Free software to download (for research purposes)
and material accompanying my latest book:
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The software is provided under the GNU license for Research purposes only (for commercial use - please, contact Prof. Angelov, Lancaster University; the open source code does NOT provide rights to use the software (and patented IP methods for any commercial purposes without a license agreement with Lancaster University). In any publication when using the software and the methods, please, make appropriate references to avoid plagiarism claims.
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​A. Code @ GitHub for:
A1. IDEAL (Neurocomputing paper D2)
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A2. xDNN (Neural Networks paper D35)
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A3. Deep Fake Detection method (ICCV and CVPR papers E13 and E9, respectively)
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A4. ViT unsueprvised domain adaptation (ICLR paper E10)
A5. xDNN application to Covid CT scan data (Evolving Systems paper D9, TexRxiv paper F12 and medRxiv paper F14)
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​​​B. Accompanying material to my latest book
"Empirical Approach to Machine Learning"
Springer, 2018, ISBN 9783030023836; DOI 10.1007/978-3-030-02384-3
(for the URL - please, press the book image)
B1. Lecture Notes and Lab session notes forming a course
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B2. The software accompanying the book can be downloaded from the
​​(see also the hyperlinks below to specific algorithms/functions)
​​C. ALMMo (Autonomous Learning Multi-Model) Systems family:
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DRB Classifier
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Semi-supervised DRB (SS-DRB) Classifier
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Autonomous Data Partitioning (ADP) Algorithm
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Self-Organising Fuzzy Logic Classifier
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SODA (Self-organising Direction-aware) Partitioning
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D. Empirical Fuzzy Sets (EFS) software
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E. Autonomous Anomaly Detection Algorithm
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F. Autonomous Data-Density-based (ADD) Clustering
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G. Autonomous Data-Driven Clustering for Streaming Data
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H. TEDA Clustering - parallel version
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​I. EST Matlab package can be received upon request and includes:
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eTS+ (2001-2010)
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eClass (2006-2008)
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eClustering
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RDE (2001-2010)
