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Loukas Dimitriou - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy rule based System approach to combining traffic count forecasts
    Transportation Research Record, 2010
    Co-Authors: Antony Stathopoulos, Matthew G. Karlaftis, Loukas Dimitriou
    Abstract:

    Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance Systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information Systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an eff...

  • Fuzzy Rule-Based System Approach to Combining Traffic Count Forecasts
    Transportation Research Record: Journal of the Transportation Research Board, 2010
    Co-Authors: Antony Stathopoulos, Matthew G. Karlaftis, Loukas Dimitriou
    Abstract:

    Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance Systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information Systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an efficient real-time forecasting mechanism. Results from the application of the proposed framework on data acquired from realistic signalized urban network data (of Athens, Greece) and for a diversity of locations exhibit its potential

Antony Stathopoulos - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy rule based System approach to combining traffic count forecasts
    Transportation Research Record, 2010
    Co-Authors: Antony Stathopoulos, Matthew G. Karlaftis, Loukas Dimitriou
    Abstract:

    Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance Systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information Systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an eff...

  • Fuzzy Rule-Based System Approach to Combining Traffic Count Forecasts
    Transportation Research Record: Journal of the Transportation Research Board, 2010
    Co-Authors: Antony Stathopoulos, Matthew G. Karlaftis, Loukas Dimitriou
    Abstract:

    Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance Systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information Systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an efficient real-time forecasting mechanism. Results from the application of the proposed framework on data acquired from realistic signalized urban network data (of Athens, Greece) and for a diversity of locations exhibit its potential

Matthew G. Karlaftis - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy rule based System approach to combining traffic count forecasts
    Transportation Research Record, 2010
    Co-Authors: Antony Stathopoulos, Matthew G. Karlaftis, Loukas Dimitriou
    Abstract:

    Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance Systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information Systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an eff...

  • Fuzzy Rule-Based System Approach to Combining Traffic Count Forecasts
    Transportation Research Record: Journal of the Transportation Research Board, 2010
    Co-Authors: Antony Stathopoulos, Matthew G. Karlaftis, Loukas Dimitriou
    Abstract:

    Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance Systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information Systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an efficient real-time forecasting mechanism. Results from the application of the proposed framework on data acquired from realistic signalized urban network data (of Athens, Greece) and for a diversity of locations exhibit its potential

Maged Kamel N Boulos - One of the best experts on this subject based on the ideXlab platform.

  • expert System shells for rapid clinical decision support module development an esta demonstration of a simple rule based System for the diagnosis of vaginal discharge
    Healthcare Informatics Research, 2012
    Co-Authors: Maged Kamel N Boulos
    Abstract:

    Objectives: This study demonstrates the feasibility of using expert System shells for rapid clinical decision support module development. Methods: A readily available expert System shell was used to build a simple Rule-Based System for the crude diagnosis of vaginal discharge. Pictures and ‘canned text explanations’ are extensively used throughout the program to enhance its intuitiveness and educational dimension. All the steps involved in developing the System are documented. Results: The System runs under Microsoft Windows and is available as a free download at http://healthcybermap.org/vagdisch.zip (the distribution archive includes both the program’s executable and the commented knowledge base source as a text document). The limitations of the demonstration System, such as the lack of provisions for assessing uncertainty or various degrees of severity of a sign or symptom, are discussed in detail. Ways of improving the System, such as porting it to the Web and packaging it as an app for smartphones and tablets, are also presented. Conclusions: An easy-to-use expert System shell enables clinicians to rapidly become their own ‘knowledge engineers’ and develop concise evidence-based decision support modules of simple to moderate complexity, targeting clinical practitioners, medical and nursing students, as well as patients, their lay carers and the general public (where appropriate). In the spirit of the social Web, it is hoped that an online repository can be created to peer review, share and re-use knowledge base modules covering various clinical problems and algorithms, as a ser vice to the clinical community.

Massimo Tisi - One of the best experts on this subject based on the ideXlab platform.

  • Automatically Summarizing all the Problems of a Rule-Based System
    2020
    Co-Authors: Jean-claude Royer, Massimo Tisi
    Abstract:

    Looking for conflicts in software policies is a critical activity in many contexts, like security, expert Systems, and databases. These policies are often written or specified as logical Rule-Based Systems, which is a flexible and modular formalism. However, this formalism does not guarantee by itself the safety of the System. While several works address conflict detection, as far as we know there is little research on making explicit all these problems in an abstract and simplified form. In this paper, we compare different techniques to extract all the problems in a Rule-Based System. We define an exact algorithm and an application on three middle-size case studies, but as expected with poor time performances. We improve this with a combine algorithm and a few simple heuristics that accelerate the computation of all the problems in the case studies by several orders of magnitude.

  • Efficiently Characterizing the Undefined Requests of a Rule-Based System
    2018
    Co-Authors: Zheng Cheng, Jean-claude Royer, Massimo Tisi
    Abstract:

    Rule-Based Systems are used to define complex policies in several contexts, because of the flexibility and modularity they provide. This is especially critical for security Systems, which may require to compose evolving policies for privacy, accountability, access control, etc. The inclusion of conflicting rules in complex policies, results in the inability of the System to unambiguously answer to certain requests, with possibly unpredictable effects. The static identification of these undefined requests is particularly challenging for unconstrained Rule-Based Systems, including quantifiers, computations and chaining of rules. In this paper we introduce a static method to precisely characterize the set of all undefined requests for a given unconstrained Rule-Based System, providing the user with a global view of the rule conflicts. We propose an enumerative approach, made usable in practice by two key performance optimizations: a finer classification of the rules and the resort of the topological sorting. We demonstrate its application on a well-known policy with more than fifty rules.

  • IFM - Efficiently Characterizing the Undefined Requests of a Rule-Based System
    Lecture Notes in Computer Science, 2018
    Co-Authors: Zheng Cheng, Jean-claude Royer, Massimo Tisi
    Abstract:

    Rule-Based Systems are used to define complex policies in several contexts, because of the flexibility and modularity they provide. This is especially critical for security Systems, which may require to compose evolving policies for privacy, accountability, access control, etc. The inclusion of conflicting rules in complex policies, results in the inability of the System to unambiguously answer to certain requests, with possibly unpredictable effects. The static identification of these undefined requests is particularly challenging for unconstrained Rule-Based Systems, including quantifiers, computations and chaining of rules. In this paper we introduce a static method to precisely characterize the set of all undefined requests for a given unconstrained Rule-Based System, providing the user with a global view of the rule conflicts. We propose an enumerative approach, made usable in practice by two key performance optimizations: a finer classification of the rules and the resort of the topological sorting. We demonstrate its application on a well-known policy with more than fifty rules.