AIMC Topic: Machine Learning

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Sparse solution of least-squares twin multi-class support vector machine using ℓ and ℓ-norm for classification and feature selection.

Neural networks : the official journal of the International Neural Network Society
In the realm of multi-class classification, the twin K-class support vector classification (Twin-KSVC) generates ternary outputs {-1,0,+1} by evaluating all training data in a "1-versus-1-versus-rest" structure. Recently, inspired by the least-square...

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses.

Journal of biomedical optics
SIGNIFICANCE: Diabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabetes management, and noninvasive methods can be more convenient and less painful than traditional finge...

Machine learning prediction and classification of behavioral selection in a canine olfactory detection program.

Scientific reports
There is growing interest in canine behavioral research specifically for working dogs. Here we take advantage of a dataset of a Transportation Safety Administration olfactory detection cohort of 628 Labrador Retrievers to perform Machine Learning (ML...

Childhood Leukemia Classification via Information Bottleneck Enhanced Hierarchical Multi-Instance Learning.

IEEE transactions on medical imaging
Leukemia classification relies on a detailed cytomorphological examination of Bone Marrow (BM) smear. However, applying existing deep-learning methods to it is facing two significant limitations. Firstly, these methods require large-scale datasets wi...

Genomics and Artificial Intelligence: Prostate Cancer.

The Urologic clinics of North America
Artificial intelligence (AI) is revolutionizing prostate cancer genomics research. By leveraging machine learning and deep learning algorithms, researchers can rapidly analyze vast genomic datasets to identify patterns and correlations that may be mi...

Learning domain invariant representations by joint Wasserstein distance minimization.

Neural networks : the official journal of the International Neural Network Society
Domain shifts in the training data are common in practical applications of machine learning; they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of these shifts, for example, by l...

Enhancing robustness, precision, and speed of traction force microscopy with machine learning.

Biophysical journal
Traction patterns of adherent cells provide important information on their interaction with the environment, cell migration, or tissue patterns and morphogenesis. Traction force microscopy is a method aimed at revealing these traction patterns for ad...

Super-resolution of magnetic resonance images using Generative Adversarial Networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less measurements yields the potential to relax these inherent forfeits. Recent breakthr...

Memristor-based spiking neural network with online reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Neural networks implemented in memristor-based hardware can provide fast and efficient in-memory computation, but traditional learning methods such as error back-propagation are hardly feasible in it. Spiking neural networks (SNNs) are highly promisi...