AIMC Topic: Neural Networks, Computer

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Multi-head ensemble of smoothed classifiers for certified robustness.

Neural networks : the official journal of the International Neural Network Society
Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple Deep Neural Networks (DNNs) has shown state-of-the-art performances due to its variance reduction effect over Gaussian noises. Ho...

ULST: U-shaped LeWin Spectral Transformer for virtual staining of pathological sections.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
At present, pathological section staining faces several challenges, including complex sample preparation and stringent infrastructure requirements. Virtual staining methods utilizing deep neural networks to automatically generate stained images are g...

Predicting Final Restoration Color Using Neural Network Models: The Impact of Substrate Lightness Versus Ceramic Shade, Translucency and Thickness.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
OBJECTIVES: This study aimed to assess the influence of background color, ceramic shade, translucency, and thickness on the color matching of lithium disilicate restorations and to use a neural network model to predict the optimal parameters for shad...

Perspectives: Comparison of deep learning segmentation models on biophysical and biomedical data.

Biophysical journal
Deep learning-based approaches are now widely used across biophysics to help automate a variety of tasks including image segmentation, feature selection, and deconvolution. However, the presence of multiple competing deep learning architectures, each...

ResTransUNet: A hybrid CNN-transformer approach for liver and tumor segmentation in CT images.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Accurate medical tumor segmentation is critical for early diagnosis and treatment planning, significantly improving patient outcomes. This study aims to enhance liver and tumor segmentation from CT and liver images by develo...

Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model.

Journal of chemical information and modeling
Accurate retention time (RT) prediction in liquid chromatography remains a significant consideration in molecular analysis. In this study, we explore the use of a transformer-based language model to predict RTs by treating simplified molecular input ...

Surrogate Model Development for Digital Experiments in Welding.

Journal of visualized experiments : JoVE
The manufacturing industry heavily relies on welding processes to join materials, forming integral components across various sectors. Many aspects will influence the quality of the weld and finally affect the structure integrity of the weldment. Weld...

Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach.

Head & face medicine
BACKGROUND: Facial aesthetics is one of major motivations for seeking orthodontic treatment. However, even for experienced professionals, the impact and extent of incisor and soft tissue changes remain largely empirical. With the application of inter...

Extract optimization and biological activities of Otidea onotica using Artificial Neural Network-Genetic Algorithm and response surface methodology techniques.

BMC biotechnology
In this study, the biological activities of Otidea onotica were investigated using two optimization methods, Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). The extracts were tested for phenolic content, a...

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification.

BMC bioinformatics
The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural ...