As the possibilities of combining experts become a more important direction in intelligent systems, difficulties arise in ways of generating these various experts and how to effectively use them concurrently. This is very evident in designing multiple classifier systems. The degree and method by which multiple classifier systems share training resources among their components can be a measure o...

Anomaly payload detection looks for payloads that deviate from a predefined model of normality. Defining normality requires an intelligent approach. Machine learning algorithms have been widely applied to build classifiers that distinguish normal from anomalous activity. These algorithms construct vectors of features extracted from raw payloads of a given dataset and train the classifier with t...

Introduction: Predicting acute respiratory insufficiency due to coronavirus disease 2019 (COVID-19) can diminish the severe complications and mortality associated with the disease. This study aimed to develop an intelligent system based on machine learning (ML) models for frontline clinicians to effectively triage high-risk patients and prioritize who needs mechanical intubation (MI). Material...

In many security applications a pattern recognition system faces an adversarial classification problem, in which an intelligent, adaptive adversary modifies patterns to evade the classifier. Several strategies have been recently proposed to make a classifier harder to evade, but they are based only on qualitative and intuitive arguments. In this work, we consider a strategy consisting in hiding...

The aim of this article is to compare two neural classifiers applied to micro component recognition. These micro components can be used for the automation of production and assembly processes of solar concentrators. The image databases have been developed and include mixed and heaped-up micro work pieces. Two neural classifiers, the limited receptive area classifier (LIRA) and the permutation c...

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In ...

An accurate reservoir characterization is a crucial task for the development of quantitative geological models and reservoir simulation. In the present research work, a novel view is presented on the reservoir characterization using the advantages of thin section image analysis and intelligent classification algorithms. The proposed methodology comprises three main steps. First, four classes of...