Forthcoming and Online First Articles

International Journal of Computational Complexity and Intelligent Algorithms

International Journal of Computational Complexity and Intelligent Algorithms (IJCCIA)

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International Journal of Computational Complexity and Intelligent Algorithms (2 papers in press)

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  • DeepSEA: Sentiment Embedding Analysis for Arabic People's Preferences on the Web   Order a copy of this article
    by Marwan Al Omari 
    Abstract: This research paper proposes four unique deep learning (DL) architectures based on previous studies through using long-short-term memory (LSTM), convolutional neural networks (CNNs), and Word2vec embedding layer. The Word2vec model built from 16 Arabic datasets to create a feature map, which is a numerical representation of text reviews. The experiments of the four models run over a single dataset, named DeepSEA, which combines all the 16 publicly available Arabic datasets for the purpose of sentiment analysis (SA) in multiple areas and domains of expertise, including movie, product, restaurant, hotel, book, tourism, and news reviews. The DeepSEA dataset contains 111,844 text reviews with only two distinguished classes: positive and negative. After running the four experiments, results have been attained in training and testing the four hybrid architectures of LSTM and CNNs with Word2vec semantic relationship model. First, the simple LSTM model has achieved 0.83 testing, 0.82 validation, and 0.993 training accuracies. Stacked LSTM model (with Word2vec) achieved 0.83 testing, 0.82 validation, and 0.95 training accuracies. Simple LSTM (with Word2vec) has scored 0.82 testing, 0.82 validation, and 0.95 training accuracies. The LSTM-CNNs (with Word2vec) achieved 0.83 testing, and 0.83 validation, and 0.995 training accuracies.
    Keywords: Arabic sentiment analysis; ASA; Arabic data; Arabic Word2vec; natural language processing; NLP; Arabic NLP.
    DOI: 10.1504/IJCCIA.2020.10032767
     
  • Artificial Bee Colony Algorithm With Hyperbolic Spiral Based Local Search   Order a copy of this article
    by SHIV KUMAR AGARWAL, Surendra Yadav 
    Abstract: Swarm intelligence based algorithms successfully solved various complex optimization problems In this class, artificial bee colony algorithm added in year 2005 by Dervis Karaboga that simulates the intelligent behavior of honey bees In order to search a quality food source honey bee update their position using some steps In ABC step size is the combination of the random component and a difference vector of existing and randomly chosen solution Sometimes this step size is very large due to huge difference between the vectors and large value of random component. This large step size leads to poor exploitation Therefore, to improve exploitation potential of ABC algorithm, a local search step added in basic ABC The new local search inspired by hyperbolic spiral and named as hyperbolic spiral local search (HSLS) The proposed variant of ABC is named as hyperbolic spiral based ABC (HSABC).
    Keywords: Swarm Intelligence; Meta-heuristics; Nature inspired algorithm; Optimization; Hyperbolic Spiral.
    DOI: 10.1504/IJCCIA.2019.10035969
     

 

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