Parul Kalra

An IOT by information retrieval approach: Smart lights controlled using WiFi

In recent years, the rapid development of Internet of Technology (IOT) makes the intelligent home come true as people expect. The intelligent home system creates the more comfortable, safer, humane and intelligent living environment. It can resolve the problems facing by the people who have busy schedules and get a very less amount of time to spend at home which is increasing rapidly around the world. For the solution of this problem, user can depend on the automated machines and gadgets like smart phones. These smart gadgets are using cloud computing which sends and receives signal o the cloud. The data that is of our use can be fetched by matching some key values using the concept of information retrieval. The key objective of this paper is to create a full-fledged application which could let user to operate the lights of their house from any remote location. The user have a list of options to select which light is to be on and when. The only requirement is to have working wifi at home to which the lights are connected. The is developed in Lua Language by using the Esplorer Integrated Development Environment (IDE). We have also used the micro controller chip ESP 8266 to build our board.

Big Data: A security compliance model

This paper proposed a security compliance model that presents security and access control features which are employed at the time of origin of Big Data. The growth rate of data dealing devices and generation of data has increased exponentially which led to the massive explosion of data availability both in structured or unstructured form also known as big data. The challenge is to analyze this unstructured form of data and categories in a momentous form.The tool design to process big data without considering the security and access control feature in mind that its security is not inbuilt in these systems. Moreover, because of the huge size data it becomes a very tedious task to incorporate security feature at a later stage. So the security should be embedded in the initial or design stage.

Prediction of Carbon Stock Available in Forest Using Naive Bayes Approach

Carbon plays an essential role in the environment for climate change. The presence and absence of carbon directly affects all living beings. Trees inhale carbon for giving us oxygen. The environmental study of carbon is a major concern these days. Carbon Dioxide is stored in different five carbon pools of forest. Many countries are innolved in the research of environmental factors these days. The focus of this paper is to build a system using Naïve Bayes Approach that trains a model to classify forest on the basis of carbon stock and predict the level of carbon stock in the forest. The model is validated using dataset of the previous year data.

Analysis of Factors Affecting Infant Mortality Rate Using Decision Tree in R Language

This is a study done for the social cause that was increasing at an alarming rate and was creating a situation of panic among the people of the world, Mortality Rate. This situation was analyzed by analyzing various factors such as birth rate, literacy rate, number of health centers, etc. using the decision tree technique in R tool which illustrated trees of two different decades separately and analyzed the factors affecting the mortality rate with their contribution in driving its rate, and also the summary of decision tree will indicate its accuracy and kappa factor to judge the authenticity of the factors chosen. This will be useful to the governing bodies to get to know about the factors and work upon them for the decrement of the infant mortality rate.

Field Based Weighting Information Retrieval on Document Field of Ad Hoc Dataset

Information retrieval is a process of representing, retrieving and normalising data items. The retrieval system is a method that verifies how a system responds against users’ needs. The accessing of useful information is directly related by the user’s job and the conceptual view of the information possessed by the retrieval system. In order to increase the efficiency of the retrieval system, the authors have considered new fields (TITLE and DESC) for evaluating the recall and precision parameters. This paper demonstrates the comparison of baseline probabilistic models with document fields in the retrieval process and for experimental analysis. The authors’ have used the standard TREC Ad hoc test collections based on the weighting and field models.

Comparison and Analysis of Information Retrieval DFR Models

The paper reviews the research of Information Retrieval models of DFR. Tipster Ad hoc Association Press newswire retrieval task is mainly applied in IR systems. The evaluation of the AP (Associated Press) newswire is based on the values obtained in Mean Average Precision (MAP). The purpose of the paper is to come up with the most optimized DFR model for the newswire so that the user can retrieve best results according to their queries. The results reveal that a higher performance of the probabilistic DFR models is achieved in contrast to other IR models.

A fuzzy association rule mining approach using movie lens dataset

In the modern era of e-commerce and social networking, people wants to get everything best and better from every other person in their group. There is always an urge within people to find products, services and opportunities better than everyone else. Nowadays, business grows by advertising it online because people are always online and they find it more interesting to review products on a social platform. Mining of association rules is an advanced data mining research area. Currently many works are undergoing in this domain. Completeness is one of the key strengths of fuzzy association rule mining. Fuzzy cannot work with large datasets. The massive amount of candidate item sets sometimes makes it unproductive for a data mining system to analyze them. At the end, it produces a significant number of fuzzy associations. The research focuses on helping the needful to find the best suited movies depending on the previous ratings. Rules have been generated by using fuzzy association rule mining.

Agglomerative hierarchical Clustering technique for partitioning patent dataset

Mining right patents from database have always been interesting and the most difficult task for analysis purposes. The choice of right data mining tool and algorithm is requisite for reducing the search space, thus enabling extraction of meaningful and useful information for technology forecasting. With clustering approach, this can be easily achieved. This paper discusses about Clustering technique called Agglomerative hierarchical clustering using Tanagra tool. It groups patents with similar characteristics into one cluster based on between sum of square (BSS ratio) and Gap parameters.

Hybrid filtering for opinion mining

The growth in involvement of social media with current business world has influenced online customers by unbalanced opinions about the product and services. The main objective of this paper is to standardize the opinion given by the masses about any product or services in various social media communities. Enormous opinions on a specific product or service is available due to vast exposure available for publicly voice their opinion through social media. There are enormous customer reviews available about various products and services which are not systematically arranged. Moreover, there is no proper mechanism to identify the reliable or genuine reviews. This motivates the organizations and researchers to create tools which can automatically analyze and systematically arrange only those opinions that are genuine and filter out the fake reviews. The principal focus is to frame a filtering model with the help of truth test. Every source needs to undergo through a truth test, if the source is genuine, then the opinion is concluded while making a decision else it is marked as a fake review and hence filtered from the system.

Relationship classifier and stress analyzer in the mobile messaging application network through text mining

Emotions are at the peculiar element for thoughtfulness and perception ourselves and others, and the computerized annotation and recognition of emotion could enhance our knowledge and understanding with technologies. Nowadays, mobile messaging application as well as other social applications has become a dominant part of an individuals social interaction and relationships. The aura around these applications where people share their expression, feelings and perception are emotionally contented. The proposed framework for differentiating emotional interactions messaging application, and then to distinguish relationships as friends, family, and professional colleagues. The aim is to mine the emotional content of textual expression in the mobile messaging system. The framework also includes the generation of special dictionaries for endless short form languages, for emotags, interjections and for frequently used foreign language words.This Mobile message emotion extraction model consist of two modules, one for relationship classifier and Stress analyzer and reliever, which includes different levels for mining the emotional data, i.e. text gathering, database tables, text processing and emotional text mining then through an unsupervised technique which uses the kmeans clustering algorithm to determine individuality of texts and predict relationships.