Radial Basis Kernel Regressive Feature Extraction and Robert Ensembled Brown Boost Classifier for Attack Detection in Cloud Environment

Cloud computing shares the resource in information technology field. The existing technique is failed to provide better results for identifying unknown attacks with higher accuracy and lesser time consumption. In order to address these problems, Radial Basis Kernel Regressive Feature Extracted Brown Boost Classification (RBKRFEBBC) method is introduced for performing the attack detection in cloud computing. The main objective of RBKRFEBBC method is to improve the attack detection performance with higher accuracy and minimal time consumption. Dichotomous radial basis kernelized regressive function is used in RBKRFEBBC method to extract the relevant features through determining the correlation between the output and one or more input variables (i.e., features of patient transaction data). After extracting relevant features, GRNBBC algorithm is used in RBKRFEBBC method to improve the secured data communication performance through classifying the patient data transaction as attack presence or attack absence. By this way, attack detection is carried out in accurate manner. Experimental evaluation is carried out by NSL-KDD dataset using different metrics like attack detection accuracy, attack detection time and error rate. The evaluation result shows RBKRFEBBC method improves the accuracy and minimizes the time consumption as well as error rate than existing works.

The Local LinearM-Estimation with Missing Response Data

This paper studies the nonparametric regressive function with missing response data. Three local linearM-estimators with the robustness of local linear regression smoothers are presented such that they have the same asymptotic normality and consistency. Then finite-sample performance is examined via simulation studies. Simulations demonstrate that the complete-case dataM-estimator is not superior to the other two local linearM-estimators.

Spatial Complex Model for Wind Farm Site Assessment

Our research is on the spatial allocation of possible wind energy usage. We would like to carry this out with a newly developed model (CMPAM = Complex Multifactoral Polygenetic Adaptive Model), which basically is a climateoriented system, but other kind of factors are also considered. With this model those areas and terrains can be located where construction of wind farms would be reasonable. The wind field modeling core of CMPAM is mainly based on sequential Gaussian simulation (sGs) otherwise known as geostatistics. But concepts from atmospheric physics and Geographical Information Systems (GIS) are used as well. For application for Hungary WAsP generated 10 m wind speed data was used as input data. The geocorrection (geometric correction) of this data was performed by us. Using optimized variography and sGs, our results were applied for Hungary in different heights. Simulation results for different heights are summarized furthermore, an exponential regressive function describing the vertical wind profile was also established. From the complex analyses of CMPAM, results derived to the 100 m height are also included and explained in a map in this paper. This produces a basis for certain several possible sites for the utilization of wind energy, under given conditions.

An Md-class of sets indexed by a regressive function

This paper deals with the study of a particular md-class of sets. The underlying theory was introduced and studied by J. C. E. Dekker in [4]. We shall assume that the reader is familiar with the terminology and main results of this paper; in particular with the concepts of md-class of sets, gc-class of sets, gc-set, gc-function and the RET of a gc-class of sets. We also use the following notations of [4]: ε = the set of all non-negative integers (numbers), R = Req (ε).

Double Series of Isols

It is assumed that the reader is familiar with the following notions: regressive function, regressive set, regressive isol, infinite series of isols, the minimum of two regressive isols, combinatorial function, and canonical extension. We shall use the slightly more general definition of a regressive function introduced in (3). The next three notions are defined in (2), the fifth in (3), and the last two in (7 and 8).