AIMC Topic: Machine Learning

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RECIPE COMPLETION USING MACHINE LEARNING TECHNIQUES.

Communications in agricultural and applied biological sciences
Completing a recipe is a non-trivial task, as the success of ingredient combinations depends on a multitude of factors such as taste, smell, texture, etc. The aim of our work is to build a model that adds one or more ingredients to a given number of ...

Fool-proofing design and crisis management for customized intelligent physical fitness and healthcare system.

Technology and health care : official journal of the European Society for Engineering and Medicine
In recent years, it is quite important to develop a customized system which can enhance physical fitness and health for people. And the system reliability is more important. In the paper, a fool-proofing design and crisis management for customized ph...

Learning multiple distributed prototypes of semantic categories for named entity recognition.

International journal of data mining and bioinformatics
The scarcity of large labelled datasets comprising clinical text that can be exploited within the paradigm of supervised machine learning creates barriers for the secondary use of data from electronic health records. It is therefore important to deve...

Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression.

International journal of data mining and bioinformatics
In silico prediction of novel miRNAs from genomic sequences remains a challenging problem. This study presents a genome-wide miRNA discovery software package called GenoScan and evaluates two hairpin classification methods. These methods, one ensembl...

Hematocrit estimation using online sequential extreme learning machine.

Bio-medical materials and engineering
Hematocrit is a blood test that is defined as the volume percentage of red blood cells in the whole blood. It is one of the important indicators for clinical decision making and the most effective factor in glucose measurement using handheld devices....

A label distance maximum-based classifier for multi-label learning.

Bio-medical materials and engineering
Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label ...

A hybrid ensemble method based on double disturbance for classifying microarray data.

Bio-medical materials and engineering
Microarray data has small samples and high dimension, and it contains a significant amount of irrelevant and redundant genes. This paper proposes a hybrid ensemble method based on double disturbance to improve classification performance. Firstly, ori...

Classification of imbalanced bioinformatics data by using boundary movement-based ELM.

Bio-medical materials and engineering
To address the imbalanced classification problem emerging in Bioinformatics, a boundary movement-based extreme learning machine (ELM) algorithm called BM-ELM was proposed. BM-ELM tries to firstly explore the prior information about data distribution ...

HClass: Automatic classification tool for health pathologies using artificial intelligence techniques.

Bio-medical materials and engineering
The classification of subjects' pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine...

Fuzzy Naive Bayesian for constructing regulated network with weights.

Bio-medical materials and engineering
In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair...