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. Chapter 1 Introduction and advances to neutrosophic probability and statistics and plithogenic probability and statistics and their applications in bioinformatics and other fields (review chapter) - p. 1 2. Chapter 2 Neutrosophic Weibull model with applications to survival studies - p. 25 3. Chapter 3 Using the four-valued Rasch model in the preparation of neutrosophic form of risk maps for the spread of COVID-19 in Turkey - p. 43 4. Chapter 4 Neutrosophic linear models for bioinformatics - p. 71 5. Chapter 5 Modeling epidemics based on quantum decision-making model by the qutrit states and employing neutrosophic form of percolation analysis - p. 87 6. Chapter 6 Use of neutrosophic statistics to analyze the measured data of diabetes - p. 119 7. Chapter 7 Analysis of changes in blood pressure of women during pregnancy through neutrosophic statistics - p. 137 8. Chapter 8 Neutrosophic statistical analysis of changes in blood pressure, pulse rate and temperature of human body due to COVID-19 - p. 153 9. Chapter 9 A study of human respiration rate through neutrosophic statistics - p. 173 10. Chapter 10 Neutrosophic diagnosis of rural women vulnerable to anemia - p. 189 11. Chapter 11 Various sampling inspection plans for cancer patients using gamma distribution under indeterminacy - p. 203 12. 11.1: Introduction - p. 203 13. 11.2: Gamma distribution for indeterminacy - p. 205 14. 11.3: Single sampling plan under indeterminacy - p. 206 15. 11.4: Repetitive sampling plan under indeterminacy - p. 217 16. 11.5: Conclusions - p. 233 17. References - p. 234 18. Chapter 12 Improved correlation coefficients in neutrosophic statistics for COVID patients using pentapartitioned neutrosophic sets - p. 237 19. Chapter 13 Post-pandemic impact on the occupational shift of rural populace—a case study using neutrosophic comparison t-test - p. 259 20. Chapter 14 A novel approach of neutrosophic continuous probability distributions using AH-isometry with applications in medicine - p. 267 21. Chapter 15 Monitoring COVID-19 cases under uncertainty - p. 287 22. Chapter 16 Neutrosophic statistical analysis of hybrid work model of medical professionals - p. 305 23. Chapter 17 Neutrosophic regression cum ratio estimators for the population mean: an application in medical science - p. 313 24. 17.1: Introduction - p. 313 25. 17.2: Proposed neutrosophic estimators - p. 315 26. 17.3: Empirical study - p. 325 27. 17.4: Results and discussion - p. 327 28. 17.5: Conclusion - p. 330 29. Acknowledgments - p. 330 30. References - p. 331 31. Chapter 18 Effectiveness on impact of COVID vaccines on correlation coefficients of pentapartitioned neutrosophic pythagorean statistics - p. 335 32. 18.1: Introduction and motivation - p. 335 33. 18.2: Background, definitions, and notations - p. 337 34. 18.3: Correlation measure of pentapartitioned neutrosophic pythagorean sets - p. 339 35. 18.4: Interval-valued pentapartitioned neutrosophic Pythagorean set - p. 346 36. 18.5: Application - p. 351 37. 18.6: Conclusion - p. 356 38. References - p. 356 39. Chapter 19 Neutrosophic statistics and the medical data: a systematic review - p. 357 40. Chapter 20 Comparative behavior of systolic blood pressure under indeterminacy - p. 373 41. Chapter 21 Effects of wild medicinal edible plant to prevent COVID-19: a survey by neutrosophic statistics in tribal people of Kokrajhar, Assam, India - p. 393 42. Chapter 22 Generalized robust-type neutrosophic ratio estimators of pharmaceutical daily stock prices - p. 417 43. Chapter 23 Multi-attribute decision-making problem in medical diagnosis using neutrosophic probabilistic distance measures - p. 431 44. 23.1: Introduction - p. 431 45. 23.2: Preliminaries - p. 434 46. 23.3: Probabilistic distance measure on single-valued neutrosophic sets - p. 436 47. 23.4: Methodologies in neutrosophic multi-attribute decision-making problems - p. 439 48. 23.5: Numerical example: application of single-valued neutrosophic probabilistic distance measures in medical diagnosis - p. 443 49. 23.6: Results and discussion - p. 449 50. 23.7: Advantages and limitations - p. 450 51. 23.8: Conclusions and future work - p. 451 52. References - p. 451 53. Index - p. 455
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. Chapter 1 Introduction and advances to neutrosophic probability and statistics and plithogenic probability and statistics and their applications in bioinformatics and other fields (review chapter) - p. 1 2. Chapter 2 Neutrosophic Weibull model with applications to survival studies - p. 25 3. Chapter 3 Using the four-valued Rasch model in the preparation of neutrosophic form of risk maps for the spread of COVID-19 in Turkey - p. 43 4. Chapter 4 Neutrosophic linear models for bioinformatics - p. 71 5. Chapter 5 Modeling epidemics based on quantum decision-making model by the qutrit states and employing neutrosophic form of percolation analysis - p. 87 6. Chapter 6 Use of neutrosophic statistics to analyze the measured data of diabetes - p. 119 7. Chapter 7 Analysis of changes in blood pressure of women during pregnancy through neutrosophic statistics - p. 137 8. Chapter 8 Neutrosophic statistical analysis of changes in blood pressure, pulse rate and temperature of human body due to COVID-19 - p. 153 9. Chapter 9 A study of human respiration rate through neutrosophic statistics - p. 173 10. Chapter 10 Neutrosophic diagnosis of rural women vulnerable to anemia - p. 189 11. Chapter 11 Various sampling inspection plans for cancer patients using gamma distribution under indeterminacy - p. 203 12. 11.1: Introduction - p. 203 13. 11.2: Gamma distribution for indeterminacy - p. 205 14. 11.3: Single sampling plan under indeterminacy - p. 206 15. 11.4: Repetitive sampling plan under indeterminacy - p. 217 16. 11.5: Conclusions - p. 233 17. References - p. 234 18. Chapter 12 Improved correlation coefficients in neutrosophic statistics for COVID patients using pentapartitioned neutrosophic sets - p. 237 19. Chapter 13 Post-pandemic impact on the occupational shift of rural populace—a case study using neutrosophic comparison t-test - p. 259 20. Chapter 14 A novel approach of neutrosophic continuous probability distributions using AH-isometry with applications in medicine - p. 267 21. Chapter 15 Monitoring COVID-19 cases under uncertainty - p. 287 22. Chapter 16 Neutrosophic statistical analysis of hybrid work model of medical professionals - p. 305 23. Chapter 17 Neutrosophic regression cum ratio estimators for the population mean: an application in medical science - p. 313 24. 17.1: Introduction - p. 313 25. 17.2: Proposed neutrosophic estimators - p. 315 26. 17.3: Empirical study - p. 325 27. 17.4: Results and discussion - p. 327 28. 17.5: Conclusion - p. 330 29. Acknowledgments - p. 330 30. References - p. 331 31. Chapter 18 Effectiveness on impact of COVID vaccines on correlation coefficients of pentapartitioned neutrosophic pythagorean statistics - p. 335 32. 18.1: Introduction and motivation - p. 335 33. 18.2: Background, definitions, and notations - p. 337 34. 18.3: Correlation measure of pentapartitioned neutrosophic pythagorean sets - p. 339 35. 18.4: Interval-valued pentapartitioned neutrosophic Pythagorean set - p. 346 36. 18.5: Application - p. 351 37. 18.6: Conclusion - p. 356 38. References - p. 356 39. Chapter 19 Neutrosophic statistics and the medical data: a systematic review - p. 357 40. Chapter 20 Comparative behavior of systolic blood pressure under indeterminacy - p. 373 41. Chapter 21 Effects of wild medicinal edible plant to prevent COVID-19: a survey by neutrosophic statistics in tribal people of Kokrajhar, Assam, India - p. 393 42. Chapter 22 Generalized robust-type neutrosophic ratio estimators of pharmaceutical daily stock prices - p. 417 43. Chapter 23 Multi-attribute decision-making problem in medical diagnosis using neutrosophic probabilistic distance measures - p. 431 44. 23.1: Introduction - p. 431 45. 23.2: Preliminaries - p. 434 46. 23.3: Probabilistic distance measure on single-valued neutrosophic sets - p. 436 47. 23.4: Methodologies in neutrosophic multi-attribute decision-making problems - p. 439 48. 23.5: Numerical example: application of single-valued neutrosophic probabilistic distance measures in medical diagnosis - p. 443 49. 23.6: Results and discussion - p. 449 50. 23.7: Advantages and limitations - p. 450 51. 23.8: Conclusions and future work - p. 451 52. References - p. 451 53. Index - p. 455
