sampling methods
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2022 ◽  
Vol 114 ◽  
pp. 103805
Breno Dias Vitorino ◽  
Angélica Vilas Boas da Frota ◽  
Pietro Kiyoshi Maruyama ◽  
Josué Ribeiro da Silva Nunes ◽  
Jeferson Vizentin-Bugoni

2022 ◽  
Vol 9 (2) ◽  
pp. 205-234
Xin Liu ◽  
Mingyu Yan ◽  
Lei Deng ◽  
Guoqi Li ◽  
Xiaochun Ye ◽  

2022 ◽  
Vol 3 (1) ◽  
pp. 78-88
Luthfi Ismawati ◽  
Isnanita Noviya Andriyani

This study aims to determine whether there is a correlation between self-efficacy and adversity quotient for students of SMK Muhammadiyah 2 Wedi Klaten. This type of research uses quantitative research methods with a correlational approach. Sampling methods using simple random sampling with data collection techniques in the form of a scale. The research subjects were 70 students from class XI of SMK Muhammadiyah 2 Wedi, Klaten. The results obtained correlation analysis (rxy) of 0.708 with a p-value 0.000 that is lower than 0.05, meaning that there is a significant positive correlation between self-efficacy and the student's adversity quotient. This can indicate that the higher the student's self-efficacy, higher the adversity quotient of the student, then Ho is rejected and Ha is accepted. Self efficacy and adversity quotient in students of SMK Muhammadiyah 2 Wedi, Klaten are classified as moderate. The coefficient of determination (R2) of the correlation is 0.724, meaning that self-efficacy contributes effectively to the adversity quotient by 72%, which means there are 28% of the other factors that affect students adversity quotient

2022 ◽  
Vol 12 (1) ◽  
Belal Alsinglawi ◽  
Osama Alshari ◽  
Mohammed Alorjani ◽  
Omar Mubin ◽  
Fady Alnajjar ◽  

AbstractThis work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3–100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3–100%, and 97%, CI 95%: 93.7–100% SMOTE-Tomek, and SMOTE-ENN respectively). Under-sampling methods reported the least important AUC results (50%, with CI 95%: 40.2–59.8%) for both (ENN and Tomek- Links). Using ML explainable technique called SHAP, we explained the outcome of the predictive model (RF) with SMOTE class balancing technique to understand the most significant clinical features that contributed to predicting lung cancer LOS with the RF model. Our promising framework allows us to employ ML techniques in-hospital clinical information systems to predict lung cancer admissions into ICU.

Mohammad Zoynul Abedin ◽  
Chi Guotai ◽  
Petr Hajek ◽  
Tong Zhang

AbstractIn small business credit risk assessment, the default and nondefault classes are highly imbalanced. To overcome this problem, this study proposes an extended ensemble approach rooted in the weighted synthetic minority oversampling technique (WSMOTE), which is called WSMOTE-ensemble. The proposed ensemble classifier hybridizes WSMOTE and Bagging with sampling composite mixtures to guarantee the robustness and variability of the generated synthetic instances and, thus, minimize the small business class-skewed constraints linked to default and nondefault instances. The original small business dataset used in this study was taken from 3111 records from a Chinese commercial bank. By implementing a thorough experimental study of extensively skewed data-modeling scenarios, a multilevel experimental setting was established for a rare event domain. Based on the proper evaluation measures, this study proposes that the random forest classifier used in the WSMOTE-ensemble model provides a good trade-off between the performance on default class and that of nondefault class. The ensemble solution improved the accuracy of the minority class by 15.16% in comparison with its competitors. This study also shows that sampling methods outperform nonsampling algorithms. With these contributions, this study fills a noteworthy knowledge gap and adds several unique insights regarding the prediction of small business credit risk.

Owner ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 580-592
Rismawandi Rismawandi ◽  
Indah Rahayu Lestari ◽  
Rinny Meidiyustiani

The purpose of this study was to assess the effect of implementing SAK EMKM for SMEs through four indicators of reflection, namely the quality of human resources as measured by five indicators. Small business perception is measured by seven reflex indicators. MSME understanding is measured by five specific indicators. Socialization of SAK EMKM is measured using five reflection indicators. This type of research is a descriptive quantitative research. The population in this study was 11,746 MSMEs in the food and beverage sector in the city of Tangerang. The survey sample was 100 respondents from MSME actors, obtained using practical or random sampling methods. The data analysis used, with SmartPLS software version 3.2.1, is based on a model that you can think of as an outer model, and an inner model, with hypothesis testing. This study found that the quality of human resources affected the implementation of SAK EMKM, the perception of SAK EMKM actors did not affect the implementation of SAK EMKM, the understanding of SMEs affected the implementation of SAK EMKM, and SAK EMKM socialization did not affect SAK EMKM.

Daria Bajerlein ◽  
Mateusz Jarmusz ◽  
Aleksandra Gregor ◽  
Andrzej Grzywacz

Abstract Our knowledge on many necrophilous dipterans was limited for a long time mainly due to taxonomic issues. Therefore, findings on the sex-related associations with pig carcasses in Dryomyzidae, Fanniidae, Muscidae, and Piophilidae are presented. Moreover, the effectiveness of pitfall traps and sweep nets in fly sampling were compared, since the correct method of collecting insects is critical for the development and application of entomological tools for forensic purposes. The trapped numbers of all species differed significantly in respect to both sexes. The sex ratios were strongly female-biased in most species of Muscidae and in Fanniidae. More females were also collected in Piophilidae, but the proportion of males was higher when compared to muscids. Within Dryomyzidae, the sex ratio differed according to species. Only Dryomyza flaveola (Fabricius, 1794) (Diptera: Dryomyzidae) and Muscina pascuorum (Meigen, 1826) (Diptera: Muscidae) had male-biased sex ratios. Differences in residency patterns between sexes in Stearibia nigriceps (Meigen, 1826) (Diptera: Piophilidae), Liopiophila varipes (Meigen, 1830) (Diptera: Piophilidae), and D. flaveola were statistically insignificant. All studied species were recorded for each sampling method. The effect of the sampling method on the number of individuals was statistically significant for all dipterans but Piophilidae. Pitfall traps outcompeted the aerial sweep net in representing Dryomyzidae and Muscidae, whereas both methods were almost equally effective in the collection of Piophilidae. A varied effectiveness of sampling methods was observed in Fanniidae. The sampling method significantly affected the determination of the appearance time of flies on carcasses, but the effect of species and sampling method was insignificant. Implications for forensic entomology are discussed.

2022 ◽  
Vol 52 (1) ◽  
pp. 045
Rodrigo Otávio Veiga Miranda ◽  
Felipe De Antoni Zarpelon ◽  
Síntia Valério Kohler ◽  
Alvaro Augusto Vieira Soares ◽  
Izabele Domingues Soares Miranda ◽  

Different sampling methods can be used in forest surveys. It is important to know the precision and accuracy of these sampling methods, and which one is the most appropriate in specific conditions of the forest population. The aim of this study was to compare estimates of a forest inventory performed by different sampling methods with forest census results. The sampling methods evaluated were the fixed-area method and the variable-area methods of Bitterlich, Prodan, and Strand. The data were obtained in a 15-year-old thinned stand of Pinus taeda L., located in the municipality of Teixeira Soares, southern Brazil, with a total area of 12.80 ha. Initially, the forest census was carried out, and subsequently, the sample units for each sampling method were distributed in the stand, with a common starting point. The variables used to compare the sample results with the census means were quadratic diameter, number of trees, basal area, and volume, per hectare. Precision and accuracy were evaluated by sampling error and whether the confidence intervals covered the population means, respectively. The fixed-area and Bitterlich methods stood out in precision for all variables analysed. The fixed-area, Bitterlich and Strand methods with proportion to height provided more accurate estimates. The Prodan method provided inaccurate and imprecise estimates for the variables under analysis, except for the quadratic diameter.

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