Hojjatollah Farahani – författare

Introduction to Intricate Artificial Psychology with Python unlocks the mysteries of Intricate Artificial Psychology (iAp). This comprehensive guide takes readers through advanced cognitive frameworks and the complex landscape of artificial psychology using Python. Starting with an introduction to iAp, the book explores degrees of prediction and applies Fuzzy Cognitive Maps (IAP). Special focus is given to detecting implicit bias through a combination of Fuzzy Cognitive Maps and SHAP values, offering a unique perspective on artificial intelligence and psychological phenomena. The book covers forecasting in iAp, complex network analysis, and psychological graph analysis (Pga).It delves into the intersection of deep learning and neuroimaging, as well as machine learning techniques in neuroimaging. It includes practical case studies, allowing readers to apply cutting-edge techniques to real-world psychological scenarios.Examines how to utilize and analyze predictive models and psychological graphsIllustrates how to apply machine learning and deep learning techniques in neuroimagingIncludes specific code examples in Python

Introduction to Intricate Artificial Psychology with Python unlocks the mysteries of Intricate Artificial Psychology (iAp). This comprehensive guide takes readers through advanced cognitive frameworks and the complex landscape of artificial psychology using Python. Starting with an introduction to iAp, the book explores degrees of prediction and applies Fuzzy Cognitive Maps (IAP). Special focus is given to detecting implicit bias through a combination of Fuzzy Cognitive Maps and SHAP values, offering a unique perspective on artificial intelligence and psychological phenomena. The book covers forecasting in iAp, complex network analysis, and psychological graph analysis (Pga).It delves into the intersection of deep learning and neuroimaging, as well as machine learning techniques in neuroimaging. It includes practical case studies, allowing readers to apply cutting-edge techniques to real-world psychological scenarios. - Examines how to utilize and analyze predictive models and psychological graphs- Illustrates how to apply machine learning and deep learning techniques in neuroimaging- Includes specific code examples in Python

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explainthe set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificialpsychology. This book implements models using R software.

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explainthe set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificialpsychology. This book implements models using R software.

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explainthe set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificialpsychology. This book implements models using R software.

This book is an innovative resource designed to bridge the gap between traditional psychological research methods and contemporary data science techniques. This book provides a comprehensive introduction to using Python for analyzing psychological data, enabling researchers, educators, and students to harness the analytical power of data science within their work. The volume is structured into four parts, encompassing programming skills, data preparation, advanced data processing, and the interpretation of results, each reinforced with practical examples and case studies.The content starts with the basics of Python programming, tailored specifically for psychological research applications. It then progresses to the sophisticated analysis of psychological data using statistical models, machine learning, and artificial intelligence, with a strong focus on Python's capabilities in these areas. This includes detailed discussions on Confirmatory Factor Analysis, machine learning algorithms like SVMs, and innovative techniques such as metaheuristics and simulations.This book is particularly timely as psychological research becomes increasingly data-driven, necessitating a deeper understanding of complex datasets and the development of more sophisticated analytical tools. "Data Science in Psychology" addresses this need by providing not only the technical skills required but also a deep understanding of how these techniques can be applied specifically to psychological research. The primary audience, including psychology researchers, academics, and advanced students, will find this book invaluable for integrating data science into their daily toolkit, thus leveling up their research capabilities and broadening their methodological approaches in an era where interdisciplinary skills are an added value.