Detection of Breast Cancer using Convolutional Neural Networks with Learning Transfer Mechanisms

Breast cancer is the leading cause of mortality in women worldwide. One of the biggest challenges for physicians and technological support systems is early detection, because it is easier to treat and establish curative treatments. Currently, assistive technology systems use images to detect patterns of behavior with respect to patients who have been found to have some type of cancer. This work aims to identify and classify breast cancer using deep learning models and convolutional neural networks (CNN) with transfer learning. For the breast cancer detection process, 7803 real images with benign and malignant labels were used, which were provided by BreaKHis on the Kaggle platform. The convolutional basis (parameters) of pre-trained models VGG16, VGG19, Resnet-50 and Inception-V3 were used. The TensorFlow framework, keras and Python libraries were also used to retrain the parameters of the models proposed for this study. Metrics such as accuracy, error ratio, precision, recall and f1-score were used to evaluate the models. The results show that the models based on VGG16, VGG19 ResNet-50 and Inception-V3 obtain an accuracy of 88%, 86%, 97% and 96% respectively, recall of 84%, 82%, 96% and 96% respectively, in addition to f1-score of 86%, 83%, 96% and 95% respectively. It is concluded that the model that shows the best results is Resnet-50, obtaining high results in all the metrics considered, although it should be noted that the Inception-V3 model achieves very similar results in relation to Resnet-50, in all the metrics. In addition, these two models exceed the 95% threshold of correct results

Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system

Computational Analysis of T Cell Receptor Repertoire and Structure

The human adaptive immune system has evolved to provide a sophisticated response to a vast body of pathogenic microbes and toxic substances. The primary mediators of this response are T and B lymphocytes. Antigenic peptides presented at the surface of infected cells by major histocompatibility complex (MHC) molecules are recognised by T cell receptors (TCRs) with exceptional specificity. This specificity arises from the enormous diversity in TCR sequence and structure generated through an imprecise process of somatic gene recombination that takes place during T cell development. Quantification of the TCR repertoire through the analysis of data produced by high-throughput RNA sequencing allows for a characterisation of the immune response to disease over time and between patients, and the development of methods for diagnosis and therapeutic design. The latest version of the software package Decombinator extracts and quantifies the TCR repertoire with improved accuracy and compatibility with complementary experimental protocols and external computational tools. The software has been extended for analysis of fragmented short-read data from single cells, comparing favourably with two alternative tools. The development of cell-based therapeutics and vaccines is incomplete without an understanding of molecular level interactions. The breadth of TCR diversity and cross-reactivity presents a barrier for comprehensive structural resolution of the repertoire by traditional means. Computational modelling of TCR structures and TCR-pMHC complexes provides an efficient alternative. Four generalpurpose protein-protein docking platforms were compared in their ability to accurately model TCR-pMHC complexes. Each platform was evaluated against an expanded benchmark of docking test cases and in the context of varying additional information about the binding interface. Continual innovation in structural modelling techniques sets the stage for novel automated tools for TCR design. A prototype platform has been developed, integrating structural modelling and an optimisation routine, to engineer desirable features into TCR and TCR-pMHC complex models

CACIC 2015 : XXI Congreso Argentino de Ciencias de la Computación. Libro de actas

Actas del XXI Congreso Argentino de Ciencias de la Computación (CACIC 2015), realizado en Sede UNNOBA Junín, del 5 al 9 de octubre de 2015.Red de Universidades con Carreras en Informática (RedUNCI