Force and Proficient Data Replica Discovery in WSN

We propose the distributed clone detection method and star topology is used to discover the replica data in the wireless sensor networks. In this paper vitality practiced space careful clone guarantee prosperous clone assault location and carry on satisfactory system period of time. Solidly, we tend to abuse the realm information of sensors and subjectively separate witnesses located in an exceedingly ring region to envision the genuineness of sensors and to report known clone assaults. Besides the clone detection likelihood, we tend to additionally contemplate energy consumption and memory storage within the style of clone detection protocol, i.e., associate degree energy- and memory economical distributed clone detection protocol with random witness choice theme in WSNs

Query Extraction Using Filtering Technique over the Stored Data in the Database

Many variety of users approaching server to perform their continuous queries which incorporates the knowledge desires and obtain notified at anytime supported the question that has been printed. To makes this task with efficiency servers ought to keep classification methodology that compares the knowledge in information. we tend to gift a unique question classification and reorganization formula that supports mathematician IF and that we determine totally different reorganization choices for the indexes and demonstrate the importance of question insertion order within the construction of the classification structure. we tend to through an experiment judge completely different reorganization methods and showcase their impact in filtering potency victimization 2 different real-world datasets and each artificial and real question sets. we tend to planned a CF primarily based algorithms for economical filtering performance. It doesn't base on the insertion of queries in information

A Recommendation for Online Social Voting using the Evidence based Filtering Method

Marvelous growth within the quality of on-line social networks (OSNs) in recent years. Most of existing on-line social networks like Face book & Twitter area unit designed to bias towards data speech act to an outsized audience and additionally raises variety of privacy and security problems. Though OSNs permits one user to limit access to her/his knowledge, presently they are doing not give any mechanism to enforce privacy considerations over knowledge related to multiple users. During this paper, we tend to propose associate approach to facilitate cooperative privacy management of shared knowledge in OSNs. we tend to extend and formulate a multiparty access management model, named Evidence based aggregation method to capture the essence of voting in OSNs, beside a multiparty policy specification theme and a policy social control mechanism. We tend to additionally demonstrate the relevancy of our approach by implementing a proof-of-concept example hosted in Face book

Smart Health Predicting System Using Data Mining

An overview of the data mining techniques with its applications, medical, and educational aspects of Clinical Predictions. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available. Such a large amount of data cannot be processed by humans in a short time to make diagnosis, and treatment schedules. A major objective is to evaluate datamining techniques in clinical and health care applications to develop accurate decisions. It also gives a detailed discussion of medical data mining techniques which can improve various aspects of Clinical Predictions. It is a new powerful technology which is of high interest incomputer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of machine learning and database management to extract new patterns from large datasets and the knowledge associated with these patterns. The actual task is to extract data by automatic orsemi- automatic means. The different parameters included in data mining include clustering, forecasting, path analysis and predictive analysis. It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reason. The Health Prediction system is an end user support and online consultation project. Here we propose a system that allows users to get instant guidance on their health issues through an intelligent health care system online. The system is fed with various symptoms and the disease/illness associated with those systems. The system allows user to share their symptoms and issues. It then processes userssymptoms to check for various illness that could be associated with it. Here we use some intelligent data mining techniques to guess the most accurate illness that could be associated with patient’s symptoms. If the system is not able to provide suitable results, it informs the user about the type of disease or disorder it feels user’s symptoms are associated with. If users symptoms do not exactly match any disease in our database

Measuring Semantic Similarity among Text Snippets and Page Counts in Data Mining

Measuring the semantic similarity between words is an important component in various tasks on the web such as relation extraction, community mining, document clustering, and automatic metadata extraction. Despite the usefulness of semantic similarity measures in these applications, accurately measuring semantic similarity between two words (or entities) remains a challenging task. We propose an empirical method to estimate semantic similarity using page counts and text snippets retrieved from a web search engine for two words. Specifically, we define various word co-occurrence measures using page counts and integrate those with lexical patterns extracted from text snippets. To identify the numerous semantic relations that exist between two given words, we propose a novel pattern extraction algorithm and a pattern clustering algorithm. The optimal combination of page counts-based co-occurrence measures and lexical pattern clusters is learned using support vector machines. The proposed method outperforms various baselines and previously proposed web-based semantic similarity measures on three benchmark data sets showing a high correlation with human ratings. Moreover, the proposed method significantly improves the accuracy in a community mining task

Effectiveness of Social Media Community Using Optimized Clustering Algorithm

Now-a-days social media is used to the introduce new issues and discussion on social media. More number of users participates in the discussion via social media. Different users belong to different kind of groups. Positive and negative comments will be posted by user and they will participate in discussion. Here we proposed system to group different kind of users and system specifies from which category they belong to. For example film industry, politician etc. Once the social media data such as a user messages are parsed and network relationships are identified, data mining techniques can be applied to group of different types of communities. We used K-Means clustering algorithm to cluster data. In this system we detect communities by the clustering messages from large streams of social data. Our proposed algorithm gives better a clustering result and provides a novel use-case of grouping user communities based on their activities. This application is used to the identify group of people who viewed the post and commented on the post. This helps to categorize the users

Design and Implementation an RFID Customer Shopping Behaviour Mining System

Shopping behavior data is of great an importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of the capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers within physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this study, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that phase readings of tags attached to items will demonstrate distinct yet stable patterns in a time-series when customers look at, pick out, or turn over desired items. We design Shop Miner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of Shop Miner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that Shop Miner is able to identify customer shopping behaviors with high accuracy and low overhead, and is robust to interference

Isolated oculomotor nerve palsy in patients with mild head injury

Isolated oculomotor nerve palsy following head injury is uncommon. It can only be diagnosed with confidence if it is known to have developed immediately following trauma and if adequate investigations exclude secondary causes. The recovery is only partial and this has repercussion on patients' quality of life