Forthcoming articles


International Journal of Innovative Computing and Applications


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International Journal of Innovative Computing and Applications (4 papers in press)


Regular Issues


  • An Empirical Study of Statistical Language Models: N-gram Language Models vs. Neural Network Language Models   Order a copy of this article
    by Freha MEZZOUDJ, Abdelkader BENYETTOU 
    Abstract: Statistical language models are an important module in many areas of successful applications such as speech recognition and machine translation. And N-gram models are basically the state-of-the-art. However, due to sparsity of data, the modelled language cannot be completely represented in the n-gram language model. In fact, if new words appear in the recognition or translation steps, we need to provide a smoothing method to distribute the model probabilities over the unknown values. Recently, neural networks were used to model language based on the idea of projecting words onto a continuous space and performing the probability estimation in this space. In this experimental work, we compare the behavior of the most popular smoothing methods with statistical n-gram language models and neural network language models in different situations and with different parameters. The language models are trained on two corpora of French and English texts. Good empirical results are obtained by the recurrent neural network language models.
    Keywords: language models; n-grams; Kneser-Ney smoothing; modified Kneser-Ney smoothing; Good-Turing smoothing; interpolation; back-off ; feed-forward neural networks; continuous space language models; recurrent neural networks; speech recognition; machine translation.

  • An Algorithm Based on Voronoi Diagrams for the Multi-Stream Multi-Source Multicast Routing Problem   Order a copy of this article
    by Romerito Andrade, Marco Goldbarg, Elizabeth Goldbarg 
    Abstract: In this study, we present a new heuristic for the multi-stream multi-source multicast routing problem. The core of the heuristic proposed in this study is based on a generalization of Voronoi Diagrams in graphs. It allows building the trees needed to serve the demands of multiple sessions efficiently. Also, the proposed algorithm supports multiple sources. We performed an extensive experimental analysis of different network and problem configurations such as the number of sessions, nodes, sources and participants per session. We compare the proposed algorithm to heuristics proposed previously. The results of the experiments showed that the heuristic proposed in this study finds high-quality solutions efficiently.
    Keywords: multicast routing; multi-source; multi-session; Voronoi diagram.

  • Modelling and implementation of an energy management simulator based on agents using optimized fuzzy rules: Application to an electric vehicle   Order a copy of this article
    by Rachid El Amrani 
    Abstract: This paper presents an intelligent algorithm based on multi agent systems to manage the energy in a hybrid electrical vehicle using a model of lithium metal polymer (LMP) battery and a model of an electrical double layer capacitor (EDLC). The algorithm uses fuzzy rules optimized by a genetic algorithm to control the flow of energy inside the system. The LMP battery is linked to a boost converter to insure the autonomy of the electrical vehicle, while the EDLC is linked to a back boost converter that provides the highly demanded energy in a short time and guarantees the temporarily energy storage when the vehicle is braking (no energy is demanded). The hybrid electrical vehicle is simulated in different driving cycles to analyze the behaviour of the LMP battery and the EDLC. Results showed that the used hybrid strategy was able to ensure the autonomy of the vehicle in terms of energy since it has performed a minimum energy cost and a maximum profit in autonomy, which means a longer life of the Hybrid Electric Source.
    Keywords: Hybrid vehicle; Battery; Capacitor; Modelling; Genetic optimization; Fuzzy control; Multi-agent system; Energy management.

  • Efficiency Analysis of Maximum Power Point Tracking Techniques for Photovoltaic Systems under variable conditions   Order a copy of this article
    by Rajanand Patnaik Narasipuram, Chaitanya Somu, Ravindranath Tagore Yadlapalli, Lakshmi Sirisha Simhadri 
    Abstract: With the rapid increase in development of solar energy, researchers are concentrated on developing the maximum power point tracking (MPPT) techniques for extracting the power efficiently. It was environmentally friendly, low maintenance cost, no noise, used in remote areas, and long lasting life. The output power of PV module is depending on the solar irradiance and temperature. So, to extract more power MPPT techniques are employed. This paper describes the comparative analysis of various MPPTs like Perturb & Observe (P&O), Incremental and Conductance (IC), Fuzzy Logic Controller (FL), and Neuro-Fuzzy (NF) technique. The controlled output is fed as input to the boost DC-DC converter and the effective performance of the MPPTs are checked under different irradiations and constant temperature. And also, this paper gives mathematical analysis, design and operation of converter and MPPT techniques. In addition, steady state and dynamic performance of MPPT techniques are also analyzed. The simulations are performed under MATLAB/Simulink environment.
    Keywords: DC-DC converter; Photovoltaic Module; PV; Maximum Power Point Tracking; MPPT; Perturb & Observe; P&O; Incremental and Conductance; IC; Fuzzy Logic Controller; FL; Neuro-Fuzzy; NF;.