Estimation model is key factor to calculate the size of project and in turn estimate the efforts. Based on these efforts project commitment can be provided. However, there is no accurate model which can predict exact size and efforts in new generation projects which are based on agile development methodology.
In earlier days of project development methodology were of conventional types where requirements were almost freeze at the start and waterfall model was good to serve the purpose. But in today’s scenarios market changes very fast and development needs are changed accordingly so the agile development is need of the era. The user story estimation approach is very much acquainted for agile development and serve the purpose. Estimations are fairly good with the user story approach, also phase delivery life cycle model leverage the advantages of phase wise estimation. To summarize the context, phase wise project lifecycle and phase wise estimation model provides and improved estimation model where the productivity is also based on the actual results of previous projects.Keywords: Life cycle model, Estimation, Project Size, User Stories, Agile development method.
As security and privacy of the cloud and data are usually handled by the service providers, the data owners may not even be fully aware of the underlying security challenges and solutions. Substantial scale organizations and utilization of distributed computing in industry is went with and in a similar time hampered by concerns in regards to security of information took care of by Distributed computing suppliers. One of the outcomes of the moving information preparing and capacity organization premises is that associations have less control over their framework. Subsequently, cloud benefit (CS) customers must assume that the CS supplier can Shield their information and Framework from both outside and inside assaults.
Having the instruments to perform such verifications before the dispatch of the VM occasion enables the CS customers to choose in runtime whether certain information ought to be put away or estimations ought to be made on the VM occurrence offered by the CS supplier.
This theory consolidates three components trusted computing, virtualization, Innovation and distributed computing stages to address issues of trust and security in broad daylight distributed computing situations of the three components virtualization, innovation has had the longest development and is a foundation for the acknowledgment of distributed computing. Trusted computing is the current industry activity that accepts to execute the base of trust in an equipment part, the put stock is in stage module. The activity has been formalized in an arrangement of details and is at present at rendition distributed computing stages pool virtualized computing, stockpiling and system assets keeping in mind the end goal to serve countless that utilization in a multi- inhabitant multiplexing model to on request self benefit over wide system. Open source distributed computing stages are, like put stock in computing, a genuinely late innovation in dynamic advancement.Keywords: Virtual Machine, Cloud Computing, Security.
Data mining is a method in which the valuable data is mined from the rough data. The futuristic outcomes are forecasted using recent information in the prediction analysis. This research work deals with the prediction of the heart disease. There are several steps that are included in the heart disease prediction. The pre-processing, feature extraction and classification are some of these steps. The random forest and logistic regression based the hybrid scheme is introduced. The features are abstracted using RF(Random Forest). The implementation of LR (Logistic Regression) is done for classification. The analysis of performance of the recommended model for acquiring accuracy, precision and recall is completed in this research. The accuracy has obtained in predicting the heart disease from this model is evaluated 95%.Keywords: Cardio-Vascular Disease, Data Mining, Random Forest, K-Means, Random Forest Classifier.