http://bibliotecatede.uninove.br/handle/tede/35 2026-04-05T00:02:49Z http://bibliotecatede.uninove.br/handle/tede/3920 Título: Inteligência artificial com processamento de linguagem natural em people analytics para predição de clima organizacional Autor: Vasconcellos, Sabrina da Silva Primeiro orientador: Belan, Peterson Adriano Abstract: Context: The growing adoption of artificial intelligence (AI) technologies in organizations has driven significant advances in how human behavior in the workplace is analyzed. Although areas such as Human Resources (HR) collect large volumes of natural language data such as exit interviews, feedback, climate surveys, and eNPS (Employee Net Promoter Score) these data are still not fully leveraged in people analytics. Objective: To apply natural language processing (NLP) techniques to develop predictive methods for organizational climate, using exit interviews as the textual basis. Method: To this end, a supervised sentiment analysis model based on Random Forest was conducted, with and without the use of synthetic data. The direct scale dimensions (1–5), (0–10) and sentiment labels (detractor, neutral, and promoter) were manually defined by three HR specialists (E1, E2, and E3) and consolidated through a consensus model (E4). One of the specialists was from the studied organization, while the others came from different organizations, broadening the market perspective. Indirect eNPS dimensions were also considered, manually defined by specialist E2, including leadership, career, communication, diversity, health and well-being, retention, teams, training, innovation, and engagement, and associated with the questions and responses from the interviews. Model performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. Additionally, the study incorporated sentiment analyses grouped by indirect dimensions, applied statistics, visualizations, and temporal analyses, enabling observation of organizational climate evolution over time. Results: The final Random Forest model, trained exclusively on real data, achieved the best performance, reaching an accuracy of 75%. Conclusion: This result demonstrates the potential of the proposed approach to support data-driven strategic decisions, guide more targeted interventions, and contribute to more precise and human-centered people management in organizations. Instituição: Universidade Nove de Julho Tipo do documento: Dissertação 2025-12-16T00:00:00Z http://bibliotecatede.uninove.br/handle/tede/3919 Título: Detecção e mensuração de fissuras em edificações combinando o uso de drones com visão computacional e aprendizagem de máquina Autor: Paz, Gabriel Rosa Primeiro orientador: Araújo, Sidnei Alves de Abstract: The inspection of structures and buildings is an essential activity to ensure safety, performance, and durability in civil construction. However, traditional methods based on visual inspection present significant limitations, such as difficulty accessing elevated regions, exposure of professionals to risks, and dependence on the inspector’s interpretation. In this context, technologies based on drones, computer vision, and machine learning emerge as alternatives to automate the process and increase the reliability of the analysis. This work proposes a computational method for the automatic detection and measurement of cracks in buildings using images captured by drones. The method consists of two stages: the first involves automatic crack detection using a model of convolutional neural network, trained on images of cracks in civil works; the second stage performs the measurement of the detected crack (main length and predominant orientation), using techniques such as histogram of oriented gradients (HOG), skeletonization, and distance transform, as well as the conversion of pixel-based measurements into metric units. The results obtained in the detection stage (precision of 81.1%, recall of 69.7%, and mAP@50 of 71.1%) and in the measurement stage (average error of 2.24%), together with experiments using images from real building and civil construction environments and the simulation of a continuous monitoring scenario during drone flight, demonstrate the feasibility of the proposed method. Although opportunities for improvement were identified, such as expanding the training dataset and refining the calibration of the geometric measurement procedure, the present work advances the development of an integrated computational method that combines modern AI techniques, computer vision, and the use of drones to support inspections of buildings and civil engineering structures. Instituição: Universidade Nove de Julho Tipo do documento: Dissertação 2025-12-16T00:00:00Z http://bibliotecatede.uninove.br/handle/tede/3918 Título: Elements of education 4.0 as support for promoting education 5.0 in technological higher education institutions Autor: Rego, Emilia Augusta Primeiro orientador: Gaspar, Marcos Antônio Abstract: The rapid advancement of digital technologies is accelerating educational transformation worldwide while also deepening disparities in developing countries, where infrastructure and educator training remain uneven. This context reinforces the transition from Education 4.0, which emphasizes digitalization and automation, toward Education 5.0, characterized by human-centered, AI-supported, and sustainability-oriented learning ecosystems. In Brazil, Technological Higher Education Institutions (THEIs) continue to face persistent barriers to this transition, including limited Information and Communication Technology (ICT) resources, insufficient digital readiness, and unequal access to emerging technologies. The objective of this research is to identify and characterize the main elements of current Education 4.0 frameworks that could facilitate the adoption of Education 5.0 in Brazilian THEIs. This study adopts a qualitative, exploratory-descriptive design, developed through a Systematic Literature Review (SLR) that analyzed five consolidated Education 4.0 frameworks, followed by validation by three experts. The empirical phase included 54 PhD professors and researchers from 32 federal, state, private, and community THEIs across 14 Brazilian states. Data were collected through structured questionnaires combining Likert-type evaluation and open-ended questions, enabling refinement of the proposed elements. The results validate three dimensions—Technology, Skills, and Innovation—comprising a total of 35 elements identified, refined, and approved by the expert committee. The Technology dimension includes 13 elements, with highly rated components such as Responsible and Sustainable Tech Training, AI Literacy and Computational Thinking, Smart Infrastructure for Adaptive and Technology-Enhanced Learning, High-Speed Networks, and AI-Enhanced Ecosystems. The Skills dimension consists of 10 elements, emphasizing Analytical and Critical Thinking, Socioemotional Competencies, Cognitive and Socioemotional Skills for 21st-Century Professionals, Integration of Human and Technical Skills, and Complex Problem-Solving. The Innovation dimension contains 12 elements, highlighting Reflective and Collaborative Teaching, Inclusion, Sustainability and Societal Impact, Problem-Based and Scenario-Based Learning, Technology-Enhanced Learning Analytics, and Neuroscience. In conclusion, the three validated dimensions and 35 elements constitute a foundational theoretical structure to support the evolution of Education 5.0 in Technological Higher Education Institutions in Brazil. As a contribution to academia, this study advances the literature by consolidating, organizing, and validating key elements from Education 4.0 frameworks into a structured and empirically supported set of elements aligned with Education 5.0 principles. As a contribution to practitioners and society, the findings provide evidence-based guidance for institutional leaders and educators to strengthen digital transformation strategies, improve teacher development, and promote more equitable, ethical, and sustainable learning environments. Instituição: Universidade Nove de Julho Tipo do documento: Dissertação 2026-02-10T00:00:00Z http://bibliotecatede.uninove.br/handle/tede/3835 Título: Framework COBIT 2019 expandido com os elementos fundamentais da governança de dados Autor: Monteiro, Rogerio Carlos Primeiro orientador: Costa, Ivanir Abstract: . Instituição: Universidade Nove de Julho Tipo do documento: Dissertação 2025-09-15T00:00:00Z