Customer Relationship Management (CRM) has been an important ally to companies willing to improve their customer experiences. Lately, Machine Learning (ML) and its techniques have been transforming the way companies interact with their customers through data analysis. This paper reviews the literature on the application of ML techniques to improve CRM processes and provides an overview of used techniques and their application to each CRM dimension and element. In addition, the practical implications are analyzed based on recent developments of CRM tools in the field of ML. The paper delivers insights for future studies as well as for CRM tools willing to improve their analytical and automated services and features.

Emerging deep learning-based applications in precision medicine include computational histopathological analysis. However, there is a lack of the required training image datasets to generate classification and detection models. This phenomenon occurs mainly due to human factors that make it difficult to obtain well-annotated data. The present study provides a curated public collection of histopathological images (DeepHP) and a convolutional neural network model for diagnosing gastritis. Images from gastric biopsy histopathological exams were used to investigate the performance of the proposed model in detecting gastric mucosa with Helicobacter pylori infection. The DeepHP database comprises 394,926 histopathological images, of which 111 K were labeled as Helicobacter pylori positive and 283 K were Helicobacter pylori negative. We investigated the classification performance of three Convolutional Neural Network architectures. The models were tested and validated with two distinct image sets of 15% (59K patches) chosen randomly. The VGG16 architecture showed the best results with an Area Under the Curve of 0.998%. The results showed that CNN could be used to classify histopathological images from gastric mucosa with marked precision. Our model evidenced high potential and application in the computational pathology field.

É inegável o aumento da pervasividade e relevância das mídias sociais em nosso cotidiano. Desde 2012, o Brazilian Workshop on Social Network Analysis and Mining (BraSNAM) representa um importante fórum para reunir pesquisadores a fim de discutir métodos de análise, tendências e fenômenos que ocorrem nas redes sociais. Neste 2021, este evento completa 10 anos, com 230 trabalhos apresentados até o momento. Além disso, possui uma comunidade composta por 527 pesquisadores e pesquisadoras de 95 diferentes instituições. À luz deste marco, esse trabalho apresentam uma análise da comunidade BraSNAM. Os resultados atestam o crescimento sustentável da comunidade, sobretudo em relação ao seu impacto técnico-científico. Os achados do trabalho podem auxiliar o comitê organizador no planejamento estratégico das próximas edições.