• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.49-57
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• 2013

A novel approach for the feature selection is proposed, which is the important preprocessing task of on-line document classification. In previous researches, the features based on information from their single population for feature selection task have been selected. In this paper, a mixed feature set is constructed by selecting features from multi-population as well as single population based on various information. The mixed feature set consists of two feature sets: the original feature set that is made up of words on documents and the transformed feature set that is made up of features generated by LSA. The hybrid feature selection method using both filter and wrapper method is used to obtain optimal features set from the mixed feature set. We performed classification experiments using the obtained optimal feature sets. As a result of the experiments, our expectation that our approach makes better performance of classification is verified, which is over 90% accuracy. In particular, it is confirmed that our approach has over 90% recall and precision that have a low deviation between categories.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.605-616
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• 2021

While malwares must be accurately identifiable from arbitrary programs, existing studies using classification techniques have limitations that they can only be applied to limited samples. In this work, we propose a method to utilize API call frequency to detect and classify malware families from arbitrary programs. Our proposed method defines a rule that checks whether the call frequency of a particular API exceeds the threshold, and identifies a specific family by utilizing the rate information on the corresponding rules. In this paper, decision tree algorithm is applied to define the optimal threshold that can accurately identify a particular family from the training set. The performance measurements using 4,443 samples showed 85.1% precision and 91.3% recall rate for family detection, 97.7% precision and 98.1% reproduction rate for classification, which confirms that our method works to distinguish malware families effectively.

• Clinics in Shoulder and Elbow
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• v.25 no.1
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• pp.49-56
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• 2022

Background: The treatment approach for proximal humeral fractures is determined by various factors, including patient age, sex, dominant arm, fracture pattern, presence of osteoporosis, preexisting arthritis, rotator cuff status, and medical comorbidities. However, there is a lack of consensus in the literature regarding the optimal treatment for displaced proximal humeral fractures. This study aimed to assess and quantify the decision-making process for either conservative or surgical treatment and the choice of surgical method among shoulder surgeons when treating proximal humeral fractures. Methods: Forty sets of true anteroposterior view, scapular Y projection view, and three-dimensional computed tomography of proximal humeral fractures were provided to 12 shoulder surgeons along with clinical information. Surveys regarding Neer classification, decisions between conservative and surgical treatments, and chosen methods were conducted twice with an interval of 2 months. The factors affecting the treatment plans were also assessed. Results: The inter-rater agreement was fair for Neer classification (kappa=0.395), moderate for the decision between conservative and surgical treatments (kappa=0.528), and substantial for the chosen method of surgical treatment (kappa=0.740). The percentage of agreement was 71.1% for Neer classification, 84.6% for the decision between conservative and surgical treatment, and 96.4% for the chosen method of surgical treatment. The fracture pattern was the most crucial factor in deciding between conservative and surgical treatments, followed by age and physical activity. Conclusions: The decision between conservative and surgical treatment for proximal humeral fractures showed good agreement, while the chosen method between osteosynthesis and arthroplasty showed substantial agreement among shoulder surgeons.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.994-1003
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• 1994

In this paper, a scheme of noise cancellation using spectral subreaction method with single input in an autombile noise environment is proposed. In order to remove the changing automonile noise components form the noisy speech signal, the noise of various states is analyzed and its characteristics are presented. For the decision of speech/nonspeech and the estimation of noise spectrum, a classification function is proposed on the basis of noise analysis. This function presents the precise decision of speech/nonspeech and the optimal estimation of noise spectrum with less computation. As the result of the estimation of noise spectrum by the proposed classification function, the clean speech signal is extracted from the noisy speech signal with high signal-to-ratio.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.331-340
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• 2008

Feature Selection is one of methods to improve the classification accuracy of data in the field of machine learning. Many feature selection algorithms have been proposed and discussed for years. However, the problem of finding the optimal feature subset from full data still remains to be a difficult problem. Bio-inspired algorithms are well-known evolutionary algorithms based on the principles of behavior of organisms, and very useful methods to find the optimal solution in optimization problems. Bio-inspired algorithms are also used in the field of feature selection problems. So in this paper we proposed new improved bio-inspired algorithms for feature selection. We used well-known bio-inspired algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), to find the optimal subset of features that shows the best performance in classification accuracy. In addition, we modified the bio-inspired algorithms considering the prior importance (prior relevance) of each feature. We chose the mRMR method, which can measure the goodness of single feature, to set the prior importance of each feature. We modified the evolution operators of GA and PSO by using the prior importance of each feature. We verified the performance of the proposed methods by experiment with datasets. Feature selection methods using GA and PSO produced better performances in terms of the classification accuracy. The modified method with the prior importance demonstrated improved performances in terms of the evolution speed and the classification accuracy.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.169-176
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• 2020

This paper studied the collision detection of robot manipulators for safe collaboration in human-robot interaction. Based on sensor-based collision detection, external torque is detached from subtracting robot dynamics. To detect collision using joint torque sensor data, a comparative study was conducted using data-based machine learning algorithm. Data was collected from the actual 3 degree-of-freedom (DOF) robot manipulator, and the data was labeled by threshold and handwork. Using support vector machine (SVM), decision tree and k-nearest neighbors KNN method, we derive the optimal parameters of each algorithm and compare the collision classification performance. The simulation results are analyzed for each method, and we confirmed that by an optimal collision status detection model with high prediction accuracy.

• Journal of Sensor Science and Technology
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• v.17 no.6
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• pp.406-413
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• 2008

We have been implemented simple statistical pattern recognition methods for harmful gases classification using gas sensors array fabricated by MEMS (Micro Electro Mechanical System) technology. The performance of pattern recognition method as a gas classifier is highly dependent on the choice of pre-processing techniques for sensor and sensors array signals and optimal classification algorithms among the various classification techniques. We carried out pre-processing for each sensor's signal as well as sensors array signals to extract features for each gas. We adapted simple statistical pattern recognition algorithms, which were PCA (Principal Component Analysis) for visualization of patterns clustering and MLR (Multi-Linear Regression) for real-time system implementation, to classify harmful gases. Experimental results of adapted pattern recognition methods with pre-processing techniques have been shown good clustering performance and expected easy implementation for real-time sensing system.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.93-100
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• 1988

The Auto Regressive Parameter Estimation and Pattern Classification of EKG Signal for Automatic Diagnosis. This paper presents the results from pattern discriminant analysis of an AR (auto regressive) model parameter group, which represents the HRV (heart rate variability) that is being considered as time series data. HRV data was extracted using the correct R-point of the EKG wave that was A/D converted from the I/O port both by hardware and software functions. Data number (N) and optimal (P), which were used for analysis, were determined by using Burg's maximum entropy method and Akaike's Information Criteria test. The representative values were extracted from the distribution of the results. In turn, these values were used as the index for determining the range o( pattern discriminant analysis. By carrying out pattern discriminant analysis, the performance of clustering was checked, creating the text pattern, where the clustering was optimum. The analysis results showed first that the HRV data were considered sufficient to ensure the stationarity of the data; next, that the patern discrimimant analysis was able to discriminate even though the optimal order of each syndrome was dissimilar.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• Fisheries and Aquatic Sciences
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• v.4 no.1
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• pp.1-9
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• 2001

This paper describes a procedure extracting feature vector of a target cell more precisely in the case of identifying specified cell. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of the object So, the feature vector plays very important role in classifying objects. Because the feature vectors is affected by noises and holes, it is necessary to remove noises contaminated in original image to get feature vector extraction exactly. In this paper, we propose the following method to do to get feature vector extraction exactly. First, by Otsu's optimal threshold selection method and morphological filters such as cleaning, filling and opening filters, we separate objects from background an get rid of isolated particles. After the labeling step by 4-adjacent neighborhood, the labeled image is filtered by the area filter. From this area-filtered image, feature vector such as area, complexity, centroid, rotation angle, effective diameter, the perimeter based on chain code and the width and height based on rotation matrix are extracted. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxn. It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.