In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

Interval methods for verified integration of initial value problems (IVPs) for ODEs have been used for more than 40 years. For many classes of IVPs, these methods have the ability to compute guaranteed error bounds for the flow of an ODE, where traditional methods provide only approximations to a solution. Overestimation, however, is a potential drawback of verified methods. For some problems, the computed error bounds become overly pessimistic, or integration even breaks down. The dependency problem and the wrapping effect are particular sources of overestimations in interval computations. Berz (see [1]) and his co-workers have developed Taylor model methods, which extend interval arithmetic with symbolic computations. The latter is an ef

This paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar

Includes search unemployment concept ... types, graduate unemployment a model introduction to the researcher tackled the problem of unemployment being dangerous to the community, it's also growing in size year after year is a waste of a clear human capabilities, also addressed the importance of the research being a touch on the problem of unemployment and its concept and try to find solutions to them , and then came the goals set by the search researcher identifies unemployment and their causes and consequences and to provide a true picture of the situation of unemployed graduates and disclosure about how they treat their graduates for jobs provide him with a decent life problem. And adopted a researcher on the use of a questionnaire add

الخلاصة

اهتم الفكر السياسي في القرنين الاخيرين بدراسة الطبقات على نحو غير مسبوق, واصبح موضوع التحليل الطبقي المعني بالطبقات من حيث تعريفها, وتحديد موقعها في السلم الاجتماعي, فضلاً عن نوعية العلاقة بين شرائحها وفئاتها المختلفة من حيث الصراع والتناغم, المادة الرئيسة والموضوع الاكثر اهمية في دراسات الفكر السياسي والاجتماعي.ومن بين الطبقات, احتلت الطبقة الوسطى مكا

This paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar

The constructivist learning model is one of the models of constructivist theory in learning, as it generally emphasizes the active role of the learner during learning, in addition to that the intellectual and actual participation in the various activities to help students gain the skills of analyzing artistic works. The current research aims to know the effectiveness of the constructivist learning model in the acquisition of the skills of the Institute of Fine Arts for the skills of (technical work analysis). To achieve the goal, the researcher formulated the following hypothesis: There are no statistically significant differences between the average scores of the experimental group students in the skill test for analyzing artworks befor

Gas and Downhole Water Sink, Gravity Drainage, GDWS-AGD, Enhance the Recovery of Oil

The use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree mode