The word Quantum is one of the hottest terms since the beginning of this century. Quantum computer, quantum dot TV, quantum communication, quantum battery, quantum dot mobile phone, and quantum printer are gradually becoming the hot terms in our lives. Quantum is not a new concept in physics, as it has been discovered for more than one hundred years. It carries extensive connotations: quantum technologies are deeply changing our present and future lives, the quantum philosophy contains the explanations on the source of substantiality and a brand new world view, and the quantum mechanics may be recognized as the most successful theory in the history of physics. Quantum has become the symbol of our time - at least many people believe that our time belongs to quantum.

The tropics is characterized with high temperature but in recent time with the advent of global warming that had shot the world temperature so high ,cascaded transformers failure is imminent due to excessive heat stored in the windings that could not be dispensed equitably. Understanding the thermal behavior of the windings in cascaded transformers is complicated, it is difficult to predict the location and strength of the hottest temperature in the winding (i.e. hotspot temperature). To initiate an improved understanding of the thermal behavior, a thermal analysis (steady and transient ), numerical should be done. A model of cascaded transformer is made using Creo Element and built with Ansys Work Bench to analyze. The steady and transient thermal analysis with maximum -temperature distribution of 96.325oC ,and total heat flux ⁄ on a steady analysis and maximum temperature of 114.62C and toatal heat flux of ⁄ .

An excellent source of collecting the reviews on specific product is various online shopping sites where people share their reviews on products and their shopping experience. People may come through the wrong opinions known as review spam. Therefore, for this it is essential to detect it by some means. In this paper, presents methods for detection of spam users using feature extraction and discretization, in combination with EM algorithm. Our framework can detect multiple spammers by knowing only small set of spammer sets. Proposed method effectively selects relevant features and builds features set to identify the spammers. In this paper, we have blocked the users with fake id or who are predicted as spammer.

Sequential rule mining is a fundamental technique of data mining which has many application one of which is in the area of bioinformatics. Bioinformatics is an application of information technology to store gigantic biological data in an organised way so that the data can easily be analysed. These biological data is in form of sequences of proteins and sequence analysis is one of the major research areas in bioinformatics. These Bioinformatics data are incremental datasets and at the present era of cheap information technology these bioinformatics data has become big data. Efficient mining of big data require parallel as well as iterative techniques. Here we propose a technique to analyse these sequential data and extract sequential rule using map reduce Framework of Hadoop mounted over genetic algorithm. Map reduce is responsible for parallelizing of mining technique where as genetic algorithm would work in an iterative manner to generate sequential rules from DNA sequences.

Now-a-days, Online Social Networking sites are important part of human society. In other way a person maintains a social networking site account which is then used for the following purposes like building relations, for business, exchange of data etc. The use of OSN’s is therefore tremendously increasing day by day and its demanding for concentration over protection of private information of user’s, their likes and dislikes etc. There are different filtering criteria’s provided by various OSN’s but the role of user to prevent his/her privacy is very less with the help of those criteria’s. This paper represents a model that helps to allow the user to take over the control of their profile’s and walls. The key parameters of the proposal illustrate are that the message posted on user wall’s will be filtered for unwanted content in any form and type and will be posted with the users consent only. The feasibility of this model is presented considering OSN’s scenarios.

Sustainable development, on the one hand, helps preserve the environment, on the other hand, it helps to maintain the social and economic structures of the city. In the context of planning urban development, the economic, social, cultural and environmental objectives are at the top of their policies, and the realization of these goals requires interaction between people and the state, the presence and participation of people is necessary and inevitable in the sustainable development. Indeed, although participation in the sense of cooperation has a long history in Iran, it has not yet been institutionalized in our society, and the formation of city councils and people's representatives is one of the measures that reveals the necessity for people to participate in urban affairs with a focus on sustainable development to resolve many problems of the city and, by studying the experiences of most countries in the field of citizen participation in urban affairs, and the effect of this participation on sustainable urban development, one can consider the necessities of measures for the participation of people in urban affairs, which emphasizes that sustainable development is a fundamental concept and plays a vital role in sustainable urban development. Reflecting on the needs of the current generation and preserving resources and wealth for future generations, this paper addresses the challenges of citizen participation in city decision-making and urban governance and planning.

Data mining is the combination of data assembled by customary information mining philosophies and procedures with data accumulated over large no of data sources. Time-Series clustering is one of the important concepts of data mining that is used to gain insight into the mechanism that generate the time-series and predicting the future values of the given time-series. Time series clustering is a task that aims to assign each of the time series a class label from two or more classes having some training data. Classification is needed to distinguish between different types of time series. K-Means is one of the common and simplest unsupervised algorithms for clustering. It classifies data on the basis of Euclidian distance. In data mining and statistics, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Time Series based clustering is used to classify time series data by K-Means or Hierarchical clustering. The proposed work aims to extract important information from Time Series sales data for analysing current sale trends for efficient Market Basket Analysis. The work also aims to provide better analysis of the proposed approach on the basis of obtained numerical results.

With the rapid development of urbanization process, the rise of smart city has become a new direction for building the future city. However, the congestion, pollution and waste problems have being happened in various cities, which are in urgent need of a scientific evaluation system to weigh. This paper has studied a number of cities in different continents, while making a detailed analysis about the impact indicators, and through the principal component analysis to select the indicators further, so the establishment of useful output data is accomplish. Then in the construction of comprehensive evaluation system, we choose the combination of heuristic algorithm—SA and BP neural network, which can improve the speed of operation and avoid falling into the local minimum. In the 79 large and medium-sized cities we randomly calculate 2 cities, which are used to do the simulation. The results show that the predicted value and the actual value of the error there are meet the requirements, thus it proves that comprehensive model established is effective.

Thin films of ZnO were deposited on corning glass (by rf sputtering and spray pyrolysis) and annealed at 573 and 673 K respectively. It has been noted that the samples deposited by Spray pyrolysis have an average transmittance, refractive index and extinction coefficient as 80% to 90%, 1.33 to 1.44 and 13.11 to 27.52 respectively. While those deposited by sputtering method have 34% to 80%, 1.51to 1.58 and 0.11 to 0.37. The XRD patterns of the samples shows that they are polycrystalline. Other optical parameters analyzed include the refractive index (n), absorption coefficient ( ) and the extinction coefficient (k).