The Experts below are selected from a list of 7506 Experts worldwide ranked by ideXlab platform
Kae Doki - One of the best experts on this subject based on the ideXlab platform.
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Modeling method of execution timing of operation to analyze the reaction time from judgment to execution of operation
IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 2016Co-Authors: Kohjiro Hashimoto, Shinji Doki, Kae DokiAbstract:In this study, we aim to evaluate a driver skill based on the driver behavior model generated by the operation and environment data under driving vehicle. When skill of driver is reduced, the operator behavior is delayed, it takes along time of operation, and execution timing of operation is no reproducibility. These mean that reducing skill of driver affects time information of the driver operation. Therefore, we study on the model structure incorporated time information expresses the execution timing of driver's operation to evaluate driver's skill. In particular, we propose a new model structure incorporated timing Probabilistic Distribution expresses the time until the execution of next operation. Moreover, we propose a modeling method of operator behavior according to proposed model structure. It is considered that the reaction time from the decision to execution of operation and ambiguity of the decision can be read from the timing Probabilistic Distribution. In this paper, the above usefulness is examined through some experimental results. Moreover, the applicability of the driving skill evaluation based on the proposed operation model is examined.
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IECON - Estimation of next human action and its timing based on the human action model with timing Probabilistic Distribution
IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 2015Co-Authors: Kohjiro Hashimoto, Shinji Doki, Kae DokiAbstract:In order to give a suitable support to a person timely, it is necessary for the system to estimate the next human action and its execution timing. Therefore, we have proposed a modeling method of human actions based on the causality between the situation around a person and human action change. Our previous modeling method makes it possible to estimate the next human action and its execution timing based on Hidden Markov Model which expresses the situation around a person. However, Hidden Markov Model was not enough to express the temporal information such as execution timing of next human action. According to this reason, we propose a new model structure of human action that a Probabilistic Distribution of timing information is incorporated into Hidden Markov Model. And we propose a new modeling method of human action based on the above new Probabilistic model.
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Estimation of next human action and its timing based on the human action model with timing Probabilistic Distribution
IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 2015Co-Authors: Kohjiro Hashimoto, Shinji Doki, Kae DokiAbstract:In order to give a suitable support to a person timely, it is necessary for the system to estimate the next human action and its execution timing. Therefore, we have proposed a modeling method of human actions based on the causality between the situation around a person and human action change. Our previous modeling method makes it possible to estimate the next human action and its execution timing based on Hidden Markov Model which expresses the situation around a person. However, Hidden Markov Model was not enough to express the temporal information such as execution timing of next human action. According to this reason, we propose a new model structure of human action that a Probabilistic Distribution of timing information is incorporated into Hidden Markov Model. And we propose a new modeling method of human action based on the above new Probabilistic model.
Phond Phunchongharn - One of the best experts on this subject based on the ideXlab platform.
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iCAST - Liver Cancer Prediction Using Synthetic Minority based on Probabilistic Distribution (SyMProD) Oversampling Technique
2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 2019Co-Authors: Intouch Kunakorntum, Woranich Hinthong, Sumet Amonyingchareon, Phond PhunchongharnAbstract:Liver cancer is challenging to diagnose in general. Moreover, liver cancer prediction can be hindered by skewed data between majority and minority classes, and missing values. Many existing prediction models do not address these two limitations that can make classification results ignore minority instances (i.e., patients with liver cancer are not detected). In this paper, we present a liver cancer prediction model with a new oversampling technique called Synthetic Minority based on Probabilistic Distribution (SyMProD) to handle skewed patients’ data from Chulabhorn hospital. SyMProD removes noisy data based on z-score normalization value and adaptively selects referenced data using probability Distribution from the ratio of minority and majority closeness factor. The proposed method oversamples minority instances from several minority nearest neighbors to cover the Distribution. We employ Random Forest (RF) and Gradient Boosted Tree (GBT) to generate prediction models with stratified five-fold cross-validation. Results demonstrate that GBT with our proposed oversampling technique achieves a better result than other techniques. These results from our technique generate new instances in the minority Distribution, avoid the majority region, remove the overgeneralization problem, and reduce possibilities of creating noise and overlapping classes. Our prediction model may help prompt high-risk patients to get a proper diagnosis and treatments in time.
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Liver Cancer Prediction Using Synthetic Minority based on Probabilistic Distribution (SyMProD) Oversampling Technique
2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 2019Co-Authors: Intouch Kunakorntum, Woranich Hinthong, Sumet Amonyingchareon, Phond PhunchongharnAbstract:Liver cancer is challenging to diagnose in general. Moreover, liver cancer prediction can be hindered by skewed data between majority and minority classes, and missing values. Many existing prediction models do not address these two limitations that can make classification results ignore minority instances (i.e., patients with liver cancer are not detected). In this paper, we present a liver cancer prediction model with a new oversampling technique called Synthetic Minority based on Probabilistic Distribution (SyMProD) to handle skewed patients' data from Chulabhorn hospital. SyMProD removes noisy data based on z-score normalization value and adaptively selects referenced data using probability Distribution from the ratio of minority and majority closeness factor. The proposed method oversamples minority instances from several minority nearest neighbors to cover the Distribution. We employ Random Forest (RF) and Gradient Boosted Tree (GBT) to generate prediction models with stratified five-fold cross-validation. Results demonstrate that GBT with our proposed oversampling technique achieves a better result than other techniques. These results from our technique generate new instances in the minority Distribution, avoid the majority region, remove the overgeneralization problem, and reduce possibilities of creating noise and overlapping classes. Our prediction model may help prompt high-risk patients to get a proper diagnosis and treatments in time.
C. Barbulescu - One of the best experts on this subject based on the ideXlab platform.
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Network usage with Probabilistic Distribution factors method
2015 IEEE Eindhoven PowerTech, 2015Co-Authors: Cosmin Oros, C. Barbulescu, Stefan KilyeniAbstract:This paper presents a Probabilistic network usage computing using Distribution factors method. A software tool is developed in Mathlab environment and contains a part dedicated to Probabilistic power flow computing and another part allocated to network usage allocated to generators and consumers. Case study is applied on West, South-West and North-West parts of Romanian Power System. Useful results have been provided.
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Probabilistic Distribution factors assessment using OptimalPowerPrice Mathematica software, case study: Test 25 buses test power system
2009 5th International Symposium on Applied Computational Intelligence and Informatics, 2009Co-Authors: D. Paunescu, St. Kilyeni, M. Nemes, A. Kilyeni, C. BarbulescuAbstract:Competition within the electric power systems has proven the importance of Distribution factors assessment. In addition, load characteristic is unpredictable, leading to a diversity of supply paths. As a result, Probabilistic power flow analysis can provide better tools and information for determining the tracing of the generator-consumer path. In this paper, the authors analyze the Probabilistic Distribution power factors method using the OptimalPowerPrice Mathematica software. Moreover, a correlation between the generalized Distribution factors is obtained by means of Probabilistic and deterministic methods. The case study in Section IV presents the above approach using a 25 buses test power system.
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SACI - Probabilistic Distribution factors assessment using OptimalPowerPrice Mathematica software, case study: Test 25 buses test power system
2009 5th International Symposium on Applied Computational Intelligence and Informatics, 2009Co-Authors: D. Paunescu, St. Kilyeni, M. Nemes, A. Kilyeni, C. BarbulescuAbstract:Competition within the electric power systems has proven the importance of Distribution factors assessment. In addition, load characteristic is unpredictable, leading to a diversity of supply paths. As a result, Probabilistic power flow analysis can provide better tools and information for determining the tracing of the generator-consumer path. In this paper, the authors analyze the Probabilistic Distribution power factors method using the OptimalPowerPrice Mathematica software. Moreover, a correlation between the generalized Distribution factors is obtained by means of Probabilistic and deterministic methods. The case study in Section IV presents the above approach using a 25 buses test power system.
Kohjiro Hashimoto - One of the best experts on this subject based on the ideXlab platform.
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Modeling method of execution timing of operation to analyze the reaction time from judgment to execution of operation
IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 2016Co-Authors: Kohjiro Hashimoto, Shinji Doki, Kae DokiAbstract:In this study, we aim to evaluate a driver skill based on the driver behavior model generated by the operation and environment data under driving vehicle. When skill of driver is reduced, the operator behavior is delayed, it takes along time of operation, and execution timing of operation is no reproducibility. These mean that reducing skill of driver affects time information of the driver operation. Therefore, we study on the model structure incorporated time information expresses the execution timing of driver's operation to evaluate driver's skill. In particular, we propose a new model structure incorporated timing Probabilistic Distribution expresses the time until the execution of next operation. Moreover, we propose a modeling method of operator behavior according to proposed model structure. It is considered that the reaction time from the decision to execution of operation and ambiguity of the decision can be read from the timing Probabilistic Distribution. In this paper, the above usefulness is examined through some experimental results. Moreover, the applicability of the driving skill evaluation based on the proposed operation model is examined.
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IECON - Estimation of next human action and its timing based on the human action model with timing Probabilistic Distribution
IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 2015Co-Authors: Kohjiro Hashimoto, Shinji Doki, Kae DokiAbstract:In order to give a suitable support to a person timely, it is necessary for the system to estimate the next human action and its execution timing. Therefore, we have proposed a modeling method of human actions based on the causality between the situation around a person and human action change. Our previous modeling method makes it possible to estimate the next human action and its execution timing based on Hidden Markov Model which expresses the situation around a person. However, Hidden Markov Model was not enough to express the temporal information such as execution timing of next human action. According to this reason, we propose a new model structure of human action that a Probabilistic Distribution of timing information is incorporated into Hidden Markov Model. And we propose a new modeling method of human action based on the above new Probabilistic model.
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Estimation of next human action and its timing based on the human action model with timing Probabilistic Distribution
IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 2015Co-Authors: Kohjiro Hashimoto, Shinji Doki, Kae DokiAbstract:In order to give a suitable support to a person timely, it is necessary for the system to estimate the next human action and its execution timing. Therefore, we have proposed a modeling method of human actions based on the causality between the situation around a person and human action change. Our previous modeling method makes it possible to estimate the next human action and its execution timing based on Hidden Markov Model which expresses the situation around a person. However, Hidden Markov Model was not enough to express the temporal information such as execution timing of next human action. According to this reason, we propose a new model structure of human action that a Probabilistic Distribution of timing information is incorporated into Hidden Markov Model. And we propose a new modeling method of human action based on the above new Probabilistic model.
Intouch Kunakorntum - One of the best experts on this subject based on the ideXlab platform.
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iCAST - Liver Cancer Prediction Using Synthetic Minority based on Probabilistic Distribution (SyMProD) Oversampling Technique
2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 2019Co-Authors: Intouch Kunakorntum, Woranich Hinthong, Sumet Amonyingchareon, Phond PhunchongharnAbstract:Liver cancer is challenging to diagnose in general. Moreover, liver cancer prediction can be hindered by skewed data between majority and minority classes, and missing values. Many existing prediction models do not address these two limitations that can make classification results ignore minority instances (i.e., patients with liver cancer are not detected). In this paper, we present a liver cancer prediction model with a new oversampling technique called Synthetic Minority based on Probabilistic Distribution (SyMProD) to handle skewed patients’ data from Chulabhorn hospital. SyMProD removes noisy data based on z-score normalization value and adaptively selects referenced data using probability Distribution from the ratio of minority and majority closeness factor. The proposed method oversamples minority instances from several minority nearest neighbors to cover the Distribution. We employ Random Forest (RF) and Gradient Boosted Tree (GBT) to generate prediction models with stratified five-fold cross-validation. Results demonstrate that GBT with our proposed oversampling technique achieves a better result than other techniques. These results from our technique generate new instances in the minority Distribution, avoid the majority region, remove the overgeneralization problem, and reduce possibilities of creating noise and overlapping classes. Our prediction model may help prompt high-risk patients to get a proper diagnosis and treatments in time.
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Liver Cancer Prediction Using Synthetic Minority based on Probabilistic Distribution (SyMProD) Oversampling Technique
2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 2019Co-Authors: Intouch Kunakorntum, Woranich Hinthong, Sumet Amonyingchareon, Phond PhunchongharnAbstract:Liver cancer is challenging to diagnose in general. Moreover, liver cancer prediction can be hindered by skewed data between majority and minority classes, and missing values. Many existing prediction models do not address these two limitations that can make classification results ignore minority instances (i.e., patients with liver cancer are not detected). In this paper, we present a liver cancer prediction model with a new oversampling technique called Synthetic Minority based on Probabilistic Distribution (SyMProD) to handle skewed patients' data from Chulabhorn hospital. SyMProD removes noisy data based on z-score normalization value and adaptively selects referenced data using probability Distribution from the ratio of minority and majority closeness factor. The proposed method oversamples minority instances from several minority nearest neighbors to cover the Distribution. We employ Random Forest (RF) and Gradient Boosted Tree (GBT) to generate prediction models with stratified five-fold cross-validation. Results demonstrate that GBT with our proposed oversampling technique achieves a better result than other techniques. These results from our technique generate new instances in the minority Distribution, avoid the majority region, remove the overgeneralization problem, and reduce possibilities of creating noise and overlapping classes. Our prediction model may help prompt high-risk patients to get a proper diagnosis and treatments in time.
