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This paper presents the comparison of two metaheuristic approaches: Differential Evolution (DE) and Particle Swarm Optimization (PSO) in the training of feed-forward neural network to predict the daily stock prices. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price could yield significant profit. The feasibility, effectiveness and generic nature of both DE and PSO approaches investigated are exemplarily demonstrated. Comparisons were made between the two approaches in terms of the prediction accuracy and convergence characteristics. The proposed model is based on the study of historical data, technical indicators and the application of Neural Networks trained with DE and PSO algorithms. Results presented in this paper show the potential of both algorithms applications for the decision making in the stock markets, but DE gives better accuracy compared with PSO.

Clinical records contain massive heterogeneity number of data types, generally written in free-note without a linguistic standard. Other forms of medical data include medical images with/without metadata (e.g., CT, MRI, radiology, etc.), audios (e.g., transcriptions, ultrasound), videos (e.g., surgery recording), and structured data (e.g., laboratory test results, age, year, weight, billing, etc.). Consequently, to retrieve the knowledge from these data is not trivial task. Handling the heterogeneity besides largeness and complexity of these data is a challenge. The main purpose of this paper is proposing a framework with two-fold. Firstly, it achieves a semantic-based integration approach, which resolves the heterogeneity issue during the integration process of healthcare data from various data sources. Secondly, it achieves a semantic-based medical retrieval approach with enhanced precision. Our experimental study on medical datasets demonstrates the significant accuracy and speedup of the proposed framework over existing approaches.

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The demand for real-time database is increasing. Indeed, most real-time systems are inherently distributed in nature and need to handle data in a timely fashion. Obtaining data from remote sites may take long time making the temporal data invalid. This results in large number of tardy transactions with their catastrophic effect. Clustering the database sites nodes can help distributed real-time database systems to face the challenges meeting their time requirements. Reducing the large number of network sites into many clusters with smaller number of sites will effectively decrease the response time, resulting in better meeting of time constraints. In this paper, we introduce a clustering algorithm for distributed real-time database that depend on both the communication time cost and the timing properties of data. The results show the effectiveness of the proposed approach via achieving lower communication time, higher database performance and better meeting of timing requirements. Keywords-clustering; database; real-time; distributed systems I. INTRODUCTION Recently, the demand for real-time database is increasing. Many applications such as e-commerce, mobile communication, accounting, information services, medical monitoring, nuclear reactor control, traffic control systems and telecommunications are some examples of application which require real-time data support [1]. A real-time database system (RTDBS) is defined in [2] as a database system that includes all features of traditional database system, while enforcing real-time constraints or deadlines. According to [3], the time constraints can be on the data level in the form of time validation attribute making temporal data whose validity is lost after the elapse of some pre_specified time interval, or on the transaction level in the form of deadline used by the real-time scheduling and concurrency control. Real-time systems are often classified depending on the value of the deadline. There are three types of deadlines, hard, firm or soft, depending on the resulting value of the computation when missing a deadline. If the hard deadline is missed, a large or infinity penalty returns. when a firm deadline is missed, no value returns. while some value from the computation may be still for some time if the soft deadline is lost [1]. A transaction that executes outside of the deadline boundaries has less value or may damage the system, depending on the type of deadline associated with it [4]. Like any information systems, database is the main component of real-time information system. However, most real-time systems are inherently distributed in nature. Such critical systems always require to deal with their data in a timely fashion [5]. Sometimes data which is required at a particular location is not available, and it has to be obtained from remote site. This may take long time which consumes the validation duration of data make them invalid. This leads to large number of tardy transactions (transactions that miss their deadline).