Link prediction is an important problem in social network analysis and has been applied in a variety of fields. Link prediction aims to estimate the likelihood of the existence of links between nodes by the known network structure. The time complexity of link prediction algorithms in huge-scale networks remains unexplored and unsolved, especially for sparse networks. In this project, we will ex...

Many statistical models for environmental studies can be run at different scales, e.g., for daily, weekly or monthly data. It is important to know how these models differ in terms of prediction. We provide some theoretical results to compare these models. We show that when there is no high order terms of explanatory variables, the small scale model yields more efficient estimators for model par...

This paper presents a simulation-based performance prediction framework for large scale data-intensive applications on large scale machines. Our framework consists of two components: application emulators and a suite of simulators. Application emulators provide a parameterized model of data access and computation patterns of the applications and enable changing of critical application component...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...

This study examined the risk prediction efficiency of the Reasons for Living Inventory Survival and Coping Beliefs Scale, Beck Hopelessness Scale, Beck Depression Inventory, and the Life Experiences Survey with a sample of 51 newly hospitalized parasuicides. The index of suicidal potential chosen for this study was suicide intent as measured by Beck's Suicide Intent Scale. Regression analyses i...

prediction of climatic variables on a local scale by general circulation models of the atmosphere is impossible because the models have large-scale network of resolution. therefore, downscaling methods are used to solve this problem. since the climate change phenomenon can affect different systems such as, water resources, agriculture, environment, industry and economy as well, selection of the...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...