IEEE AIML4COINS 2020 : IEEE AIML4COINS2020 | Artificial Intelligence | Machine Learning | Deep Learning | Machine Vision | Big Data Analytics | Video Analytics | Speech Recognition | NLP

  in Conferences   Posted on October 28, 2019

Conference Information

Submission Deadline Friday 06 Mar 2020 Proceedings indexed by :
Conference Dates Jul 27, 2020 - Jul 29, 2020
Conference Address Barcelona, Spain
Conference & Submission Link
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Call for Papers

Supported and organized by IEEE Robotics and Automation Society, IEEE Council on Electronic Design Automation, IEEE Circuits and Systems Society, IEEE e-Health Technical Committee (ComSoc), and IEEE Big Data Technical Committee (ComSoc).

The Artificial Intelligence, Machine Learning, and Advanced Analytics track at COINS encourages original and high-quality submissions related to one or more of the following topics (but not limited to):

Machine learning architectures and formulations

Machine learning and artificial intelligent systems analysis, modelling, simulation, and application in different domains

Platforms, architecture and infrastructure for efficient data analytics

Novel computational intelligence methods and improvement of existing algorithms

Human-driven Artificial Intelligence for Smarter Hyper-Connected Societies

Learning from IoT Data

Regression, Classification and Clustering for big data analysis

Intelligence inference in Edge/Fog/Cloud computing and IoT

Reinforcement learning

Data acquisition and cleaning techniques

Data modeling, representation, transformation, and integration techniques

Data mining and machine learning algorithms for IoT Big Data

Intelligent algorithms for Fog and cloud-based Internet of Things

Machine learning on chip and at edge

Deep learning models, architectures and algorithms

Big Data, Large Scale Methods

Brain-inspired representations learning of Big Data

Streaming data learning algorithms

Evolutionary computing in Big Data

Swarm Intelligence and Big data for IoT

AI-driven performance optimization for IoT

Distributed Intelligent Information Systems

Transfer, low-shot, semi- and un- supervised learning

Speech Recognition

3D computer vision

Action and behavior recognition

Adversarial learning, adversarial attack and defense methods

Biometrics, face, gesture, body pose

Computational photography, image and video synthesis

Datasets and evaluation

Image retrieval

Motion and tracking

Neural generative models, auto encoders, GANs

Optimization and learning methods

Recognition (object detection, categorization)

Representation learning, deep learning

Scene analysis and understanding

Segmentation, grouping and shape

Video analysis and understanding

Visual reasoning and logical representation

Natural Language Processing

Crowd Sensing and Human Intelligence

Open issues for data management and analytics in IoT

Case studies in different domains

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