Fuzzy Hybrid Approach for Ranking and Selecting Services in Cloud-based Marketplaces

Background and Objective: The popularity cloud computing has led to the proliferation of services that are commoditized and traded on cloud e-marketplaces. Besides, user’s cloud service requirements-QoS preferences and aspiration are often shrouded in vagueness and subjectivity. Therefore, cloud service selection can be overwhelming and lead to service choice overload. Existing cloud service selection approaches rarely provide mechanisms to elicit both the QoS preferences and aspirations, but rather considers either of them. This study aimed to design fuzzy-based model for service selection in e-market places that articulates both QoS preferences and aspirations. Materials and Methods: This model comprised a fuzzy Analytic Hierarchy Process (AHP) method for deriving relative priority weights of QoS attributes, a fuzzy decision-making method for obtaining user’s QoS aspiration values and a fuzzy multi-objective optimization module for evaluating the services with respect to user requirements. A simulated experiment was conduct using publicly QoS dataset and ranking accuracy produced by the proposed approach compared to existing methods was measured using Normalize Discounted Cumulative Gain (NCDG) metric. Results: The descriptive and inferential analyses of the ranking results from both versions of the proposed approach produce better accuracy results based on the NCDG metric and were in all cases closer to the benchmark metric than the other two existing methods used in this simulation. Conclusion: Results from current simulation experiment showed that the ranking accuracy of this model is not compromised by subjective QoS information from users and this approach is applicable use the subjective QoS requirements of user’s in ranking services in the cloud e-marketplaces

Technological Innovation Capability and Firm’s Performance in New Product Development

Technological innovation is one of the driving and fundamental instruments of growth strategies. The main objective of this study is to provide the understanding the way in which technological innovation capabilities affect the efficiency and potential of firm performance. The study attempted to draw on the theoretical literature and empirical studies on innovation, management and capabilities of technology in an effort to explore the role of technological innovation on new product development. The study posits the importance of technological innovation as an essential ingredient of competitive advantage for new product development. The study is different from previous research and focuses on an integrated framework of potential influence on innovation incorporating other variables. Adopting the Principal Component Analysis (PCA) approach, we were able to reduce the larger set of variables into a more manageable set of scales. A PCA with varimax rotation was conducted to find out the underlying dimensions of innovations and firm performance. We used the SPSS for window 12.0 software pack as our statistical analysis tool for all the data, and Pearson\u27s analysis to verify the relationship between technological innovation and new product development, and t-test to verify the hypotheses. In this study, the researcher constructed research variables for measurement (α) was used to measure the internal consistent of the study. For Cooper and Emory (1995) if Cronbach’s alpha (α) is between 0.70-0.98, then the reliability is higher but if it is lower than 0.35, then the results are not reliable and should be refused. For this study, Cronbach’s α was above 0.80, indicating that the results of the survey were all well within the parameters of reliability. The survey findings verify the existence of correlation between technological innovation and firm performance on new product development. Based on the findings, recommendations were proffered which have crucial role for innovative capabilities

A PREFERENCE-BASED GRADE RECOMMENDER TOWARDS THE ATTAINMENT OF A TARGET GRADE POINT AVERAGE (GPA)

A number of GPA calculators exist to automate the calculations of GPA, and it is used by college students to anticipate the amount of study required to accomplish a desired academic target. However, many of these apps do not sufficiently satisfy the user experience realities of the academic aspect of college life because they require excessive user inputs; grades combination that approximates their target GPA is known through a painstaking series of trials; they do not consider user’s subject preference in recommending grades. A model of a grade recommender towards the attainment of a target GPA based on a self-efficacy reports and mathematical optimization is proposed. A prototype was developed as a proof of concept and its viability was demonstrated using three illustrative scenarios. The algorithm assigns lower grades to courses with low subject preference, and upper grades are allotted to courses with higher self-efficacy evaluation towards the attainment of a target GPA. An integration of the full implementation of the proposed model into a student information system will serve as a very useful resource to help college student achieve their academic goals

Forecasting Gas Compressibility Factor Using Artificial Neural Network Tool for Niger-Delta Gas Reservoir

Accurate prediction of gas compressibility factor is important in engineering applications such as gas metering, pipeline design, reserves estimation, gas flow rate, and material balance calculations. This factor also is important in calculating gas properties such as gas formation volume factor, gas isothermal compressibility, viscosity and density. Compressibility factor value shows how much the real gas deviates from the ideal gas at a given pressure and temperature. Most often, compressibility factor values can be determined experimentally from collected laboratory samples but frequently this measurement is not always available. In such cases, the natural gas property can be determined using empirical correlations or iteratively using equation of state (EOS). Therefore, the aim of this work is to develop ANN model to accurately predict the gas compressibility factor; as well to compare its performance with existing empirical gas compressibility factor correlations. The new model was developed using 513 PVT data points obtained from Niger-Delta region of Nigeria. The data used wasrandomly divided into three parts, of which 60% was used for training, 20% for validation, and 20% for testing. Both quantitative and qualitative assessments were employed to evaluate the accuracy of the new model to the existing empirical correlations. The ANN model performed better than the existing empirical correlations by the statistical parameters used having the lowest rank of 1.37 and better performance plot