AIMC Topic: Algorithms

Clear Filters Showing 1971 to 1980 of 28713 articles

Multi-modal sentiment recognition with residual gating network and emotion intensity attention.

Neural networks : the official journal of the International Neural Network Society
Multimodal emotion recognition focuses on the prediction of emotions using text, visual and acoustic modalities, and some results have been generated in this field. Previous approaches fall short in two aspects, one is the processing of complementary...

Kernel-free quadratic surface SVM for conditional probability estimation in imbalanced multi-class classification.

Neural networks : the official journal of the International Neural Network Society
For the multi-class classification problems, we propose a new probabilistic output classifier called kernel-free quadratic surface support vector machine for conditional probability estimation (CPSQSVM), which is based on a newly developed binary cla...

Communication-efficient distributed learning with Local Immediate Error Compensation.

Neural networks : the official journal of the International Neural Network Society
Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compressio...

Layer Frozen Multi-Net & Latent Space Feature-Concealed Backdoor Samples Detection.

Neural networks : the official journal of the International Neural Network Society
Identifying feature-concealed backdoor samples that entangle with benign semantics of target-class or possess dynamic triggers challenges backdoor attack detection. Existing methods focus on sample distribution differences in latent space of victim m...

A systematic review of machine learning algorithms for breast cancer detection.

Tissue & cell
Breast cancer is one of the leading causes of death and morbidity among women worldwide. Identifying cancerous cells remains a complex and time-consuming task, particularly when performed manually by radiologists or pathologists, contributing to high...

An explainable artificial intelligence framework for weaning outcomes prediction using features from electrical impedance tomography.

Computer methods and programs in biomedicine
BACKGROUND: Prolonged mechanical ventilation (PMV) might cause ventilator-associated pneumonia and diaphragmatic injury, and may lead to worsening clinical weaning outcomes. The present study proposes a comprehensive machine learning (ML) framework f...

A multi-task neural network for full waveform ultrasonic bone imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: It is a challenging task to use ultrasound for bone imaging, as the bone tissue has a complex structure with high acoustic impedance and speed-of-sound (SOS). Recently, full waveform inversion (FWI) has shown promising imagi...

FovealNet: Advancing AI-Driven Gaze Tracking Solutions for Efficient Foveated Rendering in Virtual Reality.

IEEE transactions on visualization and computer graphics
Leveraging real-time eye tracking, foveated rendering optimizes hardware efficiency and enhances visual quality virtual reality (VR). This approach leverages eye-tracking techniques to determine where the user is looking, allowing the system to rende...

Artificial intelligence based vision transformer application for grading histopathological images of oral epithelial dysplasia: a step towards AI-driven diagnosis.

BMC cancer
BACKGROUND: This study aimed to classify dysplastic and healthy oral epithelial histopathological images, according to WHO and binary grading systems, using the Vision Transformer (ViT) deep learning algorithm-a state-of-the-art Artificial Intelligen...

A population based optimization of convolutional neural networks for chronic kidney disease prediction.

Scientific reports
Chronic kidney disease (CKD) is a global public health concern, and the timely detection of the disease is priceless. Most of the classical machine learning models have the major drawbacks of being unsophisticated, non-robust, and non-accurate. This ...