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

Computational Approaches to Emotion in Artificial Psychology provides readers with a comprehensive introduction to how emotions can be processed by AI systems. It offers theoretical and practical guidance on data preprocessing and emotion analysis techniques, explores diverse real-world applications, and bridges the gap between AI and psychology.Beginning with an introduction to the emerging field of artificial psychology, it explores the study, understanding, and recognition of emotions in various bodily signals, including facial expressions, voice, heart rate, and neural mechanisms. The book delves into data preprocessing for embodied emotion analysis, encompassing multiple data modalities like text, audio, visual, and gaze data, with a focus on Python basics for emotional AI. Additionally, it discusses EEG-based emotion decoding, emotional insights from medical imaging, affective image analysis, text-based emotion recognition, multimodal data integration, unsupervised learning for embodied emotion discovery, reinforcement learning, emotion elicitation, and predicting personality and emotional abilities using machine learning. The book concludes by examining the close relationship between cognition and emotion from the perspective of the universal structure of language and describing the use of deep fuzzy cognitive maps in diagnosing coronary artery disease.By promoting research and innovation through case studies and experiments, it addresses the current lack of comprehensive resources in this interdisciplinary field, making it an essential reference for researchers, practitioners, students, and professionals seeking to navigate the intersection of AI and emotions.

Computational Approaches to Emotion in Artificial Psychology provides readers with a comprehensive introduction to how emotions can be processed by AI systems. It offers theoretical and practical guidance on data preprocessing and emotion analysis techniques, explores diverse real-world applications, and bridges the gap between AI and psychology.Beginning with an introduction to the emerging field of artificial psychology, it explores the study, understanding, and recognition of emotions in various bodily signals, including facial expressions, voice, heart rate, and neural mechanisms. The book delves into data preprocessing for embodied emotion analysis, encompassing multiple data modalities like text, audio, visual, and gaze data, with a focus on Python basics for emotional AI. Additionally, it discusses EEG-based emotion decoding, emotional insights from medical imaging, affective image analysis, text-based emotion recognition, multimodal data integration, unsupervised learning for embodied emotion discovery, reinforcement learning, emotion elicitation, and predicting personality and emotional abilities using machine learning. The book concludes by examining the close relationship between cognition and emotion from the perspective of the universal structure of language and describing the use of deep fuzzy cognitive maps in diagnosing coronary artery disease.By promoting research and innovation through case studies and experiments, it addresses the current lack of comprehensive resources in this interdisciplinary field, making it an essential reference for researchers, practitioners, students, and professionals seeking to navigate the intersection of AI and emotions.

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 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.