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.
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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].
and several selections of publications:
B9. F. Smarandache, Introduction to Neutrosophic Measure, Neutrosophic Integral,
and Neutrosophic Probability, Sitech Publishing House, Craiova, 2013,
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,
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,
Editors: Usama Afzal, School of Microelectronics, Tianjin University, Tianjin, China, and Muhammad Aslam, Department of Statistics, King Abdulaziz University, Jeddah,Saudi Arabia
Submission Procedures:
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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September 3rd, 2024 
Daniela Lopez de Luise 
