AIMC Topic: Algorithms

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Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images.

Sensors (Basel, Switzerland)
Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the ...

An Emotion Recognition Method for Humanoid Robot Body Movements Based on a PSO-BP-RMSProp Neural Network.

Sensors (Basel, Switzerland)
This paper mainly explores the computational model that connects a robot's emotional body movements with human emotion to propose an emotion recognition method for humanoid robot body movements. There is sparse research directly carried out from this...

Persistence landscapes: Charting a path to unbiased radiological interpretation.

Oncotarget
Persistence landscapes, a sophisticated tool from topological data analysis, offer a promising approach to address biases in radiological interpretation and AI model development. By transforming complex topological features into statistically analyza...

Validation of Vetscan Imagyst, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.

Parasites & vectors
BACKGROUND: Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intell...

Facial Image expression recognition and prediction system.

Scientific reports
Facial expression recognition system is an advanced technology that allows machines to recognize human emotions based on their facial expressions. In order to develop a robust prediction model, this research work proposes three distinct architectural...

Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation.

PloS one
Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-s...

Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?

International endodontic journal
BACKGROUND: Artificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision-making and problem-solving through complex datasets. In endodontics, AI is transfor...

Predicting the Risk of Driving Under the Influence of Alcohol Using EEG-Based Machine Learning.

Computers in biology and medicine
Driving under the influence of alcohol (DUIA) is closely associated with alcohol use disorder (AUD). Our previous study on machine learning (ML) algorithms revealed a very high accuracy of decision trees with neuropsychological features in predicting...

Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, a...

MRI denoising with a non-blind deep complex-valued convolutional neural network.

NMR in biomedicine
MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal...