1. A New Generation of Artificial Neural Networks

2.  Support Vector Machines (SVM) appeared in the early nineties in the COLT92 ACM Conference.  SVM have increasingly turned into a standard methodology in the computer science and engineering communities.  These techniques are easy to tune and, in addition, mathematically well-founded.

3.  Security in Public Buildings and Facilities  Image Processing  Improvement of Internet Resources  Energy Efficiency Management  Detection and Prevention of Diseases  Analysis of Genetic Samples  Prevention of Natural Disasters

5. An individual is detected: The main features of the individual are extracted: The features are compared with a database: The identification can be made within a Real-Time schedule

7.  Postal services need automatic Real-Time procedures to identify zip codes.  For this task, the error rate of these methods is 3.2%, similar to the human error, known to be around 2.5%.

8.  Wrongly written digits are automatically detected by the method.  In this way, human work reduces to checking the small set of wrongly written digits detected.

9.  For instance, from a database with 10.000 images (some of them, non-digits), the method has detected the following wrongly written digits:

11.  Results of web search engines can be refined using these methods.  Once a web search has been made, these methods can be used to organize the resulting webpages, according to the user’s explicit or implicit preferences.

12.  Spam is the use of electronic messaging systems to send unsolicited bulk messages indiscriminately.  Results using these methods for spam filtering are promising, with rates of successful filtering over 90%.

14.  Load forecasting is an important issue in the electric power supply industry. The goal is to supply the prediction of maximum daily values of electrical loads.  The prediction accuracy of these methods is over 97% for this task.

15.  The load forecasting procedure can be integrated into a Complex Decision Support System (DSS) for Energy Efficiency and Risk Management.  A DSS enables operators to improve energy efficiency by providing integrated management of:  cost minimisation,  meeting energy,  emission-reduction requirements,  or risk management.

17.  The inputs to the Distributed Energy Resources Customer Adoption Model (DER-CAM) are:  the customer’s energy loads,  energy prices,  and information on Distributed Energy Resources equipment.

18.  The outputs of the Distributed Energy Resources Customer Adoption Model are:  the optimal capacity adoption of Distributed Energy Resources technology,  the optimal operating schedule for each time period of the year,  the system energy efficiency,  and the level of CO 2 emissions.

21.  These techniques can be used to distinguish malignant from benign breast cytology, using characteristics of the cell nuclei present in a digitized image.  Their accuracy in detecting this disease is over 97%, with a very low rates of false positives and false negatives.

22. Images of Benign CellsImages of Malignant Cells

24.  The previous ones are only some of the many applications where these techniques are a very successful or promising tool. Some other examples are:  Analysis of Genetic Samples: Tissue Classification, Gene Function Prediction  Prevention of Natural Disasters: Earthquakes, Tsunamis