Analysis the Impact of Game theory (Pay-off Matrix, Nash Equilibrium) in the High Tech Industry: AI – Digital Assistant

International audienceIn this paper we are going to discuss and analysis the formal application of game theory which require the identity of independent actors, their preferences, their knowledge, strategic acts they are allowed to make. Each independent actor is assumed to be coherent. This game theory is not limited to case analysis rather psychology, tag of war, business, economy etc. Our focus is to use the game theory in information technology more specifically in Artificial Inelegance, Digital Assistant solution. Analysis the outcomes of game theory to determine the strategy for market penetration. The study however reveals some challenges such as affordance, access to information, cost advantage and subsidy or support or funding that change the total game of the deployment plan in order not to exacerbate the problem of digital divide

Optimal allocation of FACTS devices in distribution networks using Imperialist Competitive Algorithm

Copyright © 2005-2015 Praise Worthy Prize. The publisher granted a permission to the author to archive this article in BURA.FACTS devices are used for controlling the voltage, stability, power flow and security of transmission lines. Imperialist Competitive is a recently developed optimization technique, used widely in power systems. This paper presents an approach to finding the optimal location and size of FACTS devices in a distribution network using the Imperialist Competitive technique. IEEE 30-bus system is used as a case study. The results show the advantages of the Imperialist Competitive technique over the conventional approaches. © 2013 Praise Worthy Prize S.r.l. - All rights reserved

Developing new models for flyrock distance assessment in open-pit mines

Peer ReviewedObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches

Detection and estimation of damage in structures using imperialist competitive algorithm

Abstract. This paper presents a method for detection and estimation of structural damage on the basis of modal parameters of a damaged structure using imperialist competitive algorithm. The imperialist competitive algorithm was developed over the last few years in an attempt to overcome inherent limitations of traditional optimize method. In this research, imperialist competitive algorithm has been employed due to its favorable performance in detection of structural damages. The performance of the proposed method has been verified through using a benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring and a number of numerical examples. By way of comparison between location and amount of damage obtained from the proposed method and simulation model, it was concluded that the method is sensitive to the location and amount of damage. The results clearly revealed the superiority of the presented method in comparison with energy index method

A Continuous Review inventory Control Model within Batch Arrival Queuing Framework: A Parameter-Tuned Imperialist Competitive Algorithm

In this paper, a multi-product continues review inventory control problem within batch arrival queuing approach (MQr/M/1) is modeled to find the optimal quantities of maximum inventory. The objective function is to minimize summation of ordering, holding and shortage costs under warehouse space, service level, and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Np-Hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, a simulated annealing algorithm has been utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analyzed using some numerical illustrations

Technical and Fundamental Features Analysis for Stock Market Prediction with Data Mining Methods

Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.154 - Katedra financívyhově

Well Placement Optimization using Imperialist Competitive Algorithm

An efficient and optimized field development plan is a crucial and primary aspect of maximizing well productivities and improving the recovery factors of oil and gas fields, and thereby most effectively increasing profitability. In this research, we apply a meta-heuristics algorithm known as the imperialist competitive algorithm (ICA) to determine optimal well locations for maximum well productivity. The ICA, an evolutionary algorithm that mimics socio-political imperialist competition, uses an initial population that consists of colonies and imperialists that are assigned to several empires. The empires then compete with each other, which cause the weak empires to collapse and the powerful empires to dominate and overtake their colonies. We compared the ICA performance with that of particle swarm optimization (PSO) and the genetic algorithm (GA) in the following four optimization scenarios: 1) a vertical well in a channeled reservoir, 2) a horizontal well in a channeled reservoir, 3) placement of multiple vertical wells, and 4) placement of multiple horizontal wells. In all four scenarios, the ICA achieved a better solution than did the PSO or GA in a fixed number of simulation runs. We also applied the ICA optimization algorithm to optimize well placement, well type (producer/injector), well configuration (vertical/directional), wellbore length, and drilling schedules for a sector of a Middle East reservoir. In addition, we conducted sensitivity analyzes on three important parameters (revolution ratio, assimilation coefficient, and assimilation angle), and the analyses show that the recommended ICA default parameters generally led to acceptable performances in our examples. However, to obtain optimum performance, we recommend tuning the three main ICA parameters with respect to specific optimization problems

On the Processing of Highly Nonlinear Solitarywaves and Guided Ultrasonic Waves for Structural Health Monitoring and Nondestructive Evaluation

The in-situ measurement of thermal stress in civil and mechanical structures may prevent structural anomalies such as unexpected buckling. In the first half of the dissertation, we present a study where highly nonlinear solitary waves (HNSWs) were utilized to measure axial stress in slender beams. HNSWs are compact non-dispersive waves that can form and travel in nonlinear systems such as one-dimensional chains of particles. The effect of the axial stress acting in a beam on the propagation of HNSWs was studied. We found that certain features of the solitary waves enable the measurement of the stress. In general, most guided ultrasonic waves (GUWs)-based health monitoring approaches for structural waveguides are based on the comparison of testing data to baseline data. In the second half of the dissertation, we present a study where some baseline-free signal processing algorithms were presented and applied to numerical and experimental data for the structural health monitoring (SHM) of underwater or dry structures. The algorithms are based on one or more of the following: continuous wavelet transform, empirical mode decomposition, Hilbert transform, competitive optimization algorithm, probabilistic methods. Moreover, experimental data were also processed to extract some features from the time, frequency, and joint timefrequency domains. These features were then fed to a supervised learning algorithm based on artificial neural networks to classify the types of defect. The methods were validated using the numerical model of a plate and a pipe, and the experimental study of a plate in water. In experiment, the propagation of ultrasonic waves was induced by means of laser pulses or transducer and detected with an array of immersion transducers. The results demonstrated that the algorithms are effective, robust against noise, and able to localize and classify the damage