The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
Esra Cengiz-yanardag - One of the best experts on this subject based on the ideXlab platform.
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Clinical performance of clinical-Visual Examination, digital bitewing radiography, laser fluorescence, and near-infrared light transillumination for detection of non-cavitated proximal enamel and dentin caries.
Lasers in Medical Science, 2020Co-Authors: Nazan Koçak, Esra Cengiz-yanardagAbstract:The aim of this study was to compare the clinical performance of clinical-Visual Examination using the International Caries Detection and Assessment System (ICDAS) II, digital bitewing radiography, near-infrared light transillumination (NIR-LT), and laser fluorescence (LF) for the detection of non-cavitated proximal enamel and dentin caries. The study included 335 patients, aged 12-18 years, with no cavities in the posterior teeth. Clinical-Visual inspections of 335 non-cavitated proximal caries were performed by two examiners. For enamel caries, clinical validation included a combination of clinical-Visual and digital bitewing radiography assessments. For dentin caries, the clinical validation was opening the cavity. The accuracy rate, sensitivity, specificity, predictive values, and areas under receiver operating characteristic curves were determined. The agreement between the examiners' measurements was calculated using the kappa coefficient. The sensitivity, specificity, and accuracy of the methods were compared using the McNemar test. The significance level was set at p < 0.05. Digital bitewing radiography had the highest sensitivity (0.96) and accuracy (0.96), and LF had the lowest sensitivity (0.38) and accuracy (0.39). After separation of the lesions into enamel and dentin caries, clinical-Visual Examination had the highest sensitivity (0.98) and accuracy (0.98) for enamel caries, while digital bitewing radiography had the highest sensitivity (0.97) and accuracy (0.97) for dentin caries. The NIR-LT method had a higher sensitivity for enamel caries (0.86). Each method also differed significantly from the others (p < 0.001). Digital bitewing radiography gave the best prediction of proximal enamel and dentin caries. NIR-LT showed good potential for detection of proximal caries.
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Clinical performance of clinical-Visual Examination, digital bitewing radiography, laser fluorescence, and near-infrared light transillumination for detection of non-cavitated proximal enamel and dentin caries
Lasers in Medical Science, 2020Co-Authors: Nazan Koçak, Esra Cengiz-yanardagAbstract:The aim of this study was to compare the clinical performance of clinical-Visual Examination using the International Caries Detection and Assessment System (ICDAS) II, digital bitewing radiography, near-infrared light transillumination (NIR-LT), and laser fluorescence (LF) for the detection of non-cavitated proximal enamel and dentin caries. The study included 335 patients, aged 12–18 years, with no cavities in the posterior teeth. Clinical-Visual inspections of 335 non-cavitated proximal caries were performed by two examiners. For enamel caries, clinical validation included a combination of clinical-Visual and digital bitewing radiography assessments. For dentin caries, the clinical validation was opening the cavity. The accuracy rate, sensitivity, specificity, predictive values, and areas under receiver operating characteristic curves were determined. The agreement between the examiners’ measurements was calculated using the kappa coefficient. The sensitivity, specificity, and accuracy of the methods were compared using the McNemar test. The significance level was set at p
Yusuke Nojima - One of the best experts on this subject based on the ideXlab platform.
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Visual Examination of the behavior of EMO algorithms for many-objective optimization with many decision variables
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Hiroyuki Masuda, Yusuke Nojima, Hisao IshibuchiAbstract:Various evolutionary multiobjective optimization (EMO) algorithms have been proposed in the literature. They have different search mechanisms for increasing the diversity of solutions and improving the convergence to the Pareto front. As a result, each algorithm has different characteristics in its search behavior. Multiobjective search behavior can be Visually shown in an objective space for a test problem with two or three objectives. However, such a Visual Examination is difficult in a high-dimensional objective space for many-objective problems. The use of distance minimization problems has been proposed to examine many-objective search behavior in a two-dimensional decision space. This idea has an inherent limitation: the number of decision variables should be two. In our former study, we formulated a four-objective distance minimization problem with 10, 100, and 1000 decision variables. In this paper, we generalize our former study to many-objective problems with an arbitrary number of objectives and decision variables by proposing an idea of specifying reference points on a plane in a high-dimensional decision space. As test problems for computational experiments, we generate six-objective and eight-objective problems with 10, 100, and 1000 decision variables. Our experimental results on those test problems show that the number of decision variables has large effects on multiobjective search in comparison with the choice of an EMO algorithm and the number of objectives.
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IEEE Congress on Evolutionary Computation - Visual Examination of the behavior of EMO algorithms for many-objective optimization with many decision variables
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Hiroyuki Masuda, Yusuke Nojima, Hisao IshibuchiAbstract:Various evolutionary multiobjective optimization (EMO) algorithms have been proposed in the literature. They have different search mechanisms for increasing the diversity of solutions and improving the convergence to the Pareto front. As a result, each algorithm has different characteristics in its search behavior. Multiobjective search behavior can be Visually shown in an objective space for a test problem with two or three objectives. However, such a Visual Examination is difficult in a high-dimensional objective space for many-objective problems. The use of distance minimization problems has been proposed to examine many-objective search behavior in a two-dimensional decision space. This idea has an inherent limitation: the number of decision variables should be two. In our former study, we formulated a four-objective distance minimization problem with 10, 100, and 1000 decision variables. In this paper, we generalize our former study to many-objective problems with an arbitrary number of objectives and decision variables by proposing an idea of specifying reference points on a plane in a high-dimensional decision space. As test problems for computational experiments, we generate six-objective and eight-objective problems with 10, 100, and 1000 decision variables. Our experimental results on those test problems show that the number of decision variables has large effects on multiobjective search in comparison with the choice of an EMO algorithm and the number of objectives.
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Many-objective and many-variable test problems for Visual Examination of multiobjective search
2013 IEEE Congress on Evolutionary Computation, 2013Co-Authors: Hisao Ishibuchi, Masakazu Yamane, Naoya Akedo, Yusuke NojimaAbstract:In the development of evolutionary multiobjective optimization (EMO) algorithms, it is important to implement a good balancing mechanism between the convergence of solutions towards the Pareto front and their diversity over the Pareto front. When an EMO algorithm is applied to a two-objective problem, the balance can be easily Visualized by showing all solutions at each generation in the two-dimensional objective space. However, such a Visual Examination of the multiobjective search is difficult for many-objective problems with four or more objectives. The use of many-objective test problems with two decision variables has been proposed in some studies to Visually examine the search behavior of EMO algorithms. Such test problems are defined by a number of points in a two-dimensional decision space where the distance minimization from each point is an objective. Thus the number of objectives is the same as the number of points. The search behavior of EMO algorithms can be Visually examined in the two-dimensional decision space. In this paper, we propose the use of many-objective test problems for Visual Examination of the search behavior in a high-dimensional decision space. More specifically, our m-objective test problem with n variables is generated by specifying m points on a plane in an n-dimensional decision space. We examine the behavior of EMO algorithms through computational experiments on such an m-objective n-variable test problem. Our experimental results show that the number of variables has a large effect on the search behavior of EMO algorithms with respect to the diversity of solutions.
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IEEE Congress on Evolutionary Computation - Many-objective and many-variable test problems for Visual Examination of multiobjective search
2013 IEEE Congress on Evolutionary Computation, 2013Co-Authors: Hisao Ishibuchi, Masakazu Yamane, Naoya Akedo, Yusuke NojimaAbstract:In the development of evolutionary multiobjective optimization (EMO) algorithms, it is important to implement a good balancing mechanism between the convergence of solutions towards the Pareto front and their diversity over the Pareto front. When an EMO algorithm is applied to a two-objective problem, the balance can be easily Visualized by showing all solutions at each generation in the two-dimensional objective space. However, such a Visual Examination of the multiobjective search is difficult for many-objective problems with four or more objectives. The use of many-objective test problems with two decision variables has been proposed in some studies to Visually examine the search behavior of EMO algorithms. Such test problems are defined by a number of points in a two-dimensional decision space where the distance minimization from each point is an objective. Thus the number of objectives is the same as the number of points. The search behavior of EMO algorithms can be Visually examined in the two-dimensional decision space. In this paper, we propose the use of many-objective test problems for Visual Examination of the search behavior in a high-dimensional decision space. More specifically, our m-objective test problem with n variables is generated by specifying m points on a plane in an n-dimensional decision space. We examine the behavior of EMO algorithms through computational experiments on such an m-objective n-variable test problem. Our experimental results show that the number of variables has a large effect on the search behavior of EMO algorithms with respect to the diversity of solutions.
Nazan Koçak - One of the best experts on this subject based on the ideXlab platform.
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Clinical performance of clinical-Visual Examination, digital bitewing radiography, laser fluorescence, and near-infrared light transillumination for detection of non-cavitated proximal enamel and dentin caries.
Lasers in Medical Science, 2020Co-Authors: Nazan Koçak, Esra Cengiz-yanardagAbstract:The aim of this study was to compare the clinical performance of clinical-Visual Examination using the International Caries Detection and Assessment System (ICDAS) II, digital bitewing radiography, near-infrared light transillumination (NIR-LT), and laser fluorescence (LF) for the detection of non-cavitated proximal enamel and dentin caries. The study included 335 patients, aged 12-18 years, with no cavities in the posterior teeth. Clinical-Visual inspections of 335 non-cavitated proximal caries were performed by two examiners. For enamel caries, clinical validation included a combination of clinical-Visual and digital bitewing radiography assessments. For dentin caries, the clinical validation was opening the cavity. The accuracy rate, sensitivity, specificity, predictive values, and areas under receiver operating characteristic curves were determined. The agreement between the examiners' measurements was calculated using the kappa coefficient. The sensitivity, specificity, and accuracy of the methods were compared using the McNemar test. The significance level was set at p < 0.05. Digital bitewing radiography had the highest sensitivity (0.96) and accuracy (0.96), and LF had the lowest sensitivity (0.38) and accuracy (0.39). After separation of the lesions into enamel and dentin caries, clinical-Visual Examination had the highest sensitivity (0.98) and accuracy (0.98) for enamel caries, while digital bitewing radiography had the highest sensitivity (0.97) and accuracy (0.97) for dentin caries. The NIR-LT method had a higher sensitivity for enamel caries (0.86). Each method also differed significantly from the others (p < 0.001). Digital bitewing radiography gave the best prediction of proximal enamel and dentin caries. NIR-LT showed good potential for detection of proximal caries.
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Clinical performance of clinical-Visual Examination, digital bitewing radiography, laser fluorescence, and near-infrared light transillumination for detection of non-cavitated proximal enamel and dentin caries
Lasers in Medical Science, 2020Co-Authors: Nazan Koçak, Esra Cengiz-yanardagAbstract:The aim of this study was to compare the clinical performance of clinical-Visual Examination using the International Caries Detection and Assessment System (ICDAS) II, digital bitewing radiography, near-infrared light transillumination (NIR-LT), and laser fluorescence (LF) for the detection of non-cavitated proximal enamel and dentin caries. The study included 335 patients, aged 12–18 years, with no cavities in the posterior teeth. Clinical-Visual inspections of 335 non-cavitated proximal caries were performed by two examiners. For enamel caries, clinical validation included a combination of clinical-Visual and digital bitewing radiography assessments. For dentin caries, the clinical validation was opening the cavity. The accuracy rate, sensitivity, specificity, predictive values, and areas under receiver operating characteristic curves were determined. The agreement between the examiners’ measurements was calculated using the kappa coefficient. The sensitivity, specificity, and accuracy of the methods were compared using the McNemar test. The significance level was set at p
Hisao Ishibuchi - One of the best experts on this subject based on the ideXlab platform.
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Visual Examination of the behavior of EMO algorithms for many-objective optimization with many decision variables
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Hiroyuki Masuda, Yusuke Nojima, Hisao IshibuchiAbstract:Various evolutionary multiobjective optimization (EMO) algorithms have been proposed in the literature. They have different search mechanisms for increasing the diversity of solutions and improving the convergence to the Pareto front. As a result, each algorithm has different characteristics in its search behavior. Multiobjective search behavior can be Visually shown in an objective space for a test problem with two or three objectives. However, such a Visual Examination is difficult in a high-dimensional objective space for many-objective problems. The use of distance minimization problems has been proposed to examine many-objective search behavior in a two-dimensional decision space. This idea has an inherent limitation: the number of decision variables should be two. In our former study, we formulated a four-objective distance minimization problem with 10, 100, and 1000 decision variables. In this paper, we generalize our former study to many-objective problems with an arbitrary number of objectives and decision variables by proposing an idea of specifying reference points on a plane in a high-dimensional decision space. As test problems for computational experiments, we generate six-objective and eight-objective problems with 10, 100, and 1000 decision variables. Our experimental results on those test problems show that the number of decision variables has large effects on multiobjective search in comparison with the choice of an EMO algorithm and the number of objectives.
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IEEE Congress on Evolutionary Computation - Visual Examination of the behavior of EMO algorithms for many-objective optimization with many decision variables
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Hiroyuki Masuda, Yusuke Nojima, Hisao IshibuchiAbstract:Various evolutionary multiobjective optimization (EMO) algorithms have been proposed in the literature. They have different search mechanisms for increasing the diversity of solutions and improving the convergence to the Pareto front. As a result, each algorithm has different characteristics in its search behavior. Multiobjective search behavior can be Visually shown in an objective space for a test problem with two or three objectives. However, such a Visual Examination is difficult in a high-dimensional objective space for many-objective problems. The use of distance minimization problems has been proposed to examine many-objective search behavior in a two-dimensional decision space. This idea has an inherent limitation: the number of decision variables should be two. In our former study, we formulated a four-objective distance minimization problem with 10, 100, and 1000 decision variables. In this paper, we generalize our former study to many-objective problems with an arbitrary number of objectives and decision variables by proposing an idea of specifying reference points on a plane in a high-dimensional decision space. As test problems for computational experiments, we generate six-objective and eight-objective problems with 10, 100, and 1000 decision variables. Our experimental results on those test problems show that the number of decision variables has large effects on multiobjective search in comparison with the choice of an EMO algorithm and the number of objectives.
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Many-objective and many-variable test problems for Visual Examination of multiobjective search
2013 IEEE Congress on Evolutionary Computation, 2013Co-Authors: Hisao Ishibuchi, Masakazu Yamane, Naoya Akedo, Yusuke NojimaAbstract:In the development of evolutionary multiobjective optimization (EMO) algorithms, it is important to implement a good balancing mechanism between the convergence of solutions towards the Pareto front and their diversity over the Pareto front. When an EMO algorithm is applied to a two-objective problem, the balance can be easily Visualized by showing all solutions at each generation in the two-dimensional objective space. However, such a Visual Examination of the multiobjective search is difficult for many-objective problems with four or more objectives. The use of many-objective test problems with two decision variables has been proposed in some studies to Visually examine the search behavior of EMO algorithms. Such test problems are defined by a number of points in a two-dimensional decision space where the distance minimization from each point is an objective. Thus the number of objectives is the same as the number of points. The search behavior of EMO algorithms can be Visually examined in the two-dimensional decision space. In this paper, we propose the use of many-objective test problems for Visual Examination of the search behavior in a high-dimensional decision space. More specifically, our m-objective test problem with n variables is generated by specifying m points on a plane in an n-dimensional decision space. We examine the behavior of EMO algorithms through computational experiments on such an m-objective n-variable test problem. Our experimental results show that the number of variables has a large effect on the search behavior of EMO algorithms with respect to the diversity of solutions.
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IEEE Congress on Evolutionary Computation - Many-objective and many-variable test problems for Visual Examination of multiobjective search
2013 IEEE Congress on Evolutionary Computation, 2013Co-Authors: Hisao Ishibuchi, Masakazu Yamane, Naoya Akedo, Yusuke NojimaAbstract:In the development of evolutionary multiobjective optimization (EMO) algorithms, it is important to implement a good balancing mechanism between the convergence of solutions towards the Pareto front and their diversity over the Pareto front. When an EMO algorithm is applied to a two-objective problem, the balance can be easily Visualized by showing all solutions at each generation in the two-dimensional objective space. However, such a Visual Examination of the multiobjective search is difficult for many-objective problems with four or more objectives. The use of many-objective test problems with two decision variables has been proposed in some studies to Visually examine the search behavior of EMO algorithms. Such test problems are defined by a number of points in a two-dimensional decision space where the distance minimization from each point is an objective. Thus the number of objectives is the same as the number of points. The search behavior of EMO algorithms can be Visually examined in the two-dimensional decision space. In this paper, we propose the use of many-objective test problems for Visual Examination of the search behavior in a high-dimensional decision space. More specifically, our m-objective test problem with n variables is generated by specifying m points on a plane in an n-dimensional decision space. We examine the behavior of EMO algorithms through computational experiments on such an m-objective n-variable test problem. Our experimental results show that the number of variables has a large effect on the search behavior of EMO algorithms with respect to the diversity of solutions.
Min C. Shin - One of the best experts on this subject based on the ideXlab platform.
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Crack Segmentation by Leveraging Multiple Frames of Varying Illumination
2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 2017Co-Authors: Stephen J. Schmugge, Lance Rice, John Lindberg, Robert Grizziy, Chris Joffey, Min C. ShinAbstract:In this work, we present an automated inspection approach to assist remote Visual Examinations of nuclear power plant components. An automated approach would require detecting often low contrast cracks that could be surrounded by or even within textures with similar appearances such as welding, scratches, and grind marks. We propose a crack segmentation method for remote Visual Examination videos by aggregating the pixel-level classification confidence from multiple frames consisting of different illumination conditions. A dataset of 685 pixel-level ground truth images consisting of 37 cracks from remote Visual Examination videos is used for evaluation. The results show that the proposed method provides a significant improvement over hand-crafted feature based segmentation and 9% over convolutional neural network based method.
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WACV - Crack Segmentation by Leveraging Multiple Frames of Varying Illumination
2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 2017Co-Authors: Stephen J. Schmugge, Lance Rice, John Lindberg, Robert Grizziy, Chris Joffey, Min C. ShinAbstract:In this work, we present an automated inspection approach to assist remote Visual Examinations of nuclear power plant components. An automated approach would require detecting often low contrast cracks that could be surrounded by or even within textures with similar appearances such as welding, scratches, and grind marks. We propose a crack segmentation method for remote Visual Examination videos by aggregating the pixel-level classification confidence from multiple frames consisting of different illumination conditions. A dataset of 685 pixel-level ground truth images consisting of 37 cracks from remote Visual Examination videos is used for evaluation. The results show that the proposed method provides a significant improvement over hand-crafted feature based segmentation and 9% over convolutional neural network based method.
