In this paper, an objective function based on minimal spanning tree (MST) of data points is proposed for clustering and a density-based clustering technique has been used in an attempt to optimize the specified objective function in order to detect the “natural grouping” present in a given data set. A threshold based on MST of data points of each cluster thus found is used to remove noise (if any present in the data) from the final clustering.
A comparison of the experimental results obtained by DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm and the proposed algorithm has also been incorporated. It is observed that our proposed algorithm performs better than DBSCAN algorithm. Several experiments on synthetic data set in R^2 and R^3 show the utility of the proposed method. The proposed method has also found to provide good results for two real life data sets considered for experimentation. Note thatK-means is one of the most popular methods adopted to solve the clustering problem. This algorithm uses an objective function that is based on minimization of squared error criteria. Note that it may not always provide the “natural grouping” though it is useful in many applications.

Film industry is the most important component of entertainment industry. A large amount of money is invested in this high risk industry. Both profit and loss are very high for this business. Thus if the production houses have an option to know the probable profit/loss of a completed movie to be released then it will be very helpful for them to reduce the said risk. We know that artificial neural networks have been successfully used to solve various problems in numerous fields of application. For instance backpropagation neural networks have successfully been applied for Stock Market Prediction, Weather Prediction etc. In this work we have used a backpropagation network that is being trained using a subset of data points. These subsets are nothing but the “natural grouping” of data points, being extracted by an MST based clustering methods. The proposed method presented in this paper is experimentally found to produce good result for the real life data sets considered for experimentation.

Film industry is the most important component of entertainment industry. Both profit and loss are very high for this business. Like every other business, business prediction system plays a vital role for this industry. Before release of a particular movie, if the Production Houses or distributors get any type of prediction that how the film will do business, then it will be very useful to reduce the risk of the investors. In this paper we have proposed a method using back propagation neural network for prediction about a given movie’s profitability. Initially the entire range of profit-loss has been divided into a number of groups. The proposed algorithm can assign a given movie to it’s appropriate profit-loss group. Note that, a similar such method has been successfully applied in the field of Stock Market Prediction, Weather Prediction and Image Processing.

Film industry is the most important component of Entertainment industry. Profit and Loss both are very high for this business. Before release of a particular movie, if the Production House or distributors gets any type of prediction that how the film will do business, then it can be helpful to reduce the risk. In this paper we have proposed, back propagation neural network for prediction about the business of a movie. Note that, this method is successfully applied in the field of Stock Market Prediction, Weather Prediction and Image Processing.

A large number of the world business is going on using “INTERNET” and the data over the internet which is vulnerable for attacks from the hackers. Thus, uses of highly efficient methods are required for sensitive data transmission over the internet to ensure data security. One of the solutions to data security is to use an efficient method of steganography. The goal of steganography is to hide messages inside other ‘harmless’ messages in a way that does not allow any enemy to even detect that there is a second message present. Steganography can be used with a large number of file formats most commonly used in the digital world of today. The different file formats popularly used are .bmp, .gif, .txt etc. Thus the techniques of steganography are going to play a very important part in the future of data security and privacy on open systems such as the Internet.       

This paper presents an efficient method for hiding data into an image and send to the destination in a safe manner. This technique does not need any key for embedding and extracting data. Also, it allows hiding four bits in a block of size 5×5 with minimal distortion. The proposed algorithm ensures security and safety of the hidden information. The experimental results presented in this paper show the efficacy of the proposed method.