Cloud Computing

As this is the age of technology and every day we are receiving the news about growing popularity of internet and its applications. Cloud computing is an emerging paradigm of today that is rapidly accepted by the industry/organizations/educational institutions etc. for various applications and purpose. As computing is related to distributed and parallel computing which are from a very long time in the market, but today is the world of cloud computing that reduces the cost of computing by focusing on personal computing to data center computing. Cloud computing architecture and standard provide a unique way for delivering computation services to cloud users. It is having a simple API (Application Platform Interface) to users for accessing storage, platform and hardware by paying-as-per-use basis. Services provided by cloud computing is as same as other utility oriented services like electricity bill, water, telephone etc. over shared network. There are many cloud services providers in the market for providing services like Google, Microsoft, Manjrasoft Aneka, etc.

Metaheuristic- and Statistical-Based Sampling in Optimization

Modern engineering often uses computer simulations as a partial substitute to real-world experiments. As such simulations are often computationally intensive, metamodels, which are numerical approximations of the simulation, are often used. Optimization frameworks which use metamodels require an initial sample of points to initiate the main optimization process. Two main approaches for generating the initial sample are the ‘design of experiments' method which is statistically based, and the more recent metaheuristic-based sampling which uses a metaheuristic or a computational intelligence algorithm. Since the initial sample can have a strong impact on the overall optimization search and since the two sampling approaches operate based only widely different mechanisms this study analyzes the impact of these two approaches on the overall search effectiveness in an extensive set of numerical experiments which covers a wide variety of scenarios. A detailed analysis is then presented which highlights which method was the most beneficial to the search depending on the problem settings.

Swarm-Intelligence-Based Optimal Placement and Sizing of Distributed Generation in Distribution Network

In the distribution system, distributed generation (DG) are getting more important because of the electricity demands, fossil fuel depletion and environment concerns. The placement and sizing of DGs have greatly impact on the voltage stability and losses in the distribution network. In this chapter, a particle swarm optimization (PSO) algorithm has been proposed for optimal placement and sizing of DG to improve voltage stability index in the radial distribution system. The two i.e. active power and combination of active and reactive power types of DGs are proposed to realize the effect of DG integration. A specific analysis has been applied on IEEE 33 bus system radial distribution networks using MATLAB 2015a software.

Survey of Unknown Malware Attack Finding

Intrusion detection system(IDS) has played a vital role as a device to guard our networks from unknown malware attacks. However, since it still suffers from detecting an unknown attack, i.e., 0-day attack, the ultimate challenge in intrusion detection field is how we can precisely identify such an attack. This chapter will analyze the various unknown malware activities while networking, internet or remote connection. For identifying known malware various tools are available but that does not detect Unknown malware exactly. It will vary according to connectivity and using tools and finding strategies what they used. Anyhow like known Malware few of unknown malware listed according to their abnormal activities and changes in the system. In this chapter, we will see the various Unknown methods and avoiding preventions as birds eye view manner.

A Study on Static Hand Gesture Recognition Using Type-1 Fuzzy Membership Function

The idea of this chapter is the use of Gaussian type-1 fuzzy membership functions based approach for automatic hand gesture recognition. The process has been carried out in five stages starting with the use of skin color segmentation for the isolation of the hand from the background. Then Sobel edge detection technique is employed to extract the contour of the hand. The next stage comprises of the calculation of eight spatial distances by locating the center point of the boundary and all distances are normalized with respect to the maximum distance value. Finally, matching based on Gaussian fuzzy membership function is used for the recognition of unknown hand gestures. This simple and effective procedure produces highest accuracy of 91.23% for Gaussian membership function and a time complexity of 2.01s using Matlab R2011b run on an Intel Pentium Dual Core Processor.

An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing

This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it's a meta-heuristic. Cloud computing is a one of the emerging technology. It's enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.

Detection of Music-Induced Emotion Changes by Functional Brain Networks

This chapter discusses emotions induced by music and attempts to detect emotional states based on regional interactions within the brain. The brain network theory largely attributes statistical measures of interdependence as indicators of brain region interactions/connectivity. In this paper, the authors studied two bivariate models of brain connectivity and employed thresholding based on relative values among electrode pairs, in order to give a multivariate flavor to these models. The experimental results suggest that thresholding the brain connectivity measures based on their relative strength increase classification accuracy by approximately 10% and 8% in time domain and frequency domain respectively. The results are based on emotion recognition accuracy obtained by decision tree based linear support vector machines, considering the thresholded connectivity measures as features. The emotions were categorized as fear, happiness, sadness, and relaxation.

Robust Stability Self-Tuning Fuzzy PID Digital Controller

A self-tuning fuzzy control methodology via particle swarm optimization based on robust stability criterion, is proposed. The plant to be controlled is modeled considering a Takagi-Sugeno (TS) fuzzy structure from input-output experimental data, by using the fuzzy C-Means clustering algorithm (antecedent parameters estimation) and weighted recursive least squares (WRLS) algorithm (consequent parameters estimation), respectively. An adaptation mechanism based on particle swarm optimization is used to tune recursively the parameters of a fuzzy PID controller, from the gain and phase margins specifications. Computational results for adaptive fuzzy control of a thermal plant with time varying delay is presented to illustrate the efficiency and applicability of the proposed methodology.

Machine Learning Data Analysis for On-Line Education Environments

The increase of e-Learning resources such as interactive learning environments, learning management systems or intelligent tutoring systems has created huge repositories of educational data that can be explored. This increase generated the need of integrating machine learning methodologies into the currently existing e-Learning environments. The integration of such procedures focuses on working with a wide range of data analysis algorithms and their various implementations in form of tools or technologies. This paper aims to present a self-contained roadmap for practitioners who want to have basic knowledge about a core set of algorithms and who want to apply them on educational data. The background of this research domain is represented by state-of-the-art data analysis algorithms found in the areas of Machine Learning, Information Retrieval or Data Mining that are adapted to work on educational data. The main goal of the research efforts in the domain of Intelligent Data Analysis on Educational Data is to provide tools that enhance the quality of the on-line educational systems.

Genetic-Algorithm-Based Performance Optimization for Non-Linear MIMO System

Environmental regulations demand efficient and eco-friendly ways of power generation. Coal continues to play a vital role in power generation because of its availability in abundance. Power generation using coal leads to local pollution problems. Hence this conflicting situation demands a new technology - Integrated Gasification Combined Cycle (IGCC). Gasifier is one of the subsystems in IGCC. It is a multivariable system with four inputs and four outputs with higher degree of cross coupling between the input and output variables. ALSTOM – a multinational and Original Equipment Manufacturer (OEM) - developed a detailed nonlinear mathematical model, validated made this model available to the academic community and demanded different control strategies which will satisfy certain stringent performance criteria during specified disturbances. These demands of ALSTOM are well known as “ALSTOM Benchmark Challenges”. The chapter is addressed to solve Alstom Benchmark Challenges using Proportional-Integral-Derivative-Filter (PIDF) controllers optimised by Genetic Algorithm.