• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.1379-1382
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• 2007

In this paper, we present a simplified analytic approach for the prediction of roll force to be applicable to the grooveless rolling. The approach is based on the deformation shape deduced from physical considerations and employs the assumption that the deformation homogeneously occurs in three directions. Strain and strain rate are calculated by the geometric relationships between those components and the prescribed deformation functions. Then, stress components are obtained from the Levy-Mises' flow rule. By integrating the stress components along the rolling direction, roll force are finally obtained. The prediction accuracy of the proposed model is examined through comparison with results obtained from the finite element analysis.

• Communications for Statistical Applications and Methods
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• 제23권4호
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• pp.355-362
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• 2016

Ensemble methods often help increase prediction ability in various predictive models by combining multiple weak learners and reducing the variability of the final predictive model. In this work, we demonstrate that ensemble methods also enhance the accuracy of prediction under kernel ridge regression and kernel logistic regression classification. Here we apply bagging and random forests to two kernel-based predictive models; and present the procedure of how bagging and random forests can be embedded in kernel-based predictive models. Our proposals are tested under numerous synthetic and real datasets; subsequently, they are compared with plain kernel-based predictive models and their subsampling approach. Numerical studies demonstrate that ensemble approach outperforms plain kernel-based predictive models.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• IEIE Transactions on Smart Processing and Computing
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• 제6권4호
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• pp.281-291
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• 2017

Nowadays, as traffic jams are a daily elementary problem in both developed and developing countries, systems to monitor, predict, and detect traffic conditions are playing an important role in research fields. Comparing them, researchers have been trying to solve problems by applying many kinds of technologies, especially roadside sensors, which still have some issues, and for that reason, any one particular method by itself could not generate sufficient traffic prediction results. However, these sensors have some issues that are not useful for research. Therefore, it may not be best to use them as stand-alone methods for a traffic prediction system. On that note, this paper mainly focuses on predicting traffic conditions based on a hybrid prediction approach, which stands on accuracy comparison of three prediction models: multinomial logistic regression, decision trees, and support vector machine (SVM) classifiers. This is aimed at selecting the most suitable approach by means of integrating proficiencies from these approaches. It was also experimentally confirmed, with test cases and simulations that showed the performance of this hybrid method is more effective than individual methods.

• Communications for Statistical Applications and Methods
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• 제24권4호
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• Structural Engineering and Mechanics
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• 제72권1호
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• pp.31-41
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• 2019

Structural integrity assessment of piping components is of paramount important for remaining life prediction, residual strength evaluation and for in-service inspection planning. For accurate prediction of these, a reliable fracture parameter is essential. One of the fracture parameters is stress intensity factor (SIF), which is generally preferred for high strength materials, can be evaluated by using linear elastic fracture mechanics principles. To employ available analytical and numerical procedures for fracture analysis of piping components, it takes considerable amount of time and effort. In view of this, an alternative approach to analytical and finite element analysis, a model based on relevance vector machine (RVM) is developed to predict SIF of part through crack of a piping component under fatigue loading. RVM is based on probabilistic approach and regression and it is established based on Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Model for SIF prediction is developed by using MATLAB software wherein 70% of the data has been used for the development of RVM model and rest of the data is used for validation. The predicted SIF is found to be in good agreement with the corresponding analytical solution, and can be used for damage tolerant analysis of structural components.

• 자원환경지질
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• 제57권3호
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• pp.329-342
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• 2024

The exponential increase in nitrate pollution of river water poses an immediate threat to public health and the environment. This contamination is primarily due to various human activities, which include the overuse of nitrogenous fertilizers in agriculture and the discharge of nitrate-rich industrial effluents into rivers. As a result, the accurate prediction and identification of contaminated areas has become a crucial and challenging task for researchers. To solve these problems, this work leads to the prediction of nitrate contamination using machine learning approaches. This paper presents a novel approach known as Grey Wolf Optimizer (GWO) based on the Stacked Ensemble approach for predicting nitrate pollution in the Cauvery Delta region of Tamilnadu, India. The proposed method is evaluated using a Cauvery River dataset from the Tamilnadu Pollution Control Board. The proposed method shows excellent performance, achieving an accuracy of 93.31%, a precision of 93%, a sensitivity of 97.53%, a specificity of 94.28%, an F1-score of 95.23%, and an ROC score of 95%. These impressive results underline the demonstration of the proposed method in accurately predicting nitrate pollution in river water and ultimately help to make informed decisions to tackle these critical environmental problems.

• Journal of Information Technology Applications and Management
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• 제31권1호
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• pp.1-9
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• 2024

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

• 유통과학연구
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• 제11권3호
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• pp.5-11
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• 2013

Purpose - Going concern is one of fundamental concepts in accounting and auditing and sometimes the assessment of a company's going concern status that is a tough process. Various going concern prediction models' based on statistical and data mining methods help auditors and stakeholders suggested in the previous literature. Research design - This paper employs a data mining approach to prediction of going concern status of Iranian firms listed in Tehran Stock Exchange using Particle Swarm Optimization. To reach this goal, at the first step, we used the stepwise discriminant analysis it is selected the final variables from among of 42 variables and in the second stage; we applied a grid-search technique using 10-fold cross-validation to find out the optimal model. Results - The empirical tests show that the particle swarm optimization (PSO) model reached 99.92% and 99.28% accuracy rates for training and holdout data. Conclusions - The authors conclude that PSO model is applicable for prediction going concern of Iranian listed companies.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 춘계학술대회논문집
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• pp.149-154
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• 2001

Ship owners are demanding quieter vessels since crews have become more sensitive to their acoustic environment. Accordingly, designers of shipyards need to respond intelligently to the challenging requirements of delivering a quiet vessel. In early design stage, to predict shipboard noise the statistical approach is preferred to other methods because of simplicity. However, since the noise characteristics of the ships vary continuously with the environments, it is necessary to update the prediction formula with data base management system. This paper describes the feature of database program with the prediction method. Database management programs with GUI, are applied to Intranet system that is accessible by any users. Statistical approach to the prediction of A-weighted noise level in ship cabins, based on multiple regression analysis, is conducted. The noise levels in ship cabins are mainly affected by the parameters of the deadweight, the type of ship, the relative location of engines and cabins, the type of deckhouse, etc. As a result of verification, the formulas ensure the accuracy of 3 ㏈ in 83 % of cabins.