In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Due to imprecision and uncertainties in predicting real world problems, artificial neural network (ANN) techniques have become increasingly useful for modeling and optimization. This paper presents an artificial neural network approach for forecasting electric energy consumption. For effective planning and operation of power systems, optimal forecasting tools are needed for energy operators to maximize profit and also to provide maximum satisfaction to energy consumers. Monthly data for electric energy consumed in the Gaza strip was collected from year 1994 to 2013. Data was trained and the proposed model was validated using 2-Fold and K-Fold cross validation techniques. The model has been tested with actual energy consumption data and yields satisfactory performance.
Using Artificial Neural Network Techniques for Prediction of Electric Energy Consumption

The folksonomy is the result of free personal information or assignment of tags to an object (determined by the URI) in order to find them. The practice of tagging is done in a collective environment. Folksonomies are self constructed, based on co-occurrence of definitions, rather than a hierarchical structure of the data. The downside of this was that a few sites and applications are able to successfully exploit the sharing of bookmarks. The need for tools that are able to resolve the ambiguity of the definitions is becoming urgent as the need of simple instruments for their visualization, editing and exploitation in web applications still hinders their diffusion and wide adoption. An intelligent interactive interface design for folksonomies should consider the contextual design and inquiry based on a concurrent interaction for a perceptual user interfaces. To represent folksonomies a new concept structure called "Folksodriven" is used in this paper. While it is presented the Folksodriven Structure Network (FSN) to resolve the ambiguity of definitions of folksonomy tags suggestions for the user. On this base a Human-Computer Interactive (HCI) systems is developed for the visualization, navigation, updating and maintenance of folksonomies Knowledge Bases the FSN through the web. System functionalities as well as its internal architecture will be introduced.
Intelligent Interface Architectures for Folksonomy Driven Structure Network

The term "affordance" denotes the behavioral meaning of objects. We propose a cognitive architecture for the detection of affordances in the visual modality. This model is based on the internal simulation of movement sequences. For each movement step, the resulting sensory state is predicted by a forward model, which in turn triggers the generation of a new (simulated) motor command by an inverse model. Thus, a series of mental images in the sensory and in the motor domain is evoked. Starting from a real sensory state, a large number of such sequences is simulated in parallel. Final affordance detection is based on the generated motor commands. We apply this model to a realworld mobile robot which is faced with obstacle arrangements some of which are passable (corridor) and some of which are not (dead ends). The robot's task is to detect the right affordance ("passthroughable" or "nonpassthroughable"). The required internal models are acquired in a hierarchical training process. Afterwards, the robotic agent is able to distinguish reliably between corridors and dead ends. This realworld result enhances the validity of the proposed mental simulation approach. In addition, we compare several key factors in the simulation process regarding performance and efficiency. Funding statement: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 1 ar X iv :1 61 1. 00 27 4v 1 [ cs .A I] 1 N ov 2 01 6
Detecting Affordances by Visuomotor Simulation