AIMC Topic: Algorithms

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New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300nm have been used for determination of antihistamine decongestant con...

Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND/AIM: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning ...

Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

Neural networks : the official journal of the International Neural Network Society
To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and co...

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PloS one
BACKGROUND: Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiti...

A bag-of-paths framework for network data analysis.

Neural networks : the official journal of the International Neural Network Society
This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs-Boltzmann distribution i...

A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images.

Computational and mathematical methods in medicine
The PET and CT fusion images, combining the anatomical and functional information, have important clinical meaning. This paper proposes a novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet trans...

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer.

Computational and mathematical methods in medicine
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. A...

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

PloS one
This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. I...

Big Data and Machine Learning-Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference.

Journal of the American College of Radiology : JACR
The 38th radiology Intersociety Committee reviewed the current state and future direction of clinical data science and its application to radiology practice. The assembled participants discussed the need to use current technology to better generate a...

Machine-Learning Algorithms to Code Public Health Spending Accounts.

Public health reports (Washington, D.C. : 1974)
OBJECTIVES: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with...