Call for contribution
2ND INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21)
Machine-learning algorithms become more effective as the size of training datasets grows. So when combining big data with machine learning and Simulation, we benefit twice: the algorithms help us keep up with the continuous influx of data, while the volume and variety of the same data feeds the algorithms and helps them grow.
To promote the scientific works on these topics, The 2nd International Conference on Big Data, Modelling and Machine learning (BML’21) will be held in ENSA of Kenitra, Morocco, Jun. 5 – 6, 2021. BML’21 will provide an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of Big data, Machine learning, Simulation and Modelling.
BML’21 will be an opportunity for academics and specialists to present and publish their work and learn about trends in scientific research, as well as to establish future collaborations between research groups and professional.
This 2nd edition is the continuation of the 1st edition held in ENSA Kenitra.
- Big data :
- Big Data Models and Algorithms
- Big Data Architectures
- Big Data Management
- Big Data Security and Privacy
- Big Data in Smart Cities
- Big Data for Enterprise, Government and Society
- Big Data Search and Mining Algorithms and Systems
- Computational Modelling and Simulation :
- High Performance Computing & Simulation
- Information and Scientific Visualization
- Computing and Simulation Applications in Education
- Computing and Simulation Applications in Biology
- Computing and Simulation Applications in Environment
- Computing and Simulation Applications in Physics
- Databases and Visualization
- Finite and Boundary element Techniques
- Mathematical Modelling and Application
- Modelling, Simulation and Control of Technological Processes
- Mathematical and Numerical Methods in Simulation & Modeling
- Networked Modeling and Simulation technology
- Parallel and Distributed Computing Simulation
- Simulation, Experimental Science and Engineering
- Machine learning :
- Data Mining and Machine Learning Tools
- Machine Learning Applications
- Machine Learning Methods and analysis
- Statistical Learning
- Deep Learning
- Reinforcement Learning
- Bayesian Networks
- Support Vector Machines
- Text and Multimedia Mining
- Feature Extraction and Classification
- Distributed and Parallel Learning Algorithms and Applications
- Fuzzy Logic
- Natural Language Processing
- Neural Networks
- Convolutional Neural Networks
- Decision Support Systems
- Deep Learning and Big Data Analytics
- For Abstracts only : All the accepted and presented abstracts will be included in Abstracs book (Template of Abstract here)
- For Papers : All the accepted and presented papers (Template) will be submitted for inclusion in SciTePress Digital Library (Scopus, DBLP…)(https://www.scitepress.org)
Publication Notice – Papers:
- All submissions should be original, professional and have not been published elsewhere. Paper length should exceed 3 pages followed (Max 5 pages) and it need be formatted strictly according to the Template.
- Submissions must be original, unpublished work, and not have been submitted to another conference or journal for publication. All submission will be peer-reviewed roughly by at least 2-3 experts.
- Authors are invited to submit English papers. Please confirm your papers with clear argumentation, close core, sufficient theoretical analysis, proper language and standard grammar in English.
- Submission of a paper implies that should the paper be accepted for formal publication, at least one of the authors will register and present the paper in the conference. Plagiarism in any form is not allowed.
BML’21 uses the i-Thenticate software to detect instances of overlapping and similar text in submitted manuscripts. i-Thenticate software checks content against a database of periodicals, the Internet, and a comprehensive article database.
BML’21 uses Grammarly to check the manuscripts for Grammar, any manuscript with high rate of grammar imperfection will be not take on consideration.