# 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].

Comparison between Neutrosophic Statistics (NS) and Interval Statistics (IS)

We show that NS and IS are different, and in many applications the NS is more general than IS.

NS is not reduced to only using neutrosophic numbers in statistical applications, as some people asserted, but it is much broader. NS deals with all types of indeterminacy, while IS deals only with indeterminacy that can be represented by intervals. However, not all indeterminacies (uncertainties) may be represented by intervals.

Below we present several advantages of applying NS over IS:

-        Neutrosophic Statistics is based on Set Analysis, while Interval Statistics on Interval Analysis, therefore the Interval Statistics is a particular case of the Neutrosophic Statistics that uses all types of sets, not only intervals.

-        The numerical neutrosophic numbers permit the reduction of indeterminacy through operations, while the intervals increase the indeterminacy (see a simple example: let N1 = 4+3I, N2 = 5-2I, where indeterminacy I = [0,1];  using NS one has: N1 + N2 = 9 + I = [9, 10];

using IS one has: N1 + N2 = [4, 7] + [3, 5] = [7, 12]; clearly the indeterminacy or the real data point being in [7, 12], is bigger than the real data point being in [9, 10]).

-        Instead of intervals, for specific applications NS uses hesitant sets {discrete finite sets of numbers}, which make the calculations easier and reduce the indeterminacy (for example, if the real values may be 0.4, 7.9, or 41.5 (not sure which ones), instead of taking the interval [0.4, 41.5] as in IS, it is easier in NS to take just the hesitant set {0.4, 7.9, 41.5} of cardinal 3).

-        NS deals with sample or population whose size is not well-known.

-        NS deals with sample or population which contain individuals that only partially belong to the sample/population and others whose appurtenance is unknown.

-        NS deals with sample or population individuals whose degree of appurtenance to the sample or population may be outside of the interval [0, 1], as in neutrosophic overset (degree > 1), underset (degree < 0), and in general neutrosophic offset (both appurtenance degrees, > 1 and < 0, for various individuals) [see B4].

-        Neutrosophic (or Indeterminate) Data is a vague, unclear, incomplete, partially unknown, conflicting indeterminate data.

-        NS also deals with refined neutrosophic data used in the Big Data.

-        NS may employ partially indeterminate curves.

-        NS also uses Thick Functions (as intersections of curves, that may not be represented by intervals) as probability distributions [see B3].

-        Neutrosophic Probability Distribution (NPD) of an event (x) to occur is represented by three curves: NPD(x) = (T(x), I(x), F(x)), where T(x) represent the chance that the event E occurs, I(x) the indeterminate-chance that the event E occurs or not, and F(x) the chance that the event x does not occur. With T(x), I(x), F(x) being classical or neutrosophic (unclear, approximate, thick) functions – depending on each application, and T(x) + I(x) + F(x) in [0, 3] {see B9}.

-        Diagrams, histograms, pictographs, line/bar/cylinder graphs, plots with neutrosophic data (not represented by intervals) [see B9].

-        Not well-known (or completely unknown): the mean, variance, standard deviation, probability distribution function, and other statistic

-        The Qualitative Data is represented by a finite discrete neutrosophic label set, instead of a label interval.

-        You cannot use Interval Statistics or Interval (Imprecise) Probability to compute the probability of a die on a cracked surface, or coin on a crack surface, or s defect die or coin [see B9].

We deal with indeterminacy with respect to the probability or statistics space or space's elements, indeterminacy with respect to the observer that evaluates the event, indeterminacy with respect to the event [4].

You cannot approximate the indeterminacy from these examples by using some interval, so you need neutrosophic probability and statistics that deal with all types of indeterminacies.

-        In conclusion: we cannot represent all types of indeterminacies by intervals.

More than 100 papers, nine books, one PhD thesis, and five international scientific seminars have been published or presented on neutrosophic statistics, including many journals by Elsevier and Springer of high impact factor.

References

Books

B (sixth edition). InfoLearnQuest, 1998 - 2007, 156 p. http://fs.unm.edu/eBook-Neutrosophics6.pdf

B2. W. B. Vasantha Kandasamy, Florentin Smarandache, Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps, Xiquan, Phoenix, 211 p., 2003, http://fs.unm.edu/NCMs.pdf

B3. Florentin Smarandache: Introduction to Neutrosophic Statistics. Sitech & Education Publishing, 2014, 124 p. http://fs.unm.edu/NeutrosophicStatistics.pdf

B4. Florentin Smarandache: Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset. Similarly for Neutrosophic Over-/Under-/Off- Logic, Probability, and Statistics. Pons Editions, Brussels, 2016, 168 p. http://fs.unm.edu/NeutrosophicOversetUndersetOffset.pdf

B5. Maikel Leyva Vázquez, Florentin Smarandache: Neutrosofía: Nuevos avances en el tratamiento de la incertidumbre. Pons Editions, Bruselas, 2018, 74 p. http://fs.unm.edu/NeutrosofiaNuevosAvances.pdf

B6. Tatiana Veronica Gutierrez Quinonez, Fabian Andres Espinoza, Ingrid Kathyuska Giraldo, Angel Steven Asanza, Mauricio Daniel Montenegro: Estadistica y Probabilidades: Una Vision Neutrosofica desde el Aprendizaje Basado en Problemas en la Construccion del Conocimiento. Pons Editions, Bruselas, 2020, 131 p. http://fs.unm.edu/EstadisticaYProbabilidadNeutrosofica.pdf

B7. F. Smarandache, Neutrosophic Statistics vs. Classical Statistics, section in Nidus Idearum / Superluminal Physics, Vol. 7, third edition, p. 117, 2019, http://fs.unm.edu/NidusIdearum7-ed3.pdf .

B8. F. Smarandache, Nidus Idearum de Neutrosophia (Book Series), Editions Pons, Brussels, Belgium, Vols. 1-7, 2016-2019; http://fs.unm.edu/ScienceLibrary.htm

B9. F. Smarandache, Introduction to Neutrosophic Measure, Neutrosophic Integral, and Neutrosophic Probability, Sitech Publishing House, Craiova, 2013, http://fs.unm.edu/NeutrosophicMeasureIntegralProbability.pdf

PhD Thesis

PhD1. Rafif Alhabib: Formulation of the classical probability and some probability distributions due to neutrosophic logic and its impact on Decision Making. PhD Thesis in Arabic, held under the supervision of Dr. M. M. Ranna, Dr. H. Farah, Dr. A. A. Salama, Faculty of Science, Department of Mathematical Statistics, University of Aleppo, Syrian Arab Republic, 2019. http://fs.unm.edu/NS/FormulationOfTheClassicalProbability-PhDThesis.pdf

Scientific Presentations

SP1. Muhammad Aslam, Testing wind speed using Neutrosophic Weibull distribution, Bejing Jiaotong University, P. R. China, 14 November 2022.

Articles

1. Florentin Smarandache: Operators on Single-Valued Neutrosophic Oversets, Neutrosophic Undersets, and Neutrosophic OffsetsJournal of Mathematics and Informatics, Vol. 5, 2016, 63-67.

2. Florentin Smarandache: Interval-Valued Neutrosophic Oversets, Neutrosophic Undersets, and Neutrosophic OffsetsInternational Journal of Science and Engineering Investigations, Vol. 5, issue 54, 2016, Paper ID: 55416-01, 4 p.

3. Nouran M. Radwan, M. Badr Senousy, Alaa El Din M. Riad: Approaches for Managing Uncertainty in Learning Management SystemsEgyptian Computer Science Journal, vol. 40, no. 2, May 2016, 10 p.

4. Muhammad Aslam: A Variable Acceptance Sampling Plan under Neutrosophic Statistical Interval MethodSymmetry 2019, 11, 114, DOI: 10.3390/sym11010114.

5. Soumyadip Dhar, Malay K. KunduAccurate segmentation of complex document image using digital shearlet transform with neutrosophic set as uncertainty handing toolApplied Soft Computing, vol. 61, 2017, 412–426.

6. B. Kavitha, S. Karthikeyan, P. Sheeba MaybellAn ensemble design of intrusion system for handling uncertainty using Neutrosophic Logic ClassifierKnowlwdge-Based Systems, vol. 28, 2012, 88-96.

7. Muhammad AslamA new attribute sampling plan using neutrosophic statistical interval methodComplex & Intelligent Systems, 6 p. DOI: 10.1007/s40747-018-0088-6

8. Muhammad Aslam, Nasrullah Khan, Mohammed AlbassamControl Chart for Failure-Censored Reliability Tests under Uncertainty EnvironmentSymmetry 2018, 10, 690, DOI: 10.3390/sym10120690.

9. Muhammad Aslam, Nasrullah Khan, Ali Hussein AL-MarshadiDesign of Variable Sampling Plan for Pareto Distribution Using Neutrosophic Statistical Interval MethodSymmetry 2019, 11, 80, DOI: 10.3390/sym11010080.

10. Jun Ye, Jiqian Chen, Rui Yong, Shigui DuExpression and Analysis of Joint Roughness Coefficient Using Neutrosophic Number FunctionsInformation, Volume 8, 2017, 13 pages.

11. Jiqian Chen, Jun Ye, Shigui Du, Rui YongExpressions of Rock Joint Roughness Coefficient Using Neutrosophic Interval Statistical NumbersSymmetry, Volume 9, 2017, 7 pages.

12. Adrian Rubio-Solis, George PanoutsosFuzzy Uncertainty Assessment in RBF Neural Networks using neutrosophic sets for Multiclass Classiﬁcation. Presented at 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) July 6-11, 2014, Beijing, China, 8 pages.

13. Pierpaolo D’UrsoInformational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A reviewInformation Sciences, 400–401 (2017), pp. 30-62, 33 pages.

14. Muhammad Aslam, Mohammed AlbassamInspection Plan Based on the Process Capability Index Using the Neutrosophic Statistical MethodMathematics 2019, 7, 631, DOI: 10.3390/math7070631.

15. Mirela Teodorescu, Florentin Smarandache, Daniela GifuMaintenance Operating System Uncertainties Approached through Neutrosophic Theory. 8 p.

16. Muhammad Aslam, Rashad A. R. Bantan, Nasrullah KhanMonitoring the Process Based on Belief Statistic for Neutrosophic Gamma Distributed ProductProcesses 2019, 7, 209, DOI: 10.3390/pr7040209.

17. Rafael Rojas-Gualdron, Florentin Smarandache, Carlos Diaz-BohorquezApplication of The Neutrosophical Theory to Deal with Uncertainty in Supply Chain Risk Management. AGLALA 2019; 10 (2): 1-19.

18. Florentin Smarandache, Gheorghe SavoiuNeutrosophic Index Numbers: Neutrosophic Logic Applied In The Statistical Indicators TheoryCritical Review, Vol. XI, 2015, pp. 67-100.

19. Murat Kirisci, Necip SimsekNeutrosophic normed spaces and statistical convergenceJournal of Analysis, 11 April 2020, DOI: 10.1007/s41478-020-00234-0.

20. S.K. PatroThe Neutrosophic Statistical Distribution: More Problems, More Solutions. 17 p.

21. Deepesh Kunwar, Jayant Singh, Florentin SmarandacheNeutrosophic statistical evaluation of migration with particular reference to JaipurOctogon Mathematical Magazine, vol. 26, no. 2, October 2018, 560-568.

22. Deepesh Kunwar, Jayant Singh, Florentin SmarandacheNeutrosophic statistical techniques to find migration pattern in JaipurOctogon Mathematical Magazine, vol. 26, no. 2, October 2018, 583-592.

23. Muhammad Aslam, Osama H. Arif, Rehan Ahmad Khan SherwaniNew Diagnosis Test under the Neutrosophic Statistics: An Application to Diabetic Patients. Hindawi, BioMed Research International, Volume 2020, Article ID 2086185, 7 pages; DOI: 10.1155/2020/2086185.

24. Jose L. Salmeron, Florentin SmarandacheProcessing Uncertainty and Indeterminacy in Information Systems success mapping. 13 p., arXiv:cs/0512047v2.

25. Wenzhong Jiang, Jun Ye, Wenhua Cui: Scale Effect and Anisotropic Analysis of Rock Joint Roughness Coefficient Neutrosophic Interval Statistical Numbers Based on Neutrosophic StatisticsJournal of Soft Computing in Civil Engineering, 2-4 / 2018, 62-71; DOI: 10.5281/zenodo.3130240.

26. Muhammad Aslam, P. Jeyadurga, Saminathan Balamurali, Ali Hussein Al-MarshadiTime-Truncated Group Plan under aWeibull Distribution based on Neutrosophic StatisticsMathematics 2019, 7, 905; DOI: 10.3390/math7100905

27. A.A. Salama, M. Elsayed Wahed, Eman YousifA Multi-objective Transportation Data Problems and their Based on Fuzzy Random VariablesNeutrosophic Knowledge, vol. 1, 2020, 41-53; DOI: 10.5281/zenodo.4269558.

28. Philippe SchweizerUncertainty: two probabilities for the three states of neutrosophy. International Journal of Neutrosophic Science (IJNS), Volume 2, Issue 1, 2020, 18-26; DOI: 10.5281/zenodo.3989350.

29. Carlos N. Bouza-Herrera, Mir SubzarEstimating the Ratio of a Crisp Variable and a Neutrosophic VariableInternational Journal of Neutrosophic Science (IJNS), Volume 11, Issue 1, 2020, 9-21; DOI: 10.5281/zenodo.4275712

30. Angel Carlos Yumar Carralero, Darvin Manuel Ramirez Guerra, Giorver Perez IribarAnalisis estadistico neutrosofico en la aplicacion de ejercicios fisicos en la rehabilitacion del adulto mayor con gonartrosisNeutrosophic Computing and Machine Learning, Vol. 13, 1-9, 2020; DOI: https://zenodo.org/record/3901770.

31. Alexandra Dolores Molina Manzo, Rosa Leonor Maldonado Manzano, Blanca Esmeralda Brito Herrera, Johanna Irene Escobar JaraAnalisis estadistico neutrosofico de la incidencia del voto facultativo de los jovenes entre 16 y 18 anos en el proceso electoral del EcuadorNeutrosophic Computing and Machine Learning, Vol. 11, 9-14, 2020; DOI: https://zenodo.org/record/3474439.

32. Johana Cristina Sierra Morán, Jenny Fernanda Enríquez Chuga, Wilmer Medardo Arias Collaguazo And Carlos Wilman Maldonado Gudiño:Neutrosophic statistics applied to the analysis of socially responsible participation in the community , Neutrosophic Sets and Systems, vol. 26, 2019, pp. 19 -28.  DOI: 10.5281/zenodo.3244232

33. Paúl Alejandro Centeno Maldonado, Yusmany Puertas Martinez, Gabriela Stephanie Escobar Valverde, and Juan Danilo Inca Erazo:  Neutrosophic statistics methods applied to demonstrate the extra-contractual liability of the state from the Administrative Organic Code, Neutrosophic Sets and Systems, vol. 26, 2019, pp. 29-34.  DOI: 10.5281/zenodo.3244262

35. Lilia Esther Valencia Cruzaty, Mariela Reyes Tomalá, Carlos Manuel Castillo Gallo and Florentin Smarandache,  Neutrosophic Sets and Systems, vol. 34, 2020, pp. 33-39. DOI: 10.5281/zenodo.3843289; http://fs.unm.edu/NSS/NeutrosophicStatisticMethod.pdf

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.

38. O.H. Arif, Muhammad Aslam:  Springer: Complex & Intelligent Systems (2021); DOI: .

39. Nasrullah Khan, Muhammad Aslam, Asma Arshad, Ambreen Shafqat:  Springer: Journal of Metrology Society of India (2021); DOI: 10.1007/s12647-021-00436-2.

40. Muhammad Aslam:  Springer: Soft Computing (2021); DOI: 10.1007/s00500-021-05661-0.

41. Muhammad Aslam:  Springer: Journal of Metrology Society of India (2021); DOI: 10.1007/s12647-021-00429-1.

42. Muhammad Aslam, Nasrullah Khan:  Springer: Journal of Metrology Society of India (2021); DOI: 10.1007/s12647-020-00428-8.

43. Muhammad Aslam, Gadde Srinivasa Rao, Nasrullah Khan, Liaquat Ahmad:  Taylor&Francis: Communications in Statistics - Theory and Methods (2020); DOI: 10.1080/03610918.2019.1702212.

44. Ali Hussein Al-Marshadi, Ambreen Shafqat, Muhammad Aslam, Abdullah Alharbey: Performance of a New Time-Truncated Control Chart for Weibull Distribution Under Uncertainty. Atlantis Press: International Journal of Computational Intelligence Systems, Volume 14, Issue 1, 2021, 1256 - 1262; DOI: .

45. Muhammad Aslam: Testing average wind speed using sampling plan for Weibull distribution under indeterminacy. Nature: Scientific Reports, 11, (2021); DOI: 10.1038/s41598-021-87136-8.

46. Muhammad Aslam, G. Srinivasa Rao, Nasrullah Khan:. Springer: Complex & Intelligent Systems, 7, 891–900 (2021); DOI: .

47. Muhammad Aslam, G. Srinivasa Rao, Ambreen Shafqat, Liaquat Ahmad, Rehan Ahmad Khan Sherwani: Monitoring circuit boards products in the presence of indeterminacy. Elsevier: Measurement, Volume 168, 15 January 2021, 108404; DOI: 10.1016/j.measurement.2020.108404.

48. Mohammed Albassam, Nasrullah Khan, Muhammad Aslam: Neutrosophic D’Agostino Test of Normality: An Application to Water Data. Hindawi: Journal of Mathematics - Theory, Algorithms, and Applications within Neutrosophic Modelling and Optimisation, 2021, , 5 pages; DOI: 10.1155/2021/5582102.

49. Mohammed Albassam: Radar data analysis in the presence of uncertainty. Taylor&Francis: European Journal of Remote Sensing, 54:1, 140-144, 2021; DOI: .

50. Muhammad Aslam:  Springer: Complex & Intelligent Systems, 7, 359–365, 2021; DOI: .

51. Abdullah M. Almarashi, Muhammad Aslam: Process Monitoring for Gamma Distributed Product under Neutrosophic Statistics Using Resampling Scheme. Hindawi: Journal of Mathematics: Soft Computing Algorithms Based on Fuzzy Extensions, Volume 2021, , 12 pages; DOI: 10.1155/2021/6635846.

52. Muhammad Aslam:  Springer: Theoretical and Applied Climatology, 143, 1227–1234, 2021; DOI: 10.1007/s00704-020-03509-5.

53. Muhammad Aslam, Ali Algarni:  Hindawi: International Journal of Photoenergy, Volume 2020, , 6 pages; DOI: 10.1155/2020/6662389.

54. Muhammad Aslam:  Nature: Sc. Rep., Volume 10 (2020).

55. Azhar Ali Janjua, Muhammad Aslam, Naheed Sultana:  Springer: Theoretical and Applied Climatology, Volume 142, pages 1641–1648 (2020); DOI: 10.1007/s00704-020-03398-8.

56. Rehan Ahmad Khan Sherwan, Mishal Naeem, Muhammad Aslam, Muhammad Ali Raza, Muhammad Abid, Shumaila Abbas: University of New Mexico: Neutrosophic Sets and Systems, Vol. 41, 209-214, 2021; DOI: .

57. Muhammad Aslam, Ambreen Shafqat, Mohammed Albassam, Jean-Claude Malela-Majika, Sandile C. Shongwe: PLoS ONE 16(2): e0246185, 2021; DOI: .

58. Muhammad Aslam: Taylor&Francis: International Journal of Injury Control and Safety Promotion, Volume 28, 2021 - Issue 1, 39-45; DOI: 10.1080/17457300.2020.1835990.

59. Muhammad Aslam: Taylor&Francis: International Journal of Cast Metals Research, Volume 34, 2021 - Issue 1, 1-5; DOI: 10.1080/13640461.2020.1846959.

60. Ishmal Shahzadi, Muhammad Aslam, Hussain Aslam: University of New Mexico: Neutrosophic Sets and Systems, Vol. 39, 101-106, 2020.

61. Nasrullah Khan, Muhammad Aslam, P. Jeyadurga, S. Balamurali: Nature: Sc. Rep., volume 11 (2021).

62. Muhammad Aslam, Rashad A.R. Bantan: Elsevier: Measurement, Volume 166, December 2020, 108201; DOI: 10.1016/j.measurement.2020.108201.

63. Muhammad Aslam, Rashad A. R. Bantan, Nasrullah Khan: Springer: Soft Computing, Volume 24, 16617–16626 (2020); DOI: 10.1007/s00500-020-04964-y.

64. M. Albassam, Muhammad Aslam: IEEE Access, vol. 8, pp. 172379-172386, 2020; DOI: .

65. Ahmed Ibrahim Shawky , Muhammad Aslam, Khushnoor Khan: Hindawi: Journal of Mathematics, Volume 2020, , 14 pages; DOI: 10.1155/2020/7680286.

66. Muhammad Aslam: Science Direct: Journal of King Saud University - Science, Volume 32, Issue 6, September 2020, 2696-2700; DOI: .

67. Muhammad Aslam: MDPI: Symmetry, 2018, 10 (5), 132; DOI: .

68. Muhammad Aslam, Osama H. Arif: MDPI: Symmetry, 2018, 10 (9), 403; DOI: .

69. Muhammad Aslam, Nasrullah Khan, Muhammad Zahir Khan: MDPI: Symmetry, 2018, 10 (11), 562; DOI: .

70. Muhammad Zahir Khan, Muhammad Farid Khan, Muhammad Aslam, Abdur Razzaque Mughal: MDPI: Mathematics, 2019, 7 (1), 9; DOI: .

71. Muhammad Aslam, Ali Hussein Al-Marshadi: MDPI: Symmetry, 2018, 10 (12), 754; DOI: .

72. Muhammad Zahir Khan, Muhammad Farid Khan, Muhammad Aslam, Seyed Taghi Akhavan Niaki, Abdur Razzaque Mughal: MDPI: Information, 2018, 9 (12), 312; DOI: .

73. Muhammad Aslam, Nasrullah Khan, Mohammed Albassam: MDPI: Symmetry, 2018, 10 (12), 690; DOI: .

74. Muhammad Aslam, Mohammed Albassam: MDPI: Symmetry, 2019, 11 (3), 330; DOI: .

75. Muhammad Aslam, Mansour Sattam Aldosari: MDPI: Symmetry, 2019, 11 (2), 193; DOI: .

76. Muhammad Aslam: MDPI: Symmetry, 2019, 11 (1), 114; DOI: .

77. Muhammad Aslam, Nasrullah Khan, Ali Hussein Al-Marshadi: MDPI: Symmetry, 2019, 11 (1), 80; DOI: .

78. Muhammad Aslam, Rashad A. R. Bantan, Nasrullah Khan: MDPI: Processes, 2019, 7 (10), 742; DOI: .

79. Muhammad Aslam, Ali Hussein Al-Marshadi, Nasrullah Khan: MDPI: Mathematics, 2019, 7 (10), 957; DOI: .

80. Muhammad Aslam, P. Jeyadurga, Saminathan Balamurali, Ali Hussein Al-Marshadi: MDPI: Mathematics, 2019, 7 (10), 905; DOI: .

81. Muhammad Aslam, Osama Hasan Arif: MDPI: Mathematics, 2019, 7 (9), 870; DOI: .

82. Muhammad Aslam, Mohammed Albassam: MDPI: Mathematics, 2019, 7 (7), 631; DOI: .

83. Muhammad Aslam, Rashad A. R. Bantan, Nasrullah Khan: MDPI: Processes, 2019, 7 (4), 209; DOI: .

84. Muhammad Aslam: Hindawi: Advances in Fuzzy Systems, Volume 2019, , 8 pages; DOI: 10.1155/2019/8953051.

85. Muhammad Aslam, Rashad A. R. Bantan, Nasrullah Khan: Springer: International Journal of Fuzzy Systems, Volume 21, 433–440 (2019); DOI: 10.1007/s40815-018-0577-1.

86. Muhammad Aslam, Osama H. Arif: Hindawi: Complexity, Volume 2020, , 6 pages; DOI: 10.1155/2020/2935435.

87. Mohammed Albassam, Nasrullah Khan,Muhammad Aslam: Hindawi: Complexity, Volume 2020, , 8 pages; DOI: 10.1155/2020/3690879.

88. Muhammad Aslam, Osama H. Arif, Rehan Ahmad Khan Sherwani: Hindawi: BioMed Research International, Volume 2020, , 7 pages; DOI: 10.1155/2020/2086185.

88. Muhammad Aslam, Ali Hussein Al-Marshadi: Hindawi: Complexity, Volume 2019, , 7 pages; DOI: 10.1155/2019/8178067.

89. Muhammad Aslam, Osama H. Arif: Hindawi: Journal of Analytical Methods in Chemistry, Volume 2020, Article ID 1406028, 6 pages; DOI: .

90. Muhammad Aslam, Abdulmohsen Al-Shareef, Khushnoor Khan: Nature: Sc. Rep., Volume 10, Article number: 12182 (2020).

91. Muhammad Aslam: IEEE Access, vol. 6, pp. 64153-64158, 2018; DOI: .

92. Muhammad Aslam: IEEE Access, vol. 7, pp. 25253-25262, 2019; DOI: .

93. Muhammad Aslam, M. Azam, M. Albassam: IEEE Access, vol. 7, pp. 38568-38576, 2019; DOI: .

94. Naeem Jan, Muhammad Aslam, Kifayat Ullah, Tahir Mahmood, Jun Wang: An approach towards decision making and shortest path problems using the concepts of interval-valued Pythagorean fuzzy information. Wiley: International Journal of Intelligent Systems, Volume 34, Issue 10, October 2019, 2403-2428.

95. Muhammad Aslam: IEEE Access, vol. 7, 2019, 2163-3536; DOI: .

96. Muhammad Aslam, R. A. R. Bantan, N. Khan: IEEE Access, vol. 7, pp. 8858-8864, 2019; DOI: .

97. Muhammad Aslam, Muhammad Ali Raza: Springer: International Journal of Fuzzy Systems, volume 21, 978–992 (2019); DOI: 10.1007/s40815-018-0560-x.

98. Muhammad Aslam: Springer: International Journal of Fuzzy Systems, volume 21, 1214–1220 (2019); DOI: 10.1007/s40815-018-0588-y.

99. Muhammad Aslam: Springer: Complex & Intelligent Systems, volume 5, 403–407 (2019); DOI: .

100. Muhammad Aslam, Mohammed Albassam: Elsevier: Journal of King Saud University - Science, Volume 32, Issue 6, September 2020, 2728-2732; DOI: .

101. Muhammad Aslam, Mansour Sattam Aldosari: Elsevier: Journal of King Saud University - Science, Volume 32, Issue 6, September 2020, 2831-2834; DOI: .

102. Muhammad Aslam: Elsevier: Journal of King Saud University - Science, Volume 32, Issue 3, April 2020, 2005-2008; DOI: .

103. Muhammad Aslam: Springer: Complex & Intelligent Systems, 5, 365–370 (2019); DOI: .

104. Muhammad Aslam, Saminathan Balamurali, Jeyadurga Periyasamypandian, Ali Hussein Al-Marshadi: IEEE Access, vol. 7, 164186-164193, 2019; DOI: .

105. Muhammad Aslam, R. A. R. Bantan, N. Khan: IEEE Access, vol. 7, pp. 152233-152242, 2019; DOI: .

106. Muhammad Aslam: American Chemical Society: ACS Omega 2020, 5, 1, 914-917; DOI: .

107. Muhammad Aslam: Taylor&Francis: Journal of Taibah University for Science, Volume 14, 2020, Issue 1; DOI: .

108. Muhammad Aslam, Muhammad Ali Raza, Liaquat Ahmad: IOS Press: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7839-7850, 2019; DOI: 10.3233/JIFS-182849.

109. Muhammad Kashif, Hafiza Nida, Muhammad Imran Khan, Muhammad Aslam: University of New Mexico: Neutrosophic Sets and Systems, vol. 30, 143-148, 2019.

110. Muhammad Aslam, Nasrullah Khan: University of New Mexico: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2615-2623, 2019; DOI: 10.3233/JIFS-181767.

111. N Khan, L Ahmad, M Azam, M Aslam, F Smarandache, Control Chart for Monitoring Variation Using Multiple Dependent State Sampling Under Neutrosophic Statistics, in the book Neutrosophic Operational Research (eds. F. Smarandache, M. Abdel-Basset), Springer, pp 55-70, 10 September 2021, https://link.springer.com/chapter/10.1007/978-3-030-57197-9_4.

112. Rehan Ahmad Khan Sherwani, Muhammad Aslam, Muhammad Ali, Raza Muhammad, Farooq Muhammad, Abid Muhammad Tahir, Neutrosophic Normal Probability Distribution—A Spine of Parametric Neutrosophic Statistical Tests: Properties and Applications, in the book Neutrosophic Operational Research (eds. F. Smarandache, M. Abdel-Basset), Springer, pp 153-169, 10 September 2021, https://link.springer.com/chapter/10.1007/978-3-030-57197-9_8.

113. Rehan Ahmad Khan Sherwani, Muhammad Aslam, Huma Shakeel, Kamran Abbas, Farrukh Jamal, Neutrosophic Statistics for Grouped Data: Theory and Applications, in the book Neutrosophic Operational Research (eds. F. Smarandache, M. Abdel-Basset), Springer, pp 263-289, 10 September 2021, https://link.springer.com/chapter/10.1007/978-3-030-57197-9_14.

114. Arif, O.H., Aslam, M. A new sudden death chart for the Weibull distribution under complexity. Complex Intell. Syst. 7, 2093–2101 (2021), Springer,  https://doi.org/10.1007/s40747-021-00316-x, https://link.springer.com/article/10.1007/s40747-021-00316-x

115. Wen-Qi Duan, Zahid Khan, Muhammad Gulistan, Adnan KhurshidNeutrosophic Exponential Distribution: Modeling and Applications for Complex Data AnalysisComplexity, vol. 2021, Article ID 5970613, 8 pages, 2021. https://doi.org/10.1155/2021/5970613

116. Nasrullah Khan, Liaquat Ahmad, G. Srinivasa Rao, Muhammad Aslam, Ali Hussein AL Marshadi, A New X bar Control Chart for Multiple Dependent State Sampling Using Neutrosophic Exponentially Weighted Moving Average Statistics with Application to Monitoring Road Accidents and Road Injuries, International Journal of Computational Intelligence Systems, Springer, (2021) 14:182 https://doi.org/10.1007/s44196-021-00033-w, 30 September 2021, https://link.springer.com/content/pdf/10.1007/s44196-021-00033-w.pdf

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

Project

Pr1. F. Smarandache, Neutrosophic Statistics is a generalization of Classical and Interval Statistics, research project, ResearchGate (Germany), https://www.researchgate.net/project/Neutrosophic-Statistics-is-a-generalization-of-Classical-and-Interval-Statistics

Seminars

S1. History of Neutrosophic Set, Logic, Probability and Statistics and their Applications, Mathematics and Statistics Departments, King Abdulaziz University, Jeddah, Saudi Arabia, 19 December 2019.

S2. Neutrosophic Set and Logic / Interval Neutrosophic Set and Logic / Neutrosophic Probability and Neutrosophic Statistics / Neutrosophic Precalculus and Calculus / Symbolic Neutrosophic Theory / Open Challenges of Neutrosophic Set, lecture series, by F. Smarandache, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam, 31st May - 3th June 2016.

S3. Neutrosophic Set and Logic / Interval Neutrosophic Set and Logic / Neutrosophic Probability and Neutrosophic Statistics / Neutrosophic Precalculus and Calculus / Symbolic Neutrosophic Theory / Open Challenges of Neutrosophic Setby F. Smarandache, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam, 30th May 2016.

S4. Neutrosophic Set and Logic / Interval Neutrosophic Set and Logic / Neutrosophic Probability and Neutrosophic Statistics / Neutrosophic Precalculus and Calculus / Symbolic Neutrosophic Theory / Open Challenges of Neutrosophic Set, lecture series, by F. Smarandache, Vietnam national University, Vietnam Institute for Advanced Study in Mathematics, Hanoi, Vietnam, lecture series, 14th May – 26th May 2016.

S5. Foundations of Neutrosophic Logic, Set, Probability and Statistics and their Applications in Science. n-Valued Refined Neutrosophic Set, Logic, Probability and Statistics, by F. Smarandache, Universidad Complutense de Madrid, Facultad de Ciencia Matematicas, Departamento de Geometria y Topologia, Instituto Matematico Interdisciplinar (IMI), Madrid, Spain, 9th July 2014.

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