In 2020 and 2021, the cryptocurrency market attracted millions of new traders and investors. Lack of regulation, high liquidity, and modern exchanges significantly lowered the entry threshold for new market participants. In 2021, over 5 million Americans were regularly involved in cryptocurrency trading. At that time, the interest in market indicators and trading strategies remained low, leading to the conclusion that most investors did not use decision-support indicators. The correct and backtested use of technical analysis signals can give the trader a significant advantage over most market participants. This work introduces an algorithmic approach to examining the effectiveness of the signals generated by one of the most popular market indicators, the Relative Strength Index (RSI). A model corresponding to an actual cryptocurrency exchange was used to backtest the strategies. The results show that the RSI as a momentum indicator in the cryptocurrency market involves high risk. Using alternative RSI applications can allow traders to gain an advantage in the cryptocurrency market. Comparing the results with the traditional buy and hold strategy shows the credible potential of the indicated method and the usage of signals generated by the technical analysis indicators.

Today's knowledge and content Web-based technologies call for adaptive and intelligent algorithms for accessing the resources, especially to respond to Semantic Web, as well to performance challenges. This work presents the application of two machine learning techniques in an adaptive decision making framework, namely a fuzzy logic and neural networks, to deploy the adaptive and intelligent dispatching algorithm for resource requesting within a geographically distributed fully replicated Web sites. Our approach and algorithm can be used in content distribution and delivery networks where due to content replication each Web server can respond to the client request. Intelligent redirection mechanism redirects the request from the originating site's server to the “best” content source to deliver the content in the fastest manner. First, we introduce a broker-based system architecture. Next, we develop a new global adaptive intelligent request distribution algorithm. Our algorithm called GARDiB by a means of the fuzzy-neural techniques redirects each incoming request to local service with the least expected response time, which takes into account, the transfer time of the request and the response, as well as the time elapsed on the local service that responds to the request. Our simulations showed the effectiveness of the proposed adaptive distribution.

This paper presents a novel algorithm for distribution of user requests sent to a Web-server cluster driven by a Web switch. Our algorithm called FARD (fuzzy adaptive request distribution) is a client-and-server-aware, dynamic and adaptive dispatching policy. It assigns each incoming request to the server with the least expected response time, estimated for that individual request. To estimate the expected response times FARD uses the fuzzy estimation mechanism. With respect to the requirement of modifiability of the model, FARD uses a neural network provided with innate abilities for learning and adaptation. We implemented a prototype FARD-based Web switch that was used in experiments carried out to compare its performance to well known representative request distribution algorithms. The measurements show that FARD benefits can be significant, especially for heterogeneous Web clusters.