Database selection is one of the main problems in designing electronic medical record (EMR) Software, since there are many different data with different types and formats in it. This work provides an approach for selecting suitable database management system (DBMS) with fuzzy analytical hierarchical process that gives capabilities of requirements to electronic medical record software. Criteria are choose based upon capabilities of requirements including supporting from type of operating system and programming language, maximum of table size, indexing, replication and access control. In addition, alternatives are PostgreSQL, MySQL, Oracle, DB2 and Microsoft SQL Server. According to the application of fuzzy analytical hierarchical process a ranking is obtained to decision making the best database management system for electronic medical record software.

The presence of number of web sites has increased the user's attraction towards web objects. This tremendous use came up with the future requests prediction depending upon the current and past access behaviour. Use of Internet has boomed up a lot since the last decade. This use also came with the heavy load on the internet. In today's world, speed plays a significant role and hence the speed augmentation is one of the biggest issues. For this, web latency reduction by prefetching is one of the good ideas. For the same, web prefetching is performed, where user's next expected requests are prefetched in the web cache of the web browser. A browser is basically a GUI based application program, which provides a platform for running Internet. Mozilla Firefox is a web browser, which is in very much use these days. This paper provides an analysis of Mozilla Firefox prefetching technique (link and DNS prefetching) and then designs a new prefetching scheme for the same. The experimental results are performed in Matlab 5.0. The results show that the designed prefetching framework is more efficient in terms of the cache hit ratio.

Spectrum sensing is an important task in cognitive radio (CR). Matched filter technique one of the techniques employed for spectrum sensing in cognitive radio which faces the challenge of low frequency offset tolerance in very low SNR environments. Hybrid matched filter architecture is used to improve this frequency offset tolerance. But, overall even such kind of architecture results in coarse detection. In very low SNR environments, where primary user is highly mobile, the multipath profile results in unknown phase of signal. Such kind of signal cannot even be detected by Hybrid Matched Filter. In this paper we propose combination of Hybrid Matched Filter and Single Cycle Cyclostationary Feature Detector to enhance the detection of such architecture. This results in both high frequency offset tolerance as well as fine detection of signal with unknown phase in very low SNR environments. Simulation results show significant improvement in the probability of detection and false alarm of the proposed scheme over Hybrid Matched Filter.

This paper presents the fault detection technique in wide area power system network using Phasor Data Concentrator (PDC). Single line to ground (SLG) fault data of transmission line is processed using the WAMS technology. Phasor Data Concentrator (PDC) is primarily data collecting centres located at the control centre processing unit and is responsible for collecting all the PMUs data that is transmitted over the communication link, which will detect fault in transmission line. This will help the operators in control centre to monitor the health of wide area power system network, and can initiate corrective actions when time is the critical issue. In this paper, the voltage and current phasor information, and other transmission line parameters from 14 IEEE bus system is processed and detects the fault in transmission lines. The proposed fault detection technique in transmission line of wide area power system network is very useful for improving power system reliability and power quality.

Discuss the real line fuzzy concept into multi- dimensional based on the reference function so as to get new imprecise numbers called the two-dimensional and three-dimensional imprecise numbers and their complements. Two and three dimensional imprecise numbers are obtained in the form of Cartesian product of fuzzy numbers. To study their character some necessary definitions like partial presence, construction of membership function, membership value ,Indicator function etc. of two and three-dimensional imprecise numbers are defined with own notation. As per as possible, try to show all the properties of classical set theory that can be hold good in the present imprecise numbers with some examples. Set Operations are defined by maximum and minimum operators just like defined in the real line imprecise numbers. Further bring out a few graphical examples to verify the intersection and union of two and three dimensional imprecise numbers are the empty and the universal set respectively. Basically Intersection and union are the operators to obtain their properties. 

Crowdsourcing is a famous technique to get innovative ideas and soliciting contribution from a large online community particularly in e-business. This technique is contributing towards changing the current business techniques and practices. It is also equally famous in analysis and design of m-business services. Mobile app stores are providing an opportunity for its users' to participate and contribute in the growth of mobile app market. App reviews given by users usually contain active, heterogeneous and real life user experience of mobile app which can be useful to improve the quality of app. Best to our knowledge, the strength of mobile app reviews as a crowdsource is not fully recognized and understood by the community yet. In this paper, we have analysed a crowdsourcing reference model to find out which features of crowdsource are present and are related to our case of mobile app reviews as a crowdsource. We have analyzed and discussed each construct of the reference model from the perspective of mobile app reviews. Moreover, app reviews as a crowdsourcing technique is discussed by utilizing the four pillars of the reference model: the crowd, the crowdsourcer, the crowdsourcing, and the crowdsourcing platform. We have also identified and partially validated certain constructs of the model and highlighted the significance of app reviews as a crowdsource based on existing literature. In this study, only one crowdsourcing reference model is used which can be a limitation of our study. The study can be further investigated and compared with other crowdsourcing reference models to get better insights of app reviews as a crowdsource. We believe that the understanding of app reviews as a crowdsourcing technique can lead to the further development of the mobile app market and can open further research opportunities.

Today, in the age of images and digitization relevant retrieval is quite a topic of research. In past era, the database was having only text or database was low dimensional type. But with every new day thousands of pictures are getting added into the database making it a high dimensional data set. Therefore, from a high dimensional dataset to get a set of relevant images is quite a cumbersome task. Number of approaches for getting relevant retrieval is defined, some includes retrievals only on the basis of color, while some include more than one primitive feature to retrieve the relevant image such as color, shape and texture. In this paper experiment has been performed on the trademark images. Trademark is a very important asset for any organization and increasing trademark images have developed a quick need to organize these images. This paper includes the implementation of HSV model for fast retrieval. Which use color and texture so as to extract feature vector. Experiment takes query image and retrieve twelve most relevant images to the user. Further for performance evaluation parameter used is Precision and Recall.

The electrophysiological brain activities are nonlinear in nature as measured by Electroencephalography (EEG). There are coherent activities in brain not only seen during explicit tasks but also during rest. This article aims to employ most robust nonlinear dynamics Time - Frequency representation (TFR) techniques such as wavelet phase coherence to investigate brain activity in different frequency bands at temporal and spatial scale dynamics in form of topographic maps in resting state networks. The TFR has the advantages to study the combined effect of time and frequency domains simultaneously. The wavelet coherence computed in this way exhibit high precision to detect the phase coherence in different frequency intervals to analyze highly complex non-autonomous and non-stationary EEG signals. The spatiotemporal dynamics of resting state networks are investigated by computing coherence. We have investigated the Wavelet based Phase coherence of oscillations of eye closed and eye open signals during resting states. The wavelet coherence is computed for selected 19 electrodes according to 10-20 system from 129 channel EEG signals. The significance was obtained using Wilcoxon Signed Rank test and pairwise wavelet coherence was computed for each possible combination. The Wavelet Phase Coherence using Wavelet Transform gives significantly high results (P<0.05) in EC and EO signals during resting states in frequency interval 0.5-50 Hz overall as well as in the band intervals such as delta (05-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), beta (13-22 Hz) and gamma (22-50 Hz). By computing the spatial wavelet phase coherence, we observed significant pathways including sagittal factor (anterior-posterior interhemispheric) and lateral factor (perpendicular to anterior-posterior axis). The lateral factor differences have less affect than the sagittal factor. Each band was involved in different activities in some way, however alpha band showed distinct anterior-posterior activity when the eye-closed coherence was higher than the eye open coherence.