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One of the combinatorial optimization problems is Knapsack problem, which aims to maximize the benefit of objects whose weight not exceeding the capacity of knapsack. This paper introduces artificial bee colony algorithm to select a subset of project and represented by knapsack problem to put the best investment plan which achieve the highest profits within a determined costs, this plan is one of the applications of the financial field. The result from the proposed algorithm implemented by matlab (8.3) show the ability to find best solution with precisely and rapidity compared to genetic algorithm.

Academic website played a main role in giving information about academic institutions and view the services that introduced to their users. The purpose of this research evaluates the performance of Iraqi universities websites. A dynamic regression model was built. Created a dataset with 113 university websites then preprocessed executed at first then apply a correlation statistics method for determine the most features that influenced at the prediction of performance. Lastly, eight machine learning regression models was applied. After that compared these models, SGD regressor give the best result of evaluation metrics from other models were applied. For increasing the quality of a websites performance, academic institutions should take the result obtained from this research.

One of the combinatorial optimization problems is Knapsack problem, which aims to maximize the benefit of objects whose weight not exceeding the capacity of knapsack. This paper introduces artificial bee colony algorithm to select a subset of project and represented by knapsack problem to put the best investment plan which achieve the highest profits within a determined costs, this plan is one of the applications of the financial field. The result from the proposed algorithm implemented by matlab (8.3) show the ability to find best solution with precisely and rapidity compared to genetic algorithm