• ETRI Journal
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• 제46권4호
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• pp.648-659
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• 2024

Author verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• 인터넷정보학회논문지
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• 제20권4호
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• pp.91-102
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• 2019

자율주행 시스템에서, 카메라에 포착된 영상을 통하여 보행자를 분류하는 기능은 보행자 안전을 위하여 매우 중요하다. 기존에는 HOG(Histogram of Oriented Gradients)나 SIFT(Scale-Invariant Feature Transform) 등으로 보행자의 특징을 추출한 후 SVM(Support Vector Machine)으로 분류하는 기술을 사용했었으나, 보행자 특징을 위와 같이 수동(handcrafted)으로 추출하는 것은 많은 한계점을 가지고 있다. 따라서 본 논문에서는 CNN(Convolutional Neural Network)의 깊은 특징(deep features)과 전이학습(transfer learning)을 사용하여 보행자를 안정적이고 효과적으로 분류하는 방법을 제시한다. 본 논문은 2가지 대표적인 전이학습 기법인 고정특징추출(fixed feature extractor) 기법과 미세조정(fine-tuning) 기법을 모두 사용하여 실험하였고, 특히 미세조정 기법에서는 3가지 다른 크기로 레이어를 전이구간과 비전이구간으로 구분한 후, 비전이구간에 속한 레이어들에 대해서만 가중치를 조정하는 설정(M-Fine: Modified Fine-tuning)을 새롭게 추가하였다. 5가지 CNN모델(VGGNet, DenseNet, Inception V3, Xception, MobileNet)과 INRIA Person데이터 세트로 실험한 결과, HOG나 SIFT 같은 수동적인 특징보다 CNN의 깊은 특징이 더 좋은 성능을 보여주었고, Xception의 정확도(임계치 = 0.5)가 99.61%로 가장 높았다. Xception과 유사한 성능을 내면서도 80% 적은 파라메터를 학습한 MobileNet이 효율성 측면에서는 가장 뛰어났다. 그리고 3가지 전이학습 기법중 미세조정 기법의 성능이 가장 우수하였고, M-Fine 기법의 성능은 미세조정 기법과 대등하거나 조금 낮았지만 고정특징추출 기법보다는 높았다.

• ETRI Journal
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• 제44권2호
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• 한국지역사회생활과학회지
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• 제22권1호
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• pp.103-113
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• 2011

The purpose of the study was to examine formative features of embroidered norigae in form, color, pattern and expressive technique through positive analysis of relics and various of collections of work and to consider expressive features of embroidered norigae. The results of the study were as follows. First, embroidered norigae has handcrafted decoration. it was made by being sewed for women longing for their family's happiness. Embroidered norigae is a dress worn by women that is hung on a coat string or the waist part of a skirt. Second, it is eco-environmental. Embroidered norigae applied things seen in nature such as flowers, butterflies and bees to its pattern. Third, it has practicality. Embroidered norigae has high practical value besides a decorative function. Needle case norigae and incense case norigae provide functions in accordance with women's wisdom and skill as well as practicality. Fourth, it is praying for good luck. Women embroidered patterns symbolizing their desires in life such as their family's happiness, wealth, many sons and a long life. Fifth, it has balance and harmony. The knot of embroidered norigae has a perfect symmetry in the front/back part and in the right/left part. And the main body and tassel are symetrical in the right/left part, which gives stability and comfortableness. Embroidered norigae is classified into knot, main body and decorative part in its form. The three kinds express their unique beauty by being harmonized together. Finally, it has a property of melody. Movement of the tassel has a property of melody shaken by the wind and movement of its wearer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• 복식문화연구
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• 제22권6호
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• pp.873-889
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• 2014

The goals of this study are to analyze how fashion professionals' attitudes toward Grunge style have changed since the 1990s and to compare aesthetic features of 1990s Grunge style and the style since 2000. By searching Vogue and Women's Wear Daily articles from 1992 to 2014 according to the keyword "Grunge", three collections from the 1990s and 59 collections since 2000 were selected for analysis. Although Grunge collections of the 1990s were harshly criticized by critics and retailers as ugly, the more recent collections have been highly praised for both design and profitability. The common aesthetic features of Grunge style in the 1990s and beyond include loose silhouettes, mix-and-match layerings, plaid patterns, floral prints, and striped patterns. However, Grunge style since 2000 has new features such as ornate fabrics, handcrafted details, a formal and dressy look, and faux plaid flannel shirts in chiffon or organza. These features give the style a more luxurious, feminine, and refined appearance. The results of this study indicate that Grunge style of the 1990s changed high fashion beauty standards and today's designers and consumers prefer to mix various styles to create new ones. They typically do not consider the original spirit or identities of the varied styles.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• 전자통신동향분석
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• 제33권4호
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• pp.23-32
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• 2018

Object detection is a challenging field in the visual understanding research area, detecting objects in visual scenes, and the location of such objects. It has recently been applied in various fields such as autonomous driving, image surveillance, and face recognition. In traditional methods of object detection, handcrafted features have been designed for overcoming various visual environments; however, they have a trade-off issue between accuracy and computational efficiency. Deep learning is a revolutionary paradigm in the machine-learning field. In addition, because deep-learning-based methods, particularly convolutional neural networks (CNNs), have outperformed conventional methods in terms of object detection, they have been studied in recent years. In this article, we provide a brief descriptive summary of several recent deep-learning methods for object detection and deep learning architectures. We also compare the performance of these methods and present a research guide of the object detection field.

• 말소리와 음성과학
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• 제14권4호
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• pp.45-56
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• 2022

본 논문은 만성콩팥병 환자의 음성을 사용하여 질병을 자동으로 진단하고 중증도를 예측하는 최적의 방법론을 제안한다. 만성콩팥병 환자는 호흡계 근력의 약화와 성대 부종 등으로 인해 음성이 변화하게 된다. 만성콩팥병 환자의 음성을 음성학적으로 분석한 선행 연구는 존재했으나, 환자의 음성을 분류하는 연구는 진행된 바가 없다. 본 논문에서는 모음연장발화, 유성음 문장 발화, 일반 문장 발화의 발화 목록과, 수제 특징 집합, eGeMAPS, CNN 추출 특징의 특징 집합, SVM, XGBoost의 머신러닝 분류기를 사용하여 만성콩팥병 환자의 음성을 분류하였다. 총 3시간 26분 25초 분량의 1,523개 발화가 실험에 사용되었다. 그 결과, 질병을 자동으로 진단하는 데에는 0.93, 중증도를 예측하는 3분류 문제에서는 0.89, 5분류 문제에서는 0.84의 F1-score가 나타났고, 모든 과제에서 일반 문장 발화, 수제 특징 집합, XGBoost의 조합을 사용했을 때 가장 높은 성능이 나타났다. 이는 만성콩팥병 음성 자동 분류에는 화자의 발화 특성을 모두 반영할 수 있는 일반 문장 발화와 거기로부터 추출한 적절한 특징 집합이 효과적임을 시사한다.