Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Purchase individual online access for 1 year to this journal.
Price: EUR 140.00Impact Factor 2024: 1.4
AI Communications is a journal on Artificial Intelligence (AI) which has a close relationship to ECCAI (the European Coordinating Committee for Artificial Intelligence). It covers the whole AI community: scientific institutions as well as commercial and industrial companies.
AI Communications aims to enhance contacts and information exchange between AI researchers and developers, and to provide supranational information to those concerned with AI and advanced information processing. AI Communications publishes refereed articles concerning scientific and technical AI procedures, provided they are of sufficient interest to a large readership of both scientific and practical background. In addition it contains high-level background material, both at the technical level as well as the level of opinions, policies and news. The Editorial and Advisory Board is appointed by the Editor-in-Chief.
Authors: MohamadiBaghmolaei, Rezvan | Mozafari, Niloofar | Hamzeh, Ali
Article Type: Research Article
Abstract: Influence maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. The problem of influence maximization in social networks has been explored in many previous researches. All the existing methods have mainly relied on the constraint of binary state for each node which is either inactive or active. However in reality, changing the state of a node from inactive to active happens gradually and under the continuous effects of all its inspiring neighbors. Accordingly, a node can be inactive, active, or in any state between these two, indicating the gradual process …of inspiration. In this paper, we propose a novel influence propagation model which considers continuous states for each node instead of discrete ones. Furthermore, regarding the major role of time in pairwise propagation rates in social networks, we build on a time constrained framework to solve the influence maximization problem. In our work, we first extend the classic IC model to include continuous states and also time delays for transitions between states. Second, we find the most influential nodes in social networks considering continuity and time of the influence process simultaneously. And the last but not the least, it is applicable to well-known real social networks such as Epinions and Wikipedia. Show more
Keywords: Social networks, information diffusion, influence maximization, independent cascade model, CLIM
DOI: 10.3233/AIC-170720
Citation: AI Communications, vol. 30, no. 2, pp. 99-116, 2017
Authors: Oprea, Mihaela
Article Type: Research Article
Abstract: The paper presents a methodological framework, ABVE-Frame, for the development of virtual enterprises based on intelligent agents that are implemented as a simulation. The proposed framework combines an agent-based approach with an ontological approach and is flexible enough to be adapted for a specific domain of application. The VE lifecycle phases (creation, operation and evolution, dissolution) are mapped on the agent-based VE development lifecycle and are described as a set of algorithms. A formal description of the agent-based VE model (ABVE-Model) is proposed and a case study of applying the framework to the IT domain for computer networks development is …discussed. Show more
Keywords: Virtual enterprise, intelligent agents, ontology, VE lifecycle, development framework
DOI: 10.3233/AIC-160719
Citation: AI Communications, vol. 30, no. 2, pp. 117-140, 2017
Authors: Chaudhari, Sneha | Azaria, Amos | Mitchell, Tom
Article Type: Research Article
Abstract: Recommender Systems have become increasingly important and are applied in an increasing number of domains. While common collaborative methods measure similarity between different users, common content based methods measure similarity between different content. We propose a privacy aware recommender system that exploits relations present between entities appearing in content from user’s history and entities appearing in candidate content. In order to identify such relations, we use the knowledge graph of NELL, which encodes entities and their relations. We present a novel normalized version of Personalized PageRank, to rank candidate content. We test our approach on the movie recommendation domain and …show that the proposed method outperforms other baseline methods, including the standard Personalized PageRank. We intend to deploy our recommender system as a news recommendation app for mobile devices. Show more
Keywords: Recommender Systems, knowledge-graphs, PageRank
DOI: 10.3233/AIC-170728
Citation: AI Communications, vol. 30, no. 2, pp. 141-149, 2017
Authors: Reyes Fernández de Bulnes, Darian | Dibene Simental, Juan Carlos | Maldonado, Yazmin | Trujillo, Leonardo
Article Type: Research Article
Abstract: Operations scheduling and Lookup Table (LUT) based technology mapping are fundamental problems of mapping designs onto an electronic device, such as a Field Programmable Gate Array. We present an approach to apply two optimizations consecutively. As first optimization, we apply several metaheuristic algorithms for multi-objective optimization at the High-Level Synthesis stage. As a second optimization, we realize reductions of LUTs at the Logic Synthesis stage. Several circuit designs are represented in a Data Flow Graph (DFG) and the experiments are carried out on the standard Mediabench benchmark. In the first optimization, we compared NSGA-II, FEMO, HypE, IBEA, SPEA2 and WSGA. …Results have an average improvement 14.06% in occupied Area and 7.01% in Power consumption. Then, optimized DFG schedules are converted into Very High Description Language code using the Xilinx ISE Design Suite tool. Later, in the second optimization, The IMap algorithm is used to obtain combinational area reductions. Results show that 60% of the circuits are improved in comparison with the Xilinx ISE Design Suite. Show more
Keywords: Circuit optimization, DFG, FPGA, MOEA, VHDL
DOI: 10.3233/AIC-170727
Citation: AI Communications, vol. 30, no. 2, pp. 151-168, 2017
Authors: Tharwat, Alaa | Gaber, Tarek | Ibrahim, Abdelhameed | Hassanien, Aboul Ella
Article Type: Research Article
Abstract: Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction problems as a pre-processing step for machine learning and pattern classification applications. At the same time, it is usually used as a black box, but (sometimes) not well understood. The aim of this paper is to build a solid intuition for what is LDA, and how LDA works, thus enabling readers of all levels be able to get a better understanding of the LDA and to know how to apply this technique in different applications. The paper first gave the basic definitions and steps of how LDA technique …works supported with visual explanations of these steps. Moreover, the two methods of computing the LDA space, i.e. class-dependent and class-independent methods, were explained in details. Then, in a step-by-step approach, two numerical examples are demonstrated to show how the LDA space can be calculated in case of the class-dependent and class-independent methods. Furthermore, two of the most common LDA problems (i.e. Small Sample Size (SSS ) and non-linearity problems) were highlighted and illustrated, and state-of-the-art solutions to these problems were investigated and explained. Finally, a number of experiments was conducted with different datasets to (1) investigate the effect of the eigenvectors that used in the LDA space on the robustness of the extracted feature for the classification accuracy, and (2) to show when the SSS problem occurs and how it can be addressed. Show more
Keywords: Dimensionality reduction, PCA, LDA, Kernel Functions, Class-Dependent LDA, Class-Independent LDA, SSS (Small Sample Size) problem, eigenvectors artificial intelligence
DOI: 10.3233/AIC-170729
Citation: AI Communications, vol. 30, no. 2, pp. 169-190, 2017
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]