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Li, W., Yang, Q., & Du, W. (2022). Tourist Sentiment Mining Based on Deep Learning. In C. Thomas (Ed.), Artificial Intelligence (Vol. 8). IntechOpen. https://doi.org/10.5772/intechopen.98836

Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection.

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Caires, C. S. (2021). Towards an Approach to the Interactive Filmic Narrative. In M. J. Baltazar, T. Quadros, J. Staal, & R. Amaral (Eds.), Image in the post-millennium: mediation, process and critical tension. Onomatopee.

Bernardo Gois, F. N., Lobo Marques, J. A., & Fong, S. J. (2023). TPOT Automated Machine Learning Approach for Multiple Diagnostic Classification of Lung Radiography and Feature Extraction. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 117–135). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_8

This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity.

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Mucheroni, M. L., & Fernandes-Marcos, A. (2022). Traços transdisciplinares na obra do Padre Manuel Antunes: uma análise crítica à luz da Carta da Transdisciplinaridade /UNESCO-1994. In J. E. Franco, G. d’Oliveira Martins, & S. Alves-Jesus (Eds.), Repensar Portugal, a Europa e a Globalização – Saber Padre Manuel Antunes, SJ – 100 anos (pp. 593–602). Universidade de Coimbra. http://hdl.handle.net/10400.2/11853

Toda a obra e pensamento do Padre Manuel Antunes se revestem de características de grande abrangência e de capacidade de abertura à inovação, na perspetiva de que o pensamento crítico, sendo perscrutador do desconhecido, enquanto questiona o conhecimento adquirido ou em pesquisa, não se pode fechar em si mesmo ou separar partes do conhecimento de um todo que constitui o universo, e o homem como parte deste, já que se objetiva a compreensão última do Todo. Encontramos, portanto, traços de transdisciplinaridade na obra e pensamento do Padre Manuel Antunes indicando um pioneirismo relativamente ao movimento da transdisciplinaridade que arranca com o primeiro congresso da área e a respetiva carta daí resultante. Neste artigo, os autores propõem uma análise crítica da obra do Padre Manuel Antunes à luz dos princípios fundacionais encontrados na Carta da Transdisciplinaridade de 1994.

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Law, C. J. (2021). Translating Two Minds in the Tradition of One Faith: Considerations of Intercultural Dynamics in the parallels of John Henry Newman (1801-1890) and Joseph Ma Xiangbo (1840-1939). In Sacred and the Everyday: Comparative Approaches to Literature, Religious and Secular (pp. 173–182). University of Saint Joseph.

Interculturality is considered a constant given in the development of most major religious movements during the process of propagation coming into contact with diverse tongues, mores, and sentiments. And one of the chief, if not decisive, instruments contributing to this ever dynamic spread and reception of beliefs and cultures is translation. Christianity purports to be an incarnational religion, where the Word made flesh expresses the di-vine in human terms. Its doctrines are enshrined in a faith tradition that is developed largely through interpretation and translation. This short paper will cut into this sacral literary tradition by paralleling two influential mod-ern Christian thinkers, John Henry Newman from the Anglophone school, and Joseph Ma Xiangbo from the Orient, to see how attempts at translating the ideas and works of people from distinct cultural milieux is both reflec-tive of the necessary developmental nature of Christian teachings in the historical continuum of time and space, and indicative of the intellectual challenges that never cease to accompany the literary effervescence stem-ming from comparative religious studies.

Simoes, J. M. (2022). Um país, dois sistemas: reflexões sobre o panorama mediático em língua portuguesa em Macau. In F. Ilharco (Ed.), Em português: falar, viver e pensar no século XXI (p. 391). Universidade Católica Editora.

Chau, I. F., Cardoso, J. C. S., & Estadieu, G. (2021). Virtual Guia Fortress: A 3D-printed eXtended Reality Playset. In J. Baalsrud Hauge, J. C. S. Cardoso, L. Roque, & P. A. Gonzalez-Calero (Eds.), Entertainment Computing – ICEC 2021 (Vol. 13056, pp. 370–376). Springer International Publishing. https://doi.org/10.1007/978-3-030-89394-1_28

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Zuev, D. (2023). Visuality and Infrastructure: The Case of the Belt and Road Initiative. In P. A. B. Duarte, F. J. B. S. Leandro, & E. M. Galán (Eds.), The Palgrave Handbook of Globalization with Chinese Characteristics (pp. 285–300). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6700-9_18

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Leong, A. C. H. (2021). What Goes Around Comes Around? The (Non-)Existence of (Divine) Retribution in Ancient Chinese Literature and the Hebrew Bible. In S. Morgan (Ed.), Sacred and the Everyday: Comparative Approaches to Literature, Religious and Secular (pp. 183–196). University of Saint Joseph.

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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Duraj, J. (2021). Zen-Ignatian Training Program in Japan: A Personal Experience. In The Wheel & The Cross: An Anthology by Jesuits & Friends on Buddhism and Dialogue (pp. 1–23). MAGGA Jesuit Research Center. https://jcapsj.org/wp-content/uploads/1/2021/12/The-Wheel-The-Cross.pdf

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