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Record W2577231901 · doi:10.20965/jaciii.2005.p0003

Intelligent Systems: Methodology, Models, and Applications in Emerging Technologies

2005· article· en· W2577231901 on OpenAlexaboutno aff
Vassil Sgurev, Vladimir Jotsov, Mincho Hadjiski

Bibliographic record

VenueJournal of Advanced Computational Intelligence and Intelligent Informatics · 2005
Typearticle
Languageen
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsIntelligent decision support systemComputer scienceOntologyIntelligent agentFuzzy logicIntelligent controlArtificial intelligenceAgent architectureSemantics (computer science)Data science

Abstract

fetched live from OpenAlex

From year to year the number of investigations on intelligent systems grows rapidly. For example this year 245 papers from 45 countries were sent for the Second International IEEE Conference on Intelligent Systems (www.ieee-is.org; www.fnts-bg.org/is) and this is an increase of more than 50% by all indicators. The presented papers on intelligent systems were marked by big audiences and they provoked a significant interest that ultimately led to the formation of vivid discussions, exchange of ideas and locally provoked the creation of working groups for different applied projects. All this reflects the worldwide tendencies for the leading role of the research on intelligent systems theoretically and practically. The greater part of the presented research dealt with traditional for the intelligent systems problems like artificial intelligence, knowledge engineering, intelligent agents, neural and fuzzy networks, intelligent data processing, intelligent control and decision making systems, and also new interdisciplinary problems like ontology and semantics in Internet, fuzzy intuitionistic logic. The majority of papers from the European and American researchers are dedicated to the theory and the applications of the intelligent systems with machine learning, fuzzy inference or uncertainty. Another big group of papers focuses on the domain of building and integrating ontologies of applications with heterogeneous multiagent systems. A great number of papers on intelligent systems deals with fuzzy sets. The papers of many other researchers underscore the significance of the contemporary perception-oriented methods and also of different applications in the intelligent systems. On the first place this is valid for the paradigm of L. A. Zadeh 'computing with words'. The Guest Editors in the present specialized journal volume would like to introduce a wealth of research with an applied and theoretical character that possesses a common characteristic and it is the conference best papers complemented and updated by the new elaborations of the authors during the last half a year. A short description of the presented in the volume papers follows. In 'Combining Local and Global Access to Ontologies in a Multiagent System' R. Brena and H. Ceballos (Mexico) proposed an original way for operation with ontologies where a part of the ontology is processed by a client's component and the rest is transmitted to the other agents by an ontology agent. The inter-agent communication is improved in this way. In 'Fuzzy Querying of Evolutive Situations: Application to Driving Situations' S. Ould Yahia and S. Loriette-Rougegrez (France) present an approach to analysis of driving situations using multimedia images and fuzzy estimates that will improve the driver's security. In 'Rememberng What You Forget in an Online Shopping Context' M. Halvey and M. Keane (Ireland) presented their approach to constructing online system that predicts the items for future shopping sessions using a novel idea called Memory Zones. In 'Reinforcement Learning for Online Industrial Process Control' the authors J. Govindhasamy et al. (Ireland) use a synthesis of dynamic programming, reinforcement learning and backpropagation for a goal of modeling and controlling an industrial grinding process. The felicitous combination of methods contributes for a greater effectiveness of the applications compared to the existing controllers. In 'Dynamic Visualization of Information: From Database to Dataspace' the authors C. St-Jacques and L. Paquin (Canada) suggested a friendly online access to large multimedia databases. W. Huang (UK) redefines in 'Towards Context-Aware Knowledge Management in e-Enterprises' the concept of context in intelligent systems and proposes a set of meta-information elements for context description in a business environment. His approach is applicable in the E-business, in the Semantic Web and in the Semantic Grid. In 'Block-Based Change Detection in the Presence of Ambient Illuminaion Variations' T. Alexandropoulos et al. (Greece) use a statistic analysis, clustering and pattern recognition algorithms, etc. for the goal of noise extraction and the global illumination correction. In 'Combining Argumentation and Web Search Technology: Towards a Qualitative Approach for Ranking Results' C. Chesñevar (Spain) and A. Maguitman (USA) proposed a recommender system for improving the WEB search. Defeasible argumentation and decision support methods have been used in the system. In 'Modified Axiomatic Basis of Subjective Probability' K. Tenekedjiev et al. (Bulgaria) make a contribution to the axiomatic approach to subjective uncertainty by introducing a modified set of six axioms to subjective probabilities. In 'Fuzzy Rationality in Quantitative Decision Analysis' N. Nikolova et al. (Bulgaria) present a discussion on fuzzy rationality in the elicitation of subjective probabilities and utilities. The possibility to make this special issue was politely offered to the Guest Editors by Prof. Kaoru Hirota, Prof. Toshio Fukuda and we thank them for that. Due to the help of Kenta Uchino and also due to the new elaborations presented by explorers from Europe and America the appearance of this special issue became possible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0010.007
Scholarly communication0.0090.007
Open science0.0030.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.317
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2005
Admission routes1
Has abstractyes

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