• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.47-61
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• 2001

Lazy loaming methods including CBR have relative advantages in comparison with eager loaming methods such as artificial neural networks and decision trees. However, they are very sensitive to irrelevant features. In other words, when there are irrelevant features, larry learning methods have difficulty in comparing cases. Therefore, their performance can be degraded significantly. To overcome this disadvantage, feature weighting methods for lazy loaming methods have been studied. Most of the existing researches, however, were focused on global feature weighting. In this research, we propose a new local feature weighting method, which we shall call CBDFW. CBDFW stores classification performance of randomly generated feature weight vectors. Then, given a new query case, CBDFW retrieves the successful feature weight vectors and designs a feature weight vector fur the query case. In the test on credit evaluation domain, CBDFW showed better classification accuracy when compared to the results of previous researches.

• Bulletin of the Korean Chemical Society
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• v.29 no.1
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• pp.111-116
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• 2008

A piperazinylalkylisoxazole library containing 86 compounds was constructed and evaluated for the binding affinities to dopamine (D3) and serotonin (5-HT2A/2C) receptor to develop antipsychotics. Dopamine antagonists (DA) showing selectivity for D3 receptor over the D2 receptor, serotonin antagonists (SA), and serotonin-dopamine dual antagonists (SDA) were identified based on their binding affinity and selectivity. The analogues were divided into three groups of 7 DAs (D3), 33 SAs (5-HT2A/2C), and 46 SDAs (D3 and 5-HT2A/2C). A classification model was generated for identifying structural characteristics of those antagonists with different affinity profiles. On the basis of the results from our previous study, we conducted the generation of the decision trees by the recursive-partitioning (RP) method using Cerius2 2D descriptors, and identified and interpreted the descriptors that discriminate in-house antipsychotic compounds.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.27-35
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• 2016

Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.

• Journal of Haehwa Medicine
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• v.9 no.2
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• pp.159-171
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• 2001

In the result of investigating the drugs for replenishing Qi in many herbal books, we could get consistent relation in their immunopharmacologic effects as follows: 1. The effects of drugs for replenishing Qi was shown in the Shen Nong's Herbal, but its classification was natural such as trees and plants, insects and beasts, fruits, vegetables, and we think that trial about classification of drugs for replenishing Qi was accomplished in the ben-cao-jiu-zhen(本草求眞). 2. Main drugs for replenishing Qi was Gingseng Radix, Codonopsis pilosulae Radix, Astragali Radix, Atractylodis macrocephalae Rhizoma, Dioscoreae Rhizoma, Glycyrrhizae Radix. 3. Drugs for replenishing Qi commonly contain polysaccharides as much, and it was thought that they have also immunopharmacolologic effects by means of activating T and B cells, secreting cytokines, making lymphocytes, activating NK cells, increasing abilities of voracity and generating antibodies. 4. Drugs for replenishing Qi mainly replenish lung-Qi and Wei-Qi so that they have also effects of activating T and B cells, secreting cytokines, making lymphocytes, activating NK cells, increasing abilities of voracity and generating antibodies. 5. Drugs for replenishing Qi modulate content of cAMP and inhibit Na(+)-K(+) ATPase, so that they have effects of treating indistinctive pulse from Qi deficiency by means of inhibition delivery of chemical substances, activating lymphocytes. promoting contraction of myocardium.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.536-564
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• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.728-735
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• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• Journal of Korean Society of Transportation
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• v.24 no.1 s.87
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• pp.39-52
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• 2006

This study divided the large-scale traffic accident locations into its own characteristics by using Cluster Analysis. Also, Quantification II and Classification and Regression Tree methods were used enabling evaluation for the amount of affecting influence by the crash type. After these analyses, we tested the fitness of the results and suggested the simplification of the quantification index. With the results from the discussed procedure, obvious differences were observed by groups according to the characteristics of crash type from the Discrimination and Classification analysis of divided four groups. Thus, measures and supplementary measures for the traffic accidents could be suggested in groups systematically. However, a lot of missing values in variables caused a huge loss of data and made this study difficult for more detailed analysis, With this difficulty. recording mandatory log files with a standardized format is also recommended to Prevent this Problem in advance.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.41-47
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• 2011

Processing of the raw LiDAR data requires the high-end processor, because data form is a vector. In contrast, if LiDAR data is converted into a regular grid pattern by filltering, that has advantage of being in a low-cost equipment, because of the simple structure and faster processing speed. Especially, by using grid data classification, such as Quadtree, some of trees and cars are removed, so it has advantage of modeling. Therefore, this study presents the algorithm for automatic extraction of ground points using Quadtree and refion growing method from LiDAR data. In addition, Error analysis was performed based on the 1:5000 digital map of sample area to analyze the classification of ground points. In a result, the ground classification accuracy is over 98%. So it has the advantage of extracting the ground points. In addition, non-ground points, such as cars and tree, are effectively removed as using Quadtree and region growing method.