Support Vector Machine (SVM)
/ September 1, 2017

In recent years, the Artificial Neural Networks (ANNs) have been playing a significant role for variants of data mining tasks which is extensively popular and active research area among the researchers. To intend of neural network is to mimic the human ability to acclimatize to varying circumstances and the current environment. The subtle use of Support Vector Machine (SVM) in various data mining applications makes it an obligatory tool in the development of products that have implications for the human society. SVMs, being computationally powerful tools for supervised learning, are widely used in classification, clustering and regression problems. SVMs have been successfully applied to a variety of real-world problems like particle identification, face recognition, text categorization, bioinformatics, civil engineering and electrical engineering etc. SVM have attracted a great deal of attention in the last decade and actively applied to various domains applications. SVMs are typically used for learning classification, regression or ranking function. SVM are based on statistical learning theory and structural risk minimization principal and have the aim of determining the location of decision boundaries also known as hyperplane that produce the optimal separation of classes. Maximizing the margin and thereby creating the largest possible distance between the separating…

Wireless Sensor Network
/ September 1, 2017

Wireless Sensor Network introduction Efficient design and implementation of wireless sensor networks has become a hot area of research in recent years, due to the vast potential of sensor networks to enable applications that connect the physical world to the virtual world. By networking large numbers of tiny sensor nodes, it is possible to obtain data about physical phenomena that was difficult or impossible to obtain in more conventional ways. In the coming years, as advances in micro-fabrication technology allow the cost of manufacturing sensor nodes to continue to drop, increasing deployments of wireless sensor networks are expected, with the networks eventually growing to large numbers of nodes. Wireless Sensor Networks (WSNs) have been widely considered as one of the most important technologies for the twenty – first century. Enabled by recent advances in microelectronic mechanical systems (MEMS) and wireless communication technologies, tiny, cheap, and smart sensors deployed in a physical area and networked through wireless links and the Internet provide unprecedented opportunities for a variety of civilian and military applications, for example, environmental monitoring, battle field surveillance, and industry process control. Definition Wireless Sensor Networks (WSNs) can be defined as a self-configured and infrastructureless wireless networks to monitor physical…

Neural Network
/ August 31, 2017

Neural Network Basics An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. Neural Network  Definition Work on artificial neural networks, commonly referred to as “neural networks,” has been motivated right from its inception by the recognition that the human brain computes in an entirely different way from the conventional digital computer. The brain is a highly complex, nonlinear, and parallel computer (information-processing system). It has the capability to organize its structural constituents, known as neurons, so as to perform certain computations (e.g., pattern recognition, perception, and motor control) many times faster than the fastest digital computer in existence today. Consider, for example,…

Hidden Markov Model
/ August 23, 2017

Hidden Markov Model (HMM) The Hidden Markov Model (HMM) is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable sequence. A hidden Markov model is a doubly stochastic process, with an underlying stochastic process that is not observable (hence the word hidden), but can be observed through another stochastic process that produces the sequence of observations. The hidden process consists of a set of states connected to each other by transitions with probabilities, while the observed process consists of a set of outputs or observations, each of which may be emitted by each state according to some probability density function (pdf). Depending on the nature of this pdf, several HMM classes can be distinguished. If the observations are naturally discrete or quantized using vector quantization. The Hidden Markov Model is a finite set of states, each of which is associated with a (generally multidimensional) probability distribution Transitions among the states are governed by a set of probabilities called transition probabilities. In a particular state an outcome or observation can be generated, according to the associated probability distribution. It is only the outcome, not the state visible to an external observer…

WormHole Attack in MANET
/ August 19, 2017

A wireless ad-hoc network is temporarily set network by wireless mobile computers moving arbitrary in the place that have no fixed infrastructure and all of the transmission links are established through wireless medium. MANETs are a kind of wireless ad-hoc network. Each node in a MANET is free to move independently in any direction leads to changing its links to other nodes frequently. Each node operates as an end system and also as a router to forward packets. The primary challenge in building a MANET is equipping each node to continuously maintain the information required to properly route traffic. Wireless ad-hoc network is promising in solving many challenging real-world problems like military field operation, communication in emergency response system and oil drilling and mining operation. Wireless mobile ad-hoc networks are vulnerable to many security attacks because of shared channel, insecure operating environment, lack of central authority, limited resource availability, dynamically changing network topology, resource constraints. MANET’s open issues are like security problem, finite transmission bandwidth, abusive broadcasting messages, reliable data delivery, dynamic link establishment and restricted hardware caused processing capabilities emerges into new horizon of different research areas. Wormhole Attack In MANETs, each node communicates directly with its neighboring nodes…

Black-hole Attack in MANET
/ August 17, 2017

Mobile Ad-hoc Network(MANET) Attack Wireless networks can be basically either infrastructure based networks or infrastructure less networks. The infrastructure based networks uses fixed base stations, which are responsible for coordinating communication between the mobile hosts (nodes). The ad hoc networks falls under the class of infrastructure less networks, where the mobile nodes communicate with each other without any fixed infrastructure between them. An ad hoc network is a collection of nodes that do not rely on a predefined infrastructure to keep the network connected. So the functioning of Ad-hoc networks is dependent on the trust and co-operation between nodes. Nodes help each other in conveying information about the topology of the network and share the responsibility of managing the network. Hence in addition to acting as hosts, each mobile node does the function of routing and relaying messages for other mobile nodes. Black-hole Attack Security in a MANET is an essential component for basic network functions like packet forwarding and routing.  Before we survey the solutions that can help secure the mobile ad hoc network, we think it necessary to find out how we can judge if a mobile ad hoc network is secure or not, or in other words,…

Decision Trees Applications
/ August 14, 2017

Decision Tree Overview In data mining techniques two kinds of basic learning processes are available namely supervised and unsupervised. when we talk about the supervised learning techniques the decision tree learning is one of the most essential technique of classification and prediction. A number of different kinds of decision tree algorithms are available i.e. ID3, C4.5, C5.0, CART, SLIQ and others. All these algorithms the used to generate the transparent data models. these data models can be evaluated using the paper and pencil. therefore that is an effective data modeling technique. Applications of Decision Trees Application of Decision Tree Algorithm in Healthcare Operations [1]: the decision trees are used to visualize the data patterns in form of tree data structure. that help to also prepare the relationship among the attributes and the final class labels. thus the patient’s different health attributes can help to understand the symptoms and possibility by comparing the historical data available with the similar attributes. Manufacturing and Production: in a large production based industries where the regulation of production and planning is required. the decision tree models helps for understanding the amount of production, time of production and other scenarios. that can be evaluated using the past scenarios of…

ID3 Decision Tree in Data Mining
/ August 11, 2017

ID3 Decision Tree Overview Engineered by Ross Quinlan the ID3 is a straightforward decision tree learning algorithm. The main concept of this algorithm is construction of the decision tree through implementing a top-down, greedy search by the provided sets for testing every attribute at each node of decision. With the aim of selecting the attribute which is most useful to classify a provided set of data, a metric is introduced named as Information Gain [1]. To acquire the finest way for classification of learning set, one requires to act for minimizing the fired question (i.e. to minimize depth of the tree). Hence, some functions are needed that is capable of determine which questions will offer the generally unbiased splitting. One such function is information gain metric. Entropy In order to define information gain exactly, we require discussing entropy first. Let’s assume, without loss of simplification, that the resultant decision tree classifies instances into two categories, we’ll call them ​$$[ P_{positive} ] and [ N_{negative} ]$$​ Given a set S, containing these positive and negative targets, the entropy of S related to this Boolean classification is: ​$$[ P_{positive} ]$$​: proportion of positive examples in S ​\( [ N_{negative} ]…

k Nearest Neighbor (KNN)
/ August 11, 2017

k Nearest Neighbor (KNN): introduction The necessity of data mining techniques has emerged quite immensely nowadays due to massive increase in data. Data mining is the process of extracting patterns and mining knowledge from data. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970’s as a non-parametric technique. The model for KNN is the entire training dataset. When a prediction is required for a unseen data instance, the KNN algorithm will search through the training dataset for the k-most similar instances. The prediction attribute of the most similar instances is summarized and returned as the prediction for the unseen instance. Nearest neighbor classifiers is a lazy learner’s method and is based on learning by analogy. It is a supervised classification technique which is used widely. Unlike the previously described methods the nearest neighbor method waits until the last minute before doing any model construction on a given tuple. In this method the training tuples are represented in N-dimensional space. When given an unknown tuple, k-nearest neighbor classifier searches the k…

Introduction of Decision Trees
/ August 11, 2017

Decision Tree: Overview Data mining techniques that help to make decisions using the available facts can be termed as Decision Tree. Decision trees are not only useful for decision-making applications it is also used for classification and prediction task. There are some popular decision Tree algorithms namely C4.5, ID3, and CART. These algorithms are supervised learning algorithms. During training, the input samples are represented as a tree data structure. example An example of a decision tree is given in figure 1, where nodes of the tree show the attributes of the data set. Additionally, edges create a relationship among two nodes using the values of available attributes. The leaf node of the tree is recognized as the decision node. Figure 1 decision tree example Figure 1, demonstrates a decisions tree. Here decision labels are (yes or no), it is also known as class labels. In decision trees, the class label is placed on leaf nodes. Additionally, the nodes humidity, outlook, and wind are attributes in data-set. Because the data set contains decision and attributes and decision tree graphically represents the data. Therefore it is helpful to understand the relationship between them. Sometimes the decision trees are used in form of IF…