A Comparative Analysis of RAD and Agile Technique for Management of Computing Graduation Projects

Computing students face the problem with time and quality of the work while managing their graduation/senior projects.Rapid Application Development(RAD)model is based on continual user involvement for the process of requirement gathering via prototyping.After each iteration,the developers can validate the requirements that are completed in the iteration.Managing a project with RAD is easier but not flexible.On the other hand,Agile project management techniques focus on flexibility,agility,teamwork and quality based on user stories.Continual user involvement is avoided,which requires extensive maintenance time for fixing iteration and release of the story points.This also makes it necessary to provide onsite training to the users of the application.This research provides the pros and cons of RAD and Agile project management techniques,to help students in deciding the best approach for managing their graduation projects.For the evaluation of these techniques,similar case studies were given to the senior project students(having similar CGPAs)for building similar functionality-based applications.The two projects“Life Organizer”developed and managed using RAD and“Smart Patient Assistant(SPA)”developed and managed through Agile methodology were evaluated against the quality assurance criteria for senior projects.The study found that the project developed with RAD methodology performed 13.33%better in providing extensive and elaborated documentation than the students following the Agile technique.On the other hand,SPA-Agile based project,due to teamwork had 2.5%better implementation than Life Organizer-RAD based project.

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.

Application of Linear Programming for Optimal Investments in Software Company

We apply the simplex algorithm which is a branch of linear programming to efficiently determine the allocation of resources required to operate a company in the software development field. The main aim of applying this technique is to maximize the profit of a company under certain limitations. This can be done using the trial-and-error approach. However, this tedious process can be replaced by user-level tools such as Excel which are based on linear programming that will give more accurate results. Small software companies cannot afford to hire a high number of senior programmers to produce the required level of quality and to keep up with the demand for adding new features. On the other hand, lowering the quality of the product will reduce the number of customers and decrease profit. Another aspect is maximizing the utilization of hosting servers which are required for providing the services to customers since the cost of buying servers and maintaining them is extremely high. The simplex algorithm in linear programming will take the specified constraints into account to compute the optimal allocation of the available resources to maximize profit and limit the cost. This paper will present a model that uses the simplex algorithm with a set of constraints to determine how many projects of each type a company should take in one period of time.