AIMC Topic: Algorithms

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Towards an Optimized Ensemble Feature Selection for DDoS Detection Using Both Supervised and Unsupervised Method.

Sensors (Basel, Switzerland)
With recent advancements in artificial intelligence (AI) and next-generation communication technologies, the demand for Internet-based applications and intelligent digital services is increasing, leading to a significant rise in cyber-attacks such as...

The search for new efficient inhibitors of SARS-COV-2 through the drug design developed by artificial intelligence.

Journal of biomolecular structure & dynamics
The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzy...

AI-Based Video Segmentation: Procedural Steps or Basic Maneuvers?

The Journal of surgical research
INTRODUCTION: Video-based review of surgical procedures has proven to be useful in training by enabling efficiency in the qualitative assessment of surgical skill and intraoperative decision-making. Current video segmentation protocols focus largely ...

Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Whole slide image (WSI) classification and lesion localization within giga-pixel slide are challenging tasks in computational pathology that requires context-aware representations of histological features to adequately infer...

Deep Learning-Based Framework for Fast and Accurate Acoustic Hologram Generation.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Acoustic holography has been gaining attention for various applications, such as noncontact particle manipulation, noninvasive neuromodulation, and medical imaging. However, only a few studies on how to generate acoustic holograms have been conducted...

Path sampling of recurrent neural networks by incorporating known physics.

Nature communications
Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledg...

Deep learning-regularized, single-step quantitative susceptibility mapping quantification.

NMR in biomedicine
The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning-regularized, single-step QSM quantifica...

Artificial intelligence in the management and treatment of burns: A systematic review and meta-analyses.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
INTRODUCTION AND AIM: Artificial Intelligence (AI) is already being successfully employed to aid the interpretation of multiple facets of burns care. In the light of the growing influence of AI, this systematic review and diagnostic test accuracy met...

NeXtQSM-A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data.

Medical image analysis
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo ...

A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images.

Medical image analysis
Mitosis counting of biopsies is an important biomarker for breast cancer patients, which supports disease prognostication and treatment planning. Developing a robust mitotic cell detection model is highly challenging due to its complex growth pattern...