Cloud computing paradigm is a service oriented system that delivers services to the customer at low cost. Cloud computing needs to address three main security issues: confidentiality, integrity and availability. In this paper, we propose user identity management protocol for cloud computing customers and cloud service providers. This protocol will authenticate and authorize customers/providers in other to achieve global security networks. The protocol will be developed to achieve the set global security objectives in cloud computing environments. Confidentiality, integrity and availability are the key challenges of web services' or utility providers. A layered protocol design is proposed for cloud computing systems, the physical, networks and application layer. However, each layer will integrate existing security features such as firewalls, NIDS, NIPS, Anti-DDOS and others to prevent security threats and attacks. System vulnerability is critical to the cloud computing facilities; the proposed protocol will address this as part of measures to secure data at all levels. The protocol will protect customers/cloud service providers' infrastructure by preventing unauthorized users to gain access to the service/facility.
Artificial Intelligence techniques have been used in petroleum engineering to predict various reservoir properties such as porosity, permeability, water saturation, lithofacie and wellbore stability. The most extensively used of these techniques is Artificial Neural Networks (ANN). More recent techniques such as Support Vector Machines (SVM) have featured in the literature with better performance indices. However, SVM has not been widely embraced in petroleum engineering as a possibly better alternative to ANN. ANN has been reported to have a lot of limitations such as its lack of global optima. On the other hand, SVM has been introduced as a generalization of the Tikhonov Regularization procedure that ensures its global optima and offers ease of training. This paper presents a comparative study of the application of ANN and SVM models in the prediction of porosity and permeability of oil and gas reservoirs with carbonate platforms. Six datasets obtained from oil and gas reservoirs in two different geographical locations were used for the training, testing and validation of the models using the stratified sampling approach rather than the conventional static method of data division. The results showed that the SVM model performed better than the popularly used Feed forward Back propagation ANN with higher correlation coefficients and lower root mean squared errors. The SVM was also faster in terms of execution time. Hence, this work presents SVM as a possible alternative to ANN, especially, in the characterization of oil and gas reservoir properties. The new SVM model will assist petroleum exploration engineers to estimate various reservoir properties with better accuracy, leading to reduced exploration time and increased production. 1. Introduction Petrophysical properties such as porosity and permeability are two important properties of oil and gas reservoirs that relate to the amount of fluid in them and their ability to flow. These properties have significant impact on petroleum field operations and reservoir management. They both serve as standard indicators of reservoir quality in the oil and gas industry (Jong-Se, 2005). Porosity is the percentage of voids and open spaces in a rock or sedimentary deposit. The greater the porosity of a rock, the greater its ability to hold water and other materials, such as oil. It is an important consideration when attempting to evaluate the potential volume of hydrocarbons contained in a reservoir (Schlumberger, 2007a). Permeability is the ease with which fluid is transmitted through a rock's pore space. It is a measure of how interconnected the individual pore spaces are in a rock or sediment (Schlumberger, 2007b). It is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, many Petroleum Engineering problems cannot be solved without having an accurate permeability value. Many reports such as Ali (1994) and Mohagheh (1994) have featured the successful application of Artificial Neural Networks (ANN) as the pioneer Artificial Intelligence (AI) technique in oil and gas reservoir characterization over the years. Despite this, ANN has been reported to have some drawbacks (Petrus et al., 1995). The recent introduction of Support Vector Machines (SVM) that is based on the concepts of Tikhonov Regularization and Structural Risk Minimization (SRM) was introduced to overcome some of the limitations of ANN. Many reports such as such as Anifowose and Abdulraheem (2010); and Helmy et al. (2010) have presented SVM as a promising predictive technique in a good number of applications. This paper focuses on the study and analysis of the comparative performance of ANN and SVM in the prediction of porosity and permeability of some Middle East and American oil and gas reservoirs. To achieve this aim, Section 2 presents a succinct survey of ANN and SVM. Section 3 describes the experimental methodology, structure of datasets and the evaluation criteria for the study. Section 4 presents the results of the study with a detailed discussion while conclusion is presented in Section 5.
<p class="Default">Yorùbá numerals have been seen as one of the most interesting but quite complicated numeral system. In this paper we present the development of a web-based English to Yorùbá numeral translation system. The system translates English numbers both in figure and text to its standard Yorùbá form. The computational processes underlying both numerals were used to formulate the model for the work. Unified Modeling language (UML) and Automata theory was used for the system design and specification. The designed system was implemented using Google Web App Engine with support for python. The result of the system evaluation using mean opinion score approach shows that the system gives a recall of 100% on all the output considered.</p>
The proliferation of web services; and users appeal for high scalability, availability and reliability of web servers to provide rapid response and high throughput for the Clients’ requests occurring at anytime. Distributed Web Servers (DWSs) provide an effective solution for improving the quality of web services. This paper addresses un-regulated jobs/tasks migration among the servers. Considering distributed web services with several servers running, a lot of bandwidth is wasted due to unnecessary job migration. Having considered bandwidth optimization, it is important to develop a policy that will address the bandwidth consumption while loads/tasks are being transferred among the servers. The goal of this work is to regulate this movement to minimize bandwidth consumption. From literatures, little or no attention was given to this problem, making it difficult to implement some of these policies/schemes in bandwidth scarce environment. Our policy “Cooperative Adaptive Symmetrical Initiated Dynamic/Diffusion (CASID)” was developed using Java Development Environment (JADE) a middle ware service oriented environment which is agent-based. The software was used to simulate events (jobs distribution) on the servers. With no job transfer allowed when all servers are busy, any over loaded server process jobs internally to completion. We achieved this by having two different agents; static cognitive agents and dynamic cognitive agents. The results were compared with the existing schemes. CASID policy outperforms PLB scheme in terms of response time and system throughput
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.