Published: Jan 1, 2018
Converted to Gold OA:
DOI: 10.4018/IJALR.2018010101
Volume 8
Research Article
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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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
Research Article
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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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
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DOI: 10.4018/IJALR.2018010103
Volume 8
Research Article
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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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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Published: Jul 1, 2018
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DOI: 10.4018/IJALR.20180701.pre
Volume 8
Guest Editorial Preface
R. Sudhakar, P. Vijaya
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Sudhakar, R., and P. Vijaya. "Special Issue on Recent Trends and Developments in Artificial Intelligence and its Applications." IJALR vol.8, no.2 2018: pp.5-6. http://doi.org/10.4018/IJALR.20180701.pre
APA
Sudhakar, R. & Vijaya, P. (2018). Special Issue on Recent Trends and Developments in Artificial Intelligence and its Applications. International Journal of Artificial Life Research (IJALR), 8(2), 5-6. http://doi.org/10.4018/IJALR.20180701.pre
Chicago
Sudhakar, R., and P. Vijaya. "Special Issue on Recent Trends and Developments in Artificial Intelligence and its Applications," International Journal of Artificial Life Research (IJALR) 8, no.2: 5-6. http://doi.org/10.4018/IJALR.20180701.pre
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Published: Jul 1, 2018
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DOI: 10.4018/IJALR.2018070101
Volume 8
Research Article
Puri Vishal, Ramesh Babu A.
Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a...
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Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.
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Vishal, Puri, and Ramesh Babu A. "Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm." IJALR vol.8, no.2 2018: pp.1-24. http://doi.org/10.4018/IJALR.2018070101
APA
Vishal, P. & Ramesh Babu A. (2018). Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm. International Journal of Artificial Life Research (IJALR), 8(2), 1-24. http://doi.org/10.4018/IJALR.2018070101
Chicago
Vishal, Puri, and Ramesh Babu A. "Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm," International Journal of Artificial Life Research (IJALR) 8, no.2: 1-24. http://doi.org/10.4018/IJALR.2018070101
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Published: Jul 1, 2018
Converted to Gold OA:
DOI: 10.4018/IJALR.2018070102
Volume 8
Research Article
Yarrapragada K.S.S. Rao, Bala Krishna B.
This article addresses the issue regarding the exploitation of conventional fuel diesel. To overcome this issue, the Tamanu oil-diesel oil blend is introduced, where a new neural model is proposed...
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This article addresses the issue regarding the exploitation of conventional fuel diesel. To overcome this issue, the Tamanu oil-diesel oil blend is introduced, where a new neural model is proposed, which is trained by renowned firefly algorithm, termed as FF-NM. In addition, different compression ratios such as 15, 16, 17, 17.5 and blend ratios like 5:95, 6:94, 7:93, 8:92, and 9:91and 10:90 is exploited. The emission analysis and the combustion characteristics of the TO-diesel oil blend are evaluated as well as the MSE analysis is carried out for the proposed FF-NM method. For all the predicted parameters, the MSE of the proposed method is low for varying blend as well as the compression ratios. Moreover, the emission characteristics of the HC, CO2, NOx, CO, as well as O2 at different CR concerning the actual, and FF-NM is computed with the chosen blend ratios. From analysis, it is recognized that the estimation errors are less for the FF-NM approach. Hence, the simulation outcomes demonstrate the better performance of the proposed FF-NM approach under various compression ratios of 15, 16, 17 and 17.5, respectively.
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Rao, Yarrapragada K.S.S., and Bala Krishna B. "A Comprehensive Analysis on Biodiesel Blend Model." IJALR vol.8, no.2 2018: pp.25-46. http://doi.org/10.4018/IJALR.2018070102
APA
Rao, Y. K. & Bala Krishna B. (2018). A Comprehensive Analysis on Biodiesel Blend Model. International Journal of Artificial Life Research (IJALR), 8(2), 25-46. http://doi.org/10.4018/IJALR.2018070102
Chicago
Rao, Yarrapragada K.S.S., and Bala Krishna B. "A Comprehensive Analysis on Biodiesel Blend Model," International Journal of Artificial Life Research (IJALR) 8, no.2: 25-46. http://doi.org/10.4018/IJALR.2018070102
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Published: Jul 1, 2018
Converted to Gold OA:
DOI: 10.4018/IJALR.2018070103
Volume 8
Research Article
Shashikant Patil, Vaishali Kulkarni, Archana Bhise
Tooth caries or cavities diagnosing are concerned as the most significant research work, as this is the common oral disease suffered by humans. Many approaches have been proposed under the topics...
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Tooth caries or cavities diagnosing are concerned as the most significant research work, as this is the common oral disease suffered by humans. Many approaches have been proposed under the topics including demineralization and decaying as well. However, the imaging modalities often suffer from various critical or complex aspects that struggles the methods to attain accurate diagnosis. This article turns to introduce a new cavity diagnosis model with three phases: (i) pre-processing (ii) feature extraction (iii) classification. In the first phase, a new bi-histogram equalization with adaptive sigmoid functions (BEASF) is introduced to enhance the image quality followed by other enhancements models like grey thresholding and active contour. Then, the features are extracted using multilinear principal component analysis (MPCA). Further, the classification is done via neural network (NN) classifier. After the implementation, the proposed model compares its performance over other conventional methods like principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) and the performance of the approach is analyzed in terms of measures such as accuracy, sensitivity, specificity, precision, false positive rate (FPR), false negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR), F1Score and Mathews correlation coefficient (MCC), and proves the superiority of proposed work.
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Patil, Shashikant, et al. "BEASF-Based Image Enhancement for Caries Detection Using Multidimensional Projection and Neural Network." IJALR vol.8, no.2 2018: pp.47-66. http://doi.org/10.4018/IJALR.2018070103
APA
Patil, S., Kulkarni, V., & Bhise, A. (2018). BEASF-Based Image Enhancement for Caries Detection Using Multidimensional Projection and Neural Network. International Journal of Artificial Life Research (IJALR), 8(2), 47-66. http://doi.org/10.4018/IJALR.2018070103
Chicago
Patil, Shashikant, Vaishali Kulkarni, and Archana Bhise. "BEASF-Based Image Enhancement for Caries Detection Using Multidimensional Projection and Neural Network," International Journal of Artificial Life Research (IJALR) 8, no.2: 47-66. http://doi.org/10.4018/IJALR.2018070103
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Published: Jul 1, 2018
Converted to Gold OA:
DOI: 10.4018/IJALR.2018070104
Volume 8
Research Article
Ch.Ram Mohan, A. Venugopal Reddy
One of the infrastructureless networks built by various independent mobile nodes is mobile ad hoc network (MANET), which is an emerging technology, requiring a secure routing protocol for data...
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One of the infrastructureless networks built by various independent mobile nodes is mobile ad hoc network (MANET), which is an emerging technology, requiring a secure routing protocol for data transmission. Accordingly, literature presents various secure routing protocols for MANETs by utilizing trust and data encryption. In this article, a whale optimization algorithm (WOA) is utilized for selecting the optimal secured routing path in the MANET. The WOA algorithm utilizes the trust factor and the distance between the nodes for computing the fitness for the routing path. Overall, the steps involved in the proposed routing algorithm are as follows: i) Measuring the trust and the distance-based metrics for every node; ii) Discovering k-disjoint path; and iii) Determining the optimal path based on the trust and the distance-based metrics. The performance of the trust-based WOA (T-Whale) is analyzed using the metrics, energy, throughput, and packet delivery rate. From the simulation results, it is evident that the T-Whale algorithm has the improved energy, throughput, and PDR values of 27.4520, 0.4, and 0.4, at the simulation time of 10 sec over the conventional trust random search algorithm when the node is under attack.
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Mohan, Ch.Ram, and A. Venugopal Reddy. "T-Whale: Trust and Whale Optimization Model for Secure Routing in Mobile Ad-Hoc Network." IJALR vol.8, no.2 2018: pp.67-79. http://doi.org/10.4018/IJALR.2018070104
APA
Mohan, C. & Reddy, A. V. (2018). T-Whale: Trust and Whale Optimization Model for Secure Routing in Mobile Ad-Hoc Network. International Journal of Artificial Life Research (IJALR), 8(2), 67-79. http://doi.org/10.4018/IJALR.2018070104
Chicago
Mohan, Ch.Ram, and A. Venugopal Reddy. "T-Whale: Trust and Whale Optimization Model for Secure Routing in Mobile Ad-Hoc Network," International Journal of Artificial Life Research (IJALR) 8, no.2: 67-79. http://doi.org/10.4018/IJALR.2018070104
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