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Item type:Publication, Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks ApplicationArtificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. ©The authors ©MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving a Biological Extracts Company’s Cash Cycle by Simulating Discrete Events: First Steps Towards Designing a Digital Twin(Elsevier BV, 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Strategy, Power and CSR: Practices and Challenges in Organizational Management(2020); ; ; Organizational management, along with strategy, is the most important part of business administration. Directors must know how to manage people, make decisions and, above all, have the ability to create strategies that help organizations achieve their objectives, greater strategic competitiveness, and above-average returns. In today’s global and complex environment, traditional views towards organizational management are not enough for businesses to thrive. It’s only by bringing together different approaches can management styles develop fast enough to keep pace with the ever-changing big picture. In this innovative new look at organizational management, expert authors Santiago García-Álvarez and Connie Atristain-Suárez explore how looking through lenses of philosophy, health, communication, law, engineering, pedagogy and policy can affect a modern organization’s prospects. Built through the collective and collaborative work of the research professors at the Universidad Panamericana, this work includes interdisciplinary approaches to real-world problems. For students and researchers of business and management, this is an unmissable read. ©2020 Emerald Publishing Limited.Scopus© Citations 1 47 1