• 대한전기학회논문지:시스템및제어부문D
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• 제52권9호
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• pp.540-546
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• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• 대한원격탐사학회지
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• 제29권6호
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• pp.611-619
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• 2013

Although the need of crop production has increased in Mongolia, crop cultivation is very limited because of the harsh climatic and topographic conditions. Crop lands are sparsely distributed with relatively small sizes and, therefore, it is difficult to survey the exact area of crop lands. The study aimed to find an easy and effective way of accurate classification to map crop lands in Mongolia using satellite images. To classify the crop lands over the study area in northern Mongolia, four classifications were carried out by using 1) Thematic Mapper (TM) image August 23, 2) TM image of July 6, 3) combined 12 bands of TM images of July and August, and 4) both TM images of July and August by layered classification. Wheat and potato are the major crop types and they show relatively high variation in crop conditions between July and August. On the other hands, other land cover types (forest, riparian vegetation, grassland, water and bare soil) do not show such difference between July and August. The results of four classifications clearly show that the use of multi-temporal images is essential to accurately classify the crop lands. The layered classification method, in which each class is separated by a subset of TM images, shows the highest classification accuracy (93.7%) of the crop lands. The classification accuracies are lower when we use only a single TM image of either July or August. Because of the different planting practice of potato and the growth condition of wheat, the spectral characteristics of potato and wheat cannot be fully separated from other cover types with TM image of either July or August. Further refinements on the spatial characteristics of existing crop lands may enhance the crop mapping method in Mongolia.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권12호
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• pp.555-560
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• 2021

최근 안면 피부 미용에 대한 사람들의 관심이 높아짐에 따라 딥 러닝을 활용한 안면 피부 미용을 위한 피부 질환 인식 연구가 진행되고 있다. 이러한 연구들은 여드름을 비롯한 다양한 피부 질환을 인식한다. 기존의 연구들은 단일 피부 질환만을 인식하지만, 안면에 발생하는 피부 질환은 더 다양하고 복합적으로 발생할 수 있다. 따라서 본 논문에서는 Inception-ResNet V2 모델을 활용하여 다중 레이블 분류 방법으로 여드름, 블랙헤드, 주근깨, 검버섯, 일반 피부, 화이트헤드에 관한 복합적인 피부 질환을 인식한다. 사용한 평가 지표 중 정확도는 98.8%, 해밍 손실은 0.003을 달성하였고, 단일 클래스별 정밀도, 재현율, F1-점수는 모두 96.6% 이상을 달성하였다.

• Structural Monitoring and Maintenance
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• 제2권3호
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• pp.181-197
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• 2015

Assessing the condition of paint on civil structures is an important but challenging and costly task, in particular when it comes to large and complex structures. Current practices of visual inspection are labour-intensive and time-consuming to perform. In addition, this task usually relies on the experience and subjective judgment of individual inspectors. In this study, hyperspectral imaging and classification techniques are proposed as a method to objectively assess the state of the paint on a civil or other structure. The ultimate objective of the work is to develop a technology that can provide precise and automatic grading of paint condition and assessment of degradation due to age or environmental factors. Towards this goal, we acquired hyperspectral images of steel surfaces located at long (mid-range) and short distances on the Sydney Harbour Bridge with an Acousto-Optics Tunable filter (AOTF) hyperspectral camera (consisting of 21 bands in the visible spectrum). We trained a multi-class Support Vector Machines (SVM) classifier to automatically assess the grading of the paint from hyperspectral signatures. Our results demonstrate that the classifier generates highly accurate assessment of the paint condition in comparison to the judgement of human experts.

• 센서학회지
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• 제24권1호
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• pp.47-53
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• 2015

Fall-related injuries in elderly people are a major health care problem. This paper introduces determination of fall direction before impact using support vector machine (SVM). Once a falling phase is detected, dynamic characteristic parameters measured by the accelerometer and gyroscope and then processed by a Kalman filter are used in the SVM to determine the fall directions, i.e., forward (F), backward (B), rightward (R), and leftward (L). This paper compares the determination sensitivities according to the selected parameters for the SVM (velocities, tilt angles, vs. accelerations) and sensor attachment locations (waist vs. chest) with regards to the binary classification (i.e., F vs. B and R vs. L) and the multi-class classification (i.e., F, B, R, vs. L). Based on the velocity of waist which was superior to other parameters, the SVM in the binary case achieved 100% sensitivities for both F vs. B and R vs. L, while the SVM in the multi-class case achieved the sensitivities of F 93.8%, B 91.3%, R 62.3%, and L 63.6%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.632-657
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• 2022

Detecting web attacks is a major challenge, and it is observed that the use of simple models leads to low sensitivity or high false positive problems. In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall. Normal and abnormal classification is carried out in the first stage of the proposed WAF model. The classification process of the types of abnormal traffics is postponed to the second stage and carried out using an integrated stacked ensemble model. By this way, clients' requests can be served without time delay, and attack types can be detected with high sensitivity. In addition to the high accuracy of the proposed model, by using the statistical similarity and diversity analyses in the study, high generalization for the ensemble model is achieved. Within the study, a comprehensive, up-to-date, and robust multi-class web anomaly dataset named GAZI-HTTP is created in accordance with the real-world situations. The performance of the proposed WAF model is compared to state-of-the-art deep learning models and previous studies using the benchmark dataset. The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.372-378
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• 2009

For effective mobility control of unmanned ground vehicles in outdoor off-road environments, terrain cover classification technology using passive sensors is vital. This paper presents a novel method far terrain classification based on color and texture information of off-road images. It uses a neural network classifier and wavelet features. We exploit the wavelet mean and energy features extracted from multi-channel wavelet transformed images and also utilize the terrain class spatial coordinates of images to include additional features. By comparing the classification performance according to applied features, the experimental results show that the proposed algorithm has a promising result and potential possibilities for autonomous navigation.

• 한국컴퓨터정보학회논문지
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• 제26권1호
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• pp.27-35
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• 2021

본 연구에서는 레이블 임베딩의 분포를 반영하는 딥러닝 모형을 위한 새로운 스태킹 앙상블 방법론을 제안하였다. 제안된 앙상블 방법론은 기본 딥러닝 분류기를 학습하는 과정과 학습된 모형으로 부터 얻어진 레이블 임베딩을 이용한 군집화 결과로부터 소분류기들을 학습하는 과정으로 이루어져 있다. 본 방법론은 주어진 다중 분류 문제를 군집화 결과를 활용하여 소 문제들로 나누는 것을 기본으로 한다. 군집화에 사용되는 레이블 임베딩은 처음 학습한 기본 딥러닝 분류기의 마지막 층의 가중치로부터 얻어질 수 있다. 군집화 결과를 기반으로 군집화 내의 클래스들을 분류하는 소분류기들을 군집의 수만큼 구축하여 학습한다. 실험 결과 기본 분류기로부터의 레이블 임베딩이 클래스 간의 관계를 잘 반영한다는 것을 확인하였고, 이를 기반으로 한 앙상블 방법론이 CIFAR 100 데이터에 대해서 분류 성능을 향상시킬 수 있다는 것을 확인할 수 있었다.

• 방송공학회논문지
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• 제27권6호
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• pp.885-896
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• 2022

제조에서의 모터 불량은 향후 A/S 및 신뢰성에 중요한 역활을 한다. 모터의 불량 구분은 소리, 전류, 진동등의 측정을 통해 검출한다. 본 논문에서 사용한 데이터는 자동차 사이드미러 모터 기어박스의 소리를 사용하였다. 모터 소리는 3가지의 클래스로 구성되어 있다. 소리 데이터는 멜스펙트로그램을 통한 변환 과정을 거쳐 네트워크 모델에 입력된다. 본 논문에서는 불량 모터 구분 성능을 올리기 위한 데이터 증강, 클래스 불균형에 따는 다양한 데이터 재샘플링, 재가중치 조절, 손실함수의 변경, 표현 학습과 클래스 구분의 두 단계 분리 방법 등 다양한 방법을 적용하였으며, 추가적으로 커리큘럼 러닝 방법, 자기 스페이스 학습 방법 등을 Bidirectional LSTM Attention, Convolutional Recurrent Neural Network, Multi-Head Attention, Bidirectional Temporal Convolution Network, Convolution Neural Network 등 총 5가지 네트워크 모델을 통하여 비교하고, 모터 소리 구분에 최적의 구성을 찾을 수 있었다.