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The purpose of this study is to introduce software technologies and modelsand artificial intelligence algorithms to improve the weaknesses of CBT(Cognitive Behavior Therapy) method in psychotherapy. The presentation methodfor this purpose is the implementation of psychometric experiments in which thehidden human variables are inferred from the answers of tests. In this report,we describe the various models of Item Response Theory and measure the hiddencomponents of ability and complementary parameters of the reality of theindividual's situation. Psychometrics, selecting the appropriate model andestimating its parameters have been introduced and implemented using R languagedeveloped libraries. Due to the high flexibility of the Multi variant Raschmixture Model, machine learning has been applied to this method of datamodeling. BIC and CML were used to determine the number of hidden classes ofthe model and its parameters respectively, to obtain Measurement Invariance.The sensitivity of items to hidden attributes varies between groups (DIF), somethods for detecting it are introduced. This simulation is done based on theVerbal Aggression Dataset. We also analyze and compile a reference model basedon this certificate based on the discovered patterns of software engineering.Other achievements of this study are related to providing a solution to explainthe reengineering problems of the mind, by preparing an identity card for theclients by an ontology. Finally, applying the developed knowledge in the formof system thinking and recommended patterns in software engineering during thetreatment process is pointed out.

Supply of engineering techniques and software design patterns in psychoanalysis and psychometrics sciences