The reorganization of the world and its globalization, a new turn of migration processes led to the appearance of problems that had not previously disturbed into the modern society. Consequently, nowadays there is a need to study the historical past so that we could understand the modern trends. The origin of modern problems, phenomena, processes and, especially, their appearance can be traced using the example of economic and political systems that have existed before. In this regard, it would be especially interesting to trace the specific aspects of modern taxation, why the Eastern and European tax collection systems occurred to be different, what influenced the formation of the mechanism of tax collection in different countries in the past, and, most importantly, how the interaction of the Asian and European taxation systems created the specifics of tax collection in the "middle" countries of Eastern Europe. The presented article is devoted to the analysis of the development of the taxation system of the feudal states of Eastern Europe such as Volga Bulgaria, Ulus Dzhuchi and the Kazan vilayet in the first half of the 16th century. While comparing them with the fiscal systems of the countries of the Muslim East, using the reports of Arab-Persian travelers, information from the Russian sources and information from Khan yarlyks, the authors analyze the diversity in the evolution of the of tax system and the extortion of a huge part of the population of Eastern Europe.

<abstract> <p>Customer churn prediction (CCP) is among the greatest challenges faced in the telecommunication sector. With progress in the fields of machine learning (ML) and artificial intelligence (AI), the possibility of CCP has dramatically increased. Therefore, this study presents an artificial intelligence with Jaya optimization algorithm based churn prediction for data exploration (AIJOA-CPDE) technique for human-computer interaction (HCI) application. The major aim of the AIJOA-CPDE technique is the determination of churned and non-churned customers. In the AIJOA-CPDE technique, an initial stage of feature selection using the JOA named the JOA-FS technique is presented to choose feature subsets. For churn prediction, the AIJOA-CPDE technique employs a bidirectional long short-term memory (BDLSTM) model. Lastly, the chicken swarm optimization (CSO) algorithm is enforced as a hyperparameter optimizer of the BDLSTM model. A detailed experimental validation of the AIJOA-CPDE technique ensured its superior performance over other existing approaches.</p> </abstract>

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