Hybrid journal (It can contain Open Access articles) ISSN (Print) 1755-0556 - ISSN (Online) 1755-0564 Published by Inderscience Publishers[450 journals]
Authors:Khalil Ibrahim Hamzaoui, Mohammed Berrajaa, Mostafa Azizi, Giuseppe Lipari, Pierre Boulet Pages: 4 - 16 Abstract: Energy consumption is the result of interactions between hardware, software, users, and the application environment. Optimisation of energy consumption has become crucial, and energy is considered a critical resource, so it is important to know and understand both how energy is measured and consumed on mobile devices. An accurate knowledge will allow us to develop efficient solutions to reduce energy consumption in order to improve the user experience. In this paper, we propose an experimental methodology to build a model of the energy consumption of mobile applications. Based on precise measurements, we elaborate predictive models of energy consumption for both unconnected and connected applications. Keywords: mobile computing; operating system; energy consumption modelling Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 12, No. 1 (2020) pp. 4 - 16 PubDate: 2020-02-10T23:20:50-05:00 DOI: 10.1504/IJRIS.2020.105007 Issue No:Vol. 12, No. 1 (2020)
Authors:Nadia Ben Seghier, Okba Kazar Pages: 17 - 33 Abstract: Web services are meaningful only if potential users may find and execute them. Universal Description Discovery and Integration (UDDI) help businesses, organisations, and other web services providers to discover and reach to the service(s) by providing the URI of the WSDL file. However, it does not offer a mechanism to choose a web service based on its quality. The standard also lacks sufficient semantic description in the content of web services, this lack makes it difficult to find and compose suitable web services during analysis, search, and matching processes. In addition, a central UDDI suffers from one centralised point problem and the high cost of maintenance. To get around these problems, the authors propose in this paper a novel framework based on mobile agent, metadata catalogue and user profile for web services discovery in order to reduce the search space and increase the number of relevant services. Keywords: semantic web service; ontology; matchmaking; metadata catalogue; mobile agent; distributed architecture; user profile representation; customer satisfaction; service quality Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 12, No. 1 (2020) pp. 17 - 33 PubDate: 2020-02-10T23:20:50-05:00 DOI: 10.1504/IJRIS.2020.105003 Issue No:Vol. 12, No. 1 (2020)
Authors:Nassira Chekkai, Ilyes Chorfi, Souham Meshoul, Badreddine Chekkai, Didier Schwab, Mohamed Belaoued, Amel Ziani Pages: 34 - 50 Abstract: Recommender systems (RSs) have recently gained significant attention from both research and industrial communities. These systems generate the recommendations of items in one of two ways, namely collaborative or content-based filtering. Collaborative filtering is a technique used by recommender systems in order to suggest to the user a set of items based on the opinions of other users who share with him the same preferences. One of the key issues in collaborative filtering systems (CFSs) is how to generate adequate recommendations for newcomers who rate only a small number of items, a problem known as cold start user. Another interesting problem is the cold start item when a new item is introduced in the system and cannot be recommended. In this paper, we present a clustering-based approach SCOL that aims to alleviate the cold start challenges; by identifying the most effective opinion leaders among the social network of the CFS. SCOL clustering focuses on the credibility and correlation similarity concepts. Keywords: collaborative filtering; recommender systems; cold start problem; social network; graph theory; credibility; correlation similarity Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 12, No. 1 (2020) pp. 34 - 50 PubDate: 2020-02-10T23:20:50-05:00 DOI: 10.1504/IJRIS.2020.105006 Issue No:Vol. 12, No. 1 (2020)
Authors:Mohamed Hamlich, Abdelkarim El Khantach, Noureddine Belbounaguia Pages: 51 - 59 Abstract: The false data injection in the power grid is a major risk for a good and safety functioning of the smart grid. The false data detection with conventional methods are incapable to detect some false measurements, to remedy this, we have opted to use machine learning which we used five classifiers to conceive an effective detection [k-nearest neighbour (KNN) algorithm, random trees, random forest decision trees, multilayer perceptron and support vector machine]. Our analysis is validated by experiments on a physical bus feeding system performed on PSS/in which we have developed a dataset for real measurement. Afterward we worked with MATLAB software to construct false measurements according to the Jacobean matrix of the state estimation. We tested the collected data with different classification algorithms, which gives good and satisfactory results. Keywords: smart grid; state estimation; false data injection; machine learning Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 12, No. 1 (2020) pp. 51 - 59 PubDate: 2020-02-10T23:20:50-05:00 DOI: 10.1504/IJRIS.2020.104991 Issue No:Vol. 12, No. 1 (2020)
Authors:Alessia Amelio, Ivo Rumenov Draganov Pages: 60 - 69 Abstract: This paper explores a new reasoning-based approach for measuring the extremely low frequency magnetic field emitted by a portable computer. The introduced approach and the widely accepted TCO standard are compared each other and discussed. This comparison shows that the well-known magnetic field measurement TCO standard has important limitations and disadvantages. In fact, the new reasoning-based approach obtains measurement results of the extremely low frequency magnetic field which are closer to the working conditions of the portable computers' users. Accordingly, the introduced measurement methodology is more user-centric than the TCO standard measurement and should be employed in a future standardisation. Keywords: magnetic field; measurement; methodology; self-organising-map; SOM; artificial intelligence; pattern recognition; portable computers; standardisation; magnetic field; TCO standard Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 12, No. 1 (2020) pp. 60 - 69 PubDate: 2020-02-10T23:20:50-05:00 DOI: 10.1504/IJRIS.2020.105010 Issue No:Vol. 12, No. 1 (2020)
Authors:Krassimira Stoyanova, Vassil Guliashki Pages: 70 - 79 Abstract: This paper presents a two-stage portfolio risk optimisation based on Markowitz's mean variance optimisation (MVO) model. Historical return data for six asset classes are used to calculate the optimal proportions of assets, included in a portfolio, so that the expected return of each asset is no less than in advance given target value. Optimisation procedure is performed at the first stage, in order to select a limited number of assets among a large assets sample. At the second stage the optimal proportions of selected assets in the portfolio are calculated, minimising a risk objective function for a given rate of return. Ten optimisation problems are solved for different expected rate of return. The optimisation is performed in MATLAB. The proposed approach is robust and could be used successfully to solve large-scale portfolio optimisation problems. Keywords: portfolio optimisation; mean variance optimisation model; MATLAB Citation: International Journal of Reasoning-based Intelligent Systems, Vol. 12, No. 1 (2020) pp. 70 - 79 PubDate: 2020-02-10T23:20:50-05:00 DOI: 10.1504/IJRIS.2020.105011 Issue No:Vol. 12, No. 1 (2020)
Hybrid journal (It can contain Open Access articles)
[450 journals]
