On the Synergy of Network Science and Artificial Intelligence
نویسنده
چکیده
Traditionally science is done using the reductionism paradigm. Artificial intelligence does not make an exception and it follows the same strategy. At the same time, network science tries to study complex systems as a whole. This Ph.D. research takes an alternative approach to the reductionism strategy, and tries to advance both fields, i.e. artificial intelligence and network science, by searching for the synergy between them, while not ignoring any other source of inspiration, e.g. neuroscience.
منابع مشابه
Curl Size and Pelt Color Determination of Zandi Lambs Using Image Processing and Artificial Neural Network
In this study, a method based on using image processing and artificial neural network is introduced to determine pelt color and curl size of newborn lambs in Zandi sheep. The data was collected from 300 newborn lambs reared in the Zandi sheep breeding centre of Khojir, Tehran. Primarily, curl size and pelt color of new born lambs was recorded by experienced appraisers, and at the same time, sev...
متن کاملUsing emotional intelligence to predict job stress: Artificial neural network and regression models
Introduction: These days, there is a consensus that emotional intelligence plays an important role in the success of individuals in different areas of life. Persons with higher emotional intelligence had lower stress in dealing with demands and pressures in the workplace. The purpose of this study was to use artificial neural network to predict job stress and to compare the performance of this ...
متن کاملControl over Power Conversion Efficiency of BHJ Solar Cells: Learn more from Less, with Artificial Intelligence
Harvesting the energy from the sun through the bulk heterojunction (BHJ) solar cells need materials with specific electronic characteristics. However, any promising material if cast improperly in cells will end into low or even null power conversion efficiency (PCE). Cell casting optimization is a time/material consumable step in any photovoltaic manufacturing practice. In this study, we sh...
متن کاملPresenting a Novel Algorithm to Optimal Designing Power Distribution Network in the Presence of DG
Regarding the nature of non-linear discrete placement, and in order to determine the optimal capacity of the substation, the goal in the present study will be a number of local optimum points. In this research, the problem of optimal placement posts to reduce power losses by considering Distributed Generation (DG). In formulating the objective function, geographical distribution density in the ...
متن کاملArtificial Intelligence for prediction of porosity from Seismic Attributes: Case study in the Persian Gulf
Porosity is one of the key parameters associated with oil reservoirs. Determination of this petrophysical parameter is an essential step in reservoir characterization. Among different linear and nonlinear prediction tools such as multi-regression and polynomial curve fitting, artificial neural network has gained the attention of researchers over the past years. In the present study, two-dimensi...
متن کامل