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  • Open Access

    ARTICLE

    Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

    Omer Berat Sezer*, Ahmet Murat Ozbayoglu

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 323-334, 2020, DOI:10.31209/2018.100000065

    Abstract Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our… More >

  • Open Access

    ARTICLE

    Enhancing the Classification Accuracy in Sentiment Analysis with Computational Intelligence Using Joint Sentiment Topic Detection with MEDLDA

    PCD Kalaivaani1,*, Dr. R Thangarajan2

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 71-79, 2020, DOI:10.31209/2019.100000152

    Abstract Web mining is the process of integrating the information from web by traditional data mining methodologies and techniques. Opinion mining is an application of natural language processing to extract subjective information from web. Online reviews require efficient classification algorithms for analysing the sentiments, which does not perform an in–depth analysis in current methods. Sentiment classification is done at document level in combination with topics and sentiments. It is based on weakly supervised Joint Sentiment-Topic mode which extends the topic model Maximum Entropy Discrimination Latent Dirichlet Allocation by constructing an additional sentiment layer. It is assumed that topics generated are dependent… More >

  • Open Access

    ARTICLE

    African Buffalo Optimization Algorithm for Collision-Avoidance in Electric Fish

    Julius Beneoluchi Odili1,*, A. Noraziah2, Mohd Helmy Abd Wahab3

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 41-51, 2020, DOI:10.31209/2018.100000059

    Abstract This paper presents the African Buffalo Optimization algorithm for collision avoidance among electric fishes. Collision-avoidance in electric fish finds correlation with the Travelling Salesman avoiding the cities he has earlier visited. Collision avoidance in electric is akin to collision-avoidance in modern day driverless cars being promoted by Google Incorporation and other similar companies. The concept of collision-avoidance is also very useful to persons with visual impairment as it will help them avoid collision with objects, vehicles, persons, especially other visually-impaired. After a number of experimental procedures using the concept of the travelling salesman’s problem to simulate collision-avoidance in electric fish,… More >

  • Open Access

    ARTICLE

    System Integration for Cognitive Model of a Robot Partner

    Jinseok Woo, János Botzheim, Naoyuki Kubota

    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 829-841, 2018, DOI:10.1080/10798587.2017.1364919

    Abstract This paper introduces the integrated system of a smart-device-based cognitive robot partner called iPhonoid-C. Interaction with a robot partner requires many elements, including verbal communication, nonverbal communication, and embodiment as well. A robot partner should be able to understand human sentences, as well as nonverbal information such as human gestures. In the proposed system, the robot has an emotional model connecting the input information from the human with the robot’s behavior. Since emotions are involved in human natural communication, and emotion has a significant impact on humans’ actions, it is important to develop an emotional model for the robot partner… More >

  • Open Access

    ARTICLE

    Aircraft Structural Integrity Assessment through Computational Intelligence Techniques

    RamanaM. Pidaparti1

    Structural Durability & Health Monitoring, Vol.2, No.3, pp. 131-148, 2006, DOI:10.3970/sdhm.2006.002.131

    Abstract This paper provides an overview of the computational intelligence methods developed for the structural integrity assessment of aging aircraft structures. Computational intelligence techniques reviewed include artificial neural networks, inverse neural network mapping, wavelet based image processing methods, genetic algorithms, spectral element methods, and particle swarm optimization. Multi-site damage, corrosion, and corrosion-fatigue damage in aging aircraft is specifically discussed. Results obtained from selected computational intelligence methods are presented and compared to the existing alternate solutions and experimental data. The results presented illustrate the applicability of computational intelligence methods for assessing the structural integrity of aging aircraft structures and materials. More >

  • Open Access

    ARTICLE

    Application of Artificial Neural Networks in Design of Steel Production Path

    Igor Grešovnik1,2, Tadej Kodelja1, Robert Vertnik2,3, Bojan Senčič3,2,3, Božidar Šarler1,2,4

    CMC-Computers, Materials & Continua, Vol.30, No.1, pp. 19-38, 2012, DOI:10.3970/cmc.2012.030.019

    Abstract Artificial neural networks (ANNs) are employed as an alternative to physical modeling for calculation of the relations between the production path process parameters (melting of scrap steel and alloying, continuous casting, hydrogen removal, reheating, rolling, and cooling on a cooling bed) and the final product mechanical properties (elongation, tensile strength, yield stress, hardness after rolling, necking) of steel semi products. They provide a much faster technique of response evaluation complementary to physical modeling. The Štore Steel company process path for production of steel bars is used as an example for demonstrating the approach. The applied ANN is of a multilayer… More >

  • Open Access

    ARTICLE

    Data Mining and Machine Learning Methods Applied to 3 A Numerical Clinching Model

    Marco Götz1,*, Ferenc Leichsenring1, Thomas Kropp2, Peter Müller2, Tobias Falk2, Wolfgang Graf1, Michael Kaliske1, Welf-Guntram Drossel2

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 387-423, 2018, DOI:10.31614/cmes.2018.04112

    Abstract Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised. The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design. Multiple analysis methods are known and available to gain insight into existing models. In this contribution, selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process. The selection of introduced methods comprises techniques of machine learning and data mining, in which the utilization is aiming at a decreased numerical effort. The methods of choice are basically… More >

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