Conference Information

MDA-AI&PR 2022: International Conference on Mass Data Analysis of Images and Signals in Artificial Intelligence and Pattern Recognition

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Submission Date:
2022-03-15 Extended
Notification Date:
2022-04-18
Conference Date:
2022-07-11
Location:
New York, USA
Years:
17
Viewed: 14903   Tracked: 1   Attend: 0

Conference Partner Index (CP-I)

48.5 / 100
Ranked #1,955 of 5,684 conferences · Top 35%

#88 of 245 in Computer Vision & Pattern Recognition #204 of 741 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
78
Community attention (10%)
17
Public record completeness (15%)
25

Inputs used: Editions on record: 17 · Researchers following it here: 1 · Researchers who opened this page in the past 24 months: 3

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-24

Call For Papers

MDA-AI&PR 2022 (International Conference on Mass Data Analysis of Images and Signals in Artificial Intelligence and Pattern Recognition) is an academic conference held in New York, USA on 2022-07-11. The paper submission deadline is 2022-03-15 (extended). Acceptance notifications are sent on 2022-04-18.

The Scope of the Conference The automatic analysis of images and signals in medicine, biotechnology, System Biology, chemistry, biometry, drug discovery and information robots is a challenging and demanding field. Signal-producing procedures by microscopes, spectrometers and other sensors have found their way into wide fields of medicine, biotechnology, economy and environmental analysis. With this arises the problem of the automatic mass analysis of signal information. Signal-interpreting systems which generate automatically the desired target statements from the signals are therefore of compelling necessity. The continuation of mass analyses on the basis of the classical procedures leads to investments of proportions that are not feasible. New procedures and system architectures are therefore required. The scope of conference is to bring together researcher, praticioners and industry people who are dealing with mass analysis of images and signals to present and discuss recent research in these fields. The goals of this conference are to: provide a forum for identifying important contributions and opportunities for research on mass data analysis of signals and images, promote the systematic study of how to apply automatic signal/image analysis and interpretation procedures to that field show case applications of mass data analysis in biology, medicine, and chemistry. Topics of interest include (but are not limited to): Applications in Medicine, r/g/b Biotechnology, Food Industries and Dietetics, Biometry and Agriculture Techniques and developments of signal and image producing procedures Object matching and object tracking in microscopic and video microscopic images 1D, 2D and 3D shape analysis and description 1D, 2D and 3D feature extraction of texture, structure and location Algorithms for 1D, 2D and 3D signal analysis and interpretation Image segmentation algorithms Parallelization of image analysis and interpretation algorithms Semantic tagging of microscopic images Applications in medicine, biotechnology, chemistry and others Applications in crystallography Applications in proteomics Applications in 2D and 3D cell images analysis Image Acquisition procedures for mass data analysis Methods and solutions for biometrical systems (Face-, Iris and Fingerprint Recognition) Recognition methods for moving objects (face, iris, body) Methods for data encryption (e.g. crypotographic methods, watermarking)
Last updated by Dou Sun on

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