• 제목/요약/키워드: Class Model

검색결과 3,326건 처리시간 0.034초

A Hybrid Multi-Level Feature Selection Framework for prediction of Chronic Disease

  • G.S. Raghavendra;Shanthi Mahesh;M.V.P. Chandrasekhara Rao
    • International Journal of Computer Science & Network Security
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    • 제23권12호
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    • pp.101-106
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    • 2023
  • Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

Self-terminated carbonation model as an useful support for durable concrete structure designing

  • Woyciechowski, Piotr P.;Sokolowska, Joanna J.
    • Structural Engineering and Mechanics
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    • 제63권1호
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    • pp.55-64
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    • 2017
  • The paper concerns concrete carbonation, the phenomena that occurs in every type of climate, especially in urban-industrial areas. In European Standards, including Eurocode (EC) for concrete structures the demanded durability of construction located in the conditions of the carbonation threat is mainly assured by the selection of suitable thickness of reinforcement cover. According to EC0 and EC2, the thickness of the cover in the particular class of exposure depends on the structural class/category and concrete compressive strength class which is determined by cement content and water-cement ratio (thus the quantitative composition) but it is not differentiated for various cements, nor additives (i.e., qualitative composition), nor technological types of concrete. As a consequence the selected thickness of concrete cover is in fact a far estimation - sometimes too exaggerated (too safe or too risky). The paper presents the elaborated "self-terminated carbonation model" that includes abovementioned factors and enables to indicate the maximal possible depth of carbonation. This is possible because presented model is a hyperbolic function of carbonation depth in time (the other models published in the literature use the parabolic function that theoretically assume the infinite increase of carbonation depth value). The paper discusses the presented model in comparison to other models published in the literature, moreover it contains the algorithm of concrete cover design with use of the model as well as an example of calculation of the cover thickness.

Baltic Ice Class IA를 적용한 115K Ice Tanker 개발 (Development of 115K Tanker Design Adopted Ice Class 1A)

  • 김현수;하문근;백명철;김수형;박종우;전호환
    • 대한조선학회논문집
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    • 제41권6호
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    • pp.120-125
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    • 2004
  • There are very few numbers of 115K FPP (Fixed Pitch Propulsion) Tankers for the Baltic ice class IA because the minimum power requirement of FMA (Finish- Swedish Maritime Association) needs quite large engine power and the 40 m Beam is out of calculation range of FMA minimum power requirements. The shipyard has no choice except to increase the engine power to satisfy FMA minimum power requirement Rule. And the operation cost, efficiency of hullform and its building cost are not good from the ship owners' point of view To solve this problem, the experience of ice breaking tanker development and the ice tank test results were adopted. The main idea to reduce the ice resistance is by reducing waterline angle at design load waterline. The reason behind the main idea is to reduce the ice-clearing force. Two hull forms were developed to satisfy Baltic Ice class IA. Two ice tank tests and one towing tank test was performed at MARC (Kvaener-Masa Arctic Research Center) and SSMB (Samsung Ship Model Basin) facilities, respectively. The purpose of these tests was to verify the performance in ice and open water respectively The hull form 2 shows less speed loss compared to Hull form 1 in open water operation but hull form 2 shows very good ice clearing ability. finally the Hull Form 2 satisfying Baltic ice class IA. The merit of this hull form is to use the same engine capacity and no major design changes in hull form and other related designs But the hull structure has to be changed according to the ice class grade. The difference in two hull form development methods, ice model test methods and analysis methods of ice model test will be described in this paper.

Numerical study on the structural response of energy-saving device of ice-class vessel due to impact of ice block

  • Matsui, Sadaoki;Uto, Shotaro;Yamada, Yasuhira;Watanabe, Shinpei
    • International Journal of Naval Architecture and Ocean Engineering
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    • 제10권3호
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    • pp.367-375
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    • 2018
  • The present paper considers the contact between energy-saving device of ice-class vessel and ice block. The main objective of this study is to clarify the tendency of the ice impact force and the structural response as well as interaction effects of them. The contact analysis is performed by using LS-DYNA finite element code. The main collision scenario is based on Finnish-Swedish ice class rules and a stern duct model is used as an energy-saving device. For the contact force, two modelling approaches are adopted. One is dynamic indentation model of ice block based on the pressure-area curve. The other is numerical material modelling by LS-DYNA. The authors investigated the sensitivity of the structural response against the ice contact pressure, the interaction effect between structure and ice block, and the influence of eccentric collision. The results of these simulations are presented and discussed with respect to structural safety.

2 단 Self-Organizing Feature Map 을 사용한 변환 영역 영상의 벡터 양자화 (Image VQ Using Two-Stage Self-Organizing Feature Map in the Transform Domain)

  • 이동학;김영환
    • 전자공학회논문지B
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    • 제32B권3호
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    • pp.57-65
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    • 1995
  • This paper presents a new classified vector quantization (VQ) technique using a neural network model in the transform domain. Prior to designing a codebook, the proposed approach extracts class features from a set of images using self-organizing feature map (SOFM) that has the pattern recognition characteristics and the same as VQ objective. Since we extract the class features from the training images unlike previous approaches, the reconstructed image quality is improved. Moreover, exploiting the adaptivity of the neural network model makes our approach be easily applied to designing a new vector quantizer when the processed image characteristics are changed. After the generalized BFOS algorithm allocates the given bits to each class, codebooks of each class are also generated using SOFM for the maximal reconstructed image quality. In experimental results using monochromatic images, we obtained a good visual quality in the reconstructed image. Also, PSNR is comparable to that of other classified VQ technique and is higher than that of JPEG baseline system.

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ENGLISH RESTRUCTURING AND A USE OF MUSIC IN TEACHING ENGLISH PRONUNCIATION

  • Kim, Key-Seop
    • 대한음성학회:학술대회논문집
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    • 대한음성학회 2000년도 7월 학술대회지
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    • pp.117-134
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    • 2000
  • Kim, Key-Seop(2000). English Restructuring and A Use of Music in Teaching English Pronunciation. JSEP 2000 voU This study has two-fold aims: one is to clarify the restructuring of English in utterance, and the other is to relate it to teaching English pronunciation for listening and speaking with a use of music and song by suggesting a model of 10-15 minute pronunciation class syllabus for every period in class. Generally, English utterances are restructured by stress-timed rhythm, irrespective of syntactic boundaries. So the rhythmic units are arranged in isochronous groups, of which the making is to attach clitic(s) to a host or head often leftwards and sometimes rightwards, which results in linking, contraction, reduction, sound change and rhythm adjustment in utterance, just as in music and song. With English restructuring focused on, a model of English pronunciation class syllabus is proposed to be put forward in class for every period of a lesson or unit. It tries to relate the focused factor(s) in pronunciation to the integrated, with teaching techniques and music made use of.

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열린 수학교육 교수 학습 모형 연구 (A Study on Teaching-Learning Model for Open Education in Mathematics)

  • 최택영;이교희
    • 한국수학교육학회지시리즈A:수학교육
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    • 제38권1호
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    • pp.61-75
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    • 1999
  • This study has its purpose to develop an optimal teaching model in math class leading to an effective device of open education in mathematics being transformed from the current teacher-centered teaching to the individually specified student-centered one on the basis of the definitions and methods of open education learned from sundry literature references. Accordingly, this paper established several patterns of effective open math class for teaching specific math's contents, followed by developing applicable teaching-learning models for class situation rested on each math lesson's features. Unit learning models for open education in mathematics, which were made step by step according to each unit's contents were also presented to be applied to real class situations.

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LoRaWAN에서 LoRa Class B의 에너지 소비 모델 (Energy Consumption Model of LoRa Class B in LoRaWAN)

  • 홍지연;이경헌;윤주상
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2019년도 제60차 하계학술대회논문집 27권2호
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    • pp.105-107
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    • 2019
  • 최근 몇 년간 소량의 데이터를 송수신하는 Massive IoT 네트워크에 많은 관심을 가지고 있다. 이러한 환경을 구축하기 위해서 저전력 광역 네트워크(LPWAN) 기술 중 LoRa(Long Range) 네트워크를 사용하고 있다. 대부분의 IoT 응용 서비스는 디바이스가 장시간 안정적으로 작동해야 하므로 에너지 효율성을 중점으로 두고 LoRa 디바이스의 수명을 최대화하기 위한 디바이스의 여러 동작들을 설계하는 에너지 소비 모델링이 중요하다. 따라서 본 논문에서는 LoRa Class B 통신 방식의 에너지 소비 모델을 정의하고 성능을 평가한다.

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윤리적 의사결정모형 기반 토론식 수업이 윤리 지식, 수업만족도 및 윤리적 가치관에 미치는 효과 (The Effects of Debate Classes based on an Ethical Decision-Making Model on Ethical Knowledge, Class Satisfaction, and Ethical Values)

  • 김창희;정선영
    • 디지털융복합연구
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    • 제12권10호
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    • pp.405-414
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    • 2014
  • 본 연구는 간호학생의 윤리적 의사결정 역량 강화 방안으로 2013년 3월 4일에서 6월 3일까지 간호대학 4학년 82명을 대상으로 실시한 비동등성 대조군 사전 사후 유사실험연구이다. 토론식 수업을 적용한 실험군과 전통적 강의식 수업을 적용한 대조군 간의 윤리 지식, 수업만족도 및 윤리적 가치관 차이를 확인하였다. 토론식 수업은 공리주의, 의무론에 기초한 3단계 수정모형과 Value-Be-Do 모형을 포함한 윤리적 의사결정 모형을 적용하였다. 연구결과 수업 후 윤리 지식 점수는 실험군이 유의하게 높았다. 윤리적 가치관은 두 군 모두 수업 전 후 차이가 없었다. 두 군은 수업 전 후 협동자관계 영역, 대상자관계 영역에서 의무론적 성향을 보였지만, 인간생명 영역에서 공리주의적 성향을 보였다. 수업만족도는 내용이해와 실무적용 가능성 영역에서 실험군이 유의하게 높았다. 결론적으로 본 윤리적 의사결정모형 기반 토론식 수업을 효과적인 간호윤리 의사결정 훈련 방안으로 활용할 것을 제안한다.

Comparison Study of Multi-class Classification Methods

  • Bae, Wha-Soo;Jeon, Gab-Dong;Seok, Kyung-Ha
    • Communications for Statistical Applications and Methods
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    • 제14권2호
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    • pp.377-388
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    • 2007
  • As one of multi-class classification methods, ECOC (Error Correcting Output Coding) method is known to have low classification error rate. This paper aims at suggesting effective multi-class classification method (1) by comparing various encoding methods and decoding methods in ECOC method and (2) by comparing ECOC method and direct classification method. Both SVM (Support Vector Machine) and logistic regression model were used as binary classifiers in comparison.