American Journal of Business and Operations Research

This article is devoted to the issues of regulating tolling operations implemented in the process of receiving raw materials supplied by the customer, their processing and subsequent return of products to customers. In addition, the article investigates the procedure for document flow, obligatory requisites of source documents drawn up at the customer and during processing, as well as the procedure for their compilation, acceptance, and storage.

The article analyzes the economic factors affecting the export and import of goods, gives a statistical assessment of the features of foreign trade and economic situations in Uzbekistan in modern conditions. The analysis and conclusions from the study carried out are important for drawing lessons for the future. Foreign economic activity, in particular the result of the foreign trade policy implemented in the country, will directly affect the growth of the country's economy.

This study investigates the application of time series models, specifically ARIMA (Auto Regressive Integrated Moving Average) and SARIMAX (Seasonal Autoregressive Integrated Moving Average with eXogenous regressors), in the context of climate change. The ARIMA and SARIMAX models are mathematical methods that can be used to forecast future values of a time series related to climate change, taking into account trends and seasonality, as well as incorporating additional information through exogenous variables. The paper also delves into the mathematical foundations of the ARIMA and SARIMAX models, including the various operators used to eliminate trends, the use of lag polynomials to represent the autoregressive and moving average components of the model, and the incorporation of exogenous variables in the SARIMAX model. The study aims to provide a better understanding of the use of these models in analyzing and predicting the effects of climate change.

Nowadays, the banking industry is no exception to the general trend of massive data production in all spheres of modern life. In this research, we analyze the categorization of marketing data from banks using a variety of machine learning techniques. The term "banking" refers to the supply of services by a bank to an individual consumer. The data was first compiled from the UCI Machine Learning repository and the Kaggle website. Phone-based banking marketing statistics are the focus of this data set. Python is utilized as the language of implementation, and the Machine Learning concept is employed for statistical learning and data analysis in this work. An improved prediction is the primary goal of machine learning's model-building phase. In order to classify the results, a supervised Naive Bayes algorithm is used to the data. The primary goal of the modeling effort is to characterize whether or not the consumer has chosen a term deposit. The bank should devote substantial time to returning phone calls from prospective customers. Accuracy, precision, recall, and F1 score were all evaluated as a consequence of this study in the direction of term deposit forecasting.

Cryptocurrency is a technology that uses an encrypted peer-to-peer network to facilitate digital barter. Bitcoin, the first and most popular cryptocurrency, is paving the way as a disruptive technology to long-standing and unchanging financial payment systems. While cryptocurrencies are unlikely to replace traditional fiat currency, they have the potential to alter how Internet-connected global markets interact with one another, removing the restrictions that exist around traditional national currencies and exchange rates. Technology advances at a breakneck pace, and a technology's success is almost entirely determined by the market it tries to improve. Cryptocurrencies have the potential to change digital trade marketplaces by enabling a fee-free trading mechanism. A SWOT analysis of Bitcoin is offered, which highlights some of the recent events and movements that may have an impact on whether Bitcoin contributes to a paradigm change in economics. Cryptocurrency is a relatively new payment option, and users are naturally drawn to it because it offers privacy. To measure the impact of cryptocurrency on the world payment system, we use a Cryptocurrency extra data – Bitcoin. The proposed algorithm uses Random Forest Algorithm for prediction. The RFPA has achieved a 0.073 MSE. The RFPA has achieved the best results as it can handle huge datasets with a lot of dimensionality. It improves the model's accuracy and eliminates the problem of overfitting. When compared to other algorithms, it takes less time to train.