In recent years, cloud computing research, specifically data replication techniques and their applications, has been growing. If the replicas number is raised and put in multiple positions, it will be expensive to maintain the data usability, performance and stability of the application systems. In this paper, two bio- inspired algorithms were proposed to improve both selection and placement of data replicas in the cloud environment. The suggested algorithms for dynamic data replication are multi-objective particle swarm optimization (MO-PSO) and ant colony optimization (MO-ACO). The first suggested algorithm, i.e ., MO-PSO, is employed to obtain the best selected data replica depend on the most frequent one. However, the second suggested algorithm, i.e ., MO-ACO, is employed to obtain the best data replica placement depend on the shortest distance, and the replicas availability. A simulation of the suggested strategy was carried out using CloudSim. Each data center (DC) includes hosts with set of virtual machines (VMs). The data replication order is determined at random from a thousand cloudlets. All replication files are randomly distributed in the proposed architecture. The performance of suggested techniques was evaluated against several approaches including: Adaptive Replica Dynamic Strategy (ARDS), Enhance Fast Spread (EFS), Genetic Algorithm (GA), Replica Selection and Placement (RSP), Popular File Replication First (PFRF), and Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S). The simulation results prove that MOPSO gives improved data replication compared against other algorithms. Additionally, MOACO realizes higher data availability, lower cost, and less bandwidth consumption compared with other algorithms.

Cloud computing is a modern technology for dealing with large-scale data. The Cloud has been used to process the selection and placement of replications on a large scale. Most previous studies concerning replication used mathematical models, and few studies focused on artificial intelligence (AI). The Artificial Bee Colony (ABC) is a member of the family of swarm intelligence based algorithms. It simulates bee direction to the final route and has been proven to be effective for optimization. In this paper, we present the different costs and shortest route sides in the Cloud with regard to replication and its placement between data centers (DCs) through Multi-Objective Optimization (MOO) and evaluate the cost distance by using the knapsack problem. ABC has been used to solve shortest route and lower cost problems to identify the best selection for replication placement, according to the distance or shortest routes and lower costs that the knapsack approach has used to solve these problems. Multi-objective optimization with the artificial bee colony (MOABC) algorithm can be used to achieve highest efficiency and lowest costs in the proposed system. MOABC can find an optimal solution for the best placement of data replicas according to the minimum distance and the number of data transmissions, affording low cost with the knapsack approach and availability of data replication.Low cost and fast access are characteristics that guide the shortest route in the CloudSim implementation as well. The experimental results show that the proposed MOABC is more efficient and effective for the best placement of replications than compared algorithms.

Categorisation of text is significant trend that ultimately appears owing to the internet revolution nowadays resulting in enormous amounts data that depend on various languages. The Arabic language is one of the most commonly used languages all over the world; it is considered the fifth most spoken one. Various challenges occur through processing and classifying of Arabic text since it has more sophisticated techniques than the English language. These challenges are clear owing to the Arabic language's variation in shape, structure and component; besides, there is a lack of adequate studies discussing Arabic text classification. This research seeks to form a general point of view by categorising different techniques of Arabic text classification for helping new researches concerning this domain. Also, it shows some of prior information and innovative designs about Arabic text classification. Besides, it mentions various works that have discussed classifying Arabic text, with regard to data sets, categories and pre-processes steps, classification mechanism and assessment procedure for those techniques. These discussions aim to conclude a comprehensive overview through forming a general framework for all researchers about this domain via examining the defects of the prior studies, and then the possibility of presenting more advanced directions.

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.

Developing an automatic framework for updating/ upgrading a system or a software is a challenging task due to the need of the updated or upgraded system to be easy for all users, satisfying all users' requirements, and decreasing the response time of the system. The main objective of this paper is to propose a framework for automating the requirement analysis steps such as identifying and classifying stakeholders, gathering requirements, and classifying requirements. The proposed automatic framework integrates rough set theory, Mendelow's power-interest model, and online questionnaires for the automation process. Rough set theory based on Mendelow's power-interest model supports identifying and classifying stakeholders step. A prepared online questionnaire supports gathering both stakeholders and requirements. In addition, the prepared online questionnaire attempts to classifying requirements based on importance and urgency attributes. After the data is captured automatically by online questionnaire, the analysis of results can be started straight away by presenting the results in tables, charts and graphs. This research contributes to a better understanding of how the integration of technologies (i.e., rough set theory, Mendelow's model and online questionnaire) to requirements analysis steps will ensure the quality, less effort and delivery time for software development. A Comparison between the proposed automated framework and the traditional manual requirement analysis steps on a case study for updating a faculty website is presented to show the effectiveness of the proposed method.