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.

In this study, a total of 209 individuals of leeches were collected from Al-Hindyia River / Babil Province. 116 individuals were identified as Erpobdella octaculata (Linnaeus, 1758), 50 individuals as Erpobdella punctata (Leidy,1870) and 43 individuals as Hemiclepsis marginata (Müller, 1774). Four samples were collected monthly during a period from February to June 2018. Some physical and chemical water properties were also examined, including air and water temperature, potential of hydrogen pH, Electrical Conductivity EC, Total Dissolved Solid TDS, Dissolved Oxygen DO, and the Biological Oxygen Demand BOD₅. Air and water temperature were ranged 19.5-29, & 14.6-23.2 °C respectively. The values of pH ranged 6.2-7.6. EC ranged 1104-1581 μs/cm². The TDS recorded 669-767 mg/l, while the DO reached 1.3-8.5 mg / l, the BOD₅ ranged 3.5-5.7 mg/l.

Character recognition has been very popular and interested area for researches, and it continues to be a challenging and impressive research topic due to its diverse applicable environment. The optical character recognition has been introduced as a fast and accurate method to convert both existing text images as well as large archives of existing paper documents to editable digital text format.However, existing optical character recognition algorithms suffer from flawed tradeoffs between accuracy and speed, making them less effective and impractical for large and complex documents. This paper describes a suggested method for Assyrian optical character recognition using modified back propagation artificial neural network based on moments. The experimental results show that the proposed method achieves higher recognition accuracy rate in compared with the standard algorithm.

We have proposed in this research the design of a new protocol named VMSI coherence protocol in the cache in order to solve the problem of coherence which is the incompatibility of data between caches that appeared in recent multiprocessors system through the operations of reading and writing. The main purpose of this protocol is to increase processor efficiency by reducing traffic between processor and memory that have been achieved through the removal of the write back to the main memory in the case of reading or writing of shared caches because it depends on existing directory inside that cache which contains all the data that represents a subset of main memory.

The cache coherence is the most important issue that rapidly affected the performance of a multicore processor as a result of increasing the number of cores on chip multiprocessors and the shared memory program that will be run on these processors. "Snoopy protocols" and "directory based protocols" are two types of protocols that are used to achieve coherence between caches. The main objective of these Protocols is to achieve consistency and validation of the data value in the caches of a multi core processor so that any reading of a memory address via any caches will returns the latest data written to that address.In this paper, a new protocol has been designed to solve a problem of a cache coherence that combines the two schemes of coherency: snooping and directory depending on the states of MESI protocol. The MESI protocol is a version of the snooping cache protocol which based on four (Modified, Exclusive, Shared, Invalid) states that a block in the cache memory can have. The proposed protocol has the same states of MESI protocol but the difference is in laying the directory inside a shared cache instead of main memory to make the processor more efficient by reducing the gap between fast CPU and slow main memory.