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"Cortez, Paulo Cesar"

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Faculty of Business and Law
- Alexandre Lobo

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Ip, C. H., Lobo Marques, J. A., Dos Santos Silva, B. R., Cortez, P. C., & Barbosa, A. (2023). A Review on Open Innovation and Absorptive Capacity in Small and Medium Enterprises during the last decade - Analyzing Bibliometrics to Understand the Development of the Field. Proceedings of the 2023 14th International Conference on E-Business, Management and Economics, 417–422. https://doi.org/10.1145/3616712.3616786

Small and medium-sized enterprises (SMEs) can benefit significantly from open innovation by gaining access to a broader range of resources and expertise using absorptive capacitive, and increasing their visibility and reputation. Nevertheless, multiple barriers impact their capacity to absorb new technologies or adapt to develop them. This paper aims to perform an analysis of relevant topics and trends in Open Innovation (OI) and Absorptive Capacity (AC) in SMEs based on a bibliometric review identifying relevant authors and countries, and highlighting significant research themes and trends. The defined string query is submitted to the Web of Science database, and the bibliometric analysis using VOSviewer software. The results indicate that the number of scientific publications has consistently increased during the past decade, indicating a growing interest of the scientific community, reflecting the industry interest and possibly adoption of OI, considering Absorptive. This bibliometric analysis can provide insights on the most relevant regions the research areas are under intensive development.

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Albuquerque, V., Gois, F. B., Lobo Marques, J. A., Silva, B. R. dos S., & Cortez, P. C. (2024). Automl-Based Eeg Signal Analysis in Neuro-Marketing Classification Using Biclustering Method. 1–29. https://doi.org/10.2139/ssrn.4871053

Consumer neuroscience analyzes individuals’ preferences through the assessment of physiological data monitoring, considering brain activity or other bioinformation to assess purchase decisions. Traditional marketing tactics include customer surveys, product evaluations, and comments. For product or brand marketing and mass production, it is important to understand consumer neurological responses when seeing an ad or testing a product. In this work, we use the bi-clustering method to reduce EEG noise and automatic machine learning to classify brain responses. We analyze a neuromarketing EEG dataset that contains EEG data from product evaluations from 25 participants, collected with a 14 channel Emotiv Epoch + device, while examining consumer items. Four components comprised the research methodology. Initially, the Welch Transform was used to filter the EEG raw data. Second, the best converted signal biclusterings are used to train different classification models. Each biclustering is evaluated with a separate classifier, considering F1-Score. After that, the H2O.ai AutoML library is used to select the optimal biclustering and models. Instead of traditional procedures, two thresholds are used. First-threshold values indicate customer satisfaction. Low values of the second threshold reflect consumer dissatisfaction. Values between the first and second criteria are classified as uncertain values. We outperform the state of the art with a 0.95 F1-Score value.

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dos Santos Silva, B. R., Cortez, P. C., da Silva Neto, M. G., & Lobo Marques, J. A. (2023). X-Ray Machine Learning Classification with VGG-16 for Feature Extraction. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 65–78). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_5

The Covid-19 pandemic evidenced the need Computer Aided Diagnostic Systems to analyze medical images, such as CT and MRI scans and X-rays, to assist specialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the spread of the virus. There are some types of CAD systems, such as Machine and Deep Learning-based and Transfer learning-based. This chapter proposes a pipeline for feature extraction and classification of Covid-19 in X-ray images using transfer learning for feature extraction with VGG-16 CNN and machine learning classifiers. Five classifiers were evaluated: Accuracy, Specificity, Sensitivity, Geometric mean, and Area under the curve. The SVM Classifier presented the best performance metrics for Covid-19 classification, achieving 90% accuracy, 97.5% of Specificity, 82.5% of Sensitivity, 89.6% of Geometric mean, and 90% for the AUC metric. On the other hand, the Nearest Centroid (NC) classifier presented poor sensitivity and geometric mean results, achieving 33.9% and 54.07%, respectively.

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Kehdi, R. C., Silva, M. F. S., Cavalcante, L. R. L., Fiorenza, N. G., Viana, M., Leite, I. B., Silva, B. R. dos S., Assis, D. F. de, Cortez, P. C., Bezerra, D. L., Motta, P. C., Lobo Marques, J. A., Filho, A. J. M. C., Correia, É., Farias, A. A., Miyajima, F., Teixeira, A. L., Thomason, M. E., Carvalho, F. H. C., & Macedo, D. S. (2025). Cord blood cytokines/chemokines linked to delays in toddlers exposed to SARS-CoV-2 prenatally. Pediatric Research. https://doi.org/10.1038/s41390-025-04192-w

Background: Maternal infections are linked to neurodevelopmental impairments, highlighting the need to investigate SARS-CoV-2-induced immune activation. Objective: This study aimed to evaluate the impact of maternal infection on neurodevelopment and investigate whether cytokine and chemokine profiles predict delays at 24 months. Methods: Conducted in Brazil (January 2021–March 2022), this follow-up study included 18 SARS-CoV-2 positive pregnant women at 35–37 weeks’ gestation, 15 umbilical cord blood samples, and blood samples from 15 children at 6 months and 14 at 24 months. Developmental delay was defined using the Bayley Scales of Infant and Toddler Development, Third Edition, with scores below 90 in cognitive, communication, or motor domains. Results: At 6 months, 33.3% of infants exhibited cognitive delays, 20% communication delays, and 40% motor delays, increasing to 35.71%, 64.29%, and 57.14% at 24 months, respectively. Elevated interferon-gamma and tumor necrosis factor-alpha in cord blood correlated with cognitive delays, while interleukin (IL)-6, IL-8, IL-17, and IL-1β were associated with motor delays. Increased C-X-C motif chemokine ligand 10 and other cytokines were associated with communication delays. Conclusion: Maternal SARS-CoV-2 may impact infant neurodevelopment, as early cytokine elevations correlate with delays, highlighting the importance of early monitoring and interventions to reduce long-term effects. Impact: Prenatal SARS-COV-2 infection in pregnant women is linked to developmental delays in toddlers, with cytokine and chemokine changes associated with neurodevelopmental outcomes at 24 months. This study shows the long-term impact of maternal SARS-COV-2 infection on child development, highlighting inflammatory markers like IFN-γ, TNFα, IL-6, IL-8, IL-17, IL-1β, and CXCL10. Identifying specific cytokines correlating with cognitive, communication, and motor delays suggests potential biomarkers for early intervention. Conducted in Fortaleza, Brazil, the study emphasizes understanding local epidemiological impacts on child development, especially in regions with high infection rates. (Figure presented.)

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