The Process Capability Index (PCI) has been widely used in industry to advance the quality of a product. Neutrosophic statistics is the more generalized form of classical statistics and is applied when the data from the production process or a product lot is incomplete, incredible, and indeterminate. In this paper, we will originally propose a variable sampling plan for the PCI using neutrosophic statistics. The neutrosophic operating function will be given. The neutrosophic plan parameters will be determined using the neutrosophic optimization solution. A comparison between plans based on neutrosophic statistics and classical statistics is given. The application of the proposed neutrosophic sampling plan will be given using company data.

The Process Capability Index (PCI) has been widely used in industry to advance the quality of a product. Neutrosophic statistics is the more generalized form of classical statistics and is applied when the data from the production process or a product lot is incomplete, incredible, and indeterminate. In this paper, we will originally propose a variable sampling plan for the PCI using neutrosophic statistics. The neutrosophic operating function will be given. The neutrosophic plan parameters will be determined using the neutrosophic optimization solution. A comparison between plans based on neutrosophic statistics and classical statistics is given. The application of the proposed neutrosophic sampling plan will be given using company data.

Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics investigates and presents the many applications that have arisen in the last ten years using neutrosophic statistics in bioinformatics, medicine, agriculture and cognitive science. This book will be very useful to the scientific community, appealing to audiences interested in fuzzy, vague concepts from which uncertain data are collected, including academic researchers, practicing engineers and graduate students. Neutrosophic statistics is a generalization of classical statistics. In classical statistics, the data is known, formed by crisp numbers. In comparison, data in neutrosophic statistics has some indeterminacy. This data may be ambiguous, vague, imprecise, incomplete, and even unknown. Neutrosophic statistics refers to a set of data, such that the data or a part of it are indeterminate in some degree, and to methods used to analyze the data. Introduces the field of neutrosophic statistics and how it can solve problems working with indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data Presents various applications of neutrosophic statistics in the fields of bioinformatics, medicine, cognitive science and agriculture Provides practical examples and definitions of neutrosophic statistics in relation to the various types of indeterminacies

Contributors to current issue (listed in papers’ order): Noel Batista Hernández; C.V. Valenzuela Chicaiza; O.G. Arciniegas Paspuel; P.Y. Carrera Cuesta; D.R. Álvarez Hernández, C.E. Pozo Hernández; E.T. Mejía Álvarez; E.T. Villa Shagnay; S. Guerrón Enríquez; M.A. Tello Cadena; E.M. Pinos Medina; M. Jaramillo Burgos; F. Jara Vaca; R. Aguilar Berrezueta; E.M. Sandoval; B. Villalta Jadán; D. Palma Rivera; L.E. Valencia Cruzaty; M. Reyes Tomalá; C.M. Castillo Gallo, M.R. Velázquez; M.R. Mena Peralta; L. Ricardo Domínguez; D. Andrade Santamaría; X.Cangas Oña; M. Jaramillo Burgos; G.A. Calderón Vallejo; M. Orellana Cepeda; M.F. Galarza Villalba; M.S. Serrano Viteri; I. Ramos Castro; F. Vera Díaz; N.P. Lastra Calderón; D.L. Villarruel Delgado; D. Sandoval Malquín; E. Araujo Guerrón; A.R. Pupo Kairuz; D.V. Ponce Ruiz; F. Viteri Pita; F.S. Bustillo Mena; M.E. Narváez Jaramillo; M.A. Guerrero Ayala; D.A. Flores Jurado; O.M. Alonzo Pico; A.I. Utrera Velázquez; D.A. García Coello; E. Real Garlobo; C. Escobar Vinueza; R.C. Hernández Infante; M.E. Infante Miranda; F.R. Rivadeneira Enríquez; C.J. Galeano Páez; R.M. Montalvo Pantoja; K.A. Narváez Ortiz; S. Guaytarilla Salas; A.D. Rodríguez Lara; C.P. Rendón Tello; J. Almeida Blacio; R. Hurtado Guevara; L.G. Guallpa Zatán; H.J. Paillacho Chicaiza; J. Yaguar Mariño; M. Aguilar Carrión; D.A. Viteri Intriago; L. Álvarez Gómez; D. Ponce Ruiz; L.H. Carrión Hurtado; W.R. Salas Espín; M. Benalcázar Paladines; L. Moreira Rosales; L.K. Baque Villanueva; M.A. Mendoza; R. Salcedo; A.M. Izquierdo Morán; M.A. Checa Cabrera; B.J. Ipiales Chasiguano; A.L. Sandoval Pillajo; R. Díaz Vázquez; N.P. Becerra Arévalo; M.F. Calles Carrasco; John Luis Toasa Espinoza; M. Velasteguí Córdova; V.M. Parrales Carvajal; M.T. Macías Valverde; R. Aguas Pután; N. García Arias; N. Quevedo Arnaiz; S. Gavilánez Villamarín; M. Cleonares Borbor; M.F. Galarza Villalba; R. Aguas Pután; J. Mora Romero; J.E. Espìn Oviedo; L.J. Molina Chalacán; L.O. Albarracín Zambrano; E.J. Jalón Arias; A. Zúñiga Paredes; F. Smarandache; J. Estupiñán Ricardo; E. González Caballero; M.Y. Leyva Vázquez.

In this paper, we prove that Neutrosophic Statistics is more general than Interval Statistics, since it may deal with all types of indeterminacies (with respect to the data, inferential procedures, probability distributions, graphical representations, etc.), it allows the reduction of indeterminacy, and it uses the neutrosophic probability that is more general than imprecise and classical probabilities and has more detailed corresponding probability density functions. While Interval Statistics only deals with indeterminacy that can be represented by intervals. And we respond to the arguments by Woodall et al. [1]. We show that not all indeterminacies (uncertainties) may be represented by intervals. Also, in some cases, we should better use hesitant sets (that have less indeterminacy) instead of intervals. We redirect the authors to the Plithogenic Probability and Plithogenic Statistics which are the most general forms of MultiVariate Probability and Multivariate Statistics respectively (including, of course, the Imprecise Probability and Interval Statistics as subclasses).

Neutrosophy as science has inclusive attributes that make possible to extract the contributions of neutral values in the analysis of data sets; it builds a unified field of logic for transdisciplinary studies that transcend the boundaries between natural and social sciences. Neutral philosophy seeks to solve the problems of indeterminacy that appear universally, to reform the current natural or social sciences, with an open methodology to promote innovation. The research products related in this special issue start from the premise that the difficulty is not the complexity of the social environment, but the instrumental obsolescence to observe, interpret and manage that complexity, there are bold approaches and proposals for valid solutions that come to enrich the universe of resolution through the use of neutral methods. In the last year, the use of tools related to neutrosophy and its application to the social sciences, modeling of social phenomena based on simulation agents, problems associated with health, psychology, education, environmental management and sustainability solutions and legal sciences has increased in the events organized by the Asociacion Latinoamericana de Ciencias Neutrosoficas (ALCN in Spanish). The methods of higher incidence are cognitive maps, neutral Iadovs, neutral Delphi, analytical hierarchy process methods, neutral statistics, neutral personality models, among the most significant. In this special issue, there is a predominance of research from Ecuadorian universities, demonstrating how neutrosophy and its methods are consolidated as instruments of analysis, inference and research validation.

This fourteenth volume of Collected Papers is an eclectic tome of 87 papers in Neutrosophics and other fields, such as mathematics, fuzzy sets, intuitionistic fuzzy sets, picture fuzzy sets, information fusion, robotics, statistics, or extenics, comprising 936 pages, published between 2008-2022 in different scientific journals or currently in press, by the author alone or in collaboration with the following 99 co-authors (alphabetically ordered) from 26 countries: Ahmed B. Al-Nafee, Adesina Abdul Akeem Agboola, Akbar Rezaei, Shariful Alam, Marina Alonso, Fran Andujar, Toshinori Asai, Assia Bakali, Azmat Hussain, Daniela Baran, Bijan Davvaz, Bilal Hadjadji, Carlos Díaz Bohorquez, Robert N. Boyd, M. Caldas, Cenap Özel, Pankaj Chauhan, Victor Christianto, Salvador Coll, Shyamal Dalapati, Irfan Deli, Balasubramanian Elavarasan, Fahad Alsharari, Yonfei Feng, Daniela Gîfu, Rafael Rojas Gualdrón, Haipeng Wang, Hemant Kumar Gianey, Noel Batista Hernández, Abdel-Nasser Hussein, Ibrahim M. Hezam, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Muthusamy Karthika, Nour Eldeen M. Khalifa, Madad Khan, Kifayat Ullah, Valeri Kroumov, Tapan Kumar Roy, Deepesh Kunwar, Le Thi Nhung, Pedro López, Mai Mohamed, Manh Van Vu, Miguel A. Quiroz-Martínez, Marcel Migdalovici, Kritika Mishra, Mohamed Abdel-Basset, Mohamed Talea, Mohammad Hamidi, Mohammed Alshumrani, Mohamed Loey, Muhammad Akram, Muhammad Shabir, Mumtaz Ali, Nassim Abbas, Munazza Naz, Ngan Thi Roan, Nguyen Xuan Thao, Rishwanth Mani Parimala, Ion Pătrașcu, Surapati Pramanik, Quek Shio Gai, Qiang Guo, Rajab Ali Borzooei, Nimitha Rajesh, Jesús Estupiñan Ricardo, Juan Miguel Martínez Rubio, Saeed Mirvakili, Arsham Borumand Saeid, Saeid Jafari, Said Broumi, Ahmed A. Salama, Nirmala Sawan, Gheorghe Săvoiu, Ganeshsree Selvachandran, Seok-Zun Song, Shahzaib Ashraf, Jayant Singh, Rajesh Singh, Son Hoang Le, Tahir Mahmood, Kenta Takaya, Mirela Teodorescu, Ramalingam Udhayakumar, Maikel Y. Leyva Vázquez, V. Venkateswara Rao, Luige Vlădăreanu, Victor Vlădăreanu, Gabriela Vlădeanu, Michael Voskoglou, Yaser Saber, Yong Deng, You He, Youcef Chibani, Young Bae Jun, Wadei F. Al-Omeri, Hongbo Wang, Zayen Azzouz Omar.