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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Yang, Binyi Zunyi Normal University Guizhou Province Zunyi City563006 China

ISBN: (纸本)9798331509828

News dissemination in the context of digitalization is a research focus in the field of multimedia and social networks. Stable and accurate modeling and analysis can effectively promote the development of journalism. The theoretical basis of this study is that social networks, complex networks and multimedia networks. Through deep modeling, this study constructs a new two-layer news dissemination model. In terms of topology, the high-order network centrality optimization is used to obtain the dynamic characteristics of user relationships and dissemination paths. In the part of the social network fusion multimedia analysis, this study designed an adaptive random walk algorithm to address the data sparsity and uncertainty of trust relationships. This algorithm can further optimize the trust calculation indicators of social networks. Furthermore, in the optimization of the multimedia network modeling, this study improved the stability of resource allocation through matching exchange operations to ensure the accessibility of news. In the experimental part, this study uses the topological analysis method of complex networks to visualize the structural characteristics and information dissemination efficiency of news dissemination network. This study provides a new theoretical basis for the networking and intelligence of news dissemination, and expands the scope of traditional research to a new paradigm of computer algorithm integration. © 2025 IEEE.

关键词： AI-driven Digital Media intelligent data analysis News Communication

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intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction

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AI COMMUNICATIONS 1998年第3-4期11卷 191-218页

作者： Lavrac, N Kononenko, I Keravnou, E Kukar, M Zupan, B Jozef Stefan Inst Ljubljana 1001 Slovenia Fac Comp & Informat Sci Ljubljana 1000 Slovenia Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus

Extensive amounts of knowledge and data stored in medical databases request the development of specialized tools for storing and accessing of data, data analysis, and effective use of stored knowledge and data. This paper focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension. The paper sketches the history of research that led to the development of current intelligent data analysis techniques, discusses the need for intelligent data analysis in medicine, and proposes a classification of intelligent data analysis methods. The main scope of the paper are machine learning and temporal abstraction methods and their application in medical diagnosis. A selection of methods and diagnostic domains is presented, and the performance and usefulness of approaches discussed. The paper concludes with the evaluation of selected intelligent data analysis methods and their applicability in medical diagnosis.

关键词： intelligent data analysis machine learning temporal abstraction medical applications medical diagnosis

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intelligent data analysis applied to debug complex software systems

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NEUROCOMPUTING 2009年第13-15期72卷 2785-2795页

作者： Serrano, Emilio Gomez-Sanz, Jorge J. Botia, Juan A. Pavon, Juan Univ Murcia Murcia Spain Univ Complutense Madrid Madrid Spain

The emergent behavior of complex systems, which arises from the interaction of multiple entities, can be difficult to validate, especially when the number of entities or their relationships grows. This validation requires understanding of what happens inside the system. In the case of multi-agent systems, which are complex systems as well. this understanding requires analyzing and interpreting execution traces containing agent specific information, deducing how the entities relate to each other, guessing which acquaintances are being built, and how the total amount of data can be interpreted. The paper introduces some techniques which have been applied in developments made with an agent oriented methodology, INGENIAS, which provides a framework for modeling complex agent oriented systems. These techniques can be regarded as intelligent data analysis techniques, all of which are oriented towards providing simplified representations of the system. These techniques range from raw data visualization to clustering and extraction of association rules. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Complex systems Multi-agent systems validation intelligent data analysis Agent interaction analysis

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intelligent data analysis approaches to churn as a business problem: a survey

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KNOWLEDGE AND INFORMATION SYSTEMS 2017年第3期51卷 719-774页

作者： Garcia, David L. Nebot, Angela Vellido, Alfredo Univ Politecn Catalunya UPC BarcelonaTech Dept Comp Sci Barcelona 08034 Spain

Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer's intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application.

关键词： Churn analysis intelligent data analysis Computational intelligence Customer continuity management Literature survey

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intelligent data analysis with fuzzy decision trees

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SOFT COMPUTING 2007年第5期11卷 439-457页

作者： Wang, Xiaomeng Nauck, Detlef D. Spott, Martin Kruse, Rudolf Univ Magdeburg Fac Comp Sci D-39106 Magdeburg Germany Intelligent Syst Res Ctr BT Res & Venturing Ipswich IP5 3RE Suffolk England

intelligent data analysis has gained increasing attention in business and industry environments. Many applications are looking not only for solutions that can automate and de-skill the data analysis process, but also methods that can deal with vague information and deliver comprehensible models. Under this consideration, we present an automatic data analysis platform, in particular, we investigate fuzzy decision trees as a method of intelligent data analysis for classification problems. We present the whole process from fuzzy tree learning, missing value handling to fuzzy rules generation and pruning. To select the test attributes of fuzzy trees we use a generalized Shannon entropy. We discuss the problems connected with this generalization arising from fuzzy logic and propose some amendments. We give a theoretical comparison on the fuzzy rules learned by fuzzy decision trees with some other methods, and compare our classifiers to other well-known classification methods based on experimental results. Moreover, we show a real-world application for the quality control of car surfaces using our approach.

关键词： fuzzy decision trees intelligent data analysis classification models fuzzy rule learning

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intelligent data analysis for conservation: experiments with rhino horn fingerprint identification

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KNOWLEDGE-BASED SYSTEMS 2003年第5-6期16卷 329-336页

作者： Amin, R Bramer, M Emslie, R Zool Soc London Inst Zool London NW1 4RY England Univ Portsmouth Fac Technol Dept Comp Sci & Software Engn Portsmouth PO1 3HE Hants England IUCN SSC African Rhino Specialist Grp Kwa Zulu Natal South Africa

Conservation is an area in which a great deal of data has been collected over many years. intelligent data analysis offers the possibility of analysing this data in an automatic fashion to map characteristics, identify trends and offer guidance for conservation action. This paper is concerned with the use of techniques of intelligent data analysis for an important task in animal conservation: the identification of the species and origin of illegally traded or confiscated African rhino horn. It builds on an earlier analysis by the African Rhino Specialist Group. It is demonstrated that it is possible to distinguish between both species and country of origin with a high degree of accuracy and that the results are also likely to be suitable for use in court. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： conservation rhino horn fingerprint identification intelligent data analysis data mining neural nets automatic rule induction decision trees

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intelligent data analysis As an Evidence-Based Medicine Tool

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AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS 2024年第2期58卷 129-134页

作者： Zabezhailo, M. I. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia

Some possible mathematical models and methods of intelligent data analysis (IDA) for the field of evidence-based medicine (EBM) are discussed. Two critically significant limitations for the application of traditional (statistics-based) EBM approach are considered: work with open subject areas and with small (statistically nonsignificant) collections of analyzed data. A special class of IDA methods based on the computer-oriented formalization of causal similarity heuristics using logical and algebraic means is presented. Options for clarifying the concept of evidence-based are proposed, which allow the indicated limitations of the traditional EBM approach to be circumvented. Some practically significant characteristics of this variant of the use of artificial intelligence methods in the tasks of evidence-based medicine are discussed.

关键词： artificial intelligence intelligent data analysis evidence-based medicine causal similarity heuristics reasoning

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intelligent data analysis and Its Challenges in Big data Environment

Intelligent Data Analysis and Its Challenges in Big Data Env...

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IEEE International Conference on System Science and Engineering (ICSSE)

作者： Kong, Weichang Wu, Qidi Li, Li Qiao, Fei Tongji Univ Sch Elect & Informat Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9781479943678

intelligent data analysis (IDA) is one of the most important approaches in the field of data mining, which attracts great concerns from the researchers. Based on the basic principles of IDA and the features of datasets that IDA handles, the development of IDA is briefly summarized from three aspects, i.e., algorithm principle, the scale and type of the dataset. Moreover, the challenges facing the IDA in big data environment are analyzed from four views, including big data management, data collection, data analysis, and application pattern. It is also cleared that in order to extract more values from data, the further development of IDA should combine practical applications and theoretical researches together.

关键词： intelligent data analysis big data value extraction

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intelligent data analysis for conservation: experiments with rhino horn fingerprint identification

Intelligent data analysis for conservation: experiments with...

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22nd SGAI International Conference on Knowledge Based Systems and Applied Artificial Intelligence

ISBN: (纸本)1852336730

关键词： conservation rhino horn fingerprint identification intelligent data analysis data mining neural nets automatic rule induction decision trees

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Flow graphs and intelligent data analysis

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FUNDAMENTA INFORMATICAE 2005年第1-4期64卷 369-377页

作者： Pawlak, Z Polish Acad Sci Inst Theoret & Appl Informat PL-44100 Gliwice Poland Warsaw Sch Informat Technol PL-01447 Warsaw Poland

This paper concerns a new approach to intelligent data analysis based on information flow distribution study in a flow graph. Branches of a flow graph are interpreted as decision rules, whereas a flow graph is supposed to describe a decision algorithm. We propose to model decision processes as flow graphs and analyze decisions in terms of flow spreading in a graph.

关键词： flow graphs data mining information flow intelligent data analysis Bayes' rule

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