The main goal of this paper is to propose a probability model for computing probabilities of dismissal of 10b-5 securities class-action cases filed in United States Federal district courts. By dismissal, we mean dismissal with prejudice in response to the motion to dismiss filed by the defendants, and not eventual dismissal after the discovery process. The proposed probability model is a hybrid...

The prevention of dangerous chemical accidents is a primary problem of industrial manufacturing. In the accidents of dangerous chemicals, the oil gas explosion plays an important role. The essential task of the explosion prevention is to estimate the better explosion limit of a given oil gas. In this paper, Support Vector Machines (SVM) and Logistic Regression (LR) are used to predict the explo...

BACKGROUND AND OBJECTIVE Artificial Neural Networks (ANNs) have recently been applied in situations where an analysis based on the logistic regression (LR) is a standard statistical approach; direct comparisons of the results, however, are seldom attempted. In this study, we compared both logistic regression models and feed-forward neural networks on the academic failure data set. METHODS The...

Logistic regression is a simple statistical tool used in binary classification. LR is useful for situations in which we want to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. Unlike LR, PCA is another technique used in feature extraction and in dimensionality reduction. This paper puts forth the experimental results of LR model an...

The use of Logistic Regression (LR) and Support Vector Machines (SVM) to identify the survival Covid 19 patients in a novel way is prime work. To forecast Covid-19 patients, LR algorithm SVM are iterated 20 times with sample size 10.In comparison 86.4% accuracy logistic regression technique, Machine approach offers 91.2% accuracy. In independent T-test, has high level significance. range this s...

The use of non-parametric methodologies, the introduction of non-financial variables, and the development of models geared towards the homogeneous characteristics of corporate sub-populations have recently experienced a surge of interest in the bankruptcy literature. However, no research on default prediction has yet focused on micro-entities (MEs), despite such firms’ importance in the global ...

Softmax regression, a generalization of Logistic regression (LR) in the setting of multi-class classification, has been widely used in many machine learning applications. However, the performance of softmax regression is extremely sensitive to the presence of noisy data and outliers. To address this issue, we propose a model of robust softmax regression (RoSR) originated from the self-paced lea...