Agent Based Context Aware Data Aggregation and Dissemination in Distributed Multimedia Sensor Networks

Being equipped with appropriate multimedia sensor nodes, DMSNs can enable detection of object, temperature and identification of the location of fire attack in the forest. Sensor nodes deployed in forest environment enables to gather context information such as air pressure, temperature, object awareness, location of fire, fire condition (emergency level or non emergency level), and energy awareness about each node. Data aggregation plays an important role to conserve the network life of DMSN. Hence, in this paper we propose an software agent based energy efficient context aware data aggregation and dissemination in DMSN for the targeted area. The proposed model considers the context information such as temperature, air-pressure, energy, object awareness and helps in identifying the location of fire attack in the forest. Static and mobile software agents are used along with context awareness to improve the performance of the proposed scheme. To test the operation, proposed scheme is simulated using NS2. The performance of the proposed scheme is evaluated by considering some of the parameters such as energy consumption, routing overhead, rate of redundancy of data, aggregation time and rate of dissemination of data. © 2017 IEEE

Frequent Item Set Mining Using INC_MINE in Massive Online Analysis Frame Work

Frequent Pattern Mining is one of the major data mining techniques, which is exhaustively studied in the past decade. The technological advancements have resulted in huge data generation, having increased rate of data distribution. The generated data is called as a ‘data stream’. Data streams can be mined only by using sophisticated techniques. The paper aims at carrying out frequent pattern mining on data streams. Stream mining has great challenges due to high memory usage and computational costs. Massive online analysis frame work is a software environment used to perform frequent pattern mining using INC_MINE algorithm. The algorithm uses the method of closed frequent mining. The data sets used in the analysis are Electricity data set and Airline data set. The authors also generated their own data set, OUR-GENERATOR for the purpose of analysis and the results are found interesting. In the experiments five samples of instance sizes (10000, 15000, 25000, 35000, 50000) are used with varying minimum support and window sizes for determining frequent closed itemsets and semi frequent closed itemsets respectively. The present work establishes that association rule mining could be performed even in the case of data stream mining by INC_MINE algorithm by generating closed frequent itemsets which is first of its kind in the literature

Evaluation of Principal Components Analysis (Pca) and Data Clustering Techniques (Dct) on Medical Data

The present study investigates the performance analysis of PCA filters and six clustering algorithms on the medical data (Hepatitis) which happens to be multidimensional and of high dimension with complexities much more than the conventional data. By Clustering process data reduction is achieved in order to obtain an efficient processing time to mitigate a curse of dimensionality. Usually, in medical diagnosis, the chief guiding symptoms (rubrics) coupled with the clinical tests help in accurate diagnosis of the diseases/disorders. Hence, the primary factors have maximum impact/influence on the detection of the specific disorders. Therefore, the present study is undertaken and the results predict that farthestfirst clustering algorithm happens to be the best clustering algorithm without PCA filter in general, while cobweb clustering algorithm could be preferred with PCA filter in some other medical datasets

A Comparative Study of Different Segmentation Techniques for Brain Tumour Detection

Brain tumour detection is one of the challenging tasks in medical image processing. The present study discusses in detail the segmentation process by means of histogram clustering, Global thresholding, Watershed segmentation and edge based segmentation. Six MRI images from radiologists were collected and the experiments were conducted for statistical analysis also. A comparative study is made and the results are of great interest and practical utility

A Data Centric Privacy Preserved Mining Model for Business Intelligence Applications

In present day competitive scenario, the techniques such as data warehouse and on-line analytical process (OLAP) have become a very significant approach for decision support in data centric applications and industries. In fact the decision support mechanism puts certain moderately varied needs on database technology as compared to OLAP based applications. Data centric decision support schemes (DSS) like data warehouse might play a significant role in extracting details from various areas and for standardizing data throughout the organization to achieve a singular way of detail presentation. Such efficient data presentation facilitates information for decision making in business intelligence (BI) applications in various industrial services. In order to enhance the effectiveness and robust computation in BI applications, the optimization in data mining and its processing is must. On the other hand, being in a multiuser scenario, the security of data on warehouse is also a critical issue, which is not explored till date. In this paper a data centric and service oriented privacy preserved model for BI applications has been proposed. The optimization in data mining has been accomplished by means of C5.0 classification algorithm and comparative study has been done with C4.5 algorithm. The implementation of enhanced C5.0 algorithm with BI model would provide much better performance with privacy preservation facility for Business Intelligence applications

Adaptive Data Mining Approach for Pcb Defect Detection and Classification

Objective: To develop a model for PCB defect detection and classification with the help of soft computing technique. Methodology: To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. The proposed approach is divided into three main stages: (i) data pre-processing (ii) feature selection and reduction and (iii) Classification. In this approach, pre-processing, feature selection and reduction is carried out by measuring of confidence with the adaptive genetic algorithm. Prediction and classification is carried out by using neural network classifier. A genetic algorithm is used for data preprocessing to achieve the feature reduction and confidence measurement. Findings: The system is implemented using MatLab 2013b. The resulting analysis shows that the proposed approach is capable of detecting and classifying defects in PCB board

Analysis of Facial Expressions with Respect to Navarasas in Bharathanatym Styles Using Image Processing

Facial expression analysis with respect to Bharathanatyam a classical dance style of south India is studied in this paper by using image processing techniques and several properties associated with the face are taken into consideration. The emotional changes result in the changes in the facial expression. Accordingly the curvatures developed on the face and the dimensions of the objects on the face such as eyebrows, lips and the area of the mouth also change. Naturally there exist changes in the intensity of the pixels corresponding to these objects. The natural eyes can distinguish these sharp changes in both the cases and understand the facial expressions. The experimental results predicted a definite change in every trail. These results can also be used as a tool to design intelligent system which recognizes different emotions of people in different environment. The results are found to be of immense scientific and psychological interest

Knowledge Discovery Process in the Image-Segmentation Data

This paper discusses in detail the behavior of the different classification on image segmentation data. The result predicts the different aspects of the classification model. It is found that NNEG is the best classifier with accuracy of 96.2771%. ROC|max and ROC|min are computed for different classes and are found to be interesting

Knowledge Discovery in Data Mining and Massive Data Mining

Knowledge discovery is a process of non trivial extraction of previously unknown and presently useful information. The rapid advancement of the technology resulted in the increasing rate of data distributions. The data generated from mobile applications, sensor applications, network monitoring, traffic management, weblogs etc. can be referred as a data stream. The data streams are massive in nature. The present work mainly aims at knowledge discovery using data mining and massive data mining techniques. The knowledge discovery process in both the techniques is compared by developing a classification model using Naive bayes classifier. The former case uses Edu-data, a data collected from technical education system and the latter case uses massive online analysis frame work to generate the data streams. Mining data stream is referred as Massive Data Mining. The data streams must be processed under very strict constraints of space and time using sophisticated techniques. The traditional data mining techniques are not advised on this massive data. Therefore the massive online analysis framework is used to mine the data streams. The present work happens to be unique in the literaturein