Recently, data management and processing for wireless sensor networks (WSNs) has become a topic of active research in several fields of computer science, such as the distributed systems, the database systems, and the data mining. The main aim of deploying the WSNs-based applications is to make the real-time decision which has been proved to be very challenging due to the highly resource-constrained computing, communicating capacities, and huge volume of fast-changed data generated by WSNs. This challenge motivates the research community to explore novel data mining techniques dealing with extracting knowledge from large continuous arriving data from WSNs. Traditional data mining techniques are not directly applicable to WSNs due to the nature of sensor data, their special characteristics, and limitations of the WSNs. This work provides an overview of how traditional data mining algorithms are revised and improved to achieve good performance in a wireless sensor network environment. A comprehensive survey of existing data mining techniques and their multilevel classification scheme is presented. The taxonomy together with the comparative tables can be used as a guideline to select a technique suitable for the application at hand. Based on the limitations of the existing technique, an adaptive data mining framework of WSNs for future research is proposed.
Research is vital and necessary part of modern university education; universities are producers of new knowledge. Role of universities is different from the 19 th century; demands of the 21 st century are enormously higher. The purpose of study was to find out the causes of low research productivity at university level. Population of the study was faculty members working at University. Sample consisting of 232 male and female faculty members was selected through the stratified sampling technique. Quantitative research methodology was adopted; data were collected through questionnaire. Data collected through the questionnaire was analyzed by using the statistical methods. To describe the data at the initial stage percentages were calculated. At the second stage Mean score, SD and Chi-Square, the test of significance was applied. The level of significance selected for the study was 0.05. On the basis of findings, the conclusions were drawn that extra teaching load, performance of administrative duties along with academic duties, lack of funds, nonexistence of research leave, negative attitude of the faculty towards research, lack of research skills, non availability of latest books, absence of professional journals, less number of university own journals, are the major causes of low productivity and reduced the research productivity of the university faculty members
Job satisfaction is a set of favorable or unfavorable feelings and emotions with which employees view their works. It refers to a collection of attitudes that workers have about their job. The present study was conducted to investigate the difference between gender (male and female teachers) and types of school (urban and rural) about job satisfaction. Study was descriptive in nature and Minnesota satisfaction questionnaire was used to collect data. The data were collected from 785 teachers selected from all Public High schools (192) in one district .The findings were drawn after the descriptive and inferential analysis, Means, Standard Deviation and ‘t’ test, was run to test the hypotheses. Generally teachers were less satisfied with advancement, compensation, supervision human-relation, and working conditions. Female teachers were more satisfied than their male counterparts. There was no significant difference between urban and rural teachers’ job satisfaction
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