Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models - Chapter 7 - dataset -Cognitive learning and reasoning models applied to biomedical engineering

Published: 6 June 2022| Version 1 | DOI: 10.17632/gk9d5dffjz.1
Contributor:
Jorge Garza-Ulloa

Description

“Pre-studies and pre-analysis of different Biomedical Engineering problems that need to be develop with specialized research projects applying Cognitive Learning and Reasoning (CL&R) algorithm, that can be integrated to the Proposed General Architecture framework of a Cognitive Computing Agents System (AI-CCAS)” with special emphasis on “Cognitive Learning and its relationship with the neuroscience of reasoning, to create specific AI cognitive models based on special human cognitive functionality factors of the affected patients,” in order to detect, evaluate, and classify different important human factors, such as “Gesture recognition,” “Speech generation,” “Mood behavior,” “Expressed Sentiments,” and others to obtain a real feedback of “nonmotor neurological symptoms,” that until now in general ways are evaluated based on procedures, questions, and answers of patients, caregivers, and others. Please read chapter 7 at Science Direct: https://www.sciencedirect.com/science/article/pii/B9780128207185000052 or buy the book at Elsevier, Amazon, available at bookstores worldwide. Section 7.3.1 Research 7.1 theory for an “inference engine” shows that it is possible to extract text information stored in the “Knowledge Storage AI Database” for “COVID-19 (SARS-COVS-2)” symptoms and to define their categories. Section 7.3.2 Research 7.2 “Attention network” for “NLP applying long short-term memory” to classify text of COVID-19 symptoms (MATLAB_LTSM_Text). Section 7.6.4.2.1 “Abductive reasoning—Causal arguments—Backward-chaining” Section 7.6.4.3.1 Example: “Paradigm case-based”, Section 7.6.4.4.1 Example: “Generative Coherence metric” Section 7.6.5.1 "Abductive reasoning for medical diagnosis, Metaphoric reasoning" Section 7.6.7.1 “Fuzzy System: Mamdani-type inference” , “Fuzzy Inference systems to control cough.” (MATLAB NEURO-FUZZY) Section 7.6.7.2 Example “Fuzzy System: Sugeno-type inference” in MATLAB Problem to resolve Assume again the same problem to resolve from the last example: “liquid medicine for control cough” and analysis to control “cough” (MATLAB NEURO-FUZZY) Section 7.7.1 Research 7.3 “AI model to detect and classify mood change on neurologic diseases, such as for Parkinson’s disease (PD) patients, and other neurologic diseases, by analyzing their cognitive status using their mood descriptions for yesterday and today” (folder MATLAB_COGNITIVE) Section 7.7.2 Research 7.4 “CNN-NLP model to detect and classify deductive reasoning evaluation text on neurologic diseases patients, by analyzing their cognitive status based on their answers for three types of deductive reasoning using text propositional relational arguments” (folder MATLAB_DEDUC_REASON) Section 7.7.3 Research 7.5 “Linguistic Neuro-Fuzzy Modeling to analyze breast cancer tumor.” Obtain a “Linguistic Neuro-Fuzzy model” to detect, analyze, and classify a “breast cancer tumor” as “Benign Cancer” or “Malignant Cancer" (folder MATLAB NEURO-FUZZY)

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Institutions

University of Texas at El Paso

Categories

Human Cognition, Depressive Cognition, Cognition Disorder

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