Search Results - subject:"Multilayer Perceptron"

Identifying employee engagement drivers using multilayer perceptron classifier and sensitivity analysis

Núñez-Sánchez, José M.; Molina-Gómez, Jesús; … - In: Eurasian business review 14 (2024) 4, pp. 1001-1022

Persistent link: https://www.econbiz.de/10015196590

Optimizing stock market volatility predictions based on the SMVF-ANP approach

Guan, Zhigui; Zhao, Yuanjun - In: International review of economics & finance : IREF 95 (2024), pp. 1-15

Persistent link: https://www.econbiz.de/10015145227

Can hybrid model improve the forecasting performance of stock price index amid COVID-19? : contextual evidence from the MEEMD-LSTM-MLP approach

Yang, Qu; Yu, Yuanyuan; Dai, Dongsheng; He, Qian; Lin, Yu - In: The North American journal of economics and finance : a … 74 (2024), pp. 1-20

Persistent link: https://www.econbiz.de/10015135677

Variational inference: uncertainty quantification in additive models

Lichter, Jens; Wiemann, Paul F V; Kneib, Thomas - In: AStA Advances in Statistical Analysis 108 (2024) 2, pp. 279-331

Markov chain Monte Carlo (MCMC)-based simulation approaches are by far the most common method in Bayesian inference to access the posterior distribution. Recently, motivated by successes in machine learning, variational inference (VI) has gained in interest in statistics since it promises a...

Persistent link: https://www.econbiz.de/10015361352

Comparing logistic regression and neural networks for predicting skewed credit score : a LIME-based explainability approach

Wanjohi, Jane Wangui; Mpinda, Berthine Nyunga; Awe, … - In: Statistics in transition : an international journal of … 25 (2024) 3, pp. 49-67

compares the predictive power of logistic regression and multilayer perceptron algorithms on two credit-risk datasets by … applying the Local Interpretable Model-Agnostic Explanations (LIME) explainability technique. Our results show that multilayer … perceptron outperforms logistic regression in terms of balanced accuracy, Matthews Correlation Coefficient, and F1 score. Based …

Persistent link: https://www.econbiz.de/10015127211

Predicting patent quality based on machine learning approach

Erdogan, Zulfiye; Altuntas, Serkan; Dereli, Türkay - In: IEEE transactions on engineering management : EM ; a … 71 (2024), pp. 3144-3157

Persistent link: https://www.econbiz.de/10015405946

Enhancing the accuracy of stock return movement prediction in Indonesia through recent fundamental value incorporation in multilayer perceptron

Agusta, Stiven; Rakhman, Fuad; Mustakini, Jogiyanto Hartono - In: Asian journal of accounting research 9 (2024) 4, pp. 358-377

Persistent link: https://www.econbiz.de/10015402867

An artificial neural network for exploring the relationship between learning activities and students' performance

Borhani, Kourosh; Wong, Richard T. K. - In: Decision analytics journal 9 (2023), pp. 1-19

. Statistical and exploratory analysis investigated the dataset. A multilayer perceptron model was developed, using backpropagation …

Persistent link: https://www.econbiz.de/10014532501

An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity

Alam, Shafiq; Ayub, Muhammad Sohaib; Arora, Sakshi; … - In: Decision analytics journal 9 (2023), pp. 1-14

), Naive Bayes (NB), and Multilayer Perceptron (MLP). Results demonstrate that the decision tree method is the most effective …

Persistent link: https://www.econbiz.de/10014532502

Health services and patient satisfaction in IRAN during the COVID-19 pandemic: A methodology based on analytic hierarchy process and artificial neural network

Khansari, Seyed Mohammad; Arbabi, Farzin; Jamshidi, Mir … - In: Journal of Risk and Financial Management 15 (2022) 7, pp. 1-18

) method and ANN-MLP (Artificial neural network based on multilayer perceptron model as a supervised learning algorithm) as an … private and public hospitals, followed by the interpersonal care criterion. Furthermore, we used a multilayer perceptron …

Persistent link: https://www.econbiz.de/10014332489