The purpose of this study is to construct a valid and rigorous fraudulent financial statement detection model. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements between the years 2002 and 2013. In the first stage, two decision tree algorithms, including the classification and regression trees (CART) and the Chi squared automatic interac...

Background and Objective: The COVID-19 pandemic is a phenomenon that has infected and killed many people worldwide. Underlying diseases such as diabetes mellitus, heart failure, and chronic kidney disease (CKD) can affect the severity of COVID-19 and aggravate patients' condition. This study aimed to predict the severity of the COVID-19 disease in CKD patients by combining feature selection and...

predicting different behaviors in computer networks is the subject of many data mining researches. providing a balanced intrusion detection system (ids) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. many of the proposed methods perform well in one of the two aspects, and concentrate on a su...

A new multi-objective evolutionary algorithm which can be applied to many nonlinear multi-objective optimization problems is proposed. Its aim is to obtain a discrete fixed size set approximating the complete Pareto-front quickly. It adapts ideas from different multiand single-objective optimization evolutionary algorithms, although it also incorporates new devices, namely, a new method to impr...

Abstract The main purpose of this study is to investigate the best predictor firm performance among different proxies. A sample 287 Czech firms was taken from automobile, construction, and manufacturing sectors. Panel data acquired Albertina database for time period 2016 2020. Three proxies performance, return assets (RoA), equity (RoE), capital employed (RoCE) were used as dependent variables....

This paper considers the automatic design of fuzzy rule-basedclassification systems based on labeled data. The classification performance andinterpretability are of major importance in these systems. In this paper, weutilize the distribution of training patterns in decision subspace of each fuzzyrule to improve its initially assigned certainty grade (i.e. rule weight). Ourapproach uses a punish...

The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students’ performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present inves...

BACKGROUND The most common cancer among women is breast cancer and it has been blamed as the second leading cause of cancer death in women; so far many approaches have been used to analyze and detect benign and malignant forms of cancer and understanding the features involved in proteins expressed by various types of breast cancers is crucial. METHODS Herein features of proteins expressed in ...