Carlos Arizmendi

Carlos Arizmendi, Alfredo Vellido, Enrique Romero

The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is magnetic resonance, in the modalities of imaging or spectroscopy. The latter provides plenty of metabolic information about the tumour tissue, but its high dimensionality makes resorting to pattern recognition techniques advisable. In this brief paper, an international database of brain tumours is analyzed resorting to an ad hoc spectral frequency selection procedure combined with nonlinear classification.

C J Arizmendi, W L Garcia, M A Quintero

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called smart pig in Oil and Gas pipelines . The model uses a signal noise reduction phase by means of preprocessing algorithms and attributeselection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent

Carlos Arizmendi, Jorge Pinto, Alejandro Arboleda et al.

According to the World Health Organization (WHO), pneumonia is a disease that causes a significant number of deaths each year. In response to this issue, the development of a decision support system for the classification of patients into those without pneumonia and those with viral or bacterial pneumonia is proposed. This is achieved by implementing transfer learning (TL) using pre-trained convolutional neural network (CNN) models on chest x-ray (CXR) images. The system is further enhanced by integrating Relief and Chi-square methods as dimensionality reduction techniques, along with support vector machines (SVM) for classification. The performance of a series of experiments was evaluated to build a model capable of distinguishing between patients without pneumonia and those with viral or bacterial pneumonia. The obtained results include an accuracy of 91.02%, precision of 97.73%, recall of 98.03%, and an F1 Score of 97.88% for discriminating between patients without pneumonia and those with pneumonia. In addition, accuracy of 93.66%, precision of 94.26%, recall of 92.66%, and an F1 Score of 93.45% were achieved for discriminating between patients with viral pneumonia and those with bacterial pneumonia.

Hernando Gonzalez, Carlos Julio Arizmendi, Beatriz F. Giraldo

The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficulty maintaining spontaneous breathing and ensuring a patent airway within the first 48 hours after the withdrawal of mechanical ventilation. Approximately 20 of ICU patients experience this phenomenon, which has severe repercussions on their health. It also has a substantial impact on clinical evolution and mortality, which can increase by 25 to 50. To address this issue, we propose a medical support system that uses a convolutional neural network (CNN) to assess a patients suitability for disconnection from a mechanical ventilator after a spontaneous breathing test (SBT). During SBT, respiratory flow and electrocardiographic activity were recorded and after processed using time-frequency analysis (TFA) techniques. Two CNN architectures were evaluated in this study: one based on ResNet50, with parameters tuned using a Bayesian optimization algorithm, and another CNN designed from scratch, with its structure also adapted using a Bayesian optimization algorithm. The WEANDB database was used to train and evaluate both models. The results showed remarkable performance, with an average accuracy 98 when using CNN from scratch. This model has significant implications for the ICU because it provides a reliable tool to enhance patient care by assisting clinicians in making timely and accurate decisions regarding weaning. This can potentially reduce the adverse outcomes associated with failed weaning events.

M. R, Akram, C. P et al.

The paper presents an approach to build a question and answer system that is capable of processing the information in a large dataset and allows the user to gain knowledge from this dataset by asking questions in natural language form. Key content of this research covers four dimensions which are; Corpus Preprocessing, Question Preprocessing, Deep Neural Network for Answer Extraction and Answer Generation. The system is capable of understanding the question, responds to the user's query in natural language form as well. The goal is to make the user feel as if they were interacting with a person than a machine.