The paper outlines geographic search technology as the future of research methodology. Due to the importance of geographical environment in the routine issues, the close connection between spatial extension and most of the phenomena running over the world, the geographic query process turns to be key factor in overcoming hazard decision-making and evaluation. We describe a two step process where derived output from spatio-textual research could be largely disseminated by spatial replication techniques. Spatial research differs in that it requires a combination of text and geographical data. The mathematical formula to calculate distances derives from the assertion that the Earth is nearly spherical (ignoring ellipsoidal effects) so great-circle distance formulas give the distance between points on the surface of the Earth correct to within 0.5% or so [YTA06]. Based on the search range and limit, we introduce the Cosine algorithm who offers better speed performance and data retrieval accuracy. Retrieved data exploit spatial replication technique potentiality. When it comes to spatial data we deal extensive amount of information. Web Gis Albania is an informative platform initially structured for academicals purposes further enriched with modules which support the spatial data circulation.

data circulation, replication technique, spatio-textual technology, law of cosine.

This paper claims that the contextual qualities of Cloud-enabled software architecture should be identified and understood in a different way by Cloud customers. The existing architectures are not designed to manipulate the contextual qualities of Cloud computing. The appearance of Cloud computing virtually forces Cloud customers to re-evaluate their application architectures in light of the Cloud computing viewpoint which requires relinquishing control over most architectural components to Cloud suppliers. In a cloud-enabled architecture, the move to possession and control over architectural components from customers to Cloud suppliers has the powerful impact on the ways Cloud customers design their Cloud architecture. In this perspective, we go beyond the traditional definition of Cloud architecture and present the concepts of architectural scope, control and design components of the architecture.

software architecture, conceptual components, cloud-enabled architecture, scheduling.

Multivariate Adaptive Regression Splines (MARS) are a generalization of stepwise linear regression method that is often employed to improve the efficiency of regression models. It is a useful tool to identify linear/nonlinear and interactions effects between a set of metrical and categorical covariates in regression models. In this study, the use of a modified Cox-Snell Residuals to Survival Analysis with MARS was proposed. The proposed method was compared with Martingale Residual in the Survival MARS setting. These two residual types were used as responses in the Cox proportional hazard modeling in the MARS implementations. Results from simulation studies revealed that the proposed method fitted the data better than the Martingale residual (σ Cox-Snell=0.5747 < σ Martingake=0.8356). However, further results from Monte-Carlo experiment showed that the two residual types performed better than the classical Cox Proportional Hazard (CPH) method. These methods were applied on real life dataset on Pneumocystis Carinii Pneumonia and all the results obtained actually validated those got from the simulation studies.

MARS, Martingale Residual, Modified Cox-Snell Residual, PCP and CART.

SIP servers are playing vital role in preserving multimedia, voice, data and video file storage and distribution through Voice over Internet IP services. The increase popularity of SIP servers through VOIP has attracted the attention of cyber terrorist to instigate the most damaging attacks to eavesdrop the information and compromise the servers with DDoS attacks. The recent evidences of victimization of SIP servers for the attack of DDoS through VOIP networks made this topic to be addressed with high intensity. DDoS attacks are affecting VOIP networks and SIP servers by injecting malicious virus data packets along with the normal user data sent from different locations. The proposed literature survey is focusing on measures to be taken care of to protect SIP servers from DDoS attacks with several possible novel mechanisms.

SIP Servers, DDoS Attacks, Voice, Data, Video and multimedia transmissions.

Stratified Ranked Set sampling (SRSS) helps in obtaining an unbiased estimator for population parameters with some significant gain in efficiency. This paper presents modified exponential estimators of finite population mean using co-efficient of Variation and Co-efficient of Kurtosis of auxiliary variable. The bias and mean square error (MSE) of the proposed estimators with large sample approximation were derived. A set of secondary data on students’ enrolment in secondary schools in Ogun State were used. The population was stratified into 4 strata base on political zones which are Egba, Yewa, Ijebu and Remo with 89, 91, 69 and 53 schools respectively. The sample sizes of the 4 strata based on proportional allocation are 27, 27, 21 and 15 schools respectively. The population means for students’ enrolment and number of staff are 1284.71 and 49.0 respectively. The students’ enrolment in Egba, Yewa, Ijebu and Remo zones are 140718, 137835, 56618 and 52815 respectively. The proportions of staff members to number of enrolled students are 0.042, 0,025, 0.051 and 0.047 respectively. The MSEs for four proposed estimators are 6176.84, 6503.61, 6269.63, and 6632.94. The MSEs for the corresponding four existing estimators are 9754.51, 10270.80, 9748.76, and 10561.44. The proposed estimators have least MSE, hence they are more efficient.

Heterogeous Population, Exponential Estimators, Co-efficient of Variation, Co-efficient of kurtosis and Efficiency.

Since the grid is emerging all over the internet, there comes a need for optimal job scheduling and load balancing approaches that takes various characteristics of the grid environment into consideration. Our principal seek is to present a novel fault tolerant approach for job scheduling and load balancing. Our approach called as NovelAlg_FLB mainly focuses on the architecture of the grid, bandwidth of the network, delay caused by communication, availability of resources, heterogeneity of computer resources and other features of jobs. This approach culminates the strength of various other algorithms such as load based and cluster based algorithms. The primary aim of this research is to schedule jobs in such a way that the job’s task delivers their response in minimum time. Also, this approach ensures that the computing nodes are optimally used. The secondary aim of this approach is to reduce complexity and the reduction in the total number of additional communication lines due to load balancing. We have used a java based simulation toolkit called grid GridSim to conduct the simulation of the proposed approach. Experimental results prove that our approach works extremely well in a huge and complex grid environment.

Computing Grid, Job Scheduling, Load Balancing, Fault Handler, Completion time, Resubmission time.

In every organization, effective Human Resource Management (HRM) is vital in order to meet the increasing market demands with well-qualified employees at all times. The research is aimed at providing personnel activity recording model that assists both records managers and non-records staff, including line managers and personnel staff, to manage personnel records in support of corporate accountability and good governance. Production rules technique was employed to model the decision engine of the system. The implementation machines are configured with Windows 8 operating system, Java programming language and My Structured Query Language as the backend machine. The result showed that electronic activity record system is cheaper to run and yield better returns on personnel invetsment than the conventional method of record keeping.

Human Resource Management, Production Rules, Backward Chaining, Forward Chaining.

In this study, an efficient Support Vector Machine (SVM) algorithm that incorporates feature selection procedure for efficient identification and selection of gene biomarkers that are predictive of Diffuse Large B–Cell Lymphoma (DLBCL) and Follicular Lymphoma (FL) cancer tumor samples is presented. The data employed were published real life microarray cancer data that contained 7,129 gene expression profiles measured on 77 biological samples that comprised 58 DLBCL and 19 FL tissue samples. The dimension reduction approach of the Welch statistic was employed at the feature selection phase of the SVM algorithm. The cost and kernel parameters of the SVM model were tuned over a 10–fold cross-validation to improve the efficiency of the SVM classifier. The entire sample was randomly partitioned into 95% training and 5% test samples. The SVM classifier was trained using Monte Carlo Cross-validation approach with 1000 replications. The performance of this classifier was assessed on the test samples using misclassification error rate (MER) and other performance measures. The results showed that the SVM classifier is quite efficient by yielding very high prediction accuracy of the tumor samples with fewer differentially expressed genes. The selected gene biomarkers in this work can be subjected to further clinical screening for proper determination of their biological relationship with DLBCL and FL tumour sub-groups. However, more studies with large samples might be needed in future to validate the results from this work.

SVM, Diffuse Large B-Cell Lymphoma, Follicular Lymphoma, 10-fold cross-validation, Welch Statistic.

After presenting a survey using the expert system implemented at the “Tibiscus” University of Timisoara, Romania, applied for at least five years to analyze the quality assessment of the educational process, compulsory made by the students at our university using an online web-based application and based on the requirements of the Romanian Agency for Quality Insurance in Higher Education (ARACIS), we’re now introducing the results of a survey upon the Computer Science master students. The results of the statistical analysis are used on departments to ensure the transparent policy of the educational high education evaluation. The application allows first the evaluation by students, then the interpretation of the results and finally the study of the evolution of the results. The mathematical apparatus we’re using are statistical indicators as the average, the mean squared deviations, the class values, the correlations and others. We continue to suggest that a widely implementation of our solution permits to have the same evaluation system in all universities and, by consequence, a unitary insight to the higher education level.

quality assessment, quality assurance, M.Sc., high education, statistical processing.

Medium Access Control (MAC) takes a vital role in the wireless communication networks for making efficient utilization of bandwidth, fairness among the nodes, latency management, and scalability. Unlike other networks, sensor networks required dedicated MAC protocols to consider the energy efficiency to improve the life time of the node along with efficient bandwidth utilization. In this paper, energy aware virtual back off algorithm (EAVBA) is proposed for successful channel access over the medium. In this method overall network energy consumption is managed by maintaining the details of transmission energy and receiving energy of a node. The simulation performed by considering the delay, packet delivery ratio and energy consumption factors to identify the performance of the proposed algorithm.

MAC, virtual backoff algorithm, sensor networks, energy, optimization.

Population with large number of elements remains unchanged in several occasions but the value of units changes. There are existing works on changes in population estimates from one occasion to another using double sampling approach. There is dearth of information on the estimation of population estimate using double sampling approach with stratification. This study proposed an estimator to determine the population estimates of two occasions involving stratification using successive sampling with two different secondary data. A random sample of size 120 was selected from 260 prices of milled rice and 40 from 97 immunized children on the first occasion. On the second occasion, the samples were stratified and subsamples of 60 from the price of milled rice and 20 from the immunized children were selected from each stratum which was then supplemented with a post stratified fresh sample of 60 and 40 for both data respectively. The variance of double sampling estimator and minimum variance for the successive sampling for the existing and proposed estimators were obtained. The variance of the existing estimator for double sampling for the price of milled rice and the number of immunized children were 40,914.20 and 1,870,842.00 respectively, while the variance of the proposed estimator were 26,198.11 and 799,064.40 respectively. The minimum variances for the existing estimator for the price of milled rice and the number of immunized children were 25,572.38 and 677,564.40 respectively, while the minimum variances for the proposed estimator were 21,513.36 and 577,275.30 respectively. The variance of the proposed estimator is less than that of the existing estimator, hence more efficient.

Estimator Matched and Unmatched part, Double sampling, Stratification.

The Internet of Things (IoT) offers new horizons for the development of the Internet network and business: consumer products intelligent, huge amounts of information in real time with the possibility of solving immediate problems, and innovative new types of business. But beyond these technological opportunities even discern significant risks in the operation information stored but also devices.

Internet of Things, IoT, Big Data, cibersecurity, business value.