Call For Papers


MIKE is an interdisciplinary conference that brings together researchers and practitioners from the broad domains of:

  • Evolutionary computation
    • Evolutionary Computation
    • Design of new and improved evolutionary algorithms
    • Design of new search operators
    • Diversity control
    • Hybrid approaches and memetic algorithms
    • Local search methods
    • Automatic algorithm configuration and design
    • Applications of metaheuristics to combinatorial optimization problems
    • Development of tailored evolutionary algorithms for specific applications
  • Artificial intelligence
    • Artificial intelligence and philosophy
    • Automated reasoning and inference
    • Cognitive aspects of AI
    • Common-sense reasoning
    • Constraint processing
    • Heuristic search
    • Intelligent interfaces
    • Intelligent robotics
    • Knowledge representation
    • Multi-agent systems
    • Reasoning under uncertainty or imprecision
    • Applications of AI
  • Machine learning
    • Decision tree learning
    • Association rule learning
    • Artificial neural networks
    • Deep learning
    • Support vector machines
    • Bayesian learning
    • Reinforcement learning
    • Representation learning
    • Similarity and metric learning
    • Sparse dictionary learning
    • Rule-based machine learning
    • Learning classifier systems
    • Applications of ML
    • Applications of ML to various interdisciplinary domains
    • Modeling Material Properties,
    • Predicting Material Properties
    • Predicting Mechanical Properties
    • Thermal Properties of Fluids
    • Design Materials
    • Optimizing Material properties
    • Application of Neural Networks
    • Fuzzy Logic
    • Fuzzy Systems
    • Computational Intelligence
  • Knowledge Exploration in IoT
    • Physical: world event processing and understanding: novel data collection, deep learning, real-time decision making, event processing, extracting information from large data sets);
    • Machine learning and deep learning on sensor data: deep learning models for multimodal sensing and processing, multi-sensor fusion with deep learning);
    • IoT: reliability, adaptability, and dependability;
    • Technical assessment of emerging IoT standards;
    • New hardware and system design to enable machine learning on sensor data;
    • Sensor data-related issues (e.g., structure, management, methods, tools, analysis, etc.);
    • Anomaly detection;
    • Data mining of large scale urban networks and big data;
    • Human/social behavior mining in urban environments;
    • Environmental modeling based on data mining methods.
  • Data mining and Information retrieval
    • Data and knowledge creation and discovery
    • Data and knowledge processing, modeling, mapping,
    • Data and knowledge search, interoperability, exchange, and integration
    • Data visualization
    • Databases, indexing, and query processing
    • Data mining: machine learning and deep learning
    • Behavior analytics
    • Business analytics
    • Customer analytics
    • Financial analytics
    • Information retrieval and extraction
    • Big data infrastructure and visualization
    • Scalable computing and high-performance computing for big data
    • Parallel and distributed data mining techniques
    • Computational intelligence-based theories and methods
    • Statistical and mathematical techniques
    • Large-scale optimization
    • Data characteristics and complexities
    • Bioinformatics, computational biology, health, and medical analytics
  • Medical image analysis, Pattern analysis, and Computer vision
    • Visual search
    • Computer vision theory
    • Physics-based vision and shape from-X
    • 3D computer vision
    • Vision for graphics
    • Vision for robotics
    • Vision for web
    • Computational photography, sensing, and display
    • Document and handwriting analysis
    • Image and video analysis
    • Segmentation, grouping, and shape representation
    • Motion and tracking
    • Content-based retrieval of image and video
    • RGBD sensor and analytics
    • Recognition: detection, categorization, indexing
    • Face and gesture recognition
    • Action recognition
    • Biometrics
    • Statistical learning-based applications
    • Deep learning-based applications
  • Speech and Signal processing
    • Basics of oral communication: modeling of production and perception processes;
    • phonetics and phonology; syntax; cognitive aspects
    • Models and tools for language learning: functional organization and developmental models of human language capabilities; acquisition and rehabilitation of spoken language;
    • Speech signal analysis
    • Speech recognition
    • Language identification
    • Speaker recognition
    • Speech synthesis
    • Multimodal human-computer interface: speech in combination with a gesture, handwriting, etc
    • Forensic voice comparison
    • Forensic analysis of disputed utterances
    • Speaker identification by earwitness
  • Text mining and Natural language processing
    • Named entity recognition
    • Part-of-speech tagging
    • Relationship extraction
    • Parsing
    • Sentence breaking
    • Word segmentation
    • Morphological segmentation
    • Topic segmentation and recognition
    • Automatic summarization
    • Coreference resolution
    • Machine translation
    • Natural language generation
    • Natural language understanding
    • Question answering
    • Sentiment analysis
    • Word sense disambiguation
  • Intelligent security systems
    • Blockchain in Security
    • Device log analysis
    • Correlation of events posing security threats
    • Deep packet analysis
    • Network and system behavior analysis
    • User behavior modeling and analysis
    • Intrusion detection
    • Intelligent security systems
    • Cryptanalysis
    • Steganalysis
    • Social media analysis for security
  • Smart and Intelligent systems
    • Smart Cities
    • Smart Transportation
    • Intelligent V2V and V2I Communication
    • Vehicular Networks
    • Data Analytics for Real-Time Systems
    • System Assisted Driving/ Recommendation Engine
    • Deep Learning for smart systems

All accepted papers will appear in the conference proceedings published by the Springer series

Review Criteria:

Each submission must be identified as theoretical/methodological research, applied research, or deployed application paper and will be reviewed using criteria appropriate to its category. The criteria are as follows:

Paper Category: Theoretical/Methodological research paper
Review Criteria: Scientific significance; originality; technical quality; and clarity

Paper Category: Applied research paper
Review Criteria: Significance for scientific research or innovative commercial deployment; originality; technical quality; and clarity

Paper Category: Deployed application paper

Review Criteria: Demonstrated practical, social, environmental, or economic significance; originality; treatment of issues of engineering, management & user acceptance; and clarity.

Submission Procedure:
Papers must be submitted online through EasyChair (submission link)

Springer publishes MIKE 2021 Proceedings in Lecture Notes in Artificial Intelligence (LNAI) series

Authors should note that the papers should be of a maximum of 10 A4 pages (including figures and tables) and manuscripts must be prepared with LaTex2e ( Author names and affiliations details should be anonymized during the manuscript preparation so as to enable the peer-review.


MIKE 2021 uses a double-blind review process for paper selection.

  1. Authors should not know the names of the reviewers of their papers, and reviewers should not know the name(s) of the authors.
  2. Please prepare your paper in a way that preserves the anonymity of the authors.
  3. Do not put your name(s) under the title.
  4. Avoid using phrases such as "our previous work" when referring to earlier publications by the authors.
  5. Remove information that may identify the authors in the acknowledgments (e.g., co-workers and grant IDs).
  6. Check supplemental material (e.g., titles in the video clips, or supplementary documents) for information that may identify the author's identity.
  7. Avoid providing links to websites that identify the authors.

Submission Format:

Papers MUST be submitted in Springer LNCS format, which is the format required for the final camera-ready copy. We will accept:

  • Long papers: with a maximum of 10 pages
  • Short papers: with a maximum of 6 pages

EXTRA PAGES: Authors may include the maximum of 4 additional pages at an extra fee of US$ 10 USD per page (Rs. 600 per page for Indian Authors). In exceptional cases, the author's request may be considered for the waiver of this fee based on the merits of their paper.


Both long and short papers will be considered for poster and oral (short or long) presentation at the conference based on the reviewing criteria above. Authors" instructions along with LaTeX (preferred) and Word macro files are available on the web at Springer"s Information for LNCS Authors page ( Authors of accepted papers are required to transfer their copyrights to Springer. All submissions are required to be in electronic format.

Multiple Submission Policy:

Papers submitted to other conferences or journals must state this fact. If a paper will appear in another conference or journal, it must be withdrawn from MIKE 2021. This restriction does not apply to papers appearing in proceedings of specialized MIKE workshops.

Author Registration Policy:
In order for a paper to appear in the proceedings, at least one of the authors MUST register for the conference by the camera-ready copy deadline.


The venue is in Hammamet, Tunisia.

Important Dates

31 July 5 September 2021 11:59 PM PST: Paper Submissions

31 August 25 September 2021: Paper Notifications

15 September 1 October 2021: Camera Ready and Registration

01 November 2021: Online Proceedings Ready

1-3 November 2021: Conference Dates


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