Mrutyunjaya Panda

Social Networks Analysis: Tools, Measures and Visualization

Social Network Analysis (SNA) is becoming an important tool for investigators, but all the necessary information is often available in a distributed environment. Currently there is no information system that helps managers and team leaders monitor the status of a social network. This chapter presents an overview of the basic concepts of social networks in data analysis including social network analysis metrics and performances. Different problems in social networks are discussed such as uncertainty, missing data and finding the shortest path in a social network. Community structure, detection and visualization in social network analysis is also illustrated. This chapter bridges the gap among the users by combining social network analysis methods and information visualization technology to help a user visually identify the occurrence of a possible relationship amongst the members in a social network. The chapter illustrates an online visualization method for a DBLP (Digital Bibliography Library Project) dataset of publications from the field of computer science, which is focused on the co-authorship relationship based on the intensity and topic of joint publications. Challenges to be addressed and future directions of research are presented and an extensive bibliography is also included.

Hybrid Data Mining Approach for Image Segmentation Based Classification

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function RBF neural network WRBF and feature subset harmony search based fuzzy discernibility classifier HSFD approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image MR and Computed Tomography CT Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

Performance Evaluation of Social Network Using Data Mining Techniques

Social network research relies on a variety of data sources, depending on the problem scenario and the questions, which the research is trying to answer or inform. Social networks are very popular nowadays and the understanding of their inner structure seems to be promising area. Cluster analysis has also been an increasingly interesting topic in the area of computational intelligence and found suitable in social network analysis in its social network structure. In this chapter, we use k-cluster analysis with various performance measures to analyse some of the data sources obtained for social network analysis. Our proposed approach is intended to address the users of social network, that will not only help an organization to understand their external and internal associations but also highly necessary for the enhancement of collaboration, innovation and dissemination of knowledge.

Development of a reliable trust management model in social internet of things

In this paper, we introduce the first application of the belief propagation algorithm in the design and evaluation of trust management systems with an introduction of a novel paradigm of social internet of things (SIoT), as ‘social network of intelligent objects’, that are based on the notion of social relationships among objects. Further, we address a trust-making process, where a person needs to make a judgement about the trustworthiness of another community member where they do not have any prior knowledge about each other. Our proposed model uses various performance measures such as: centrality and transitivity measures for SIoT analysis and then employs hybrid fuzzy nearest neighbour with Bayesian belief network and Bayesian belief network with conditional independence to represent a trust-based evaluation. Bayesian belief propagation technique is used here to infer trustworthiness in a social context. Finally, we perform non-parametric Friedman two tail test for statistical significance of the results obtained for various approaches. The evaluation of the model is done on datasets collected from epinion.com and slashdog.org shows promising results that enable us to steer the interaction among the billions of objects which will crowd the future IoT.

Histogram of Oriented Gradients-Based Digit Classification Using Naive Bayesian Classifier

Classification helps in grouping the objects according to their characteristics or features, which is essential for predicting the behavior of objects, simplifying the process of searching in a large database, detecting specific objects, etc. Advancement in information technology has increased the need for classification of text documents, image, video, audio dataset for easy and accurate retrieval of required information. Selecting features where the most relevant information lies is one of the important steps before classification. In this paper, gradient information is used for feature extraction with the help of histogram of oriented gradients technique. The simplicity of naive Bayesian classifier makes it suitable for large databases. The accuracy and ROC curve prove the effectiveness of the proposed method.

On the Effectiveness of Hybrid Canopy with Hoeffding Adaptive Naive Bayes Trees: Distributed Data Mining for Big Data Analytics

The Big Data, due to its complicated and diverse nature, poses a lot of challenges for extracting meaningful observations. This sought smart and efficient algorithms that can deal with computational complexity along with memory constraints out of their iterative behavior. This issue may be solved by using parallel computing techniques, where a single machine or a multiple machine can perform the work simultaneously, dividing the problem into sub problems and assigning some private memory to each sub problems. Clustering analysis are found to be useful in handling such a huge data in the recent past. Even though, there are many investigations in Big data analysis are on, still, to solve this issue, Canopy and K-Means++ clustering are used for processing the large-scale data in shorter amount of time with no memory constraints. In order to find the suitability of the approach, several data sets are considered ranging from small to very large ones having diverse filed of applications. The experimental results opine that the proposed approach is fast and accurate.

Performance analysis of supervised machine learning techniques for sentiment analysis

Wide use of internet and web applications like, feedback collection systems are now making peoples smarter. In these applications, peoples used to give their feedback about the movies, products, services, etc through which they have gone, and this feedback are publicly available for future references. It is a tedious task for the machines to identify the feedback types, i:e positive or negative. And here Machine Learning Techniques plays vital roles to train the machine and make it intelligent so that the machine will be able to identify the feedback type which may give more benefits and features for those web applications and the users. There are many supervised machine learning techniques are available so it is a difficult task to choose the best one. In this paper, we have collected the movie review datasets of different sizes and have selected some of the widely used and popular supervised machine learning algorithms, for training the model. So that the model will be able to categorize the review. Pythons NLTK package along with the WinPython and Spyder are used for processing the movie reviews. Then Pythons sklearn package is used for training the model and finding the accuracy of the model.

Performance Comparison Between Apache Hive and Oracle SQL for Big Data Analytics

Big data shall mean the massive volume of data that could not be stored, processed and managed by any traditional database management systems. Big Data Analytics becoming a comprehensive research area today this has attracted to all academia and industry to extract knowledge and information from a large amount of data. Oracle SQL is a prominent DBMS and is used worldwide. As the data goes bigger the running time is increasing in Oracle SQL. With the help of Apache Hive, we can do a large scale of data analysis in minimal time period. Apache Hive expedites for reading, writing and managing big datasets in distributed environment using SQL. Whereas Oracle SQL provides integrated development domain for running queries and scripts. In this paper, we have taken few queries for analysis for some smaller data sets as well as larger data sets and we have done an analysis for both Apache Hive and Oracle SQL environment.

Characterizing Intelligent Intrusion Detection and Prevention Systems Using Data Mining

Intrusion Detection and Prevention Systems (IDPS) are being widely implemented to prevent suspicious threats in computer networks. Intrusion detection and prevention systems are security systems that are used to detect and prevent security threats to computer networks. In order to understand the security risks and IDPS, in this chapter, the authors make a quick review on classification of the IDPSs and categorize them in certain groups. Further, in order to improve accuracy and security, data mining techniques have been used to analyze audit data and extract features that can distinguish normal activities from intrusions. Experiments have been conducted for building efficient intrusion detection and prevention systems by combining online detection and offline data mining. During online data examination, real-time data are captured and are passed through a detection engine that uses a set of rules and parameters for analysis. During offline data mining, necessary knowledge is extracted about the process of intrusion.