The amount of data in our world has been rapidly keep growing from time to time. In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.
Automatic Modulation Recognition (AMR) has a significant impact in the military as well as civil applications. Recognizing the modulation of the received signal has been considered as an intermediate step between the detection and demodulation of the signal. Which is why, in many military and communication systems, the AMR is considered as part of the system. Presently, due to increasing digital modulations in military and civil applications. Digital modulation recognition is especially important. Usually for the AMR a small number of the received signal features are obtained and utilized. The choice of the suitable feature plays an important part in the increase of AMR efficiency. The presented paper indicates hybrid intelligent system for the recognitions of digital signal types, consisting of 3 major modules: classifier module, feature extraction module and J48 Classifier that was used for the first time in our research in the field of classification of modulated signals and optimization module by Chicken Swarm Optimization (CSO). To get better results of the system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong relevant features Chicken Swarm Optimization. The results of simulation confirm the high accuracy of recognition that is related to the suggested system even at low SNR.
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