spectra are examples of high dimensional data that can often be limited in the number of samples. This is a primary concern when Deep Learning frameworks are developed for tasks such as chemical species identification, quantification, and diagn...

Convolutional neural network misclassification analysis in oral lesions: an error evaluation criterion by image characteristics.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This retrospective study analyzed the errors generated by a convolutional neural network (CNN) when performing automated classification of oral lesions according to their clinical characteristics, seeking to identify patterns in systemic e...

Deterministic learning-based neural identification and knowledge fusion.

Neural networks : the official journal of the International Neural Network Society
Recent deterministic learning methods have achieved locally-accurate identification of unknown system dynamics. However, the locally-accurate identification means that the neural networks can only capture the local dynamics knowledge along the system...

Saturation function-based continuous control on fixed-time synchronization of competitive neural networks.

Neural networks : the official journal of the International Neural Network Society
Currently, through proposing discontinuous control strategies with the signum function and discussing separately short-term memory (STM) and long-term memory (LTM) of competitive artificial neural networks (ANNs), the fixed-time (FXT) synchronization...

Graph-based methods coupled with specific distributional distances for adversarial attack detection.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks are prone to being fooled by carefully perturbed inputs which cause an egregious misclassification. These adversarial attacks have been the focus of extensive research. Likewise, there has been an abundance of research in w...

Beyond low-pass filtering on large-scale graphs via Adaptive Filtering Graph Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have emerged as a crucial deep learning framework for graph-structured data. However, existing GNNs suffer from the scalability limitation, which hinders their practical implementation in industrial settings. Many scalabl...

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction.

Computational biology and chemistry
Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been pro...

Performance of deep learning models for response evaluation on whole-body bone scans in prostate cancer.

Annals of nuclear medicine
OBJECTIVE: We aimed to develop deep learning classifiers for assessing therapeutic response on bone scans of patients with prostate cancer.

HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks.

Analytical chemistry
The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are ava...