• 제목/요약/키워드: NN techniques

검색결과 118건 처리시간 0.028초

기계학습을 기반으로 한 인터넷 학술문서의 효과적 자동분류에 관한 연구 (The Study on the Effective Automatic Classification of Internet Document Using the Machine Learning)

  • 노영희
    • 한국도서관정보학회지
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    • 제32권3호
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    • pp.307-330
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    • 2001
  • 본 연구에서는 kNN분류기를 이용한 범주화 방법에 대한 성능 실험을 하였다. kNN분류기와 같은 대부분의 예제기반 자동 분류기법은 학습문서집단의 자질을 축소하게 되는데 자질을 몇 퍼센트 축소함으로써 높은 성능을 얻을 수 있는지를 알아보고자 하였다. 또한, kNN분류기는 학습문서집단에서 검증문서와 가장 유사한 k개의 학습문서를 찾아야 하는데, 이때 가장 적합한 k값은 얼마인지를 실험을 통하여 검증하여 보고자 하였다.

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Nearest Neighbor Query Processing Techniques in Location-Aware Environment

  • Kim, Sang-Ho;Choi, Bo-Yoon;Ryu, Keun-Ho;Nam, Kwang-Woo;Park, Jong-Hyun
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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    • pp.715-717
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    • 2003
  • Some previous works for nearest neighbor (NN) query processing technique can treat a case that query/data are both moving objects. However, they cannot find exact result owing to vagueness of criterion. In order to escape their limitations and get exact result, we propose new NN query techniques, exact CTNN (continuous trajectory NN) query, approximate CTNN query, and dynamic CTNN query. These are all superior to pervious works, by reducing of number of calculation, considering of trajectory information, and using of continuous query concept. Using these techniques, we can solve any situations and types of NN query in location-aware environment.

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Utilizing Data Mining Techniques to Predict Students Performance using Data Log from MOODLE

  • Noora Shawareb;Ahmed Ewais;Fisnik Dalipi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제18권9호
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    • pp.2564-2588
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    • 2024
  • Due to COVID19 pandemic, most of educational institutions and schools changed the traditional way of teaching to online teaching and learning using well-known Learning Management Systems (LMS) such as Moodle, Canvas, Blackboard, etc. Accordingly, LMS started to generate a large data related to students' characteristics and achievements and other course-related information. This makes it difficult to teachers to monitor students' behaviour and performance. Therefore, a need to support teachers with a tool alerting student who might be in risk based on their recorded activities and achievements in adopted LMS in the school. This paper focuses on the benefits of using recorded data in LMS platforms, specifically Moodle, to predict students' performance by analysing their behavioural data and engagement activities using data mining techniques. As part of the overall process, this study encountered the task of extracting and selecting relevant data features for predicting performance, along with designing the framework and choosing appropriate machine learning techniques. The collected data underwent pre-processing operations to remove random partitions, empty values, duplicates, and code the data. Different machine learning techniques, including k-NN, TREE, Ensembled Tree, SVM, and MLPNNs were applied to the processed data. The results showed that the MLPNNs technique outperformed other classification techniques, achieving a classification accuracy of 93%, while SVM and k-NN achieved 90% and 87% respectively. This indicates the possibility for future research to investigate incorporating other neural network methods for categorizing students using data from LMS.

Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm

  • Chatterjee, Sankhadeep;Sarkar, Sarbartha;Hore, Sirshendu;Dey, Nilanjan;Ashour, Amira S.;Shi, Fuqian;Le, Dac-Nhuong
    • Structural Engineering and Mechanics
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    • 제63권4호
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    • pp.429-438
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    • 2017
  • Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

Impact of Instance Selection on kNN-Based Text Categorization

  • Barigou, Fatiha
    • Journal of Information Processing Systems
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    • 제14권2호
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    • pp.418-434
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    • 2018
  • With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Several machine learning algorithms have been proposed for text categorization. The k-nearest neighbor algorithm (kNN) is known to be one of the best state of the art classifiers when used for text categorization. However, kNN suffers from limitations such as high computation when classifying new instances. Instance selection techniques have emerged as highly competitive methods to improve kNN through data reduction. However previous works have evaluated those approaches only on structured datasets. In addition, their performance has not been examined over the text categorization domain where the dimensionality and size of the dataset is very high. Motivated by these observations, this paper investigates and analyzes the impact of instance selection on kNN-based text categorization in terms of various aspects such as classification accuracy, classification efficiency, and data reduction.

손가락 움직임 인식을 위한 웨어러블 디바이스 설계 및 ML 기법별 성능 분석 (Design and Performance Analysis of ML Techniques for Finger Motion Recognition)

  • 정우순;이형규
    • 한국산업정보학회논문지
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    • 제25권2호
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    • pp.129-136
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    • 2020
  • 손가락 움직임 인식을 통한 제어는 직관적인 인간-컴퓨터 상호작용 방법의 하나이다. 본 연구에서는 여러 가지 ML (Machine learning) 기법을 사용하여 효율적인 손가락 움직임 인식을 위한 웨어러블 디바이스를 구현한다. 움직임 인식을 위한 시계열 데이터 분석에 전통적으로 사용되어 온 HMM (Hidden markov model) 및 DTW (Dynamic time warping) 기법뿐만 아니라 NN (Neural network) 기법을 적용하여 손가락 움직임 인식의 효율성 및 정확성을 비교하고 분석한다. 제안된 시스템의 경우, 경량화된 ML 모델을 설계하기 위해 각 ML 기법에 대해 최적화된 전처리 프로세스를 적용한다. 실험 결과, 최적화된 NN, HMM 및 DTW 기반 손가락 움직임 인식시스템은 각각 99.1%, 96.6%, 95.9%의 정확도를 제공한다.

DNS key technologies based on machine learning and network data mining

  • Xiaofei Liu;Xiang Zhang;Mostafa Habibi
    • Advances in concrete construction
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    • 제17권2호
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    • pp.53-66
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    • 2024
  • Domain Name Systems (DNS) provide critical performance in directing Internet traffic. It is a significant duty of DNS service providers to protect DNS servers from bandwidth attacks. Data mining techniques may identify different trends in detecting anomalies, but these approaches are insufficient to provide adequate methods for querying traffic data in significant network environments. The patterns can enable the providers of DNS services to find anomalies. Accordingly, this research has used a new approach to find the anomalies using the Neural Network (NN) because intrusion detection techniques or conventional rule-based anomaly are insufficient to detect general DNS anomalies using multi-enterprise network traffic data obtained from network traffic data (from different organizations). NN was developed, and its results were measured to determine the best performance in anomaly detection using DNS query data. Going through the R2 results, it was found that NN could satisfactorily perform the DNS anomaly detection process. Based on the results, the security weaknesses and problems related to unpredictable matters could be practically distinguished, and many could be avoided in advance. Based on the R2 results, the NN could perform remarkably well in general DNS anomaly detection processing in this study.

유사도 임계치에 근거한 최근접 이웃 집합의 구성 (Formation of Nearest Neighbors Set Based on Similarity Threshold)

  • 이재식;이진천
    • 지능정보연구
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    • 제13권2호
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    • pp.1-14
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    • 2007
  • 사례기반추론은 다양한 예측 문제에 있어서 성공적으로 활용되고 있는 데이터 마이닝 기법 중 하나이다. 사례기반추론 시스템의 예측 성능은 예측에 사용되는 최근접 이웃 집합을 어떻게 구성하느냐에 따라 영향을 받게 된다. 최근접 이웃 집합의 구성에 있어서 대부분의 선행 연구들은 고정된 값인 K개의 사례를 포함시키는 k-NN 방법을 채택해왔다. 그러나 k-NN 방법을 채택하는 사례기반추론 시스템은 k 값을 너무 크게 혹은 작게 설정하게 되면 예측 성능이 저하된다. 본 연구에서는 이러한 문제를 해결하기 위해 최근접 이웃 집합을 구성함에 있어서 유사도의 임계치 자체를 이용하는 s-NN 방법을 제안하였다. UCI의 Machine Learning Repository에서 제공하는 데이터를 사용하여 실험한 결과, s-NN 방법을 적용한 사례기반추론 모델이 k-NN 방법을 적용한 사례기반추론 모델보다 더 우수한 성능을 보여주었다.

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텍스트 마이닝 기법을 이용한 컴퓨터 네트워크의 침입 탐지 (Using Text Mining Techniques for Intrusion Detection Problem in Computer Network)

  • 오승준;원민관
    • 한국컴퓨터정보학회논문지
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    • 제10권5호
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    • pp.27-32
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    • 2005
  • 최근 들어 데이터 마이닝 기법을 컴퓨터 네트워크의 침입 탐지에 적용하려는 많은 연구가 진행되고 있다. 본 논문에서는 침입 탐지 분야에서 프로그램 행위가 정상적인지 비정상적인지를 분류하기 위한 방법을 연구한다. 이를 위해, 택스트 마이닝 기법중의 하나인 k 최근접 이웃 (kNN) 분류기를 이용한 새로운 방법을 제안한다. 본 논문에서는 택스트 분류 기법을 적용하기 위해 각각의 시스템 호출을 단어로 간주하고, 시스템 호출의 집합들을 문서로 간주한다. 이러한 문서들은 kNN 분류기를 이용하여 분류된다. 간단한 예제를 통하여 제안하는 절차를 소개한다.

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전역근사최적화를 위한 소프트컴퓨팅기술의 활용 (Utilizing Soft Computing Techniques in Global Approximate Optimization)

  • 이종수;장민성;김승진;김도영
    • 한국전산구조공학회:학술대회논문집
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    • 한국전산구조공학회 2000년도 봄 학술발표회논문집
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    • pp.449-457
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    • 2000
  • The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

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