Soft Computing, Artificial Intelligence and Applications

Innovative Bi Approaches and Methodologies Implementing A Multilevel Analytics Platform Based on Data Mining and Analytical Models: A Case of Study in Roadside Assistance Services Alessandro Massaro, Angelo Leogrande, Palo Lisco, Angelo Galiano and Nicola Savino Dyrecta Lab, IT Research Laboratory, Italy. ABSTRACT The paper proposes an advanced Multilevel Analytics Model –MAM-, applied on a specific case of study referring to a research project involving an industry mainly working in roadside assistance service (ACI Global S.p.A.). In the first part of the paper are described the initial specifications of the research project by addressing the study on information system architectures explaining knowledge gain, decision making and data flow automatism applied on the specific case of study. In the second part of the paper is described in details the MAM acting on different analytics levels, by describing the first analyzer module and the second one involving data mining ...

Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysis of the Relation Between Economic Freedom and Quality of Life Jonathan Staufer and Patricia Brockmann Technische Hochschule Nurnberg, Germany ABSTRACT The importance of economic freedom has often been stressed by supporters of liberalism, but can its actu-al effect be observed in a data driven, objective way? To analyze this relation the Economic Freedom of the World (EFW) index and the Human Development Index (HDI) were examined with modern machine learning algorithms and a wide-ranging approach. Considering the EFW index’s preference of a liberal-istic oriented economic policy, an objective recommendation for creating an economic policy that im-proves people’s everyday lives might be derived by the analysis results. It was found that these more ad-vanced algorithms achieve a considerably stronger correlation between both indices than pure statistical means yet leave a small room for interpretati...

Intelligent Electrical Multi Outlets Controlled and Activated by a Data Mining Engine Oriented to Building Electrical Management Alessandro Massaro, Valeria Vitti, Angelo Galiano Dyrecta Lab, Italy ABSTRACT In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of thresh...

Self Learning Real Time Expert System Latha B. Kaimal, Abhir Raj Metkar and Rakesh G C-DAC,India ABSTRACT In a Power plant with a Distributed Control System ( DCS ), process parameters are continuously stored in databases at discrete intervals. The data contained in these databases may not appear to contain valuable relational information but practically such a relation exists. The large number of process parameter values are changing with time in a Power Plant. These parameters are part of rules framed by domain experts for the expert system. With the changes in parameters there is a quite high possibility to form new rules using the dynamics of the process itself. We present an efficient algorithm that generates all significant rules based on the real data. The association based algorithms were compared and the best suited algorithm for this process application was selected. The application for the Learning system is studied in a Power Plant domain. The SCADA interface was develo...

<span style="color:#0000ff;"><strong>A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA </strong></span> <p style="text-align:center;"><span style="color:#ff0000;"><strong>Doreswamy and Umme Salma M</strong></span> <span style="color:#ff0000;"><strong>Mangalore University, Mangalagangothri, Mangalore,India, </strong></span></p> <p style="text-align:justify;"><span style="color:#800000;"><strong>ABSTRACT</strong></span></p> <p style="text-align:justify;">Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large number of people around the world. Diagnosing the disease at its earliest instance ...