Considering first and Second order weighted means of order lengths and demand, sustainable trims of order one and two have been already defined. A cutting plan proposed in earlier work covers the most important component of cost minimizing problem concerning space constraint, manpower management and in particular, trim loss.In the present paper, considering a set of several datas, we have computed the sustainable trims of order one and two corresponding to each set of data. Considering the average order length as knots on the interval of the domain we have constructed a linear spline corresponding to the total trim loss obtained. Similarly, considering and as nodal points respectively on the interval of the domain linear spline functions have been constructed. It is very interesting to note that for given any arbitrary set of data, we can immediately predict the total trim loss with the help of spline curves. The authors of this paper are pioneer to implement the idea of spline approximation in the field of 1D-CSP.

Identification of a Person by using Physiological or Behavioral characteristics is critical in wide range of applications. Of all the physiological characteristics Iris can be considered as the most Biometric Technology due to its excellent features. Biometric Authentication has become the most secured means to automatically identify individuals without requiring them to carry ID cards or memorize passwords. This paper explains how Iris can be considered as the most efficient Biometric technology. Iris recognition system includes mainly iris Image Acquisition, Iris Liveness Detection, Iris Image Quality Assessment, Iris Recognition. Iris Recognition stage includes Segmentation, Normalization, Feature Extraction and Matching. This algorithm improves the speed and accuracy of the segmentation, error rate is reduced by accepting only the clear images. The Hough Transform, polynomial fitting technique, introducing specular reflections on the CASIA iris images and some morphological Operations are introduced for the effective output.

Rapid growth for storing and capturing multimedia data with digital devices, in recent years, information of multimedia retrieval has one of the most important researches and key challenging problems with image retrieval. Thus, Content-Based Image Retrieval (CBIR) is used that deals with retrieval of similar images from a large database for a given input query image. A large number of diverse methods have been proposed for CBIR using low level image content like edge, color and texture. For combination of different types of content, there is a need to train these features with different weights to achieve good results. Thus, in this paper proposes an image extractor method using genetic algorithm on multi-feature similarity synthesis. Subsequently, Genetic Algorithm-based similarity measure is performed between the query image features and the database image features. In retrieving process of most of the CBIR systems do not have potential to efficiently allied with human perception. Hence to overcome this problem in this paper we are extending this genetic algorithm based method by using relevance feedback methods to magnify retrieval performance. Also this Relevance feedback technique is helpful to trim semantic gap. We will use here both implicit and explicit feedback technique along with genetic algorithm.

Real time hand gesture recognition can be used in many applications (such as remote for television, rotate a CAD model by rotating hand, video games etc). The main objective of this paper is hand gesture should be recognized within short period of time cost of the system should be low & the system should be robust. Images are captured using web camera with static background. The paper is divided in to 2 parts, Feature extraction phase & classification phase. In feature extraction phase hand is extracted from the frame using background subtraction, & edge detection. Patterns are calculated neural network, SVM (Support vector machine) and KNN (K-nearest neighbour) which is classification phase.

Emerging technologies have become important tools in the improvement of teaching and learning in Ghanaian tertiary institutions. The aim of the research was to model the main measures that influence ease of use of emerging technologies in teaching and learning in Ghanaian public universities. The study was carried out as a quantitative research and a survey of public universities in Ghana. Lecturers who had used emerging technologies in public universities for at least a year were the sources of information in the study. The simple random and stratified sampling techniques were used to select 320 respondents. Findings of the study indicated that there is a significant positive relationship between ease of use of emerging technologies and training for lecturers, their support by their institutions, access to emerging technologies and quality of emerging technologies. This relationship is expressed in this resulting model: Y = 0.48X1+0.63X2+0.12X3+0.11X4 – 1.30, where the model accounts for about 65% of variability in Y (Ease of use) of emerging technologies. It is therefore recommended that ease of use of emerging technologies should be enhanced in teaching and learning by giving lecturers adequate training, support by their institutions, and easy and wide access to quality emerging technologies.

Data Mining refers to extracting or “Mining” knowledge from large amounts of data. Today’s Industrial scenario is having manifold of data which is data rich and information poor .The information and knowledge gained can be used for applications ranging from business management, production control ,and market analysis, to engineering design and science exploration. Data Mining can be viewed as a result of natural evolution of information technology. Association rule mining finds interesting association among a large set of data items. With massive amounts of data continuously being collected and stored. Many industries are becoming interested in mining association rules from their databases. The discovery of interesting association relationships among huge amounts of business transaction records can help in many business decision making process , such as catalogue design, cross marketing, and loss leader analysis.

This research delineates a comprehensive and successful application of decision tree induction to large mobile environment data set of different services obtained by numbers of customers. Complex interaction effects among services that lead to increased usage variability have been detected. The extracted information has been confirmed by the database managers, and used to improve the decision process. The research suggests that decision tree induction may be particularly useful when data is multidimensional, and the various process parameters and highly complex interactions.

Every colleges, offices, banks and industry are using biometric recognition system for authentication of employers. This biometric authentication system are use biometric templates or characteristics of employer and based on comparison of this template with enrolled data or template of employer for making decision about allowed enter into organization or not. So protection and recognition of human characteristics very important role play in multimodal biometric authentication system. In this review paper, we are proposed new architecture for multimodal biometric authentication system using digital watermarking and Compressive Sensing theory. In proposed architecture, we are using compressive sensing theory for generation of measurement features of biometric template of individual and embed into another biometric template of same individual to generate multimodal biometric watermarked image. This watermarked version of biometric image is use for matching with enrolled biometric templates and if matching is not possible then extracts measurement biometric features from watermarked image and match with second enrolled data of individual. This paper also shows that how improved data storage capacity at system database and protection of multibiometric template over noisy communication channel.

In the current cloud computing environments, VM resource scheduling only considers the current system condition and ignores the previous state of system which causes the system load imbalance. Number of VM migrations is more when most of the load balancing takes place. The entire migration cost becomes a problem when all the VM resources are migrated. This is largely due to granularity of VM resources and the large amount of data transferred in the migration with deferral of VM service. This paper provides a scheduling strategy to enable effective load balancing. The method used in this paper will compute its influence on the system in advance, when current VM resources are allocated to every physical node and will opt for the deployment that will have the least load on the system. This is achieved using genetic algorithm, chronological data and the current state of the system.