Introduction of Cyber Security
Technology & Science , Web Security / December 5, 2017

The term cyber security is often used interchangeably with the term information security. Cyber security is the activity of protecting information and information systems (networks, computers, data bases, data centres and applications) with appropriate procedural and technological security measures. Cyber security has become a matter of global interest and importance. Cyber security refers to a set of techniques used to protect the integrity of networks, programs and data from attack, damage or unauthorized access. Cyber security Definition Cyber security is the collection of tools, policies, security concepts, security safeguards, guidelines, risk management approaches, actions, training, best practices, assurance and technologies that can be used to protect the cyber environment and organization and user’s assets. Organization and user’s assets include connected computing devices, personnel, infrastructure, applications, services, telecommunications systems, and the totality of transmitted and/or stored information in the cyber environment. Cyber security strives to ensure the attainment and maintenance of the security properties of the organization and user’s assets against relevant security risks in the cyber environment. More specifically, “Cyber security refers to the body of technologies, processes, and practices designed to protect networks, devices, programs, and data from attack, damage, or unauthorized access. Cyber security may also be referred…

Cloud Storage and Data De-Duplication

Rendering efficient storage and security for data is very important for cloud. With the rapidly increasing data produced worldwide, networked and multi-user storage systems are becoming very popular. However, concerns over data security still prevent many users from migrating data to remote storage. Data deduplication refers to a technique for eliminating redundant data in a data set. In the process of deduplication, extra copies of the same data are deleted, leaving only one copy to be stored. Data is analysed to identify duplicate byte patterns to ensure the single instance is indeed the single file. Then, duplicates are replaced with a reference that points to the stored chunk. Data deduplication Data deduplication is a technique to reduce storage space. By identifying redundant data using hash values to compare data chunks, storing only one copy, and creating logical pointers to other copies instead of storing other actual copies of the redundant data. Deduplication reduces data volume so disk space and network bandwidth can be reduced which reduce costs and energy consumption for running storage systems Figure 1: Data de-duplication View Data deduplication is a technique whose objective is to improve storage efficiency. With the aim to reduce storage space, in traditional…

What is ACO (Ant Colony Optimization) Algorithm

There are even increasing efforts in searching and developing algorithms that can find solutions to combinatorial optimization problems. In this way, the Ant Colony Optimization Meta-heuristic takes inspiration from biology and proposes different versions of still more efficient algorithms. Ant Colony Optimization (ACO): Overview Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions. ACO is a class of algorithms, whose first member, called Ant System, was initially proposed by Colorni, Dorigo and Maniezzo The main underlying idea, loosely inspired by the behavior of real ants, is that of a parallel search over several constructive computational threads based on local problem data and on a dynamic memory structure containing information on the quality of previously obtained result. The collective behavior emerging from the interaction of the different search threads has proved effective in solving combinatorial optimization (CO) problems. More specifically, we can say that “Ant Colony Optimization (ACO) is a population-based, general search technique for the solution of difficult combinatorial problems which is…

Random Forests in Data Mining

Data analysis and machine learning have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and providing insights about the problem. Since always, artificial intelligence has been driven by the ambition to understand and uncover complex relations in data. That is, to find models that can not only produce accurate predictions, but also be used to extract knowledge in an intelligible way. This section introduces random forest description in details. Random Forest Definition A Random Forest consists of a collection or ensemble of simple tree predictors, each capable of producing a response when presented with a set of predictor values. For classification problems, this response takes the form of a class membership, which associates, or classifies, a set of independent predictor values with one of the categories present in the dependent variable. Alternatively, for regression problems, the tree response is an estimate of the dependent variable given the predictors. A Random Forest consists of an arbitrary number of simple trees, which are used to determine the final outcome.  For classification problems, the ensemble of simple trees vote for the most popular class. In…

Evaluation of Image Edges using Gabor Filter
Image Processing , Technology & Science / November 27, 2017

In the field of image processing, filters play an extremely important role. All image processing operations can be viewed as applying a series of filters to an image and transforming it in some way. Gabor filter is a particular type of filter, and it happens to be an important one. Gabor filter responses are widely and successfully used as general purpose features in many computer vision tasks, such as in texture segmentation, face detection and recognition, and iris recognition. In a typical feature construction the Gabor filters are utilized via multi-resolution structure, consisting of filters tuned to several different frequencies and orientations. The multi-resolution structure relates the Gabor features to wavelets, but the main difference, non-orthogonality, also is connected to the main weakness of the Gabor features: computational heaviness. The computational complexity prevents their use in many real-time or near real-time tasks, such as in object tracking. Gabor Filter Significance Gabor filters are orientation-sensitive filters, used for edge and texture analysis. It is named after Dennis Gabor. Certain specific bands of frequency components can be extracted by adjusting the orientation and center frequencies of the Gabor filter. They have enjoyed much attention in the field of 2D face recognition and…

Introduction of Canny Edge Detection
Image Processing , Technology & Science / November 26, 2017

The early stages of vision processing identify features in images that are relevant to estimating the structure and properties of objects in a scene. Edges are one such feature. Edges are significant local changes in the image and are important features for analyzing images. Edges typically occur on the boundary between two different regions in an image. Edge detection is frequently the first step in recovering information from images. Due to its importance, edge detection continues to be an active research area. Here we discussed about canny edge detection statement: Canny Edge Detection: Overview Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. Classical methods of edge detection involve convolving the image with an operator (a 2-D filter), which is constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions. There are an extremely large number of edge detection operators available, each designed to be sensitive to certain types of edges. Variables involved in the selection of an edge detection operator include: Edge Orientation: The geometry of the operator determines a…

Local Binary Pattern For Image Texture Analysis
Image Processing , Technology & Science / November 25, 2017

Nowadays, applications in the field of surveillance, banking and multimedia equipment are becoming more important, but since each application related to face analysis demands different requirements on the analysis process, almost all algorithms and approaches for face analysis are application dependent and a standardization or generalization is quite difficult. Local Binary Patterns (LBP) is a non-parametric descriptor whose aim is to efficiently summarize the local structures of images. Local Binary Patterns (LBP): General Overview Local Binary Patterns were first used in order to describe ordinary textures where the spatial relation was not as significant as it is for face images. A face can be seen as a composition of micro textures depending on the local situation. The LBP is basically divided into two different descriptors: a global and a local. The global is used for discriminating the most non-face objects (blocks), whereas the second provides specific and detailed face information which can be used not only to select faces, but also to provide face information for recognition. So the whole descriptor consists of a global texture and a local texture representation calculated by dividing the image into blocks and computing the texture histogram for each one. The results will be…

introduction of Regression Analysis

Extracting patterns and models of interest from large databases is attracting much attention in a variety of disciplines. Knowledge discovery in databases (KDD) and data mining are areas of common interest to researchers in machine learning, pattern recognition, statistics, artificial intelligence, and high performance computing. Regression General Overview Regression is a data mining function that predicts a number. Profit, sales, mortgage rates, house values, square footage, temperature, or distance could all be predicted using regression techniques. For example, a regression model could be used to predict the value of a data warehouse based on web-marketing, number of data entries, size, and other factors. A regression task begins with a data set in which the target values are known. For example, a regression model that predicts data warehouse values could be developed based on observed data for many data warehouses over a period of time. In addition to the value, the data might track the age of the data warehouse, size and number of clusters and so on. Data warehouse value would be the target, the other attributes would be the predictors, and the data for each data warehouse would constitute a case. In the Regression is a data mining function…

Introduction of Motion Analysis
Image Processing , Technology & Science / November 23, 2017

The ultimate goal of computer vision is to understand the scene correctly through various steps of acquiring, processing, analyzing and understanding different kinds of information obtained by different kinds of sensors. Human motion analysis, recognition, and understanding are one of the very hottest topics within computer vision. Aspect of Motion Analysis As one of the most active research areas in computer vision, visual analysis of human motion attempts to detect, track and identify people, and more generally, to interpret human behaviors, from image sequences involving humans. Human motion analysis has attracted great interests from computer vision researchers due to its promising applications in many areas such as visual surveillance, perceptual user interface, content-based image storage and retrieval, video conferencing, athletic performance analysis, virtual reality, etc. Vision based human motion recognition is a systematic approach to understand and analyze the movement of people in camera captured content. It comprises of fields such as Biomechanics, Machine Vision, Image Processing, Artificial Intelligence and Pattern Recognition. It is an interdisciplinary challenging field having grand applications with social, commercial, and educational benefits. A wide spectrum of applications demands human motion recognition. Human motion analysis is a broad concept. Figure 1: Directions of human motion analysis…

Social Network Analysis

Network analysis is still a growing field with a great deal of opportunity for new and transformative contributions. The term social network refers to the articulation of a social relationship, official or achieved, among individuals, families, households, villages, communities, regions, and so on. Each of them can play dual roles, acting both as a unit or node of a social network as well as a social actor Social Network Analysis : Definition Social network theory views a network as a group of actors who are connected by a set of relationships. Social networks develop when actors meet and form some kind of relation between each other. These can be of an informal as well as of a formal nature. Hereby actors are often people, but can also be nations, organizations, objects etc. Social Network Analysis (SNA) focuses on patterns of relations between these actors. It seeks to describe networks of relations as fully as possible. This includes teasing out the prominent patterns in such networks, tracing the flow of information through them, and discovering what effects these relations and networks have on people and organizations. It can therefore be used to study network patterns of organizations, ideas, and people that connected…

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