The Experts below are selected from a list of 23832 Experts worldwide ranked by ideXlab platform
A C Parsons - One of the best experts on this subject based on the ideXlab platform.
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power quality disturbance waveform recognition using wavelet based neural classifier ii application
IEEE Transactions on Power Delivery, 2000Co-Authors: Surya Santoso, E J Powers, W M Grady, A C ParsonsAbstract:For pt.I see ibid., vol.15, no.1, p.222-8 (2000). A wavelet-based neural classifier is constructed and thoroughly tested under various conditions, The classifier is able to provide a Degree of Belief for the identified waveform. The Degree of Belief gives an indication about the goodness of the decision made. It is also equipped with an acceptance threshold so that it can reject ambiguous disturbance waveforms. The classifier is able to achieve the accuracy rate of more than 90% by rejecting less than 10% of the waveforms as ambiguous.
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power quality disturbance waveform recognition using wavelet based neural classifier i theoretical foundation
IEEE Transactions on Power Delivery, 2000Co-Authors: Surya Santoso, E J Powers, W M Grady, A C ParsonsAbstract:Existing techniques for recognizing and identifying power quality disturbance waveforms are primarily based on visual inspection of the waveform. It is the purpose of this paper to bring to bear advances, especially in wavelet transforms, artificial neural networks, and the mathematical theory of evidence, to the problem of automatic power quality disturbance waveform recognition. Unlike past attempts to automatically identify disturbance waveforms where the identification is performed in the time domain using an individual artificial neural network, the proposed recognition scheme is carried out in the wavelet domain using a set of multiple neural networks. The outcomes of the networks are then integrated using decision making schemes such as a simple voting scheme or the Dempster-Shafer theory of evidence. With such a configuration, the classifier is capable of providing a Degree of Belief for the identified disturbance waveform.
Mahmud Hasan - One of the best experts on this subject based on the ideXlab platform.
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Avian influenza (H5N1) expert system using Dempster-Shafer theory
International Journal of Information and Communication Technology, 2012Co-Authors: Andino Maseleno, Mahmud HasanAbstract:Based on cumulative number of confirmed human cases of avian influenza (H5N1) reported to World Health Organization (WHO) in 2011 from 15 countries, Indonesia has the largest number of deaths because of avian influenza which 146 deaths. In this research, the researcher built an avian influenza (H5N1) expert system for identifying avian influenza disease and displaying the result of identification process. In this paper, we describe five symptoms as major symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. We use chicken as research object. Dempster-Shafer theory is to quantify the Degree of Belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine Beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the Belief and plausibility in our identification result. The result reveals that avian influenza (H5N1) expert system has successfully identified the existence of avian influenza and displaying the result of identification process.
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African Trypanosomiasis Detection using Dempster-Shafer Theory
arXiv: Artificial Intelligence, 2012Co-Authors: Andino Maseleno, Mahmud HasanAbstract:World Health Organization reports that African Trypanosomiasis affects mostly poor populations living in remote rural areas of Africa that can be fatal if properly not treated. This paper presents Dempster-Shafer Theory for the detection of African trypanosomiasis. Sustainable elimination of African trypanosomiasis as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we implement Dempster-Shafer theory for detecting African trypanosomiasis and displaying the result of detection process. We describe eleven symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, bleeding around the bite, joint the paint, swollen lymph nodes, sleep disturbances, meningitis and arthritis. Dempster-Shafer theory to quantify the Degree of Belief, our approach uses Dempster-Shafer theory to combine Beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the Belief and plausibility in our identification result.
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Skin Diseases Expert System using Dempster-Shafer Theory
International Journal of Intelligent Systems and Applications, 2012Co-Authors: Andino Maseleno, Mahmud HasanAbstract:Based on World Health Organization (WHO) report in the 2011 Skin diseases still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin diseases are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we are using Dempster-Shafer Theory for detecting skin diseases and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the Degree of Belief, our approach uses Dempster-Shafer theory to combine Beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the Belief and plausibility in our identification result. The result reveal that Skin Diseases Expert System has been successfully detecting skin diseases and displaying the result of identification process.
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Skin infection detection using Dempster-Shafer theory
2012 International Conference on Informatics Electronics & Vision (ICIEV), 2012Co-Authors: Andino Maseleno, Mahmud HasanAbstract:Skin infections still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin infections are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we built a Skin Infection Expert System for detecting skin infections and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the Degree of Belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine Beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the Belief and plausibility in our identification result.
Andino Maseleno - One of the best experts on this subject based on the ideXlab platform.
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the dempster shafer theory algorithm and its application to insect diseases detection
2013Co-Authors: Andino MaselenoAbstract:This paper presents Dempster-Shafer Theory for insect diseases detection. Sustainable elimination of insect diseases as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we used Dempster-Shafer theory for detecting insect diseases and displaying the result of detection process. Insect diseases which include babesiosis, dengue fever, lyme, malaria, and west nile. We describe six symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, and arthritis. Dempster-Shafer theory to quantify the Degree of Belief, our approach uses Dempster-Shafer theory to combine Beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the Belief and plausibility in our identification result.
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Avian influenza (H5N1) expert system using Dempster-Shafer theory
International Journal of Information and Communication Technology, 2012Co-Authors: Andino Maseleno, Mahmud HasanAbstract:Based on cumulative number of confirmed human cases of avian influenza (H5N1) reported to World Health Organization (WHO) in 2011 from 15 countries, Indonesia has the largest number of deaths because of avian influenza which 146 deaths. In this research, the researcher built an avian influenza (H5N1) expert system for identifying avian influenza disease and displaying the result of identification process. In this paper, we describe five symptoms as major symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. We use chicken as research object. Dempster-Shafer theory is to quantify the Degree of Belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine Beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the Belief and plausibility in our identification result. The result reveals that avian influenza (H5N1) expert system has successfully identified the existence of avian influenza and displaying the result of identification process.
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African Trypanosomiasis Detection using Dempster-Shafer Theory
arXiv: Artificial Intelligence, 2012Co-Authors: Andino Maseleno, Mahmud HasanAbstract:World Health Organization reports that African Trypanosomiasis affects mostly poor populations living in remote rural areas of Africa that can be fatal if properly not treated. This paper presents Dempster-Shafer Theory for the detection of African trypanosomiasis. Sustainable elimination of African trypanosomiasis as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we implement Dempster-Shafer theory for detecting African trypanosomiasis and displaying the result of detection process. We describe eleven symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, bleeding around the bite, joint the paint, swollen lymph nodes, sleep disturbances, meningitis and arthritis. Dempster-Shafer theory to quantify the Degree of Belief, our approach uses Dempster-Shafer theory to combine Beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the Belief and plausibility in our identification result.
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Skin Diseases Expert System using Dempster-Shafer Theory
International Journal of Intelligent Systems and Applications, 2012Co-Authors: Andino Maseleno, Mahmud HasanAbstract:Based on World Health Organization (WHO) report in the 2011 Skin diseases still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin diseases are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we are using Dempster-Shafer Theory for detecting skin diseases and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the Degree of Belief, our approach uses Dempster-Shafer theory to combine Beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the Belief and plausibility in our identification result. The result reveal that Skin Diseases Expert System has been successfully detecting skin diseases and displaying the result of identification process.
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Skin infection detection using Dempster-Shafer theory
2012 International Conference on Informatics Electronics & Vision (ICIEV), 2012Co-Authors: Andino Maseleno, Mahmud HasanAbstract:Skin infections still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin infections are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we built a Skin Infection Expert System for detecting skin infections and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the Degree of Belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine Beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the Belief and plausibility in our identification result.
Fahiem Bacchus - One of the best experts on this subject based on the ideXlab platform.
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Default Inferences From Statistical Knowledge
2007Co-Authors: Fahiem BacchusAbstract:There are two common and distinct uses of probabilities: probabilities used as Degrees of Belief and probabilities used as statistical measures. Probabilities used as statistical measures can represent various assertions about the objective statistical state of the world, while probabilities used as Degrees of Belief can represent various assertions about the subjective state of an agent’s Beliefs. In this paper we examine how an agent who knows certain statistical facts about the world might infer certain probabilistic Degrees of Beliefs in other assertions based on these statistics. For example, an agent who knows that most birds fly (a statistical fact) may have a Degree of Belief greater than 0.5 in the assertion that Tweety flies given that Tweety is a bird. This inference of Degrees of Belief from statistical facts is know as direct inference. We develop a formal logical mechanism for performing direct inference, and demonstrate how this mechanism can be applied to the problem of making default inferences as studied in AI.
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FSTTCS - Generating Degrees of Belief from Statistical Information: An Overview
Lecture Notes in Computer Science, 1993Co-Authors: Fahiem Bacchus, Adam J. Grove, Joseph Y. Halpern, Daphne KollerAbstract:Consider an agent (or expert system) with a knowledge base KB that includes statistical information (such as “90% of patients with jaundice have hepatitis”), first-order information (“all patients with hepatitis have jaundice”), and default information (“patients with jaundice typically have a fever”). A doctor with such a KB may want to assign a Degree of Belief to an assertion ϕ such as “Eric has hepatitis”. Since the actions the doctor takes may depend crucially on this Degree of Belief, we would like to specify a mechanism by which she can use her knowledge base to assign a Degree of Belief to ϕ in a principled manner. We have been investigating a number of techniques for doing so; in this paper we give an overview of one of them. The method, which we call the random worlds method, is a natural one: For any given domain size N, we consider the fraction of models satisfying ϕ among models of size N satisfying KB. If we do not know the domain size N, but know that it is large, we can approximate the Degree of Belief in ϕ given KB by taking the limit of this fraction as N goes to infinity. As we show, this approach has many desirable features. In particular, in many cases that arise in practice, the answers we get using this method provably match heuristic assumptions made in many standard AI systems.
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AAAI - Default reasoning from statistics
1991Co-Authors: Fahiem BacchusAbstract:There are two common but quite distinct interpretations of probabilities: they can be interpreted as a measure of the extent to which an agent believes an assertion, i.e., as an agent's Degree of Belief, or they can be interpreted as an assertion of relative frequency, i.e., as a statistical measure. Used as statistical measures probabilities can represent various assertions about the objective statistical state of the world, while used as Degrees of Belief they can represent various assertions about the subjective state of an agent's Beliefs. In this paper we examine how an agent who knows certain statistical facts about the world might infer probabilistic Degrees of Beliefs in other assertions from these statistics. For example, an agent who knows that most birds fly (a statistical fact) may generate a Degree of Belief greater than 0.5 in the assertion that Tweety flies given that Tweety is a bird. This inference of Degrees of Belief from statistical facts is known as direct inference. We develop a formal logical mechanism for performing direct inference. Some of the inferences possible via direct inference are closely related to default inferences. We examine some features of this relationship.
Nicholas J. J. Smith - One of the best experts on this subject based on the ideXlab platform.
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Problems of Precision in Fuzzy Theories of Vagueness and Bayesian Epistemology
Language Cognition and Mind, 2019Co-Authors: Nicholas J. J. SmithAbstract:A common objection to theories of vagueness based on fuzzy logics centres on the idea that assigning a single numerical Degree of truth—a real number between 0 and 1—to each vague statement is excessively precise. A common objection to Bayesian epistemology centres on the idea that assigning a single numerical Degree of Belief—a real number between 0 and 1—to each proposition is excessively precise. In this paper I explore possible parallels between these objections. In particular I argue that the only good objection along these lines to fuzzy theories of vagueness does not translate into a good objection to Bayesian epistemology. An important part of my argument consists in drawing a distinction between two different notions of Degree of Belief, which I call dispositional Degree of Belief and epistemic Degree of Belief.
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Vagueness, Uncertainty and Degrees of Belief: Two Kinds of Indeterminacy—One Kind of Credence
Erkenntnis, 2014Co-Authors: Nicholas J. J. SmithAbstract:If we think, as Ramsey did, that a Degree of Belief that P is a stronger or weaker tendency to act as if P , then it is clear that not only uncertainty, but also vagueness, gives rise to Degrees of Belief. If I like hot coffee and do not know whether the coffee is hot or cold, I will have some tendency to reach for a cup; if I like hot coffee and know that the coffee is borderline hot, I will have some tendency to reach for a cup. Suppose that we take Degrees of Belief arising from uncertainty to obey the laws of probability and that we model vagueness using Degrees of truth. We then encounter a problem: it does not look as though Degrees of Belief arising from vagueness should obey the laws of probability. One response would be to countenance two different sorts of Degrees of Belief: Degrees of Belief arising from uncertainty, which obey the laws of probability; and Degrees of Belief arising from vagueness, which obey a different set of laws. I argue, however, that if a Degree of Belief that P is a stronger or weaker tendency to act as if P , then this option is not open. Instead, I propose an account of the behaviour of Degrees of Belief that integrates subjective probabilities and Degrees of truth. On this account, Degrees of Belief are expectations of Degrees of truth. The account explains why Degrees of Belief behave in accordance with the laws of probability in cases involving only uncertainty, while also allowing Degrees of Belief to behave differently in cases involving only vagueness, and in mixed cases involving both uncertainty and vagueness. Justifications of the account are given both via Dutch books and in terms of epistemic accuracy.