AIMC Topic: Algorithms

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Exploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments.

Health & place
Past research has assessed the association of single community characteristics with obesity, ignoring the spatial co-occurrence of multiple community-level risk factors. We used conditional random forests (CRF), a non-parametric machine learning appr...

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

IEEE transactions on cybernetics
Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this pap...

l2 Multiple Kernel Fuzzy SVM-Based Data Fusion for Improving Peptide Identification.

IEEE/ACM transactions on computational biology and bioinformatics
SEQUEST is a database-searching engine, which calculates the correlation score between observed spectrum and theoretical spectrum deduced from protein sequences stored in a flat text file, even though it is not a relational and object-oriental reposi...

Genetic Learning Particle Swarm Optimization.

IEEE transactions on cybernetics
Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance e...

Compositional Dictionaries for Domain Adaptive Face Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We present a dictionary learning approach to compensate for the transformation of faces due to the changes in view point, illumination, resolution, and so on. The key idea of our approach is to force domain-invariant sparse coding, i.e., designing a ...

Sparse Markov chain-based semi-supervised multi-instance multi-label method for protein function prediction.

Journal of bioinformatics and computational biology
Automated assignment of protein function has received considerable attention in recent years for genome-wide study. With the rapid accumulation of genome sequencing data produced by high-throughput experimental techniques, the process of manually pre...

Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach.

BMC genomics
BACKGROUND: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Although roughly a thousand genes are expected to be important for this function in Drosophila melanogaster, just a few hund...

Computer-Aided Diagnosis System for Alzheimer's Disease Using Different Discrete Transform Techniques.

American journal of Alzheimer's disease and other dementias
The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. Th...

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

International journal of neural systems
Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recor...

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.

IEEE/ACM transactions on computational biology and bioinformatics
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up t...