Zalinda Othman

Review: Cloud computing service composition: A systematic literature review

The increasing tendency of network service users to use cloud computing encourages web service vendors to supply services that have different functional and nonfunctional (quality of service) features and provide them in a service pool. Based on supply and demand rules and because of the exuberant growth of the services that are offered, cloud service brokers face tough competition against each other in providing quality of service enhancements. Such competition leads to a difficult and complicated process to provide simple service selection and composition in supplying composite services in the cloud, which should be considered an NP-hard problem. How to select appropriate services from the service pool, overcome composition restrictions, determine the importance of different quality of service parameters, focus on the dynamic characteristics of the problem, and address rapid changes in the properties of the services and network appear to be among the most important issues that must be investigated and addressed. In this paper, utilizing a systematic literature review, important questions that can be raised about the research performed in addressing the above-mentioned problem have been extracted and put forth. Then, by dividing the research into four main groups based on the problem-solving approaches and identifying the investigated quality of service parameters, intended objectives, and developing environments, beneficial results and statistics are obtained that can contribute to future research.

Gravitational search algorithm with heuristic search for clustering problems

In this paper, we present an efficient algorithm for cluster analysis, which is based on gravitational search and a heuristic search algorithm. In the proposed algorithm, called GSA-HS, the gravitational search algorithm is used to find a near optimal solution for clustering problem, and then at the next step a heuristic search algorithm is applied to improve the initial solution by searching around it. Four benchmark datasets are used to evaluate and to compare the performance of the presented algorithm with two other famous clustering algorithms, i.e. K-means and particle swarm optimization algorithm. The results show that the proposed algorithm can find high quality clusters in all the tested datasets.

A hybrid imperialist competitive-gravitational attraction search algorithm to optimize cloud service composition

Service composition is among the most important challenges that cloud providers have ever faced. Optimization of QoS attributes when composing simple atomic services to obtain a complex service can be considered to be an NP-hard problem, which could be solved properly by using Hybrid optimization algorithms. In this research, the hybridization of an improved Gravitational Attraction Search (as a local search algorithm) with an Imperialist Competitive Algorithm has led us to introduce and apply a new memetic algorithm for gaining optimal or near optimal response time and execution fees simultaneously, for cloud computing service composition. Using a roulette wheel selection algorithm to make well-advised and non-blind decisions to choose the number of countries in each empire that should be selected to apply a local search to has assisted the hybrid algorithm at achieving better solutions. Introducing a new equation to calculate the QoS eligibility of the solutions that were generated based on the normalization of the response time and execution fee has also led us to compute the results fairly and in a scientifically based manner.

Soft computing applications and intelligent systems: Second international multi-conference on artificial intelligence technology, m-cait 2013 shah alam, august 28-29, 2013 Proceedings

The determination of real world coordinate from image coordinate has many applications in computer vision. This paper proposes the algorithm for determination of real world coordinate of a point on a plane from its image coordinate using single calibrated camera based on simple analytic geometry. Experiment has been done using the image of chessboard pattern taken from five different views. The experiment result shows that exact real world coordinate and its approximation lie on the same plane and there are no significant difference between exact real world coordinate and its approximation.

Naïve bayes variants in classification learning

Naïve Bayesian classifier is one of the most effective and efficient classification algorithms. The elegant simplicity and apparent accuracy of naive Bayes (NB) even when the independence assumption is violated, fosters the on-going interest in the model. This paper discusses issues on NB along with its advantages and disadvantages. We also present an overview of NB variants and provide a categorization of those methods based on four dimensions. These include manipulating the set of attributes, allowing interdependencies, employing local learning and adjusting the probabilities by numeric weights. Examples for each category are discussed based on 18 variants reviewed in this paper.

An adaptive guided variable neighborhood search based on honey-bee mating optimization algorithm for the course timetabling problem

A standard honey-bee mating optimization algorithm (HBMO) utilizes the steepest descent local search algorithm as a worker. The steepest descent algorithm has the advantage of being simple to understand, fast and is easy to implement. However, it can easily trapped in a local optimum and subsequently restrict the performance of HBMO. Furthermore, the type of neighborhood structures that are used within the local search algorithm might impact on the performance of algorithm. This work aimed to enhance the performance of HBMO by using an adaptive guided variable neighborhood search (AGVNS) as a worker. The AGVNS algorithm is a variant of variable neighborhood search algorithm that incorporates some problem-specific knowledge and utilizes an adaptive learning mechanism to find the most suitable neighborhood structure during the searching process. In order to evaluate the effectiveness of the proposed algorithm, the Socha course timetabling dataset has been chosen as the tested domain problem. The results demonstrated that the performance of the proposed algorithm is comparable to other approaches in the literature. Indeed, the proposed algorithm obtained the best results as compared to other approaches on some instances. These results indicate the effectiveness of combining HBMO and AGVNS for solving course timetabling problems, hence demonstrated that the AGVNS can enhance the performance of HBMO.

ERP implementation framework for Malaysian private institution of higher learning

Enterprise Resource Planning (ERP) systems are widely used by many multinational companies throughout the world. Recently, many institutions of higher learning have replaced their legacy systems to ERP systems as a means for integration advantages. Investments with this ERP system are representing the largest investment for institutions of higher learning. They invest millions of dollars and the time taken for the implementation sometimes takes two to three years, or even more. Without a solid history of successes and failures, implementers are at a disadvantage in knowing how best to implement ERP systems so that they will provide operational and strategic benefits to their owners. Due to these problems, this research is carried out in order to establish an ERP implementation framework for Malaysian private institutions of higher learning. The framework has four phases; project initiation, project implementation, realization and operation and maintenance. Every phase will be having a combination of critical success factors (CSFs), deliverables and responsibilities and this combination as unique features for the private institution of higher learning in Malaysia environment.

Associative prediction model and clustering for product forecast data

Association rules are adopted to discover the interesting relationship and knowledge in a large dataset. Knowledge may appear in terms of a frequent pattern discovered in a large number of production data. This knowledge can improve or solve production problems to achieve low cost production. To obtain knowledge and quality information, data mining can be applied to the manufacturing industry. In this study, we used one of the association rule approach, i.e. Apriori algorithm to build an associative prediction model for product forecast data. Also, we adopt the simplest method in clustering, k-means algorithm to attain the link between patterns. The real industrial product forecast data for one year duration is used in the experiment. This data consists of 42 products with two important attributes, i.e. time in the week and required quantity. Since the data mining processes need a large amount of data, we simulated these data by using the Monte Carlo technique to obtain another 15 years of simulated forecast data. There are two main experiments for the association rules mining and clustering. As a result, we obtain an associative prediction model and clustering for the forecasting data. The extracted model provides the prediction knowledge about the range of production in a certain period.

Using rough set theory for mining the level of hearing loss diagnosis knowledge

This paper focused on the development of diagnosis knowledge model of the level of hearing loss in the audiology clinic patients using rough set theory. A knowledge model contains a set of knowledge via rules that are obtained from mining certain amount of data. These data consist of valuable knowledge that impossible for the audiologist or audio therapist to extract without powerful mining techniques or tools. These rules help doctors in decision making such as setting up new strategy to improve the efficiency of the operation. In this work, a data mining technique, rough set theory was used for the knowledge modelling. It was used based on its capability of handling uncertain data that often occurs in real world problems. The results from the modelling produced a classifier called rough classifier. The classifier was used to classify the level of hearing loss. A total of 500 data obtained from the audiology clinic. The data consisted of 24 attributes from four categories namely demography, antenatal, neonatal and medical categories. These attributes were used as an input and one attribute called diagnostic category as an output. In order to facilitate the modelling process requirement, these attributes have been gone a pre-process stage. The best model has been obtained from 10 experiments using 10 sets of different training and test data. The experiment showed promising results with 76% accuracy. The developed knowledge model has a great potential to be embedded in the development of the medical decision support system.

Data mining in production planning and scheduling: A review

The paper reviews about the data mining tasks and methods, and its application in production planning and scheduling. Data mining will be reviewed in four classifications of data mining systems according to the kinds of databases mined, knowledge to discover, techniques utilized and the applications adapted. This paper also reviews in production planning and scheduling that focused in time frame range either short- to mid-range or long-range planning. In production planning, there are a lot of planning such as process planning, strategic capacity planning, aggregate planning, master scheduling, material requirements planning and order scheduling. From these activities different problems are arise because of the different time, product and environment of production.