Call For Papers
MIND-2021 will be held in virtual mode. Original contributions are invited from prospective authors with interests in the indicated conference topics and related areas of application. All contributions should be high quality, original and not published elsewhere or submitted for publication during the review period. The papers can be theoretical, practical and application oriented on the conference tracks. Every submission must identify the conference track which best relates to the contents of the paper. Papers will be peer reviewed by the International Program Committee, and may be accepted for long and short presentation. All the peer reviewed and selected papers of conference will be published in Scopus indexed proceedings with Springer in their prestigious “Lecture Notes in Electrical Engineering (LNEE)” series (Approval received from Springer).
We solicit original research and technical papers not published elsewhere in the following categories:
Full papers (10 to 12 pages in the LNEE one-column page format);
Short papers and poster papers (no less than 6 pages)
Conference Tracks
Original contribution for the following areas (but not limited to) are invited:
Machine Learning and Computational Intelligence
Theoretical Computer Science
Artificial Intelligence and Deep Learning
Pattern recognition
Computer Graphics
Virtual Reality
Distributed & Cloud Computing
Signal Processing
Soft Computing
Grid and Cluster Computing
Evolutionary Algorithms
Ubiquitous Computing
Parallel and Distributed Networks
Perceptual Computing, and related topics
Learning using Ensemble and boosting strategies
Active Machine Learning
Manifold Learning
Fuzzy Learning
Kernel Based Learning
Genetic Learning
Hybrid models
Bioinformatics and biomedical informatics
Healthcare and clinical decision support
Collaborative filtering
Information retrieval
Natural language processing
Web search
Inference dependencies on multi-layered networks
Recurrent Neural Networks and its applications
Graph wavelets
Spectral graph theory
Self-organizing networks
Multi-scale learning
Unsupervised feature learning
Clustering, Classification and regression methods
Supervised, semi-supervised and unsupervised learning
Reinforcement Learning
Optimization methods
Parallel and distributed learning
Graph embeddings
Genetic optimization
Bayesian estimation approaches
and related areas........
Image Processing and Computer Vision
Filtering, Transforms, Multi-Resolution Processing
Restoration, Enhancement, Super-Resolution
Computer Vision Algorithms and Technologies
Compression, Transmission, Storage, Retrieval
Multi-View, Stereoscopic, and 3D Processing
Multi-Temporal and Spatio-Temporal Processing
Biometrics, Forensics, and Content Protection
Biological and Perceptual-based Processing
Medical Image and Video Analysis
Document and Synthetic Visual Processing
Color and Multispectral Processing
Computational Imaging
Video Processing and Analytics
Visual Quality Assessment
Deep learning for Images and Video
Human activity recognition
Software Tools for Imaging
Image Generation, Acquisition, and Processing
Image-based Modeling and Algorithms
Mathematical Morphology
Image Geometry and Multi-view Geometry
3D Imaging
Novel Noise Reduction Algorithms
Motion and Tracking Algorithms and Applications
Watermarking Methods and Protection
Wavelet Methods
Image Data Structures and Databases
Image Compression, Coding, and Encryption
Multi-resolution Imaging Techniques
Multimedia Systems and Applications
Novel Image Processing Applications
Camera Networks and Vision
Machine Learning Technologies for Vision
Cognitive and Biologically Inspired Vision
Active and Robot Vision
Fuzzy and Neural Techniques in Vision
Novel Document Image Understanding Techniques
Novel Vision Application and Case Studies
and related areas........
Network and Cyber Security
Network Performance Analysis,
Human factors in security and privacy
Security and privacy in ad hoc networks
Machine learning for Biometric security and privacy
Machine learning for Security and privacy of Web service
Security and privacy in e-services
Security and privacy in grid computing
Security and privacy in mobile systems
Security and privacy in wireless sensor networks
Cyber risk and vulnerability assessment
Cyber-crime and warfare
Cyber threat analysis and modelling
Machine learning for Bluetooth, WiFi, WiMax security
Security and privacy in smart grid and distributed generation systems
Security and privacy in social applications and networks
Cyber forensic tools, techniques, and analysis
Visual analytics for cyber security
Security and privacy of mobile cloud computing
Cyber security testbeds, tools, and methodologies
Active and passive cyber defense techniques
Insider threat detection and prevention
Critical infrastructure protection
Security and privacy in industrial systems
Security and privacy in pervasive/ubiquitous computing
Intrusion detection and prevention
Botnet detection and mitigation
and related areas........
Data Sciences and Big Data
Big data management,
Platforms and technologies for big data,
Data retrieval,
Big data storage techniques,
Data mining and warehouse,
Data visualization,
Modelling structure and storage of big data,
Scalability and portability issues of big data,
Big data recommender systems,
Digital Forensics,
Parallel processing of big data,
Distributed access of big data,
Applications of big data and related topics,
Web mining,
Social Network Analysis,
Text Mining,
Sentiment Analysis.
Algorithms
Novel Theoretical Models
Novel Computational Models
Data and Information Quality
Data Integration and Fusion
Cloud/Grid/Stream Computing
High Performance/Parallel Computing
Energy-efficient Computing
Software Systems
Search and Mining
Data Acquisition, Integration, Cleaning
Data Visualizations
Semantic-based Data Mining
Data Wrangling, Data Cleaning, Data Curation, Data Munching
Data Analysis, , Statistical Insights
Decision making from insights, Hidden patterns
Data Science technologies, tools, frameworks, platforms and APIs
Link and Graph Mining
Efficiency, scalability, security, privacy and complexity issues in Data Science
Labelling, Collecting, Surveying, Interviewing and other tools for Data Collection
Applications in Mobility, Multimedia, Science, Technology, Engineering, Medicine, Healthcare, Finance, Business, Law, Transportation, Retailing, Telecommunication
and related areas........