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Farookh Khadeer Hussain

Bio: Farookh Khadeer Hussain is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Cloud computing & Ontology (information science). The author has an hindex of 31, co-authored 347 publications receiving 4496 citations. Previous affiliations of Farookh Khadeer Hussain include Information Technology University & Curtin University.


Papers
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Journal ArticleDOI
01 Feb 2013
TL;DR: A forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price and performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).
Abstract: 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).

391 citations

Journal ArticleDOI
TL;DR: A survey of state-of-the-art Cloud service selection approaches, which are analyzed from the following five perspectives: decision-making techniques; data representation models; parameters and characteristics of Cloud services; contexts, purposes.

248 citations

Book
16 Jun 2006
TL;DR: This book discusses Trust and Security in Service-Oriented Envirnoments, the Fuzzy and Dynamic Nature of Trust, and the Reputation Model, which helps clarify the relationship between trust and reputation.
Abstract: Preface. Author Introduction. Acknowledgement. Chapter 1 Trust and Security in Service-Oriented Envirnoments. Chapter 2 Trust Concepts and Trust Model. Chapter 3 Trustworthiness. Chapter 4 Trust Ontology for Service-Oriented Environment. Chapter 5 The Fuzzy and Dynamic Nature of Trust. Chapter 6 Trustworthiness Measure with CCCI. Chapter 7 Trustworthiness systems. Chapter 8 Reputation Concepts and the Reputation Model. Chapter 9 Reputation Ontology. Chapter 10 Reputation Calculation Methodologies. Chapter 11 Reputation Systems. Chapter 12 Trust and Reputation Prediction. Chapter 13 Trust and Reputation Modelling. Chapter 14 The Vision of Trust and Reputation Technology. References. Index.

241 citations

Journal ArticleDOI
TL;DR: A Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM is proposed.
Abstract: Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming, and a large amount of memory is involved in the migration process. To overcome these drawbacks, we propose a Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM. We also design an optimization model to migrate these extra tasks to the new host VMs by applying Particle Swarm Optimization (PSO). To evaluate the proposed method, we extend the cloud simulator (Cloudsim) package and use PSO as its task scheduling model. The simulation results show that the proposed TBSLB-PSO method significantly reduces the time taken for the load balancing process compared to traditional load balancing approaches. Furthermore, in our proposed approach the overloaded VMs will not be paused during the migration process, and there is no need to use the VM pre-copy process. Therefore, the TBSLB-PSO method will eliminate VM downtime and the risk of losing the last activity performed by a customer, and will increase the Quality of Service experienced by cloud customers.

228 citations

Proceedings ArticleDOI
30 Jun 2011
TL;DR: This paper discusses and formalizes the issue of cloud service selection in general and proposes a multi-criteria cloudService selection methodology.
Abstract: 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.

177 citations


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Book
01 Jan 1995
TL;DR: In this article, Nonaka and Takeuchi argue that Japanese firms are successful precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies, and they reveal how Japanese companies translate tacit to explicit knowledge.
Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

7,448 citations

Book ChapterDOI
01 Jan 1994
TL;DR: In this Chapter, a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view is imagined.
Abstract: In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

1,329 citations