of landslide susceptibility maps using semantics and semantic technology, a field of artificial intelligence. It provides a software framework within which a landslide expert can describe, using language as close to natural (specialist) language as possible, the properties of surface locations which are highly

method to assess landslides susceptibility. The results show That close to 30 % of the catchment area has a high to very high susceptibility to mass movements, 25 % presents medium susceptibility and 45 % a low to very low threat. The obtained results are critically compared and validated through

that influence landslide were extracted from the spatial database. The probabilistic model-frequency ratio is computed based on these factors. Landslide susceptibility indices were computed and grouped into five classes. Validation of LHS, showed an accuracy of 89 % as 25 of the 28 regions tallied with the field

to the influence to landslides, the Data layers were arranged in a hierarchy order from 1 to 6, where the bigger number, shows higher susceptibility to mass movements. Based on computed

implemented on feature extracted for each prototype facial expression was used to classify the six basic expressions. The map will compute the topological relationship between the particular expressions featured. While in recognition phase Euclidean distance measure had been used. The method tested using FACS

to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical

Recent advances in brain imaging and genetics have empowered the mapping of genetic and environmental influences on the human brain. These techniques shed light on the `nature/nurture' debate, revealing how genes determine individual differences in intelligence quotient (IQ) or risk

highest susceptibility. The landslidesusceptibility map can be used to reduce damage associated with landslides and to land cover planning.

and time consuming computation to achieve convergence. To address this, we propose an intelligent bootstrapping process and the use of parallel, communicative chains of different temperatures, commonly referred to as parallel tempering. The combined approach is shown to provide substantial improvement in a

strategies to map scalable behavior characteristics for characters as well. The authors present two important aspects of crowd simulation: the realism of the crowd behavior and the computational overhead involved. A good simulation of crowd behavior requires delicate balance between these aspects. The focus