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Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics Cognitive Data Science in Sustainable Computing Series

Langue : Anglais

Coordonnateurs : Smarandache Florentin, Aslam Muhammad

Couverture de l’ouvrage Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics

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.

1. Introduction to Neutrosophic Probability
2. Introduction to Neutrosophic Statistics
3. Applications
Applications of Neutrosophic Statistics to Medicine
Applications of Neutrosophic Statistics to Cognitive Data
Applications of Neutrosophic Statistics to Bioinformatics
Prof. Dr. Florentin Smarandache is a professor of mathematics at the University of New Mexico, United states. He is the founder of neutrosophy (generalization of dialectics), neutrosophic set, neutrosophic logic, neutrosophic probability and neutrosophic statistics since 1995 and has published hundreds of papers and books on multispace and multistructure, hypersoft set, degree of dependence and independence between neutrosophic components, refined neutrosophic set, neutrosophic over-under-off-set, plithogenic set, neutrosophic triplet and duplet structures, quadruple neutrosophic structures, extension of algebraic structures to NeutroAlgebras and AntiAlgebras, Dezert Smarandache Theory and so on to many peer-reviewed international journals and many books and he presented papers and plenary lectures to many international conferences around the world.
Muhammad Aslam is a full professor of statistics in the Department of Statistics, King Abdulaziz University, Jeddah, in Saudi Arabia. He has published over 500 research papers in national and international well-reputed journals such as IEEE Access, Journal of Applied Statistics, European Journal of Operation Research, Information Sciences, International Journal of Fuzzy Systems, International Journal of Advanced Manufacturer Technology, and has authored three books published by VDM, Germany, Springer, and Wiley. Professor Aslam is recipient of the Meritorious Service Award in research from NCBAE, as well as the Research Productivity Award from the Pakistan Council for Science and Technology and King Abdulaziz University Excellence Awards in scientific research. He introduced the concept of Neutrosophic Statistical Quality Control (NSQC). He is the founder of Neutrosophic Inferential Statistics (NIS) and NSQC. His areas of interest include industrial statistics, neutrosophic inferential statistics, neutrosophic statistics, neutrosophic quality control, neutrosophic applied statistics, and classical applied statistics.
  • 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

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Ouvrage de 494 p.

15.2x22.8 cm

Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).

145,45 €

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Thèmes de Cognitive Intelligence with Neutrosophic Statistics in... :

Mots-clés :

?AH-isometry; Age; Analysis; Anemia; Average sample number; Bias; Bioinformatics; Blood pressure; Body temperature; Boxplot; COVID-19; Cancer data; Classical statistics; Comparative analysis; Comparison t-test; Correlation coefficient; Cumulative distribution function; Diabetes; Doomsday argument; Estimation; Expectation; Exponential distribution; Food; Four-valued probability; Fuzzy theory; Gold standard test; Gonarthrosis; Heart condition; Hybrid working model; IVPNP set; Improved correlation coefficient; Indeterminacy; Interval-valued pentapartitioned neutrosophic sets; Linear model; Low; Mean square error; Medical data; Medical diagnosis; Medical professionals; Moments generating function; Monte Carlo simulation; Multi-attribute decision-making problems; Neutrosophic SRSWOR; Neutrosophic VICKOR; Neutrosophic biostatistics; Neutrosophic data; Neutrosophic diagnosis; Neutrosophic diagnosis test; Neutrosophic distance measures; Neutrosophic distribution; Neutrosophic interval method; Neutrosophic linear models; Neutrosophic number; Neutrosophic probabilistic distance measures; Neutrosophic probability; Neutrosophic probability sampling; Neutrosophic ratio-type estimators; Neutrosophic set; Neutrosophic sets; Neutrosophic statistical analysis; Neutrosophic statistics; Neutrosophic variables; Neutrosophy; Occupational shift; PNP set; Pandemic; Pandemic outbreak; Patients; Pentapartitioned neutrosophic sets; Pregnancy; Probability density function; Projection of the risks; Prostate cancer; Pulse rates; Quantum decision-making; Qudit states; Qutrit states; Regression cum ratio estimator; Repetitive sampling; Repetitive sampling plan; Respiration; Robust estimators; Rural populace; Sample size; Simulation; Single sampling plan; Single-valued neutrosophic sets; Standard deviation; Stock price; Survival measures; Syllogism; T-chart; T-test; Temperature; Traditional statistics; Tribal people of Kokrajhar; Uncertain observations; Vague data; Variance; Weibull model