A concept software-defined network is considered. Architecture of software-defined network is analyzed which, differently from traditional, foresee the separation of C-plane from a plane communication of data. The method of multicriterion optimization of multilevel networks is examined with determination of resulting objective function, which allows to carry out the synthesis of control system software-defined network (SDN) in the conditions of unforeseen changes of structure of the system.

Data Mining is gaining immense popularity in the field of education due to its predictive capabilities. But, most of the prior effort in this area is only directed towards prediction of performance in academic results only. Nowadays, education has become employment oriented. Very little attempt is made to predict students’ employability. Precise prediction of students’ performance in campus placements at an early stage can identify students, who are at the risk of unemployment and proactive actions can be taken to improve their performance.
Existing researches on students’ employability prediction are either based upon only one type of course or on single University/Institute; thus is not scalable from one context to another. With this necessity, the conception of a unified model of clustering and classification is proposed in this paper.
With the notion of unification, data of professional courses namely Engineering and Masters in Computer Applications students are collected from various universities and institutions pan India. Data is large, multivariate, incomplete, heterogeneous and unbalanced in nature. To deal with such a data, a unified predictive model is built by integrating clustering and classification techniques. Two- Level clustering (k-means kernel) with chi-square analysis is applied at the pre-processing stage for the automated selection of relevant attributes and then ensemble vote classification technique with a combination of four classifiers namely k-star, random tree, simple cart and the random forest is applied to predict students’ employability. Proposed framework provides a generalized solution for student employability prediction. Comparative results clearly depict model performance over various classification techniques. Also, when the proposed model is applied up to the level of the state, classification accuracy touches 96.78% and 0.937 kappa value.

Web search engines use various ranking methods to determine the order of web pages displayed on the Search Engine Result Page (SERP). PageRank is one of the popular and widely used ranking method. PageRank of any web page can be defined as a fraction of time a random web surfer spends on that web page on average. The PageRank method is a stationary distribution of a stochastic method whose states are web pages of the Web graph. This stochastic method is acquired by combining the hyperlink matrix of the web graph and a trivial uniform process. This combination is needed to make primitive so that stationary distribution is well defined. The combination depends on the value of damping factor α∈[0,1] in the computation of PageRank. The damping factor parameter state that how much time random web surfer follow hyperlink structure than teleporting. The value of α is exceptionally empirical and in current scenario α = 0.85 is considered as suggested by Brin and Page. If we take α =0.8 then we can say that out of total time, 80% of time is taken by the random web surfer to follow the hyperlink structure and 20% time they teleport to new web pages randomly. Today web surfer gets worn out too early on the web because of non-availability of relevant information and they can easily teleport to new web pages rather than following hyperlink structure. So we have to choose some value of damping factor other than 0.85. In this paper, we have given an experimental analysis of PageRank computation for different value of the damping factor. We have observed that for value of α=0.7, PageRank method takes fewer numbers of iterations to converge than α=0.85, and for these values of α the top 25 web pages returned by PageRank method in the SERP are almost same, only some of them exchange their positions. From the experimental results it is observed that value of damping factor α=0.7 takes approximate 25-30% fewer numbers of iterations than α=0.85 to get closely identical web pages in top 25 result pages for personalized web search, selective crawling, intra-web search engine.

An article introduces a modified architecture of the neo-fuzzy neuron, also known as a "multidimensional extended neo-fuzzy neuron" (MENFN), for the face recognition problems. This architecture is marked by enhanced approximating capabilities. A characteristic property of the MENFN is also its computational plainness in comparison with neuro-fuzzy systems and neural networks. These qualities of the proposed system make it effectual for solving the image recognition problems. An introduced MENFN’s adaptive learning algorithm allows solving classification problems in a real-time fashion.

We have proposed a new approach to identify segments, which can be executed simultaneously, or coextending to achieve high computational speed with optimized utilization of available resources. Our suggested approach is divided into four modules. In first module we have represented a program segment using Abstract Syntax Tree (AST) along with an algorithm for constructing AST and in second module, this AST has been converted into Program Dependence Graph (PDG), the detailed approach has been described in section II, The process of construction of PDG is divided into two steps: First we construct a Control Dependence Graph (CDG, In second step reachability definition algorithm has been used to identify data dependencies between the various modules of a program by constructing Data Dependence Graph (DDG). In third module an algorithm is suggested to identify parallel modules, i.e., the modules that can be executed simultaneously in the section III and in fourth module performance analysis is discussed through our approach along with the computation of time complexity and its comparison with sequential approach is demonstrated in a pictorial form.

Energy-efficient routing is an extremely critical issue in unattended, tiny and battery equipped Wireless Sensor Networks (WSNs). Clustering the network is a promising approach for energy aware routing in WSN, as it has a hierarchical structure. The Connected Dominating Set (CDS) is an appropriate and prominent approach for cluster formation. This paper proposes an Energy-efficient Self-load Balanced Clustering algorithm (SLBC) for routing in WSN. SLBC has two phases: The first phase clusters the network by constructing greedy connected dominating set and the nodes are evenly distributed among them, using the defined parent fitness cost. The second phase performs data manipulations and new on-demand re-clustering. The efficiency of the proposed algorithm is analysed through simulation study. The obtained results show that SLBC outperforms than the recent algorithms like GSTEB and DGA-EBCDS in terms of network lifetime, CDS size, load dissemination, and efficient energy utilization of the network.

Most of the man-made technologies are nature-inspired including the popular heuristics or meta-heuristics techniques that have been used to solve complex computational optimization problems. In most of the metaheuristics algorithms, adjusting the parameters has important significance to obtain the best performance of the algorithm. Cricket Chirping Algorithm (CCA) is a nature inspired meta-heuristic algorithm that has been designed by mimicking the chirping behavior of the cricket (insect) for solving optimization problems. CCA employs a set of parameters for its smooth functioning. In a metaheuristic algorithm, controlling the values of various parameters is one of the most important issues of research. While solving the problem, the parameter value control has a potential to improve the efficiency of the algorithm. The different parameters used in CCA are tuned for better performance of the algorithm and experiment its impact on a set of sample benchmark test functions, then the fine-tuned CCA is compared with some other meta-heuristic algorithms. The results show the optimal choice of the various parameters to solve optimization problems using CCA.

One of the modified versions of the PID controller is the I-PD controller. It was proposed for eliminating the proportional and derivative kick appeared during set point change. In this paper, the optimal I-PD controller design for DC motor speed control system by the adaptive tabu search (ATS), one of the most efficient metaheuristic optimization techniques, is proposed. In a control system, DC motor is the principle and it is widely used because of the power from existing direct-current lighting power distribution systems. It can be controlled over a wide range and a variable supply. In this research, TMS320F28335 DSP microcontroller is implemented for DC motor speed control. This processor consists of the several peripheral circuits for motor drive application such as analog to digital, encoder digital to analog and PWM input/output interface circuits. These interface circuits can be used in both of DC and AC motor controls. For the control algorithm development and the Code Composer Studio (CCS) compiler can be used together with TMS320F28335 DSP in MATLAB/SIMULINK. This proposed method is tested with the DC motor, 1260, 1400, 1540 rpm and 24 volts, consecutively to verify the performance of the I-PD controller designed for DC motor speed control system using the speed and control signal response to many load disturbances. The simulation of DC motor is based on MATLAB/SIMULINK. The implementation results are compared with the simulation results. The correlation in the experiment shows that they are high related. In this paper show the effectiveness of the proposed methods and discuss how they could generalize to other systems by the simulation and experimentation. The results show that the I-PD parameters can be optimized by the ATS. The controlled system with I-PD provides better responses once compared to that with a basic parallel PID controller.