One of the most important reasons for information systems failure is lack of quality. Information Systems Quality (ISQ) evaluation is important to prevent the lack of quality. ISQ evaluation is one of the most important Multi-Criteria Decision Making (MCDM) problems. The concept of Single Valued Triangular Neutrosophic Numbers (SVTrN-numbers) is a generalization of fuzzy set and intuitionistic fuzzy set that make it is the best fit in representing indeterminacy and uncertainty in MCDM. This paper aims to introduce an ISQ evaluation model based on SVTrN- numbers with introducing two types of evaluating and ranking methods. The results indicated that the proposed model can handle ill-known quantities in evaluating ISQ. Also by analyzing and comparing results of ranking methods, the results indicated that each method has its own advantage that make the proposed model introduces more than one option for evaluating and ranking ISQ.

The explosive nature of cholera epidemic over the years in different parts of the world has been a subject of interest to scientists in proffering interventions towards controlling its spread. Over the years many models has been created by the following people Capaso and Pavari – Fontana (1973), Codeco (2001), Hartley, Tien (2009), Mukandivare (2009) etc. In the present study, we modify the Cholera model proposed by Mukandivare incorporating three (3) containment options such as vaccination, Therapeutic treatment and water treatment and solved the system analytically using Homotopy Perturbation Method. The results shows that with improved use of vaccination, therapy and proper sanitation we have a more healthy population. This research is therefore recommended to modelers who desire to know how homotopy perturbation methods works. The computations were done and further analyzed mathematically using a computer symbolic package MAPLE 13.

Cloud computing has become the hottest issue due to its wide range of services. Due to a large number of users, it becomes more significant to provide high availability of services to cloud users. The majority of existing scheduling techniques in the cloud environment is NP-Complete in nature. Many researchers have utilized meta-heuristic techniques to schedule the jobs in cloud data centers. The majority of existing techniques such as Genetic Algorithm, Ant colony optimization, Non-dominated Sorting Genetic Algorithm (NSGA-III), etc. suffer from poor convergence speed. Also, most of these techniques are either based upon scheduling or load balancing. Therefore, to overcome these issues, a new Variance Honey Bee Behavior with multi-objective optimization method (VHBBMO) is proposed in this paper. Extensive experiments have been conducted by considering the various set of jobs. The experimental results have shown that the proposed method provides more significant results than available methods.

A new scheme for secure information transmission is proposed using the generalized modified projective synchronization (GMPS) method. The linear transformation of the modified Colpitts oscillator, first introduced in Cristinel and Radu (Low-Power Realizations of Secure Chaotic Communication Schemes. IEEE Asia Pacific Conference on Circuits and Systems, 2000) is investigated prior to the more detailed study by Kammogne et al. (Journal of chaos. (2014). doi: 10.1155/2014/659647). This circuit is employed to encrypt the information signal. In the receiver end, by designing the controllers and the parameter update rule, GMPS between the transmitter and receiver systems is achieved and the unknown parameters are estimated simultaneously. Based on the Lyapunov stability theory, the controllers and corresponding parameters update rule are constructed to achieve generalized modified projective synchronization between the transmitter and receiver system with uncertain parameters. The original information signal can be recovered successfully through some simple operations by the estimated parameter. The message signal can be finally recovered by the identified parameter and the corresponding demodulation method. Numerical simulations are performed to show the validity and feasibility of the presented secure communication scheme.

Selection of useful information from a large data collection is an important and challenging problem. Feature selection refers to the problem of selecting relevant features from a given dataset which produces the most predictive outcome as the original features maintain before the selection. Rough set theory (RST) and its extension are the most successful mathematical tools for feature selection from a given dataset. This paper starts with an outline of the fundamental concepts behind the rough set and fuzzy rough set based feature grouping techniques which are related to supervise feature selection. Supervised Quickreduct (QR) and fuzzy-rough feature grouping Quickreduct (FQR) algorithms are highlighted here. Then an enhanced version of FQR method is proposed here which is based on rough set dependency criteria with feature significance measure that select a minimal subset of features. Also, the termination condition of the base method is modified. Experimental studies of the algorithms are carried out on five public domain benchmark datasets available in UCI machine learning repository. JRip and J48 classifier are used to measure the classification accuracy. The performance of the proposed method is found to be satisfactory in comparison with other methods.