AIMC Topic: Algorithms

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Asymmetric lesion detection with geometric patterns and CNN-SVM classification.

Computers in biology and medicine
In dermoscopic images, which allow visualization of surface skin structures not visible to the naked eye, lesion shape offers vital insights into skin diseases. In clinically practiced methods, asymmetric lesion shape is one of the criteria for diagn...

FMCW Radar Human Action Recognition Based on Asymmetric Convolutional Residual Blocks.

Sensors (Basel, Switzerland)
Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human acti...

Improving the performance of mutation-based evolving artificial neural networks with self-adaptive mutations.

PloS one
Neuroevolution is a promising approach for designing artificial neural networks using an evolutionary algorithm. Unlike recent trending methods that rely on gradient-based algorithms, neuroevolution can simultaneously evolve the topology and weights ...

Leveraging textual information for social media news categorization and sentiment analysis.

PloS one
The rise of social media has changed how people view connections. Machine Learning (ML)-based sentiment analysis and news categorization help understand emotions and access news. However, most studies focus on complex models requiring heavy resources...

GMAC-A Simple Measure to Quantify Upper Limb Use From Wrist-Worn Accelerometers.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Various measures have been proposed to quantify upper-limb use through wrist-worn inertial measurement units. The two most popular traditional measures of upper-limb use - thresholded activity counts (TAC) and the gross movement (GM) score suffer fro...

Spectral Decomposition and Transformation for Cross-domain Few-shot Learning.

Neural networks : the official journal of the International Neural Network Society
Cross-domain few-shot Learning (CDFSL) is proposed to first pre-train deep models on a source domain dataset where sufficient data is available, and then generalize models to target domains to learn from only limited data. However, the gap between th...

LD-CSNet: A latent diffusion-based architecture for perceptual Compressed Sensing.

Neural networks : the official journal of the International Neural Network Society
Compressed Sensing (CS) is a groundbreaking paradigm in image acquisition, challenging the constraints of the Nyquist-Shannon sampling theorem. This enables high-quality image reconstruction using a minimal number of measurements. Neural Networks' po...

A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis.

Neural networks : the official journal of the International Neural Network Society
Graph convolutional networks (GCNs) as the emerging neural networks have shown great success in Prognostics and Health Management because they can not only extract node features but can also mine relationship between nodes in the graph data. However,...

Reweighted Alternating Direction Method of Multipliers for DNN weight pruning.

Neural networks : the official journal of the International Neural Network Society
As Deep Neural Networks (DNNs) continue to grow in complexity and size, leading to a substantial computational burden, weight pruning techniques have emerged as an effective solution. This paper presents a novel method for dynamic regularization-base...

CBG-Net: Cross-modality and cross-scale balance network with global semantics for multi-modal 3D object detection.

Neural networks : the official journal of the International Neural Network Society
Multi-modal 3D object detection is instrumental in identifying and localizing objects within 3D space. It combines RGB images from cameras and point-clouds data from lidar sensors, serving as a fundamental technology for autonomous driving applicatio...