Video summarization of the segmented video is an essential process for video thumbnails, video surveillance and video downloading.Summarization deals with extracting few frames from each scene and creating a summary video which explains all course of action of full video with in short duration of time.The proposed research work discusses about the segmentation and summarization of the frames.A genetic algorithm (GA) for segmentation and summarization is required to view the highlight of an event by selecting few important frames required.The GA is modified to select only key frames for summarization and the comparison of modified GA is done with the GA.

The applications require coverage of the whole monitored area for long periods of time.Clustering is a way to reduce communications, minimize energy consumption and organize messages among the cluster head and their members.The message exchange of communication and data transmission between the different sensor nodes must be minimized to keep and extended the lifetime of the network because of limited energy resources of the sensors.In this paper, we take into consideration the problem isolated nodes that are away from the cluster head (CH) and by consequence or CH is not within the reach from these nodes.To solve this problem, we propose O-LEACH (Orphan Low Energy Adaptive Clustering Hierarchy) a routing protocol that takes into account the orphan nodes.Indeed, a cluster member will be able to play the role of a gateway which allows the joining of orphan nodes.Our contribution is to election a cluster head that has enough energy for a better now to coordinate with these member nodes and maintain the full coverage for applications which requires of useful data for the entire area to be covered.The simulation results show that O-LEACH performs better than LEACH in terms of connectivity rate, energy, scalability and coverage.

Video summarization of the segmented video is an essential process for video thumbnails, video surveillance and video downloading.Summarization deals with extracting few frames from each scene and creating a summary video which explains all course of action of full video with in short duration of time.The proposed research work discusses about the segmentation and summarization of the frames.A genetic algorithm (GA) for segmentation and summarization is required to view the highlight of an event by selecting few important frames required.The GA is modified to select only key frames for summarization and the comparison of modified GA is done with the GA.

The Quantum Evolutionary Algorithm (QEA) is a new subcategory of evolutionary computation in which the principles and concepts of quantum computation are used, and to display the solutions it utilizes a probabilistic structure.Therefore, it causes an increase in the solution space.This algorithm has two major problems: hitchhiking phenomenon and slow convergence speed.In this paper, to solve the problems a multiplicative update function called quantum gate is proposed that in addition to considering the best global solution considers the best solution of each generation.The results of one max and knapsack problems and five famous numerical functions show that the proposed method has a significant advantage compared with the basic algorithm in terms of performance, quality of solutions and convergence speed.

Wireless Sensor Networks has been widely used for monitoring and control applications in our daily life due to its appealing characteristic like low cost, power efficient, easy to implement in many areas such as war zone, medical monitoring optimization and so on.However, Wireless Sensor Networks (WSN) requires a new routing algorithm for health monitoring.In this paper, an efficient routing model called Local Search and Enhanced Betweenness Routing (LS-EBR) in WSN to improve routing efficiency by increasing sensor nodes coverage and minimizing the time for routing is presented.The LS-EBR model in WSN for health monitoring uses enhanced betweenness routing that measures energy consumption of the neighboring nodes in a local search manner.First, a novel Local Search model that does not require global information about the entire network and in turn divert routing from sensor nodes that are more frequently used is used.The aim is to reduce the time for routing.Second, an Enhanced Betweenness Routing model that not only considers routing overhead but also considers the remaining energy of sensor node into account to ensure higher number of sensor nodes to be monitored, thus achieving much higher coverage is designed.Finally, working together with the above Local Search and Enhanced Betweenness Routing by applying Enhanced Betweenness Routing algorithm, reduces the average energy consumed.Simulation results showed that the proposed routing model has advantages over opportunistic routing with respect to coverage and improving routing efficiency by reducing time for routing in wireless sensor networks.

The impact of Cross Ignition process (CI) in the gas turbine operation and environmental issue is still investigated for extending the efficiency of gas turbine engines and meanwhile decreasing the environment pollution.This paper presents various constructive influential parameters and analysis of their related interaction during CI.A developed computational model for determination of cross-ignition time (CIT) is proposed, based on previous relevant models for thermal analysis and for distinguishing of heat fluxes in combustion processes.Due to the first analysis of theoretical results, experimental investigation for various operating conditions were essential to validate the developed computational model of the CIT.Thus, a simple experimental test rig is designed for this purpose, and for validation of certain conditions of the computational model. Meanwhile, for expanding the investigations in higher energy conversion and reducing expensive testprocedures, that are conducted during critical test running, a new strategy is proposed for simulating the thermal heat fluxes throughout the burners compartment model by implementation of Computational Fluid Dynamic (CFD).Finally, new constructive criteria based on the validated investigations will enable the future generation of gas turbine combustors to operate in critical conditions.

Heart disease is one of the biggest health problems in the world because of high mortality and morbidity caused by the disease.The use of data mining on medical data brought valuable and effective life achievements and can enhance medical knowledge to make necessary decisions.Data mining plays an important role in the field of medical science to solve health problems and diagnose ailments in critical conditions and in normal conditions.For this reason, in this paper, data mining techniques are used to diagnose heart disease from a dataset that includes 200 samples from different patients.Techniques used to diagnose heart disease include Bagging, AdaBoostM1, Random Forest, Naive Bayes, RBF Network, IBK, and NNge that all the techniques used to diagnose heart disease use Weka tool.Then these techniques are compared to determine which is more accurate in the diagnosis of heart disease that according to the results, it was found that the RBF Network with the accuracy of 88.2% is the most accurate classification in the diagnosis of heart disease.

Video summarization of the segmented video is an essential process for video thumbnails, video surveillance and video downloading.Summarization deals with extracting few frames from each scene and creating a summary video which explains all course of action of full video with in short duration of time.The proposed research work discusses about the segmentation and summarization of the frames.A genetic algorithm (GA) for segmentation and summarization is required to view the highlight of an event by selecting few important frames required.The GA is modified to select only key frames for summarization and the comparison of modified GA is done with the GA.

This paper attempts to improve the quality and the modification rate of a Stego Image.The input image provided for estimating the quality of an image and the modified rate is a bitmap image.The threshold value is used as a parameter for selecting the high frequency pixels from the Cover Image.The data embedding process are performed on the pixels that are found with the help of Threshold value by using LSBMR.The quality of an image is estimated by the value of PSNR and the modification rate of an image is estimated by the value of MSE.The proposed approach achieves about 0.2 to 0.6 % of improvement in the quality of an image and about 4 to 10 % of improvement in the modification rate of an image compared to the edge detection techniques such as Sobel and Canny.

Search engines today are retrieving more than a few thousand web pages for a single query, most of which are irrelevant.Listing results according to user needs is, therefore, a very real necessity.The challenge lies in ordering retrieved pages and presenting them to users in line with their interests.Search engines, therefore, utilize page rank algorithms to analyze and re-rank search results according to the relevance of the user's query by estimating (over the web) the importance of a web page.The proposed work investigates web page ranking methods and recently-developed improvements in web page ranking.Further, a new content-based web page rank technique is also proposed for implementation.The proposed technique finds out how important a particular web page is by evaluating the data a user has clicked on, as well as the contents available on these web pages.The results demonstrate the effectiveness of the proposed page ranking technique and its efficiency.