Predicting corporate failure: a systematic literature review of methodological issues
Purpose–This paper aims to review the existing literature systematically so as to contribute
towards a better understanding of methodological problems of the classical statistical …
towards a better understanding of methodological problems of the classical statistical …
Deep learning models for bankruptcy prediction using textual disclosures
This study introduces deep learning models for corporate bankruptcy forecasting using
textual disclosures. Although textual data are common, it is rarely considered in the financial …
textual disclosures. Although textual data are common, it is rarely considered in the financial …
Current evidence and future perspective of accuracy of artificial intelligence application for early gastric cancer diagnosis with endoscopy: a systematic and meta …
K Jiang, X Jiang, J Pan, Y Wen, Y Huang… - Frontiers in …, 2021 - frontiersin.org
Background & Aims: Gastric cancer is the common malignancies from cancer worldwide.
Endoscopy is currently the most effective method to detect early gastric cancer (EGC) …
Endoscopy is currently the most effective method to detect early gastric cancer (EGC) …
Classifiers consensus system approach for credit scoring
M Ala'raj, MF Abbod - Knowledge-Based Systems, 2016 - Elsevier
Banks take great care when dealing with customer loans to avoid any improper decisions
that can lead to loss of opportunity or financial losses. Regarding this, researchers have …
that can lead to loss of opportunity or financial losses. Regarding this, researchers have …
Bankruptcy visualization and prediction using neural networks: A study of US commercial banks
FJL Iturriaga, IP Sanz - Expert Systems with applications, 2015 - Elsevier
We develop a model of neural networks to study the bankruptcy of US banks, taking into
account the specific features of the recent financial crisis. We combine multilayer …
account the specific features of the recent financial crisis. We combine multilayer …
Forecast of glucose production from biomass wet torrefaction using statistical approach along with multivariate adaptive regression splines, neural network and …
Artificial intelligence (AI) has become the future trend for prediction after the data is provided
to machine learning. This study uses data analysis to optimize the experiment, find the best …
to machine learning. This study uses data analysis to optimize the experiment, find the best …
An improved boosting based on feature selection for corporate bankruptcy prediction
G Wang, J Ma, S Yang - Expert Systems with Applications, 2014 - Elsevier
With the recent financial crisis and European debt crisis, corporate bankruptcy prediction
has become an increasingly important issue for financial institutions. Many statistical and …
has become an increasingly important issue for financial institutions. Many statistical and …
Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines
This paper proposes using evolutionary multivariate adaptive regression splines (EMARS),
an artificial intelligence (AI) model, to efficiently predict the energy performance of buildings …
an artificial intelligence (AI) model, to efficiently predict the energy performance of buildings …
Predicting public corruption with neural networks: An analysis of spanish provinces
FJ López-Iturriaga, IP Sanz - Social indicators research, 2018 - Springer
We contend that corruption must be detected as soon as possible so that corrective and
preventive measures may be taken. Thus, we develop an early warning system based on a …
preventive measures may be taken. Thus, we develop an early warning system based on a …
Relative entropy fuzzy c-means clustering
Pattern recognition is a collection of computer techniques to classify various observations
into different clusters of similar attributes in either supervised or unsupervised manner …
into different clusters of similar attributes in either supervised or unsupervised manner …