Conferencia – Computación Cuántica – Áreas de desarrollo y avances recientes – CETI

September 2nd, 2024 Daniela Lopez de Luise

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Call for chapters for the collective book Artificial Intelligence with Neutrosophic Statistics

September 1st, 2024 Daniela Lopez de Luise

 Call for chapters for the collective book Artificial Intelligence with Neutrosophic Statistics from Nano to Nature,
to be published by the international Cambridge Scholars Publishing, UK-USA. 

No publication fees. 
  
We cordially invite scholars, researchers,
academicians, and professionals to contribute to an upcoming edited volume entitled
'Artificial Intelligence with Neutrosophic Statistics from Nano to
Nature.' This book explores the integration of artificial intelligence(Al) and neutrosophic statistics, focusing on their applications
across diverse environmental and material data sets, from the nano level to
large-scale natural phenomena. 
  
***************************************************************************** 
Neutrosophic  Statistics is a generalization of
Classical and Interval Statistics 
     
While the Classical Statistics deals with determinate data and determinate inference methods only, the Neutrosophic Statistics deals with indeterminate data, i.e. data that has some degree of indeterminacy (unclear, vague, partially unknown, contradictory, incomplete, etc.), and indeterminate inference methods that contain degrees of indeterminacy as well (for example, instead of crisp
arguments and values for the probability distributions, charts, diagrams,
algorithms, functions etc. one may have inexact or ambiguous arguments and
values).  

   For example, the population or sample sizes might not be
exactly known because of some individuals that partially belong to the
population or sample, and partially they do not belong, or individuals whose
membership is completely unknown. Also, there are population or sample
individuals whose data could be indeterminate.

   The Neutrosophic Statistics was founded by Prof. Dr.
Florentin Smarandache, from the University of New Mexico, United States, in
1998, who developed it in 2014 by introducing the Neutrosophic Descriptive
Statistics (NDS). Further on, Prof. Dr. Muhammad Aslam, from the King Abdulaziz
University, Saudi Arabia, introduced in 2018 the Neutrosophic Inferential
Statistics (NIS), Neutrosophic Applied Statistics (NAS), and Neutrosophic
Statistical Quality Control (NSQC).

   The Neutrosophic Statistics is also a generalization of Interval Statistics, because of, among others, while Interval Statistics is based on Interval Analysis, Neutrosophic Statistics is based on Set Analysis (meaning all kinds of sets, not only intervals, for example finite discrete sets). 
Also, when computing the mean, variance, standard deviation, probability
distributions etc. in classical and interval statistics it is automatically
assumed that all individuals belong 100% to the respective sample or
population, but in our world one often meet individuals that only partially
belong, partially do not belong, and partially their belong-ness is
indeterminate. The neutrosophic statistics results are more accurate than the
classical and interval statistics, since for example the individuals who belong
only partially do not have to be considered at the same level as one those that
fully belong.

   The Neutrosophic Probability Distributions may be represented by three curves: one representing the chance of the event to occur,
other the chance of the event not to occur, and a third one the indeterminate
chance of the event to occur or not.

   Neutrosophic Statistics is more elastic than Classical Statistics.

   If all data and inference methods are determinate, then the
Neutrosophic Statistics coincides with the Classical Statistics.

   If all sets that are used are intervals, and all individuals belong 100% to the sample and population, and there is only one probability
distribution curve, then the Neutrosophic Statistics coincides with the
Interval Statistics.

   But, since in our world we have more indeterminate data than
determinate data, therefore more neutrosophic statistical procedures are needed
than classical ones.  
    Of course, the Neutrosophic DataSets (where the data have some degree of indeterminacy) are used in Neutrosophic Statistics. 
   
     The Neutrosophic Numbers of the form N = a+bI have been defined by W. B. Vasantha Kandasamy and F. Smarandache in 2003 [see
B2], and they were interpreted as:  “a” is the determinate part
of the number N, and “bI” is the indeterminate part of the number N
by F. Smarandache in 2014 [see B3]. For the neutrosophic statistics
“I” is a subset. 
      Neutrosophic Statistics is the analysis of events described by the Neutrosophic Probability. 
     Neutrosophic Probability is a generalization of the classical probability and
imprecise probability in which the chance that an event A occurs is t% true –
where t varies in the subset T, i% indeterminate – where i varies in the subset
I, and f% false – where f varies in the subset F. In classical probability
the sum of all space probabilities is equal to 1, while in Neutrosophic
Probability it is equal to 3. 
In Imprecise Probability: the probability of an event is a subset T in [0, 1], not a number p in [0, 1], what’s left is supposed to be the opposite, subset F (also from the unit interval [0, 1]); there is no indeterminate subset I in imprecise probability
[see B9].  
The function that models the Neutrosophic Probability of a random variable x is called Neutrosophic Distribution: NP(x) = ( T(x), I(x), F(x) ), where T(x) represents the probability that value x occurs, F(x) represents the probability that value x
does not occur, and I(x) represents the indeterminate / unknown probability of
value x [see B3].  
  
See the NEUTROSOPHIC STATISTICS website https://fs.unm.edu/NS/ 
and several selections of publications: 
B3. Florentin Smarandache: Introduction to Neutrosophic Statistics. Sitech
& Education Publishing, 2014, 124 p. 
B9. F. Smarandache, Introduction to Neutrosophic Measure, Neutrosophic Integral,
and Neutrosophic Probability, Sitech Publishing House, Craiova, 2013, 
Muhammad Aslam: A Variable Acceptance Sampling Plan under Neutrosophic
Statistical Interval Method. Symmetry 2019, 11, 114, DOI: 10.3390/sym11010114 
117. F. Smarandache, Neutrosophic Statistics vs. Interval Statistics, and Plithogenic Statistics as the most general form of statistics (second edition), International Journal of Neutrosophic Science (IJNS), Vol. 19, No. 01, PP. 148-165, 2022, http://fs.unm.edu/NS/NeutrosophicStatistics-vs-IntervalStatistics.pdf  
118. Florentin Smarandache, Foundation of Appurtenance and Inclusion Equations for Constructing the Operations of Neutrosophic Numbers Needed in Neutrosophic Statistics (revised). Prospects for Applied Mathematics and Data
Analysis (PAMDA), Vol. 03, No. 01, PP. 29-48, 2023, 
11. Jiqian Chen, Jun Ye, Shigui Du, Rui Yong: Expressions of Rock
Joint Roughness Coefficient Using Neutrosophic Interval Statistical Numbers. Symmetry, Volume 9, 2017, 7 pages. 
14. Muhammad Aslam, Mohammed Albassam: Inspection Plan Based on the Process Capability Index Using the Neutrosophic Statistical Method. Mathematics 2019,
7, 631, DOI: 10.3390/math7070631. 
36. Somen Debnath: Neutrosophication of statistical data in a study to assess
the knowledge, attitude and symptoms on reproductive tract infection among
women. Journal of Fuzzy Extension & Applications (JFEA),
Volume 2, Issue 1, Winter 2021, 33-40; DOI: 10.22105/JFEA.2021.272508.1073. 
37. Muhammad Aslam, Rashad A.R. Bantan, Nasrullah Khan: Design of tests for mean and variance under complexity-an application to rock measurement data.Elsevier: Measurement, Volume 177, June 2021, 109312; DOI: 10.1016/j.measurement.2021.109312. 
100. Muhammad Aslam, Mohammed Albassam: Presenting post hoc multiple comparison tests under neutrosophic statistics. Elsevier: Journal of King Saud University – Science, Volume 32, Issue 6, September 2020, 2728-2732; DOI: 10.1016/j.jksus.2020.06.008. 
118. Florentin Smarandache, Foundation of Appurtenance and Inclusion Equations for Constructing the Operations of Neutrosophic Numbers Needed in Neutrosophic Statistics (revised). Prospects for Applied Mathematics and Data
Analysis (PAMDA), Vol. 03, No. 01, PP. 29-48, 2023, 
 
117. F. Smarandache, Neutrosophic Statistics vs. Interval Statistics, and Plithogenic Statistics as the most general form of statistics (second edition), International Journal of Neutrosophic Science (IJNS), Vol. 19, No. 01, PP. 148-165, 2022, http://fs.unm.edu/NS/NeutrosophicStatistics-vs-IntervalStatistics.pdf 
   
  
Editors: Usama Afzal, School of Microelectronics, Tianjin University, Tianjin, China, and Muhammad Aslam, Department of Statistics, King Abdulaziz University, Jeddah,Saudi Arabia 
  
Submission Procedures: 
Please send the abstracts and full chapters at both these email addresses: mohammadusamafzal7@gmail.com</span>” target=”_blank”>mohammadusamafzal7@gmail.com  andaslam_ravian@hotmail.com</span>” target=”_blank”>aslam_ravian@hotmail.com 
or via WhatsApp +92 304 6144398 
  
Important Dates: 
Abstract Submission

Deadline: 31 October 2024 
Notification of
Acceptance: 10 November 2024 
Full Chapter Submission
Deadline: 31 December 2024 
Final Manuscript
Submission: 15 February 2025  

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Invitación 4to Conversatorio CIDS

August 28th, 2024 Daniela Lopez de Luise

Estimada/o: 

Tenemos el agrado de ponernos nuevamente en contacto con Ud, a fin de hacerle llegar la invitación al 4to. Conversatorio: “Gestión integrada de los recursos hídricos en una cuenca urbano-rural de Entre Ríos: el caso del Arroyo Crespo”  a realizarse el día Jueves 29 de Agosto de 2024  a las 14.00 hs. mediante modalidad virtual. 

En esta oportunidad la actividad estará a cargo de la Co directora del CIDS, Mg. Img Tatiana Jodor. 

Les hacemos llegar la Gacetilla del evento donde se detalla la temática a trabajarse. 

La inscripción se realiza a través del siguiente formulario:

https://forms.gle/Uk636qxPf8xkGK7Z7

Saludos cordiales, Equipo CIDS. 

| Centro de Innovación y Desarrollo Sustentable

  Facultad de Ciencia y Tecnología

  Universidad Autónoma de Entre Ríos


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call for papers IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG 2025)

August 28th, 2024 Daniela Lopez de Luise
IEEE International Conference on Automatic Face and Gesture Recognition (FG 2025)

May 26 – 30, 2025, Clearwater, USA.
https://fg2025.ieee-biometrics.org

*** Call for Papers ***
The IEEE conference series on Automatic Face and Gesture Recognition is the premier international forum for research in image and video-based face, gesture, and body movement recognition. Its broad scope includes advances in fundamental computer vision, pattern recognition, computer graphics, and machine learning techniques relevant to face, gesture, and body action, new algorithms, and analysis of specific applications. The 19th IEEE International Conference on Automatic Face and Gesture Recognition will be held on 26th – 30th  May 2025 in Clearwater, USA as an in-person event. For FG 2025 we are soliciting papers in all areas of face and gesture recognition.

Topics of interest include, but are not limited to:
+ Face recognition, analysis, and synthesis,
+ Gesture recognition, analysis, and synthesis,
+ Body motion analysis and synthesis,
+ Action recognition,
+ Psychological and behavioral analysis,
+ Technologies and applications
 
*** Paper format and submission ***

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the 12th International Conf. on Robot Intelligence Tech and Apps (RiTA 2024) — Submission Deadline extended to Sep 3rd

August 28th, 2024 Daniela Lopez de Luise
Dear colleagues,

Join us for the 12th International Conference on Robot Intelligence Technology and Applications (RiTA 2024, https://2024.icrita.org/) at UNIST, Ulsan, Korea, from December 4th to 7th, 2024!

As the publicity chair, alongside General Chairs Prof. Hae-Won Park and Prof. Hyondong Oh, we would like to invite you to present your latest research, theories, or experimental developments.

As a part of the conference program, you are also invited to a technical tour to the Ulsan Plant of Hyundai Motor Company (the world's largest single automobile plant).

This prestigious conference promises high-quality discussions on a wide range of topics in robotics, including but not limited to:
–  Machine learning
–  Artificial intelligence
–  Decision making
–  Reasoning and inference
–  Perception
–  Navigation and SLAM
–  Dynamics and Control
–  Human-robot interaction
–  Manipulation
–  Swarm and multi-robot systems
–  Optimization in robotics
–  HW/SW for robot computing
–  Sensors Technology

=========================================================
< Important Dates >
Deadline of Paper Submission: September 3 (extended)
Notification of Acceptance: October 15
Final Manuscript Submission Due: October 31
=========================================================

Don't miss this opportunity to showcase your work and network with leading experts in the field.

For more details, visit our website: https://2024.icrita.org/ !

Best regards,


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