Dynamic Pricing for Cloud Service Negotiation

Big players in cloud service market like Microsoft, Amazon and Google keeps changing their service price. Increasing cloud service offerings have motivated cloud providers to develop cloud service marketplaces such as AWS, Google Apps and CloudSurfing. However, these marketplaces lack dynamic pricing mechanism. The idea of dynamic pricing in the cloud services market has emerged in recent years and there is a need to design and implement an appropriate pricing model for managing the service demand and capacity. This paper reviews dynamic pricing implementation in cloud services and similar areas. This paper reviews recent concurrent cloud SLA negotiation frameworks. A concurrent cloud SLA negotiation mechanism with adaptive pricing is proposed. The proposed mechanism is designed to address multiplicity, information sharing and complexity issues in current frameworks.

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Protocols for agent-based autonomous negotiations: A review

Autonomous negotiation needs certain protocol, a set of rules that defines the interaction boundaries between negotiating agents. This paper aims to allow readers, particularly agent-based autonomous negotiation designers to understand and differentiate various agent-based negotiation protocols. This paper reviews one-to-one, concurrent one-to-many and many-to-many negotiation protocols that are divided into general, Alternative-offers and auctions-based protocols. In total, 23 protocols are reviewed. Then, this paper discusses some limitations of current negotiation protocols. As a conclusion, there are several addressable issues arises in using the protocols in different domains. The protocols should meet negotiation objectives such as time efficiency, robustness, consistency and information sharing.
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Published in: Computer and Information Sciences (ICCOINS), 2016 3rd International Conference on

Myhealthykids: Intelligent obesity intervention system for primary school children

Sedentary lifestyles and unhealthy diet are the main reasons of childhood obesity. This paper presents MyHealthyKids: Intelligent Web-mobile Children Obesity Intervention System for Primary School to manage and reduce the problems. The main objective of the system is to prevent and to reduce childhood obesity cases that are currently increasing in primary schools in Malaysia. MyHealthyKids consists of three main modules: obesity prediction, children persuasive and recipe suggestion module. The Naïve Bayes is used to predict whether the children are prone to be obese; the persuasive technology is used to encourage the children to participate in physical activities and change their eating habits; and the knowledge-based system is used to suggest a suitable menu for canteen operators in order for them to prepare healthy food for school children. Preliminary test has shown that the system has 73.3% accuracy for prediction and gets good feedback from the children.

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A hybrid approach using Naïve Bayes and Genetic Algorithm for childhood obesity prediction

Naïve Bayes is a data mining technique that has been used by many researchers for predictions in various domains. This paper presents a framework of a hybrid approach using Naïve Bayes for prediction and Genetic Algorithm for parameter optimization. This framework is a solution applied to the childhood obesity prediction problem that has a small ratio of negative samples compared to the positive samples. The Naïve Bayes has shown a weakness in prediction involving a zero value parameter. Therefore, in this paper we propose a solution for this weakness which is using Genetic Algorithm optimization. The study begins with a literature review of the childhood obesity problem and suitable data mining techniques for childhood obesity prediction. As a result of the review, 19 parameters were selected and the Naïve Bayes technique was implemented for childhood obesity prediction. The initial experiment to identify the usability of the proposed approach has indicated a 75% improvement in accuracy.

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Published in: Computer & Information Science (ICCIS), 2012 International Conference on

Hybrid Approaches Using Decision Tree, Naive Bayes, Means and Euclidean Distances for Childhood Obesity Prediction

Even by using the data mining, many weaknesses still existed in childhood obesity prediction and it is still far from achieving perfect prediction. This paper studies previous steps involved in childhood obesity prediction using different data mining techniques and proposed hybrid approaches to improve the accuracy of the prediction. The steps taken in this study were a review of childhood obesity, data collections, data cleaning and preprocessing, implementation of the hybrid approach, and evaluation of the proposed approach. The hybrid approach consists of the classification and regression tree, Naïve Bayes, mean value identification and Euclidean distances classification. The results from the evaluation have shown that the proposed approach has 60% sensitivity for childhood obesity prediction and 95% sensitivity for childhood overweight prediction.

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A framework of a childhood obesity intervention using persuasive web-mobile technology

Childhood obesity is an increasing health problem that is being faced globally and also in Malaysia. Obesity may lead to many diseases such as heart attack and diabetes. Obese children are prone to stay obese after growing up. So, it is important to reduce obesity right from childhood. Persuasive technology can be used to motivate people to change their behavior. There are two approaches of persuasive technology that can be used in childhood obesity intervention. The first is reducing weight by means of increasing physical activity while the second is by changing the eating behavior of children. The strengths and weaknesses of these approaches will be studied and a hybrid framework will be proposed for childhood obesity intervention using persuasive web-mobile technology.

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Published in: Computer & Information Science (ICCIS), 2012 International Conference on

Parameter Identification and Selection for Childhood Obesity Prediction Using Data Mining

The accurate identification and selection of useful parameters for childhood obesity prediction are very important. This study aims to identify childhood obesity prediction parameters for children in Malaysia, and presents the methods used to identify and select the parameters from the children’s attributes, lifestyle, family, and environment. The study comprises four stages: risk factor review, data collection, parameter identifications and selection, and evaluation. Base on the results, 19 parameters were identified. The accuracy of childhood obesity prediction using the proposed parameters was 21% greater compared to a set of parameters used in a previous study

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A framework for childhood obesity classifications and predictions using NBtree

Obesity is a common issue nowadays. The numbers of obese people are increasing every year. There are evidences that childhood obesity persists into adulthood. Predicting obesity at an early age is both useful and important because preventive measures and proper interventions can be applied if the children indicated a high risk of obesity. However, the prediction of childhood obesity is a difficult task. Many ways and techniques such as assessment of body composition, data mining techniques, and logistic regression have been applied to predict childhood obesity, but only a few managed to produce accurate results. The numbers of efforts on childhood obesity prediction need to be increased and the techniques used should be improvised. The initial stage of this study involves collecting data from primary sources: parents, children and caretaker. Then, we identify risk factors such as parental obesity and education, children lifestyle and habits, and environment influences, and proposes a framework of childhood obesity prediction using NBtree.

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Published in: Information Technology in Asia (CITA 11), 2011 7th International Conference on

A survey on utilization of data mining for childhood obesity prediction

In this paper we present data mining and its utilization for childhood obesity prediction. Data mining was widely used in many childhood obesity prediction systems. Predicting obesity at an early age is both useful and important because the number of obese patients is increasing while its main cause cannot yet be defined. The ability to predict childhood obesity will help early prevention. The purpose of this survey is to provide the needed understanding of the childhood obesity problem, introduce the use of data mining on childhood obesity prediction, describe current efforts in the area, and provide the strength and weaknesses of each technique presented.

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Published in: Information and Telecommunication Technologies (APSITT), 2010 8th Asia-Pacific Symposium on

Implementation of Hybrid Naive Bayesian-Decision Tree for Childhood Obesity Predictions

Data mining techniques have been used by past researchers to predict childhood obesity, but the results are still inadequate. The purposes of this paper are to use significant parameters for childhood obesity prediction, to study suitable data mining techniques for childhood obesity predictions, and to propose a hybrid data mining technique. The proposed technique is a hybrid of Naïve Bayesian and decision tree (NBTree) that aimed to increase the accuracy of childhood obesity prediction. NBTree has managed to increase the sensitivity of Naïve Bayesian predictions from 63% to 83% but reduced it specificity from 58% to 53%. In the medical predictions, the sensitivity is often more important than the specificity. As a conclusion, the proposed hybrid technique has increased the accuracy of childhood obesity prediction.

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DOI: 10.2316/P.2012.770-044

From Proceeding (769) Modelling, Identification and Control / 770: Advances in Computer Science and Engineering – 2012