This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and Ethereum Dominance (ETH.D) in The architectural performance designed in this paper has been compared with different inputs and neural network models in terms of statistical evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time. NTRODUCTION Digital currency is a form of electronic money that operates on the internet and possesses most of the attributes of conventional money, except for its physical absence. A subset of digital currency is cryptocurrency, which is encrypted by specific algorithms. These cryptocurrencies often utilize blockchain technology to record transactions [1]. The main distinction between cryptocurrencies and other digital currencies is the level of security of the former.

This paper proposed a new estimation and control strategy to control the satellite attitude. As the attitude control strategy plays an essential role in the different kinds of space missions, scientists try to improve the performance of the satellite attitude system, regardless of the expense. In this study, we proposed an adaptive neuro-fuzzy integrated (ANFIS) satellite attitude estimation and control system. A pulse modulator is used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the ANFIS controller in closed-loop simulation, an ANFIS observer is used to estimate the attitude and angular velocities of the satellite using a magnetometer, sun sensor, and rate gyro data. Besides, a new ANFIS system will be proposed and evaluated that can simultaneously control and estimate the system. The performance of the ANFIS controller is compared with the optimal PID controller in a Monte Carlo simulation using different initial conditions, disturbance, and noise. The simulations are performed to verify the ANFIS controller's ability to decrease settling time and fuel consumption in comparison with the optimal PID controller. Also, examine the ANFIS estimator, and the results demonstrate the high skill of these designated observers. Moreover, we proposed an integrated ANFIS estimator and controller for satellite attitude control and estimation in the presence of noise and uncertainty, which can reduce the computational effort and offer smooth actuator actions.