208 research outputs found
Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models
In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared based on Kolmogorov-Smirnov Statistic, accuracy, F1 score, Type I error, Type II error, and ROC curve on the hold-out dataset with their best probability cut-offs. Results show that Bayesian Network is the most insensitive to the event rate, while Support Vector Machine is the most sensitive
COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICS
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to study the impacts of public records and firmographics and predict the bankruptcy in a 12-month-ahead period with using different classification models and adding values to traditionally used financial ratios. Univariate analysis shows the statistical association and significance of public records and firmographics indicators with the bankruptcy. Further, seven statistical models and machine learning methods were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. The performance of models were evaluated and compared based on classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset. Moreover, an experiment was set up to show the importance of oversampling for rare event prediction. The result also shows that Bayesian Network is comparatively more robust than other models without oversampling
A Product Affinity Segmentation Framework
Product affinity segmentation discovers the linking between customers and products for cross-selling and promotion opportunities to increase sales and profits. However, there are some challenges with conventional approaches. The most straightforward approach is to use the product-level data for customer segmentation, but it results in less meaningful solutions. Moreover, customer segmentation becomes challenging on massive datasets due to computational complexity of traditional clustering methods. As an alternative, market basket analysis may suffer from association rules too general to be relevant for important segments. In this paper, we propose to partition customers and discover associated products simultaneously by detecting communities in the customer-product bipartite graph using the Louvain algorithm that has good interpretability in this context. Through the post-clustering analysis, we show that this framework generates statistically distinct clusters and identifies associated products relevant for each cluster. Our analysis provides greater insights into customer purchase behaviors, potentially helping personalization strategic planning (e.g. customized product recommendation) and profitability increase. And our case study of a large U.S. retailer provides useful management insights. Moreover, the graph application, based on almost 800,000 sales transactions, finished in 7.5 seconds on a standard PC, demonstrating its computational efficiency and better facilitating the requirements of big data
Types of Flouting Maxim in Me Before You Movie
The objective of this study is to identify the specific instances in which the maxims of conversation are deliberately violated in the movie “Me Before You”. The movie serves as the primary data source for this investigation. The data in this study was gathered through the utilization of a content analysis methodology and a note-taking approach. The data is studied utilizing qualitative methodologies and interactive model analysis, encompassing data collection, reduction, exploration, drawing conclusions, and verification. Two theories used to analyze research findings. The leading theory is from Grice's (1975) Logic and Conversation, and the second theory is The Shameless Liar's Guide by Christofferson (2005). The research findings show there are sixteen flouting maxims in the film Me Before You. The flouting maxim of quantity becomes the flouting maxim with the highest frequency, ten times (62,5%). However, each of the violations committed by the characters in Me Before You does not interfere with the overall flow of communication
PERILAKU PENANGANAN NYERI DISMENORE PADA REMAJA DI SMP PGRI 5 DENPASAR: DYSMENORRHEA PAIN MANAGEMENT BEHAVIOR IN ADOLESCENTS IN SMP PGRI 5 DENPASAR
Problems that arise during menstruation such as dysmenorrhea are problems that are often experienced by 30-50% of young women in each country. The high incidence of dysmenorrhea is not matched by good behavior, where documentation is still rarely found by adolesence in School Health Unit. Proper knowledge, attitudes and actions are needed to create good behavior. The aim of this study is to determine the handling of dysmenorrhea pain in adolescents at SMP PGRI 5 Denpasar. The method used is descriptive quantitative with cross sectional approach. Data collection techniques using a questionnaire, while the sampling technique using probability sampling with stratified random sampling technique, the number of samples as many as 154 students of class IX. The results showed 116 students (75.3%) had good knowledge, 110 students (71.4%) had good attitudes, 139 students (90.3%) had less dysmenorrhea treatment measures. So it was concluded that the behavior of pain handling dysmenorrhea in adolescents in SMP PGRI 5 Denpasar included in the sufficient category (91.5%). It is hoped that this study can improve the behavior of pain handling dysmenorrhea in adolescents. The better the level of knowledge, attitudes and actions shown by adolescents, the better the behavior of dysmenorrhea pain management in adolescents.Masalah yang timbul pada saat menstruasi seperti dismenore merupakan masalah yang sering dialami oleh 30-50% remaja putri di setiap negara. Tingginya kejadian dismenore ini tidak diimbangi dengan perilaku yang baik, dimana masih jarang ditemukan dokumentasi mengenai penanganan dismenore yang dilakukan oleh remaja di UKS, maka pengetahuan, sikap dan tindakan yang baik sangat diperlukan untuk menciptakan perilaku yang baik. Tujuan penelitian ini adalah untuk mengetahui penanganan nyeri dismenore pada remaja di SMP PGRI 5 Denpasar. Metode yang digunakan adalah diskriptif kuantitatif dengan pendekatan cross sectional. Teknik pengumpulan data menggunakan kuesioner, sedangkan teknik pengambilan sampel menggunakan probability sampling dengan teknik stratified random sampling, jumlah sampel sebanyak 154 siswi kelas IX. Hasil penelitian didapatkan sebanyak 116 siswi (75,3 %) memiliki pengetahuan baik, sebanyak 110 siswi (71,4 %) yang memiliki sikap baik, sebanyak 139 siswi (90,3%) yang memiliki tindakan penanganan dismenore kurang. Sehingga didapatkan kesimpulan bahwa perilaku penanganan nyeri dismenore pada remaja di SMP PGRI 5 Denpasar termasuk dalam kategori cukup (91,5%). Diharapkan penelitian ini dapat meningkatkan perilaku penanganan nyeri dismenore pada remaja. Semakin baik tingkat pengetahuan, sikap dan tindakan yang ditunjukkan oleh remaja, maka semakin baik pula perilaku penanganan nyeri dismenore pada remaja
Pengaruh Pengetahuan, Sensitivitas Etis, Idealisme pada Persepsi Etis Mahasiswa Akuntansi Atas Perilaku Etis Akuntan
Penelitian ini bertujuan untuk membuktikan secara empiris pengaruh pengetahuan, sensitivitas etis, idealisme pada persepsi etis mahasiswa akuntansi atas perilaku etis akuntan. Penelitian menggunakan metode nonprobability sampling dengan teknik sampling jenuh sehingga total sampel adalah seluruh mahasiswa Program Pendidikan Profesi Akuntan yang berjumlah 30 orang. Berdasarkan hasil analisis regresi linear berganda, didapatkan bahwa pengetahuan, sensitivitas etis, idealisme berpengaruh positif pada persepsi etis mahasiswa akuntansi atas perilaku etis akunta
Bridging the gap: The integration of eDNA techniques and traditional sampling in fish diversity analysis
IntroductionBiodiversity loss poses a significant environmental challenge, particularly in aquatic ecosystems. The advent of environmental DNA (eDNA) sampling technology offers a promising tool for monitoring biological communities with purported high efficiency. Yet, its efficacy compared to traditional sampling methods remains underexplored, especially in fish diversity research.MethodsThis study conducted a comparative analysis of fish diversity and distribution across 29 sampling points within the rivers of the Changqing Nature Reserve, Central China, employing both eDNA techniques and traditional sampling methods.ResultsA total of 46 unique fish species were identified through this comprehensive approach. eDNA sampling detected 34 species, surpassing the 22 species identified by traditional methods. Interestingly, 10 species were detected by both methods, while traditional methods exclusively identified 12 species not detected by eDNA, and eDNA uniquely identified an additional 24 species. Despite eDNA's broader species detection range, traditional sampling methods typically yielded higher Shannon diversity index values. Both β-diversity indices (Bray-Curtis and Jaccard) and multivariate analyses (NMDS and PCoA) were applied, revealing no significant statistical differences in biodiversity measurement between the two sampling methods.DiscussionThe findings suggest that while eDNA sampling excels in identifying a wider range of species, it does not significantly outperform traditional methods in overall biodiversity assessment. By integrating both methodologies, this study demonstrates a more comprehensive and precise assessment of riverine biodiversity, underscoring the benefits of a synergistic approach for enhancing species detection and understanding distribution patterns. The combined methodology notably improves alpha diversity evaluations, particularly regarding Shannon diversity and Berger-Parker dominance. This integrated approach advocates for the amalgamation of data from both eDNA and conventional methods, fostering a robust and accurate biodiversity appraisal
Agent-based decentralised data-acquisition and time-synchronisation in critical healthcare applications
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