• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.1-10
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• 2019

Research classification of software modules was done to validate the approaches proposed for addressing limitations in existing classification approaches. The objective of this study was to replicate the experiments of a recently published research study and re-evaluate its results. The reason to repeat the experiment(s) and re-evaluate the results was to verify the approach to identify the faulty and non-faulty modules applied in the original study for the prioritization of test cases. As a methodology, we conducted this study to re-evaluate the results of the study. The results showed that binary logistic regression analysis remains helpful for researchers for predictions, as it provides an overall prediction of accuracy in percentage. Our study shows a prediction accuracy of 92.9% for the PureMVC Java open source program, while the original study showed an 82% prediction accuracy for the same Java program classes. It is believed by the authors that future research can refine the criteria used to classify classes of web systems written in various programming languages based on the results of this study.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.229-234
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• 2021

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4014-4021
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• 2021

If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails. Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation is necessary to prevent this progression. In this study, the corresponding time was defined as the golden time. To achieve the objective of accurately predicting the golden time, the prediction was performed using the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has an architecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in which the fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affect inference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regression model. By using the prediction result through the proposed DFNN with rule-dropout, it is expected to prevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery, which failed in the LOCA situation.

• Economic and Environmental Geology
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• v.56 no.1
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.102-111
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• 2022

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

• Communications for Statistical Applications and Methods
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• v.31 no.4
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• pp.441-457
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• 2024

Semicontinuous data are characterized by a mixture of a point probability mass at zero and a continuous distribution of positive values. This type of data is often modeled using a two-part model where the first part models the probability of dichotomous outcomes -zero or positive- and the second part models the distribution of positive values. Despite the two-part model's popularity, variable selection in this model has not been fully addressed, especially, in high dimensional data. The objective of this study is to investigate variable selection and prediction performance of penalized regression methods in two-part models. The performance of the selected techniques in the two-part model is evaluated via simulation studies. Our findings show that LASSO and ENET tend to select more predictors in the model than SCAD and MCP. Consequently, MCP and SCAD outperform LASSO and ENET for β-specificity, and LASSO and ENET perform better than MCP and SCAD with respect to the mean squared error. We find similar results when applying the penalized regression methods to the prediction of crime incidents using community-based data.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1863-1873
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• 2024

In the Korean domestic nuclear industry, to analyze the reliability of instrumentation and control (I&C) systems, the failure rates of the electronic components constituting the I&C systems are predicted based on the MIL-HDBK-217F standard titled 'Reliability Prediction of Electronic Equipment'. Based on these predicted failure rates, the mean time to failure of the I&C systems is calculated to determine the replacement period of the I&C systems. However, this conventional approach to the prediction of electronic component failure rates assumes that factors affecting the failure rates such as ambient temperature and operating voltage are static constants. In this regard, the objective of this study is to propose a prediction method for the remaining useful life (RUL) of electronic components considering mean time to failure calculations reflecting dynamic environments, such as changes in ambient temperature and operating voltage. Results of this study show that the RUL of electronic components can be estimated depending on time-varying temperature and electrical stress, implying that the RUL of electronic components can be predicted under dynamic stress conditions.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.239-252
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• 2002

A sort sequence $S_n$ is sequence of all unordered pairs of indices in $I_n$={1,2,…n}. With a sort sequence $S_n$ = ($s_1,S_2,...,S_{\frac{n}{2}}$),one can associate a predictive sorting algorithm A($S_n$). An execution of the a1gorithm performs pairwise comparisons of elements in the input set X in the order defined by the sort sequence $S_n$ except that the comparisons whose outcomes can be inferred from the results of the preceding comparisons are not performed. A sort sequence is said to be extremal if it maximizes a given objective function. First we consider the extremal sort sequences with respect to the objective function $\omega$($S_n$) - the expected number of tractive predictions in $S_n$. We study $\omega$-extremal sort sequences in terms of their prediction vectors. Then we consider the objective function $\Omega$($S_n$) - the minimum number of active predictions in $S_n$ over all input orderings.

• Animal Bioscience
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• v.36 no.1
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• pp.108-118
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• 2023

Objective: The objective of this experiment was to determine the net energy (NE) value of 6 wheat bran and 1 wheat shorts by indirect calorimetry and establish the NE prediction equations of wheat bran fed to growing barrows. Methods: Forty-eight growing barrows (28.5±2.4 kg body weight) were allotted in a completely randomized design to 8 dietary treatments that included a corn-soybean meal basal diet, 6 wheat bran diets and 1 wheat shorts diet. The inclusion level of wheat bran or wheat shorts in diets is 30%. Results: The addition of wheat bran reduced the apparent total tract digestibility (ATTD) of nutrients (p<0.05). The ATTD of gross energy, crude protein (CP) and dry matter (DM) in the wheat shorts were greater than that in the wheat bran. Addition of wheat bran or wheat shorts had no effect on total heat production and fasting heat production. The NE of wheat bran was negatively correlated with neutral detergent fiber (r = -0.84; p<0.05) and acid detergent fiber (r = -0.83; p<0.05), while it was positively correlated with CP (r = 0.92; p<0.01). The NE values of wheat bran ranged from 6.79 to 8.15 MJ/kg DM, and the NE value of wheat shorts was 12.47 MJ/kg DM. The ratio of NE to metabolizable energy for wheat bran fed to growing pigs was from 66.0% to 71.7%, whereas the value for wheat shorts was 83.7%. Conclusion: The NE values of wheat bran ranged from 6.79 to 8.15 MJ/kg DM, and the NE value of wheat shorts was 12.47 MJ/kg DM. The NE value of wheat bran can be well predicted based on energy content and proximate analysis.

• Animal Bioscience
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• v.36 no.7
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• pp.1003-1009
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• 2023

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. Results: The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. Conclusion: The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.