CBDA2019 | ISBN: 978-1-7281-1282-4 | IEEE Xplore | Indexed by Ei & Scopus
|
ICBDA 2021– 6th Intl. Conf. Big Data Analytics
December 29th, 2020 
Daniela Lopez de Luise IEEE NetSoft 2021 in Tokyo, Japan // Paper Submission Extended Deadline is 15 January!
December 29th, 2020 Daniela Lopez de Luise |
Machine learning is one of the highlighted topics
|
Conferences: CGI 2021 Call for Papers
December 29th, 2020 Daniela Lopez de Luise Camera-ready July 20, 2021
CALL FOR VISUAL COMPUTER JOURNAL PAPERS (FIRST CALL)
The scientific program of the conference will include accepted papers from the first call for papers and these accepted papers will be published in the Visual Computer Journal (impact factor 1.45) by Springer-Verlag.
The accepted papers from the second call for papers will be included in the CGI conference proceedings published by LNCS, Springer.
Note that for both call for papers, the review process is double blind, which requires the paper and all supplemental materials to be anonymous. Ensure that self-referencing is anonymous (refer to your full name rather than “I” or “we”). Avoid providing information that may identify the authors in the acknowledgements (e.g. co-workers and grant IDs) and in the supplemental material (e.g. titles in the movies, or attached papers). Avoid providing links to websites that identify the authors. Violation of any of these guidelines will lead to rejection without review.
We invite original contributions that advance the state-of-the-art in topics related to:
KEYWORDS
We invite original contributions that advance the state-of-the-art in topics related to:
- Rendering Techniques
- Geometric Computing
- Virtual and Augmented Reality
- Shape and Surface Modeling
- Physically Based Modeling
- Computer Vision for Computer Graphics
- Scientific Visualization
- Data Compression for Graphics
- Medical Imaging
- Computation Geometry
- Image Based Rendering
- Computational Photography
- Computer Animation
- Visual Analytics
- Shape Analysis and Image Retrieval
- Volume Rendering
- Solid Modelling
- Geometric Modelling
- Computational Fabrication
- Image Processing
- 3D Reconstruction
- Global Illumination
- Graphical Human-Computer Interaction
- Human Modelling
- Image Analysis
- Saliency Methods
- Shape Matching
- Sketch-based Modelling
- Robotics and Vision
- Stylized Rendering
- Textures
- Pattern Recognition
- Machine Learning for Graphics
Conference Chair
Nadia Magnenat Thalmann, University of Geneva, Switzerland,
Program Chairs
First track, papers submitted to CGI to be selected for Visual Computer
Computational ML, ONLINE, Feb-May 2021
December 29th, 2020 Daniela Lopez de Luise The Continuum Jumpstart Course Computational Machine Learning (ML) for Scientists and Engineers is designed to equip you with the knowledge you need to
understand, train, design and machine learning algorithms, particularly deep neural networks, and even deploy them on the cloud.
You'll learn by programming machine learning algorithms from scratch in a hands-on manner using a one-of-a-kind cloud-based interactive computational
textbook that will guide you, and check your progress, step-by-step. Using real-world datasets and datasets of your choosing, you will understand, and
we will discuss, via computational discovery and critical reasoning, the strengths and limitations of the algorithms and how they can or cannot be
overcome. You will understand how machine learning algorithms do what they claim to do so you can reproduce these while being able to reason about and
spot wild, unsupported claims of their efficacy.
By the end of the course, you will be ready to harness the power of machine learning in your daily job and prototype, we hope, innovative new ML
applications for your company with datasets you alone have access to.
It's ideal for folks who want to go deeper than a regular MOOC and want to learn by coding.
See https://continuum.engin.umich.edu/programs/jumpstart-ml/
for a description — apply by Jan 8th, 2021 for cohort starting Feb 15th.
See https://continuum.engin.umich.edu/programs/jumpstart-ml/testimonials-and-advice/
for testimonials from the pilot cohort.
Medical Informatics and Bio imaging using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments
December 29th, 2020 Daniela Lopez de Luise Book Title:
Medical Informatics and Bio imaging using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments
Publication: Studies in Computational Intelligence by Springer https://www.springer.com/series/7092
with h-index = 62 (Indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink)
Aims and Scope
Today Modern societies are witnessing an increase usage of technology in almost every domain. Healthcare is one big domain where the modern technologies are bringing a sea change and a paradigm shift in the way health care is planned, administered and implemented. Big data, networking, graphical interfaces, data mining, machine learning, pattern recognition and intelligent decision support systems are just a few of the technologies and research areas currently contributing to medical informatics. Mobility and ubiquity in healthcare systems, physiological and behavioural modelling, standardization of health records, procedures, and technologies, certification, privacy and security are some of the issues that medical informatics professionals and the Information and Communication Technology (ICT) industry and research community in general are addressing to further promote ICT in healthcare. The assistive technologies and home monitoring, applications of ICT have contributed greatly to the enhancement of quality of life and full integration of all citizens into society.
Bio imaging is a term that covers the complex chain of acquiring, processing and visualizing structural or functional images of living objects or systems, including extraction and processing of image-related information. Examples of image modalities used in bio imaging are many, including: X-ray, CT, MRI and fMRI, PET and HRRT PET, SPECT, MEG and so on. Medical imaging and microscope/fluorescence image processing are important parts of bio imaging referring to the techniques and processes used to create images of the human body, anatomical areas, tissues, and so on, down to the molecular level, for clinical purposes, seeking to reveal, diagnose, or examine diseases, or medical science, including the study of normal anatomy and physiology. Both classic image processing methods (e.g. denoising, segmentation, deconvolution and registration methods, feature recognition and classification) and modern machine, in particular deep, learning techniques represent an indispensable part of bio imaging, as well as related data analysis and statistical tools.
The trend is on the increase and we are for sure going to witness more automation & machine intelligence in the future.
This book aims at emphasizing the latest developments & achievements in the field of Artificial Intelligence and related technologies with a special focus on sustainable development and eco-friendly AI applications. This book aims at emphasizing the latest developments & achievements in the field of Medical Informatics & Bio imaging using Artificial Intelligence and related technologies with a special focus on sustainable AI applications. The book will target high quality scientific articles (theory, practical including prototype & conceptualization of ideas, case studies and critical surveys) covering all aspects of AI for Sustainable Healthcare.
Submission Guidelines
Submitted manuscripts should conform to the standard guidelines of the Springer book chapter format. Manuscripts must be prepared using Latex, or MS Word. Prospective authors should submit their manuscripts electronically through easychair submission system or email through this email: [aboitcairo@cu.edu.eg, roheet.bhatnagar@jaipur.manipal.edu, mahmoud.yasin@ai.kfs.edu.eg ] Submitted manuscripts will be refereed by at least two independent and expert reviewers for quality, correctness, originality, and relevance before being accepted for publication.
a. Submitted manuscripts should conform to the standard guidelines of the Springer book chapter format. Manuscripts must be prepared using Latex, or Word, and according to the Springer svmlt template that can be downloaded from the (link) and the Chapter should contain in between 20-25 pages. Manuscripts that do not follow the formatting rules will be ignored. Prospective authors should send their manuscripts electronically through the easychair submission system as mentioned below:
https://easychair.org/conferences/?conf=mibai2021
b. There is no submission or publication fee
c. Conference paper may be extended to be a chapter provided extension is more than 40% from the original conference paper
d. Chapters not conforming to the above mentioned guidelines shall not be considered for review purpose
The accepted contributions will be published in Studies in Computational Intelligence by Springer.
List of Topics
The readers are expected to gain knowhow on state-of-the art research challenges, results, architecture, applications, and other achievements in the following topics, but not limited to:
- Emerging and innovative technologies for next generation AI based Smart healthcare (architecture & framework in smart healthcare, sensor driven smarter health analytics, Medical Image Processing Methods)
- Implementation of AI based sustainable development goals in Bio-imaging (medical service centric architectures, algorithms for smart coordination & diagnostics, application development platform, storage & services frameworks, deployment tools and architectures, measurement and evaluation of sustainable development in healthcare)
- Feature Recognition and Extraction Methods (handling medical image data and analytics, image fusion methods)
- Applications of Machine Learning and Deep Learning in Bio imaging (intelligent patient monitoring & surveillance, intelligent tools for smart healthcare, Robotic Process Automation in Healthcare, personalised medicines, tele-medicines, infrastructure development, technology driven hospital emissions and waste management, impact of smart healthcare practices in modern societies)
- Medical Imaging and Diagnosis (tools and novel AI based methods & approaches to analyse Single-Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), High Resolution Research Tomography (HRRT), Magnetoencephalography (MEG), Ultrasound and Optical Imaging, X-ray Microscopy, Fluorescence Resonance Energy Transfer (FRET) datasets, Magnetic Resonance Imaging (MRI), Functional Magnetic Resonance Imaging (FMRI) data analysis using AI, ML and deep learning)
- Case Studies and Best Practices (government policy and regulations, privacy and data protection, futuristic tools and software with case studies)
Publication Details
The book publication schedule is as follows:
Deadline for paper submission : January 30, 2021
First round notification : February 15, 2021
Camera-ready submission : March 25, 2021
Publication date : 3rd quarter of 2021
Contact
All questions about submissions should be emailed to the Volume Editors as given below:
- Aboul Ella Hassanien, Cairo Univesrity, Egypt
- Roheet Bhatnagar, Manipal University Jaipur, Rajasthan, India
Email: roheet.bhatnagar@jaipur.manipal.edu
- Mahmoud Y. Shams, Kafrelsheikh University, Egypt
Email: mahmoud.yasin@ai.kfs.edu.eg