This paper presents an effective compression technique for lossy compression of color images. After reducing the correlation among R, G and B planes using YCoCg-R transform, the Integer Wavelet Transform (IWT) is applied on each of the transformed planes independently up to a desired level. IWT decomposes the input image into an approximation and several detail subbands. Approximation subband is compressed losslessly using prediction errors and Huffman coding, while each of the detail subbands are compressed independently using an effective quantization and Huffman coding. To show the effectiveness of proposed scheme, it is compared with several existing schemes and a state of art for image compression JPEG2000 and it is observed that the proposed scheme outperforms over the existing techniques and JPEG2000 with less degradation in the quality of reconstructed images while achieving high compression performance.

Queuing by patients in the out-patients department to access hospital services in Nigeria teaching hospitals is a teething concern to most healthcare providers. This causes inconvenience to patients and economic costs to the hospitals. Patients waiting for minutes, hours, days or months to receive medical services could result in waiting costs to them. Providing too much service could result in excessive costs. Also not providing adequate services could result in excessive waiting and costs. This study sought to determine an optimal server level and at a minimum total cost which include waiting and service costs in homogenous servers in order to reduce patients’ congestions in the hospital as low as reasonably practicable. The queuing characteristics in all the twenty-three (23) teaching hospitals in Nigeria were analysed using a Multi-server Queuing Model and the waiting and service costs determined with a view to ascertaining the optimal service level. The data for this study were collected through observations and interviews. The data was analysed using Quantitative Methods, Production and Operations Management (POM QM) and Queuing Theory Calculator Software as well as using descriptive analysis. The results of the analysis demonstrated that average queue length, waiting time of patients as well as over utilization of specialist doctors at the teaching hospitals could be reduced at an optimal server level and at a minimum total cost as against their present server level with high total cost which include waiting and service costs. Therefore, this call for refocusing so as to improve the overall patient care in our cultural context and meet the patient needs in our environment.

Face Recognition has become the challenging and interesting research topic in the last few years. The aim is to design a robust Face Recognition System under different environmental conditions like illumination, pose and occlusion. These are the three major challenges in Face Recognition which may hinder the Face Recognition system. By combining the three successful representations such as Gabor filters, CS-LBP and TLPP better performance can be achieved as compared to just considering them individually. CS-LBP is used for describing interest regions which have good tolerance to illumination and computational efficiency and TLPP is used to take the data directly in the form of tensors as input. Since the number of the combined feature sets are more only a few feature sets is selected to be trained by the Support Vector Machine classifier. A number of experiments are conducted using YouTube celebrity, McGill Face dataset and as well as the own collected sequence under different conditions such as illumination variations, different poses, occlusion including indoor and outdoor scenes. This approach provides better results compared to traditional approaches.

E-learning has become a most popular learning approach of higher education in Sri Lanka, due to the advancements of Information Technology. This study focused on the undergraduates’ acceptance of e-learning as an effective tool. The successfulness of implementing e-learning requires the understanding of end-user acceptance process. Therefore, this study focused on investigating the acceptance of e-learning approach among undergraduates in Western Province, Sri Lanka. The aim of this research was to provide a set of factors to be considered when an E-learning activity is planned and proposed to E-learners in universities of Sri Lanka. The Technology Acceptance Model which was modified by including some factors was used as the basic framework of this study which was suitable for achieving the objectives of the research. This study was based on total sample of 209 students who are following Computing degrees in the universities located in Western Province, Sri Lanka. The results of this study suggested that university students tend to focus more on perceived ease of use on e-learning and the students’ attitude has more influence on intention to use e – learning. So it could be concluded that providing more technological facilities with infrastructure would increase the positive attitude towards using e-learning among university students and behavioral intention to use.

Software testers should prioritize test cases so that important ones are run earlier in the regression testing process to reduce the cost of regression testing. Test case prioritization techniques schedule test cases for execution in an order that improves the performance of regression testing. One of the performance goals i.e. the fault detection rate, measures how quickly faults are detected during the testing process. Improved rate of fault dependency detection can provide faster feedback on software and let developers debug the leading faults at first that cause other faults to appear later. Another performance goal i.e. severity detection rate among faults, measures how quickly more severe faults are detected earlier during testing process. Previous studies addressed the second goal, but did not consider dependency among faults. In this paper an algorithm is proposed to prioritize test cases based on rate of severity detection associated with dependent faults. The aim is to detect more severe leading faults earlier with least amount of execution time and to identify the effectiveness of prioritized test case.

Image watermarking is the process of the hiding the one image into other image for the copyright protection. The process of watermarking must be done in this way that the pixels of the original image must remain in its original HD form. A lot of work has been done in this context in previous years but some techniques have their own applications, drawbacks as well as advantages. So, this paper will utilize three techniques i.e. Discreet Wavelet Transform (DWT), Neural Network (NN) and RSA encryption for image watermarking. In the end the performance of the proposed technique will be measured on the basis of PSNR, MSE, BCR, BER and NCC in MATLAB R2010a environment.

In today’s life internet is an important part. We spend most of our time on internet. One of the important features of internet is communication. Email is a mode of communication which is used for the personal and business purpose. Spam emails are the emails recipient does not wish to take delivery of; it is also called unwanted bulk email. Emails are used each day by number of user to converse around the world. At present large volumes of spam emails are reasoning serious trouble for Internet user and Internet service. Such as it degrade user investigate knowledge, it assists transmission of virus in network, it increases load on network traffic. It also misuses user time, and energy for legal emails among the spam. For evade spam there are so many conventional anti-spam technique includes Bayesian based sort, rule based system, IP blacklist, Heuristic based filter, White list and DNS black holes. These methods are based on satisfied of the post or links of the mail. In this paper we proposed an efficient spam filtering technique based on neural network. The technique used is RBF a neural network technique in which neuron are trained. The results obtained by using this technique are compared with SVM. The parameter meter for comparison is precision and accuracy. On the basis of these two parameters we compared the proposed technique with SVM.

Cloud Computing is a new paradigm which allows individuals and organizations to purchase resources like compute, storage, networking etc as a utility with minimum human intervention. According to recent surveys more than half of the large to medium size organizations have already migrated to the cloud. Despite of providing resources as a utility and its wide spread acceptance in the IT community, cloud computing is still at its prime and poses many challenges like automated service provisioning, virtual machine migration, server consolidation, energy management, traffic analysis, data security, software frameworks which needs considerable amount of research to stabilize. Conducting research on live cloud environments for individuals or small institutions is very difficult due to the costs involved in setting up a cloud. This paper presents various cloud simulators which were developed over the years that are a cost effective way of conducting cloud research tasks. This paper compares almost 17 cloud simulators based on diverse criteria and the result and explanations are presented which enables new researchers to choose a suitable cloud simulator.