Published: Jan 1, 2018
Converted to Gold OA:
DOI: 10.4018/IJALR.2018010101
Volume 8
Duc T Pham, Luca Baronti, Biao Zhang, Marco Castellani
This article describes the Bees Algorithm in standard formulation and presents two applications to real-world continuous optimisation engineering problems. In the first case, the Bees Algorithm is...
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This article describes the Bees Algorithm in standard formulation and presents two applications to real-world continuous optimisation engineering problems. In the first case, the Bees Algorithm is employed to train three artificial neural networks (ANNs) to model the inverse kinematics of the joints of a three-link manipulator. In the second case, the Bees Algorithm is used to optimise the parameters of a linear model used to approximate the torque output for an electro-hydraulic load system. In both cases, the Bees Algorithm outperformed the state-of-the-art in the literature, proving to be an effective optimisation technique for engineering systems.
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MLA
Pham, Duc T., et al. "Optimisation of Engineering Systems With the Bees Algorithm." IJALR vol.8, no.1 2018: pp.1-15. http://doi.org/10.4018/IJALR.2018010101
APA
Pham, D. T., Baronti, L., Zhang, B., & Castellani, M. (2018). Optimisation of Engineering Systems With the Bees Algorithm. International Journal of Artificial Life Research (IJALR), 8(1), 1-15. http://doi.org/10.4018/IJALR.2018010101
Chicago
Pham, Duc T., et al. "Optimisation of Engineering Systems With the Bees Algorithm," International Journal of Artificial Life Research (IJALR) 8, no.1: 1-15. http://doi.org/10.4018/IJALR.2018010101
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Published: Jan 1, 2018
Converted to Gold OA:
DOI: 10.4018/IJALR.2018010102
Volume 8
Mohammadhossein Barkhordari, Mahdi Niamanesh
When working with a high volume of information that follows an exponential pattern, the authors confront big data. This huge amount of information makes big data retrieval and analytics important...
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When working with a high volume of information that follows an exponential pattern, the authors confront big data. This huge amount of information makes big data retrieval and analytics important issues. There have been many attempts to solve data analytic problems using distributed platforms, but the main problem with the proposed methods is not observing the data locality. In this article, a MapReduce-based method called Hengam is proposed. In this method, data format unification helps nodes to have data independence. The unified format leads to an increase in the information retrieval speed and prevents data exchange betoen nodes. The proposed method was evaluated using data items from an ICT company and the information retrieval time was much better than that of other open-source distributed data warehouse software.
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MLA
Barkhordari, Mohammadhossein, and Mahdi Niamanesh. "Hengam a MapReduce-Based Distributed Data Warehouse for Big Data: A MapReduce-Based Distributed Data Warehouse for Big Data." IJALR vol.8, no.1 2018: pp.16-35. http://doi.org/10.4018/IJALR.2018010102
APA
Barkhordari, M. & Niamanesh, M. (2018). Hengam a MapReduce-Based Distributed Data Warehouse for Big Data: A MapReduce-Based Distributed Data Warehouse for Big Data. International Journal of Artificial Life Research (IJALR), 8(1), 16-35. http://doi.org/10.4018/IJALR.2018010102
Chicago
Barkhordari, Mohammadhossein, and Mahdi Niamanesh. "Hengam a MapReduce-Based Distributed Data Warehouse for Big Data: A MapReduce-Based Distributed Data Warehouse for Big Data," International Journal of Artificial Life Research (IJALR) 8, no.1: 16-35. http://doi.org/10.4018/IJALR.2018010102
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Published: Jan 1, 2018
Converted to Gold OA:
DOI: 10.4018/IJALR.2018010103
Volume 8
Daniela Lopez De Luise, Ben Raul Saad, Pablo D Pescio, Christian Martin Saliwonczyk
The main goal of this article is to present an approach that allows the automatic management of autistic communication patterns by processing audio and video from the therapy session of individuals...
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The main goal of this article is to present an approach that allows the automatic management of autistic communication patterns by processing audio and video from the therapy session of individuals suffering autistic spectrum disorders (ASD). Such patients usually have social and communication alterations that make it difficult to evaluate the meaning of those expressions. As their communicational skills may have different degrees of variation, it is very hard to understand the semantics behind the verbal behavior. The current work is based on previous work on machine learning for individual performance evaluation. Statistics show that autistic verbal behavior are physically expressed by repetitive sounds and related movements that are evident and stereotyped. The works of Leo Kanner and Ángel Riviere are also considered here. Using machine learning and neural nets with certain set of parameters, it is possible to automatically detect patterns in audio and video recording of patient's performance, which is an interesting opportunity to communicate with ASD patients.
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MLA
De Luise, Daniela Lopez, et al. "Autistic Language Processing by Patterns Detection." IJALR vol.8, no.1 2018: pp.36-61. http://doi.org/10.4018/IJALR.2018010103
APA
De Luise, D. L., Saad, B. R., Pescio, P. D., & Saliwonczyk, C. M. (2018). Autistic Language Processing by Patterns Detection. International Journal of Artificial Life Research (IJALR), 8(1), 36-61. http://doi.org/10.4018/IJALR.2018010103
Chicago
De Luise, Daniela Lopez, et al. "Autistic Language Processing by Patterns Detection," International Journal of Artificial Life Research (IJALR) 8, no.1: 36-61. http://doi.org/10.4018/IJALR.2018010103
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