AIMC Topic: Machine Learning

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A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects.

IEEE transactions on bio-medical engineering
UNLABELLED: This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a G...

Machine Learning Estimation of Atom Condensed Fukui Functions.

Molecular informatics
To enable the fast estimation of atom condensed Fukui functions, machine learning algorithms were trained with databases of DFT pre-calculated values for ca. 23,000 atoms in organic molecules. The problem was approached as the ranking of atom types w...

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

PloS one
The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought...

Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

PloS one
Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biolog...

Optimism in Active Learning.

Computational intelligence and neuroscience
Active learning is the problem of interactively constructing the training set used in classification in order to reduce its size. It would ideally successively add the instance-label pair that decreases the classification error most. However, the eff...

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach.

PloS one
The number of the overweight people continues to rise across the world. Studies have shown that being overweight can increase health risks, such as high blood pressure, diabetes mellitus, coronary heart disease, and certain forms of cancer. Therefore...

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

Computational intelligence and neuroscience
Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from b...

Large-scale machine learning for metagenomics sequence classification.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read i...

Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibra...

Hand Rehabilitation Learning System With an Exoskeleton Robotic Glove.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper presents a hand rehabilitation learning system, the SAFE Glove, a device that can be utilized to enhance the rehabilitation of subjects with disabilities. This system is able to learn fingertip motion and force for grasping different objec...