Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
System quality is key part of software system in industry, which not only directly affects the customers/users' satisfaction Software systems are increasingly required to operate in an open world, characterized by continuous changes in the environment. However, software evolution as an another important part in software system life cycle is less studied from the view point of software quality assurance. Advanced Architecture-Centric Software Evolution (AACSE) is considered as an approach to support software adaptation at a controllable level of abstraction in order to survive in the uncertain environment. Existing research and practices comprise a wide spectrum of evolution-centric approaches in terms of methods, processes and frameworks to take view on AACSE. This paper aims to present a taxonomic scheme for classification and comparison purpose and also ADLs for controlled evaluation process. An architecture-centric metric for considering structural dependencies between software components using entropies has been analyzed.
Digital technology in the medical field has grown so fast in the past few years which in turn has necessitated the need for applications that make it possible to effectively manage patient medical records and imaging data. The main objective of facilities working in the medical field worldwide is to give the highest quality of patient care at reduced costs. This does not seem to be an easy task in particular when exchanging information/images and transferring patients between different healthcare institutions. The exchanging of medical imaging is of great importance as it reduces unnecessary repeated images and unnecessary exposure to radiation. As a solution to this problem, there have been several recommendations to employ cloud computing to manage hospital information systems. Sharing and exchanging medical image information can be found in the cloud. The medical images information which can be found in the cloud, 1) can help doctors get the details needed, 2) patients can also receive medical attention in different healthcare facilities, and 3) reduces unnecessary transfers. Thus, there is pressing the need for a faster, more reliable way of exchanging or sharing of patient files/images. This will permit the three parties, viz., patients, healthcare professionals, and healthcare providers to gather, share, and see the diagnostic imaging reports electronically from any hospital; this will solve the archaic issue of copying medical images to CDs, reduce time, and cost connected with unnecessary exams and reduces unnecessary radiation exposure for patients. This paper proposes a framework that will highlight a cloud-based cross-enterprise imaging framework.
Lake is a main source of water in various places, but due to the fluctuation in the level of water, it often becomes very dangerous for the people living near the lake sides. To avoid this situation, the government does a survey of the water level in the lake and on that basis, a Meteorological Database is prepared by them. This data specifies the status of the lake whether it is an endangered or normal lake. In this paper using the Meteorological Database, the authors analyze and detect the level of the water in lakes with K-medoid Algorithm and generate a warning for the people in the surrounding areas so that they can easily understand and be cautious the water level and can vacate then habitats immediately.
Human tracking using surveillance cameras is a demanding topic of research now-a-days. Tracking and recognizing the human is much more challenging. There are many existing methods for tracking humans based on shapes and motion. In this paper, a novel algorithm for tracking human under occlusions is introduced. Gaussian Mixture Model (GMM) is used for tracking the human, which performs well under different occlusions. The new algorithm produces excellent results in case the human is occluded by another human and is obstructed by some other human, and also when there are partial occlusions. Experimental results show that the new algorithm outperforms in tracking the humans in these three cases.
There is a wealth of data archived by business organizations. Analysis of this data provides predictive information for taking proactive decisions and making statistical algorithms which are used for improving the knowledge regarding the engineering process and analysis of data. Data mining is a class of algorithms that analyses the relationship between data and identifies futuristic trends from archived data. Decision tree learning will help us to create a predictive model which will map different items consisting in the set of data and its targets in such a way that each element in this dataset is true. There are many strategies to construct the decision trees, but ID3 is one of the simplest and popularly used decision tree algorithms as there is a disadvantage in ID3 algorithm that it gives more importance to the attributes having multiple values while selecting any item affecting the decision tree. Hence in this paper, the objective is to justify that C4.5 algorithm works better than the ID3 algorithm. C4.5 system of Quinlan is one best classification algorithm that deserves a special mention for several reasons. First best reason is that it is used to represent result of research in machine learning that traces back to the ID3 system. For that reason it is taken as the point of reference for the development and analysis of novel proposals. On the other hand the results of the datasets in this paper proves that C4.5 tree-induction algorithm provides good classification, accuracy, and it is the fastest among the compared main memory algorithms for machine learning and data mining.
