Omar Khadeer Hussain

Support vector regression with chaos-based firefly algorithm for stock market price forecasting

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box-Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).

Towards Multi-criteria Cloud Service Selection

Cloud computing despite being in an early stage of adoption is becoming a popular choice for businesses to replace in-house IT infrastructure due to its technological advantages such as elastic computing and cost benefits resulting from pay-as-you-go pricing and economy of scale. These factors have led to a rapid increase in both the number of cloud vendors and services on offer. Given that cloud services could be characterized using multiple criteria (cost, pricing policy, performance etc.) it is important to have a methodology for selecting cloud services based on multiple criteria. Additionally, the end user requirements might map to different criteria of the cloud services. This diversity in services and the number of available options have complicated the process of service and vendor selection for prospective cloud users and there is a need for a comprehensive methodology for cloud service selection. The existing research literature in cloud service selection is mostly concerned with comparison between similar services based on cost or performance benchmarks. In this paper we discuss and formalize the issue of cloud service selection in general and propose a multi-criteria cloud service selection methodology.

Review: Cognitive radio network security: A survey

Recent advancements in wireless communication are creating a spectrum shortage problem on a daily basis. Recently, Cognitive Radio (CR), a novel technology, has attempted to minimize this problem by dynamically using the free spectrum in wireless communications and mobile computing. Cognitive radio networks (CRNs) can be formed using cognitive radios by extending the radio link features to network layer functions. The objective of CRN architecture is to improve the whole network operation to fulfil the users demands anytime and anywhere, through accessing CRNs in a more efficient way, rather than by just linking spectral efficiency. CRNs are more flexible and exposed to wireless networks compared with other traditional radio networks. Hence, there are many security threats to CRNs, more so than other traditional radio environments. The unique characteristics of CRNs make security more challenging. Several crucial issues have not yet been investigated in the area of security for CRNs. A typical public key infrastructure (PKI) scheme which achieves secure routing and other purposes in typical ad hoc networks is not enough to guarantee the security of CRNs under limited communication and computation resources. However, there has been increasing research attention on security threats caused specifically by CR techniques and special characteristics of CR in CRNs. Therefore, in this research, a survey of CRNs and their architectures and security issues has been carried out in a broad way in this paper.

Iaas Cloud Selection using MCDM Methods

The popularity of cloud computing and IaaS has spawned numerous cloud service providers which offer various cloud services, including IaaS, to cloud users. These services vary considerably in terms of their performance and cost and the selection of a suitable cloud service becomes a complex decision making issue for a cloud service user. Furthermore, the cloud services have several attributes all of which are the criteria that have to be taken into account when making a service selection decision. In the presence of these multiple criteria, a compromise has to be made because in most real-world situations, no single service exceeds all other services in all criteria but one service may be better in terms of some of the criteria while other services may outperform it if judged on the basis of the remaining criteria. Multi-criteria decision-making is a sub-field in operations research that deals with the techniques to solve such multi-criteria problems. There are several methods of multicriteria decision-making. In this paper, we use key multi-criteria decision-making methods for IaaS cloud service selection in a case study which contains five basic performance measurements of thirteen cloud services by a third party monitoring service. We demonstrate the use of these multi-criteria methods for cloud service selection and compare the results obtained by using each method to find out how the choice of a particular MCDM method affects the outcome of the decision-making process for IaaS cloud service selection.

Parallel Cloud Service Selection and Ranking Based on QoS History

The growing number of cloud services has made service selection a challenging decision-making problem by offering wide ranging choices for cloud service consumers. This necessitates the use of formal decision making methodologies to assist a decision maker in selecting the service that best fulfills the user’s requirements. In this paper, we present a cloud service selection methodology that utilizes quality of service history of cloud services over different time periods and performs parallel multi-criteria decision analysis to rank all cloud services in each time period in accordance with user preferences before aggregating the results to determine the overall rank of all the available options for cloud service selection. This methodology assists the cloud service user to select the best possible available service according to the requirements. The multi-criteria decision making processes used for each time period are independent of the other time periods and are executed in parallel.

A Framework for User Feedback Based Cloud Service Monitoring

The increasing popularity of the cloud computing paradigm and the emerging concept of federated cloud computing have motivated research efforts towards intelligent cloud service selection aimed at developing techniques for enabling the cloud users to gain maximum benefit from cloud computing by selecting services which provide optimal performance at lowest possible cost. Given the intricate and heterogeneous nature of current clouds, the cloud service selection process is, in effect, a multi criteria optimization or decision-making problem. The possible criteria for this process are related to both functional and nonfunctional attributes of cloud services. In this context, the two major issues are: (1) choice of a criteria-set and (2) mechanisms for the assessment of cloud services against each criterion for thorough continuous cloud service monitoring. In this paper, we focus on the issue of cloud service monitoring wherein the existing monitoring and assessment mechanisms are entirely dependent on various benchmark tests which, however, are unable to accurately determine or reliably predict the performance of actual cloud applications under a real workload. We discuss the recent research aimed at achieving this objective and propose a novel user-feedback-based approach which can monitor cloud performance more reliably and accurately as compared with the existing mechanisms.

A methodology to quantify failure for risk-based decision support system in digital business ecosystems

In digital business ecosystem architecture it is rational for the trusting agent to analyse the possible risk according to its demand before interacting with a probable trusted agent. Doing so would assist the trusting agent in its decision process and would also give the trusting agent a hint of the direction in which the interaction might head. The possible risk in an interaction is a combination of the probability of failure and the possible consequences of failure of an interaction. In this paper, we propose a methodology by which the trusting agent determines the probability of failure in interacting with a probable trusted agent. The determined probability of failure by the trusting agent is according to the specific demand of its future interaction with the probable trusted agent.

A Knapsack problem approach for achieving efficient energy consumption in smart grid for endusers' life style

In order to achieve an efficient energy consumption level in the residential sector of a smart grid, the end-users are equipped with various smart home energy controller technologies. The devices are provided to inform the consumers about their consumption pattern by showing or sending different kinds of consumptional information to them. This kind of information is provided to assist them in making decisions about altering their consumption behaviour or to urge them to modify their life style during peak hours. We propose that the energy home controllers should offer preferred and optimal scenarios to support end-users when making a decision about their consumption. Effective scenarios should emerge from consumers life style and preferences. In this paper, we will apply AHP methodology to quantify the consumers preferences for using appliances during peak periods when the price has increased, and use the Knapsack problem approach to achieve the optimal solution for managing the appliances. With this approach, not only will the cost of electricity not escalate during peak hours, but also user preferences, satisfaction and minimum change to current life style will be considered.

Special issue on cyber physical systems

This special issue is devoted to recent research findings in the field of Cyber Physical Systems. The special issue follows “The IEEE 26th International Conference on Advanced Information Networking and Applications (AINA-2012)”, held at the Fukuoka Institute of Technology, Fukuoka, Japan, March 26–29, 2012.

Identifying prosumer's energy sharing behaviours for forming optimal prosumer-communities

Smart Grid (SG) achieves bidirectional energy and information flow between the energy user and the utility grid, allowing energy users not only to consume energy, but also to generate the energy and share with the utility grid or with other energy consumers. This type of energy user who consumes energy and who also can generate the energy is called the “prosumer”. The sustainability of the SG energy sharing process heavily depends on its participating prosumers, making prosumer participation and management schemes crucial within the energy sharing approaches. However in literature, there is very little attention on prosumer management schemes. The contribution of this article is twofold. First, this article introduces a novel concept of participating and managing the prosumers in the SG energy sharing process in the form of autonomous, intelligent goal-oriented virtual communities. Here, the community of prosumers can collectively increase the amount of power to be auctioned or bought offering higher bargaining power, thereby settling for a higher price per kilowatt in long-term. According to the literature, this research is the first of this type introducing a community approach for prosumer management. The initial step to build an effective prosumer-community is the identification of those prosumers who would be suitable to make efficient prosumer communities. This leads the necessity of identifying parameters that influence the energy sharing behaviours of prosumers. The second contribution of this article is that, this comprehensively analyzes the different parameters influencing the prosumers energy sharing behaviours and thus presents multi-agent architecture for optimal prosumer-community formation.