gcosma/CalcStatPowerInPy

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Created over 5 years ago
Updated over 5 years ago

gcosma/CalcStatPowerInPy

gcosma

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PSO-FS

Particle Swarm Optimisation for Feature Selection

Deep-FS

Deep-Feature Selection

GA-FS

Genetic Algorithm for Feature Selection

adaboostCNN

Taherkhani, A, Cosma, G, McGinnity, M (2020) AdaBoost-CNN: an adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning, Neurocomputing, 404, pp.351-366, ISSN: 0925-2312. DOI: 10.1016/j.neucom.2020.03.064.

EBCS-FS

Extended Binary Cuckoo Search for Feature Selection

ACO-FS

ACO Feature Selection

DeepLearningTutorials

Deep Learning Tutorials in Colab

Jupyter Notebook

EnsembleVoltageStability

On-line voltage stability monitoring using an Ensemble AdaBoost classifier

TAGA

TAGA: Tabu Asexual Genetic Algorithm Embedded in a Filter/Filter Feature Selection Approach for High-dimensional Data

ClanBasedCulturalAlgorithFS

Clan-based Cultural Algorithm for Feature Selection

DeepGesture

Hand gesture recognition using an adapted convolutional neural network with data augmentation

SleepStageCNN

Interpreting the Filters in the First Layer of a Convolutional Neural Network for Sleep Stage Classification

BiomedDeepLearning

A Hybrid Model for Classification of Biomedical Data using Feature Filtering and a Convolutional Neural Network

Neuro-FuzzyPCaPlosOne

Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model

AIethics

AI ethics sources of info

GeneralisationPowerAnalysis

Generalisation Power Analysis for finding a stable set of features using evolutionary computation feature selection algorithms

IEEE_Access_mHealth

Python code for the paper entitled "Feature Extraction and Classification using Leading Eigenvectors: Applications to Biomedical and Multi-Modal mHealth Data", IEEE Access

Jupyter Notebook