. 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 a

planning is among the most significant in the field of robotics research.  As it is linked to finding a safe and efficient route in a cluttered environment for wheeled mobile robots and is considered a significant prerequisite for any such mobile robot project to be a success. This paper proposes the optimal path planning of the wheeled mobile robot with collision avoidance by using an algorithm called grey wolf optimization (GWO) as a method for finding the shortest and safe. The research goals in this study for identify the best path while taking into account the effect of the number of obstacles and design parameters on performance for the algorithm to find the best path. The simulations are run in the MATLAB environment to test the

An optical spectroscopic study is reported in this article to study the correlation between the supermassive black hole (SMBH) and the star formation rate (SFR) for a sample of Seyfert galaxies type (I and II). The study focused on 45 galaxy of Seyfert 1, in addition to 45 galaxy of Seyfert 2, where these samples have been selected form different survey of Salon Digital Sky Survey (SDSS). The redshift (z) of these objects were between (0.02 – 0.26). The results of Seyfert 1 galaxies shows that there good correlation between the SMBH and the SFR depending on statistical analysis parameter named Spearman’s Rank Correlation in a factor of (ρ=0.609), as well as the Seyfert 2 galaxies results show a good correlation between the SMBH

An optical spectroscopic study is reported in this article to study the correlation between the supermassive black hole (SMBH) and the star formation rate (SFR) for a sample of Seyfert galaxies type (I and II). The study focused on 45 galaxy of Seyfert 1, in addition to 45 galaxy of Seyfert 2, where these samples have been selected form different survey of Salon Digital Sky Survey (SDSS). The redshift (z) of these objects were between (0.02 – 0.26). The results of Seyfert 1 galaxies shows that there good correlation between the SMBH and the SFR depending on statistical analysis parameter named Spearman’s Rank Correlation in a factor of (ρ=0.609), as well as the Seyfert 2 galaxies results show a good correlation between the SMBH and

Many industrial systems involve multiple criteria and objectives, and they are very complex problems in computational science, such as task scheduling. We propose bi-criteria and bi-objective scheduling problems, which are solved by two nature-inspired evolutionary algorithms, such as Simulated Annealing (SA) and Bee Algorithm (BA). This problem is characterized by scheduling a batch of tasks on multiple machines, and it is fundamental because the solution should focus on the simultaneous optimization of two conflicting objectives: the makespan minimization and the total tardiness minimization. This problem is NP-Hard, and therefore, two evolutionary methods were used to search for solutions intelligently in this huge, very complex

Future wireless networks will require advance physical-layer techniques to meet the requirements of Internet of Everything (IoE) applications and massive communication systems. To this end, a massive MIMO (m-MIMO) system is to date considered one of the key technologies for future wireless networks. This is due to the capability of m-MIMO to bring a significant improvement in the spectral efficiency and energy efficiency. However, designing an efficient downlink (DL) training sequence for fast channel state information (CSI) estimation, i.e., with limited coherence time, in a frequency division duplex (FDD) m-MIMO system when users exhibit different correlation patterns, i.e., span distinct channel covariance matrices, is to date ve