Software Cost Estimation (SCE) is the emerging concern of the software companies during the development phase of the software, as it requires effort and cost factors for modelling the software. These factors are modelled using the Artificial Intelligence models, which seem to be less accurate and non-reliable by increasing the risk factor of the software projects. Thus, for estimating the software cost, meta-heuristics are employed. This paper proposes an algorithm, termed as whale-crow optimization (WCO) algorithm, which is the integration of the whale optimization algorithm (WOA) and the crow search algorithm (CSA). The main function of the WCO algorithm is to determine the Optimal Regression coefficients for the regression models, such as the Linear Regression model and the Kernel Logistic Regression model, to develop an Optimal Regression model to estimate the software cost. The experimentation is carried out using four datasets taken from the Promise software engineering repository to perform effective performance analysis. Analysis is carried out regarding the mean magnitude of relative error (MMRE) that proves that the proposed method of SCE is effective, attaining the average MMRE at a rate of 0.2442 for the proposed Linear Regression model and 0.2692 for the proposed Kernel Regression model. adhana(0123456789().,-volV)FT3 ](0123456789().,-volV)
Collaborative filtering is a successful approach where data analysis and querying can be done interactively. In large systems that contain huge data or many users, collaboration is often delayed by unrealistic runtimes. In any electronic application, the recommender systems play an important role as they help in making proper decisions on the basis of the recommendations that the system provides. Today there has been a dramatic increase in the amount of online content. Recommender system software's help users to navigate through this increased content that is collected from users. A recommender system helps a user to make decisions by predicting their preferences, during shopping, searching, or simply browsing, based on the user's past preferences as well as the preferences of other users.In this paper, we explore different recommender system algorithms such as User-Collaborative and Item-Collaborative filtering using the open source library Apache Mahout. We simulate recommendation system environments in order to evaluate the behavior of these collaborative filtering algorithms, with a focus on recommendation quality and time performance.
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