• Title/Summary/Keyword: Selection Time

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Intelligent System for the Prediction of Heart Diseases Using Machine Learning Algorithms with Anew Mixed Feature Creation (MFC) technique

  • Rawia Elarabi;Abdelrahman Elsharif Karrar;Murtada El-mukashfi El-taher
    • International Journal of Computer Science & Network Security
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    • v.23 no.5
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    • pp.148-162
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    • 2023
  • Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

A Genetic Algorithm for Materialized View Selection in Data Warehouses (데이터웨어하우스에서 유전자 알고리즘을 이용한 구체화된 뷰 선택 기법)

  • Lee, Min-Soo
    • The KIPS Transactions:PartD
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    • v.11D no.2
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    • pp.325-338
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    • 2004
  • A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored In the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks of designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views so that the total query response time over all queries can be minimized while a limited amount of time for maintaining the views is given(maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in terms of time complexity over existing search-based approaches that use heuristics. Our analysis shows that the algorithm consistently yields a solution that only has an additional 10% of query cost of over the optimal query cost while at the same time exhibits an impressive performance of only a linear increase in execution time. We have implemented a prototype version of our algorithm that is used to evaluate our approach.

Effect of part-time employment experience on adaptation to university life in dental hygiene students (일부 치위생과 학생의 아르바이트 경험이 대학생활적응에 미치는 영향)

  • Shin, Seon-Haeng
    • Journal of Korean society of Dental Hygiene
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    • v.15 no.6
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    • pp.1033-1041
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    • 2015
  • Objectives: The purpose of the study is to find out the effect of part-time employment experience on adaptation to university life in dental hygiene students. Methods: A self-reported questionnaire was completed by 288 dental hygiene students in Seoul and Gyeonggido from September to October, 2014. The questionnaire consisted of general characteristics of the subjects(5 items), part-time employment experience(6 items), and adaptation to university life(53 items). The instrument for adaptation to university life was adapted from Baker and Sirky and modified by Kwon. Likert 5 point scale adaptation included personal emotion adaptation, academic adaptation, social adaptation, and university environment adaptation. Cronbach's ${\alpha}$ was 0.80 in the study. Results: The adaptation to university life was 3.0 points. The higher the economic level was, the higher the personal emotional adaptation(p<0.001) and academic adaptation were(p<0.05). The adaptation to university life was positively influenced by tuition support by parents and part-time employment(p<0.05). The part-time employment was significantly helpful to future job selection, social adaptation, and university environment adaptation(p<0.001). Economic stability and advantage of future job selection had a positive influence on the adaptation to university life. So the adaptation to university life was proportional to younger age, economic stability, and advantage to future job selection. Conclusions: It is very important to give a positive motivation and stimulation, and a careful counseling to the students in part-time employment. In order to encourage the part-time employed students, major-related part-time job opening must be given.

Trust-based Relay Selection in Relay-based Networks

  • Wu, Di;Zhu, Gang;Zhu, Li;Ai, Bo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.10
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    • pp.2587-2600
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    • 2012
  • It has been demonstrated that choosing an appropriate relay node can improve the transmission rate for the system. However, such system improvement brought by the relay selection may be degraded with the presence of the malicious relay nodes, which are selected but refuse to cooperate for transmissions deliberately. In this paper, we formulate the relay selection issue as a restless bandit problem with the objective to maximize the average rate, while considering the credibility of each relay node, which may be different at each time instant. Then the optimization problem is solved by using the priority-index heuristic method effectively. Furthermore, a low complexity algorithm is offered in order to facilitate the practical implementations. Simulation results are conducted to demonstrate the effectiveness of the proposed trust-based relay selection scheme.

An Application of fuzzy TOPSIS in evaluating IT proposals (IT 제안서의 기술평가에서의 퍼지 TOPSIS 응용)

  • Jeong, Giho
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.13 no.1
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    • pp.197-211
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    • 2017
  • In recent years, it is natural that the development and the maintenance of information systems are strongly dependent on outside service providers for economic reasons, especially in public sector. There has been an unexpected growth in the number of selection activities for outsourcing related works. At this time, selection of the contractor generally considers the proposals received based on the RFP(requested for proposal) and determines the ranking by experts committee. However, it is difficult even for expert giving a specific numeric score in weighting criteria or rating alternatives. In this context, an extended fuzzy TOPSIS method is applied for selection problem of IT proposals. A numerical illustration is also provided to demonstrate the applicability of the approach. This approach is very practical to help decision makers in assessing proposals during the selection phase under uncertainties.

A Clonal Selection Algorithm using the Rolling Planning and an Extended Memory Cell for the Inventory Routing Problem (연동계획과 확장된 기억 세포를 이용한 재고 및 경로 문제의 복제선택해법)

  • Yang, Byoung-Hak
    • Korean Management Science Review
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    • v.26 no.1
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    • pp.171-182
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    • 2009
  • We consider the inventory replenishment problem and the vehicle routing problem simultaneously in the vending machine operation. This problem is known as the inventory routing problem. We design a memory cell in the clonal selection algorithm. The memory cell store the best solution of previous solved problem and use an initial solution for next problem. In general, the other clonal selection algorithm used memory cell for reserving the best solution in current problem. Experiments are performed for testing efficiency of the memory cell in demand uncertainty. Experiment result shows that the solution quality of our algorithm is similar to general clonal selection algorithm and the calculations time is reduced by 20% when the demand uncertainty is less than 30%.

Classification of High Dimensionality Data through Feature Selection Using Markov Blanket

  • Lee, Junghye;Jun, Chi-Hyuck
    • Industrial Engineering and Management Systems
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    • v.14 no.2
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    • pp.210-219
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    • 2015
  • A classification task requires an exponentially growing amount of computation time and number of observations as the variable dimensionality increases. Thus, reducing the dimensionality of the data is essential when the number of observations is limited. Often, dimensionality reduction or feature selection leads to better classification performance than using the whole number of features. In this paper, we study the possibility of utilizing the Markov blanket discovery algorithm as a new feature selection method. The Markov blanket of a target variable is the minimal variable set for explaining the target variable on the basis of conditional independence of all the variables to be connected in a Bayesian network. We apply several Markov blanket discovery algorithms to some high-dimensional categorical and continuous data sets, and compare their classification performance with other feature selection methods using well-known classifiers.

Deep Learning Method for Identification and Selection of Relevant Features

  • Vejendla Lakshman
    • International Journal of Computer Science & Network Security
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    • v.24 no.5
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    • pp.212-216
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    • 2024
  • Feature Selection have turned into the main point of investigations particularly in bioinformatics where there are numerous applications. Deep learning technique is a useful asset to choose features, anyway not all calculations are on an equivalent balance with regards to selection of relevant features. To be sure, numerous techniques have been proposed to select multiple features using deep learning techniques. Because of the deep learning, neural systems have profited a gigantic top recovery in the previous couple of years. Anyway neural systems are blackbox models and not many endeavors have been made so as to examine the fundamental procedure. In this proposed work a new calculations so as to do feature selection with deep learning systems is introduced. To evaluate our outcomes, we create relapse and grouping issues which enable us to think about every calculation on various fronts: exhibitions, calculation time and limitations. The outcomes acquired are truly encouraging since we figure out how to accomplish our objective by outperforming irregular backwoods exhibitions for each situation. The results prove that the proposed method exhibits better performance than the traditional methods.

Study on the Selection of Representative Pulse Wave

  • Kim, Jong-Yeol;Shin, Sang-Hoon
    • The Journal of Korean Medicine
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    • v.29 no.5
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    • pp.104-110
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    • 2008
  • Objectives : The purpose of this study is to develop the method of selecting representative pulse wave. Methods : The pulse waves were acquired at the right and the left Guan point(關部) with 1420 people who were apparently healthy. The shape agreement of right and left pulse wave and the floating-sinking ratio were compared with three cases, which were the pulse height based method, the pulse area based method, and the pulse time based method. Results : In the point of the shape accordance, the pulse time based method was the best, and the pulse area based method was the worst. In the point of the floating-sinking ratio, the pulse height based method was the worst, and the pulse time based method was the best. Conclusions : So, the pulse time based method was recommended for selecting the representative pulse wave. This study compared the selection methods of representative pulse using the physiological characteristics of pulse wave. Further studies are required, because the representative pulse wave is the main factor of determining the shape and the floating-sinking characteristic of the pulse wave.

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PORTFOLIO SELECTION WITH INCOME RISK: A NEW APPROACH

  • Lim, Byung Hwa
    • Journal of the Chungcheong Mathematical Society
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    • v.29 no.2
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    • pp.329-336
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    • 2016
  • The optimal portfolio choice problem with a stochastic income is considered in continuous-time framework. We provide a novel approach to treat the stochastic income when the market is complete. The developed method is useful to obtain closed-form solutions of the problems under borrowing constraints.