AIMC Topic: Algorithms

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Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches.

Journal of healthcare engineering
The thyroid is one of the largest endocrine glands in the human body, which is involved in several body mechanisms like controlling protein synthesis and the body's sensitivity to other hormones and use of energy sources. Hence, it is of prime import...

Molecular imaging with neural training of identification algorithm (neural network localization identification).

Microscopy research and technique
Superresolution localization microscopy strongly relies on robust identification algorithms for accurate reconstruction of the biological systems it is used to measure. The fields of machine learning and computer vision have provided promising soluti...

DGCNN: A convolutional neural network over large-scale labeled graphs.

Neural networks : the official journal of the International Neural Network Society
Exploiting graph-structured data has many real applications in domains including natural language semantics, programming language processing, and malware analysis. A variety of methods has been developed to deal with such data. However, learning grap...

Multiple Mittag-Leffler stability of fractional-order competitive neural networks with Gaussian activation functions.

Neural networks : the official journal of the International Neural Network Society
In this paper, we explore the coexistence and dynamical behaviors of multiple equilibrium points for fractional-order competitive neural networks with Gaussian activation functions. By virtue of the geometrical properties of activation functions, the...

In Silico Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods.

ChemMedChem
The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all ...

Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data.

Journal of medical Internet research
BACKGROUND: Telemonitoring of symptoms and physiological signs has been suggested as a means of early detection of chronic obstructive pulmonary disease (COPD) exacerbations, with a view to instituting timely treatment. However, algorithms to identif...

A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis.

Medicina intensiva
INTRODUCTION: Sepsis is associated to a high mortality rate, and its severity must be evaluated quickly. The severity of illness scores used are intended to be applicable to all patient populations, and generally evaluate in-hospital mortality. Howev...

A Projection Neural Network for Identifying Copy Number Variants.

IEEE journal of biomedical and health informatics
The identification of copy number variations (CNVs) helps the diagnosis of many diseases. One major hurdle in the path of CNVs discovery is that the boundaries of normal and aberrant regions cannot be distinguished from the raw data, since various ty...

Modeling growth limits of Bacillus spp. spores by using deep-learning algorithm.

Food microbiology
Growth/no growth boundary models for Bacillus spores that accounted for the effects of environmental pH, water activity (a), acetic acid, lactic acid, bacterial strain, and storage period were developed using conventional logistic regression and mach...

Cost-sensitive multi-label learning with positive and negative label pairwise correlations.

Neural networks : the official journal of the International Neural Network Society
Multi-label learning is the problem where each instance is associated with multiple labels simultaneously. Binary Relevance (BR) is a representative algorithm for multi-label learning. However, it may suffer the class-imbalance issue especially when ...