AIMC Topic: Machine Learning

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A serial image analysis architecture with positron emission tomography using machine learning combined for the detection of lung cancer.

Revista espanola de medicina nuclear e imagen molecular
INTRODUCTION AND OBJECTIVES: Lung cancer is the second type of cancer with the second highest incidence rate and the first with the highest mortality rate in the world. Machine learning through the analysis of imaging tests such as positron emission ...

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening.

PloS one
In this work we seek to enhance the frameworks practitioners in asset management and wealth management may adopt to asses how different screening rules may influence the diversification benefits of portfolios. The problem arises naturally in the area...

Machine learning in internet financial risk management: A systematic literature review.

PloS one
Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for r...

Fall prediction in a quiet standing balance test via machine learning: Is it possible?

PloS one
The elderly population is growing rapidly in the world and falls are becoming a big problem for society. Currently, clinical assessments of gait and posture include functional evaluations, objective, and subjective scales. They are considered the gol...

Apnoea detection using ECG signal based on machine learning classifiers and its performances.

Journal of medical engineering & technology
Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain diseases like stroke, depression, neurocognitive disorder, non-communicable disease, etc. We implemented ...

Artificial intelligence and machine learning responses to COVID-19 related inquiries.

Journal of medical engineering & technology
Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In th...

Comprehensive analysis of lung adenocarcinoma: Unveiling differential gene expression, survival-linked genes, subtype stratification, and immune landscape implications.

Environmental toxicology
This study offers a detailed exploration of lung adenocarcinoma (LUAD), addressing its heterogeneity and treatment challenges through a multi-faceted analysis that includes gene expression, genetic subtyping, pathway analysis, immune assessment, and ...

Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning.

Tissue engineering. Part A
In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods a...

Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk.

Journal of the science of food and agriculture
BACKGROUND: Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier-transform mid-infrared (FT-MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larg...

Contrastive Prototype-Guided Generation for Generalized Zero-Shot Learning.

Neural networks : the official journal of the International Neural Network Society
Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen classes, while only samples from seen classes are available for training. The mainstream methods mitigate the lack of unseen training data by simulating the visual unseen sa...