What is Image Retrieval
/ November 10, 2017

An image retrieval system can be defined as searching, browsing, and retrieving images from massive databases consisting of digital images. Although Conventional and common techniques of retrieving images make use of  adding metadata namely captioning keywords so as to perform annotation of words. However image search can be described by dedicated technique of search which is mostly used to find images. For searching images user provides the query image and the system returns the image similar to that of query image. Image Retrieval Architecture Image Retrieval has been adopted in most of the major search engines, including Google, Yahoo!, Bing, etc. A large number of image search engines mainly employ the surrounding texts around the images and the image names to index the images. Because there are only two main places where anyone can place text first in title (Name of image) and second in the tags which are proposed and implemented using web 2.0 concepts? Most of the time user make query in the text format for search contents over any search engine. Figure 1: General Image Retrieval System However, this limits the capability of the search engines in retrieving the semantically related images using a given query. On…

Community Detection : Unsupervised Learning
/ November 9, 2017

Advances in technology and computation have provided the possibility of collecting and mining a massive amount of real-world data. Mining such “big data” allows us to understand the structure and the function of real systems and to find unknown and interesting patterns. This section provides the brief overview of the community structure. Introduction of Community Detection In the actual interconnected world, and the rising of online social networks the graph mining and the community detection become completely up-to-date. Understanding the formation and evolution of communities is a long-standing research topic in sociology in part because of its fundamental connections with the studies of urban development, criminology, social marketing, and several other areas. With increasing popularity of online social network services like Facebook, the study of community structures assumes more significance. Identifying and detecting communities are not only of particular importance but have immediate applications. For instance, for effective online marketing, such as placing online ads or deploying viral marketing strategies [10], identifying communities in social network could often lead to more accurate targeting and better marketing results. Albeit online user profiles or other semantic information is helpful to discover user segments this kind of information is often at a coarse-grained level…

An Introduction of Computer Vision
/ November 8, 2017

Computer vision is the science and technology of machines that see, and seeing in this case means that the machine is able to extract from an image some information that is necessary for solving some task. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models to the construction of computer vision systems. Computer Vision : Overview The human ability to interact with other people is based on their ability of recognition. This innate ability to effortlessly identify and recognize objects, even if distorted or modified, has induced to research on how the human brain processes these images. This skill is quite reliable, despite changes due to viewing conditions, emotional expressions, ageing, added artifacts, or even circumstances that permit seeing only a fraction of the face. Furthermore, humans are able to recognize thousands of individuals during their lifetime. Understanding the human mechanism, in addition to cognitive aspects, would help to build a system for the automatic…

What is Steganography
/ November 3, 2017

Computer and internet are the major media that connects different parts of the world as one global virtual world in this modern era. That’s why we can exchange lots of information easily at any distance within seconds of time. But the confidential data need to be transferred should be kept confidential till the destination. Steganography: General Overview Due to advances in ICT (Information and Communication Technology), most of information is kept electronically. Consequently, the security of information has become a fundamental issue. Besides cryptography, steganography can be employed to secure information. Steganography is a technique of hiding information in digital media. In contrast to cryptography, the message or encrypted message is embedded in a digital host before passing it through the network, thus the existence of the message is unknown. Besides hiding data for confidentiality, this approach of information hiding can be extended to copyright protection for digital media: audio, video, and images. Nowadays, thanks to the stunningly fast advancement of the computer and network technology, people can easily send or receive secret information in various forms to or from almost any remotest part of the world through the Internet within seconds. In fact, there might be tons of secret…

Ariori Algorithm: Example and Algorithm Description
/ November 2, 2017

With the quick growth in e-commerce applications, there is an accumulation vast quantity of data in months not in years. Data Mining, also known as Knowledge Discovery in Databases (KDD), to find anomalies, correlations, patterns, and trends to predict outcomes. Apriori algorithm is a classical algorithm in data mining. It is used for mining frequent itemsets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. It produces association rules that indicate what all combinations of medications and patient. Figure 1 Apriori algorithm example application Ariori Algorithm :  Overview One of the first algorithms to evolve for frequent itemset and Association rule mining was Apriori. Two major steps of the Apriori algorithm are the join and prune steps. The join step is used to construct new candidate sets. A candidate itemset is basically an item set that could be either Frequent or…

FP Growth(FP-tree) Algorithm with Example
/ November 1, 2017

FP-growth algorithm : Introduction The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. The FP-Growth methods adopts a divide and conquer strategy as follows: compress the database representing frequent items into a frequent-pattern tree, but retain the itemset association information, and then divide such a compressed database into a set of condition databases, each associated with one frequent item, and mine each such database. First, a scan of database derives a list of frequent items in descending order. Then the FP – tree is constructed as follows. Create the root of the tree and scan the database second time. The items in each transaction are processed in the order of frequent items list and a branch is created for each transaction. When considering the branch to be added to a transaction, the count of each node along a common prefix is incremented by 1. After constructing the tree the mining proceeds as follows. Start from each frequent length-1 pattern, construct its conditional pattern base, then construct its conditional FP-tree and perform mining recursively on such a tree. The support of a candidate (conditional) itemset is counted traversing the tree. The sum of count values…

What is Distributed Database
/ October 31, 2017

A distributed database is a database in which portions of the database are stored in multiple physical locations and processing is distributed among multiple database nodes. Distributed databases can be homogenous or heterogeneous. In a homogenous distributed database system, all the physical locations have the same underlying hardware and run the same operating systems and database applications. In a heterogeneous distributed database, the hardware, operating systems or database applications may be different at each of the locations. Distributed Database: Overview A distributed database is a database distributed between several sites. The reasons for the data distribution may include the inherent distributed nature of the data or performance reasons. In a distributed database the data at each site is not necessarily an independent entity, but can be rather related to the data stored on the other sites.  A distributed database (DDB) is a collection of multiple, logically interrelated databases distributed over a computer network. A distributed database management system (DDBMS) is the software that manages the DDB, and provides an access mechanism that makes this distribution transparent to the user. Distributed database system (DDBS) is the integration of DDB and DDBMS. This integration is achieved through the merging the database and…

Association Rule Mining
/ October 30, 2017

Data Mining is the discovery of hidden information found in databases and can be viewed as a step in the knowledge discovery process. Data mining functions include clustering, classification, prediction, and link analysis (associations). One of the most important data mining applications is that of mining association rules. An association rule has two parts, an antecedent (if) and a consequent (then). An antecedent is an item found in the data. A consequent is an item that is found in combination with the antecedent. Association Rule Mining: Overview Association rules are created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. Support is an indication of how frequently the items appear in the database. Confidence indicates the number of times the if/then statements have been found to be true. Association rule mining has been an active research area in data mining, for which many algorithms have been developed. In data mining, association rule learning is a popular and well-accepted method for discovering interesting relations between variables in large databases. Association rules are employed today in many areas including web usage mining, intrusion detection and bioinformatics. In general, the association rule…

What is Mobile Computing
/ October 26, 2017

Mobile Computing is a technology that allows transmission of data, voice and video via a computer or any other wireless enabled device without having to be connected to a fixed physical link. Mobile computing (or ubiquitous computing as it is sometimes called) is the use of computers in a non-static environment. This use may range from using notebook-type computers away from one’s office or home to the use of handheld, palmtop-type PDA-like devices to perform both simple and complex computing tasks. Mobile Computing: General Mobile device has become essential part of human life. Apart from call and receive functions, user can access many function in his/her mobile. A user wants everything on his/her mobile device for the ease of work. Some people use tablets instead of laptop or desktop. Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. Mobile phones are set to become the universal interface to online services and cloud computing applications. However, using them for this purpose today is limited to two…

What is Phishing in Web Security
/ October 25, 2017

Phishing is one of the luring techniques used by phishing artists with the intention of exploiting the personal details of unsuspected users. Phishing is a form of identity theft that occurs when a malicious Web site impersonates a legitimate one in order to acquire sensitive information such as passwords, account details, or credit card numbers. Though there are several anti-phishing software and techniques for detecting potential phishing attempts in emails and detecting phishing contents on websites, phishers come up with new and hybrid techniques to circumvent the available software and techniques. This section provide the detail study about the online phishing and their deployment techniques. Phishing: General Description Now a day’s attacks have become major issues in networks. Attacks will intrude into the network infrastructure and collect the information needed to cause vulnerability to the networks. Security is needed to prevent the data from various attacks. Attacks may either active attack or passive attack. One type of passive attack is phishing. Phishing is a continual threat and is larger in social media such as facebook twitter. Phishing emails contain link to the infected website. Phishing email direct the user to the infected website where they are asked to enter the…

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