Jornal de Ciência da Computação e Biologia de Sistemas

Software sizing is a crucial activity among the task of software management. Work planning and subsequent estimation of effort required is based on the estimate of the software size required. Software developers are realizing the need to speed up the development process to respond to customers’ needs. This has resulted in adoption of rapid development methods and adoption of agile methodologies. Incremental method of software development has been adopted as one of the methods to speed up software development. Unfortunately there is little work that has been done to develop a clear framework to estimate software size and cost in incremental software development environment. This research work proposes the use of Pairwise Comparison matrices framework to estimate size and cost in incremental software development and evaluate the pairwise comparison framework against Putman’s size estimation model to determine if it produces more accurate results in terms of estimation of size relative to actual size.

In present scenario where various diseases are here and out of them the cancer is one of them most devastating disease. Presently the detection procedure is very much time consuming and the results obtain by them are not so fast, so here in this respect there is a need of more accurate, fast and efficient method through computing technologies. This research work incorporates Quantum computing with the clustering algorithm i.e. Shor Algorithm of Quantum Computing with various Hierarchical Clustering Technique. The Hierarchical Clustering Technique helps in clustering of results to obtain an approach for Cancer Disease Detection. Now accurate results are obtained, when adopting Shor Algorithm of quantum computing in disease detection technique with different Hierarchical Clustering. Here the Shor Algorithm helps to increase the efficiency in term of accuracy and Hierarchical Clustering Technique helps to detect the stages of Cancer. So, the proposed system gives a new approach in the field of cancer disease diagnosis. The results obtained by different Hierarchical Clustering Techniques are discussed.

Cloud computing is a new technology that delivers services to end users on the promise of supporting ondemand in a flexible manner by scheduling bandwidth, storage and compute resources on the fly. Content delivery networks (CDN) based computers are considered as the best solutions to deliver User-generated content (UGC). But none of the existing CDN based solutions can support all the required features in UGC delivery. In this paper we analyses the various mechanism for deploying video in geographically distributed data center and optimal utilization of bandwidth, storage and other computing resources. Optimal Deployment can be done by using Dominating Set Algorithm.

Background: High stress developed in the cement mantle of a total hip replacement is reported to contribute to premature failure of acetabular components. We postulate that stress level is influenced by cement mantle thickness, acetabular size, bone quality and body mass index. Methods: Finite element models of reconstructed hemi pelves of different sizes and acetabular diameters (46, 52 and 58 mm) were created from CT-Scan data. We investigated the effects of cement mantle thickness (1, 2, 3 and 4 mm), acetabular size, body mass index (BMI = 20, 25 and 30 kg/m2) and bone quality on stress level developed in the cement mantle. Findings: Peak tensile stresses in the cement mantle increased with a decrease in cement mantle thickness, acetabular size and bone quality and an increase in BMI. Interpretation: Our results indicate that a 4-mm-thick cement mantle is required in small reconstructed acetabulae of ≤ 50-mm diameters, while a 1-mm thick cement mantle can be used on larger reconstructed acetabulae of ≥ 58 mm diameter. Patients with poor bone quality require at least a 4-mm-thick cement mantle to reduce the risk failure caused by high stress level in the cement mantle.