17 th International Conference on Machine Learning and Data Mining MLDM 2021

July 16-21, 2021 New York, USA www.mldm.de

Call for Papers

17th International Conference on Machine Learning and Data Mining

MLDM´2021

www.mldm.de

July 16 - 21, 2021

New York, USA

Chair

Petra Perner

FutureLab Artificial Intelligence_IBaI_II

Institute of Computer Vision and applied Computer Sciences, IBaI

Program Committee

Reneta Barneva The State University of New York at Fredonia, USA

Sung-Hyuk Cha Pace University, USA

Ireneusz Czarnowski Gdynia Maritime University, Poland

Roberto Corrizo Universtiy of Bari, Italy

Christoph F. Eick Universtiy of Houston, USA

Mark J. Embrechts Rensselaer Polytechnic Institute and CardioMag Imaging, Inc, USA

Ana Fred Technical University of Lisboa, Portugal

Giorgio Giacinto University of Cagliari, Italy

Aminata Kane Concordia University, Canada

Piet Kommers University of Twente, The Netherlands

Olga Krasotkina Russian Stae University, Russia

Dimitris Karras Chalkis Institute of Technology, Greece

Adam Krzyzak Concordia University, Canada

Valerio Pascucci University of Utah, USA

Gianvito Pio University of Bari, Italy

Lyndon Smith University of the West of England, UK

Francis E.H. Tay National University of Singapore, Singapore

Turki Turki King Abdulaziz University, Saudi Arabia

Zeev Volkovich ORT Braude College of Engineering, Israel

Patrick Wang Northeastern University, USA

Aim of the Conference

The MLDM´2021 conference is the fourteenth event in a series of Machine Learning and Data Mining meetings. The aim of MLDM is to bring together from all over the world researchers dealing with machine learning and data mining, in order to discuss the recent status of the research in the field and to direct its further developments.

Basic research papers as well as application papers are welcome. All kinds of applications are welcome, but special preference will be given to multimedia related applications, biomedical applications, and webmining. Paper submissions should be related but not limited to any of the following topics:

Applications in Medicine

Applications of Clustering

Aspects of Data Mining

Association Rules

Audio Mining

Autoamtic Semantic Annotation of Media Content

Bayesian Models and Methods

Case-Based Reasoning and Associative Memory

case-based reasoning and learning

classification and interpretation of images, text, video

Classification and Model Estimation

Cognition and Computer Vision

conceptional learning and clustering

Content-Based Image Retrieval

Decision Trees

Deviation and Novelty Detection

Feature Grouping, Discretization, Selection and Transformation

Feature Learning

Frequent Pattern Mining

Goodness measures and evaluaion (e.g. false discovery rates)

High-Content Analysis of Microscopic Images in Medicine, Biotechnology and Chemistry

inductive learning including decision tree and rule induction learning

knowledge extraction from text, video, signals, and images

Learning and adaptive control

Learning for Handwriting Recognition

Learning for robots

Learning in Image Pre-Processing and Segmentation

Learning in process automation

Learning of action patterns

Learning of appropriate behaviour

Learning of internal representations and models

Learning of Ontologies

Learning of Semantic Inferencing Rules

Learning of Visual Ontologies

Learning/adaption of recognition and perception

mining gene data bases and biological data bases

Mining Images and Texture

Mining Images in Computer Vision

mining images, temporal-spatial data, images from remote sensing

Mining Motion from Sequence

Mining Social Media

mining structural representations such as log files, text documents and HTML documents

mining text documents

Network Analysis and Intrusion Detection

Neural Methods

Nonlinear Function Learning and Neural Net-Based Learning

organisational learning and evolutional learning

probabilistic information retrieval

Real-Time Event Learning and Detection

Retrieval Methods

Rule Induction and Grammars

Sampling methods

Selection bias

Selection with small samples

similarity

similarity measures and learning

Speech Analysis

Statistical and Conceptual Clustering Methods: Basics

Statistical and Evolutionary Learning

statistical learning and neural net-based learning

Subspace Methods

Support Vector Machines

Symbolic Learning and Neural Networks in Document Processing

Time Series and Sequential Pattern Mining

video mining

visualization and data mining

Important Dates

Deadline for paper submission: January 15, 2021

Notification of acceptance: March 18, 2021 Submission of camera-ready copy: April 05, 2021

Paper submission

Please submit the electronic version of your camera-ready paper through the conference management system (http://www.easychair.org/). If you have any problems with the system please do not hesitate to contact info@mldm.de.

Papers must be formatted in the Springer LNCS format. They should have at most 15 pages. Papers will be reviewed by the program committee. Accepted long papers will appear in the proceedings book "Machine Learning and Data Mining in Pattern Recognition". The proceedings book will be published as OPEN-ACCESS book. The book can read everybody in the world. Extended versions of selected papers will be published in a special issue of an international journal after the conference.

Tutorials

----------------------

Tutorial Standardization in Immunoflourescence

Prof. Dr. Petra Perner, Institute of Computer Vision and Applied Computer Sciences IBaI,

http://www.mda-signals.de/t_stand.php

Data Mining Tutorial

Prof. Dr. Petra Perner, Institute of Computer Vision and Applied Computer Sciences IBaI,

http://www.data-mining-forum.de/t_dm.php

Case-Based Reasoning Tutorial

Prof. Dr. Petra Perner, Institute of Computer Vision and Applied Computer Sciences IBaI,

http://www.data-mining-forum.de/t_cbr.php

Intelligent Image Interpretation and Computer Vision in Medicine, Biotechnology, Chemistry & Food Industry