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Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks Application
(MDPI AG, 2025)
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Marmolejo-Saucedo, José Antonio
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Köse, Utku
Artificial 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.
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Improving a Biological Extracts Company’s Cash Cycle by Simulating Discrete Events: First Steps Towards Designing a Digital Twin
(Elsevier BV, 2025)
Jarquin-Segovia, Ricardo
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Marmolejo-Saucedo, José Antonio
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Companies with consistently positive cash cycles tend to outperform those with negative cycles, which struggle to cover operational and capital expenses. This study focuses on a Mexican company facing financial liquidity challenges due to a high investment in finished product inventory and an 80-day cash conversion cycle, with 73 days tied to inventory. Using discrete event simulation and digital modeling, the production process of fluid extract was analyzed to identify inefficiencies and propose improvements. The simulation, conducted with Anylogic software, revealed significant delays in the percolation operation, contributing to a 20-day production process and substantial work-in-process inventory. This work lays the foundation for implementing a digital twin to evaluate real-time financial impacts of operational changes, ultimately improving the company’s financial performance. ©The authors ©IFAC-PapersOnLine ©ScienceDirect ©Elsevier.
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Foreword : Digital Transformation and XAI in Healthcare
(CRC Press, 2025)
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Uysal, Ilhan
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Jafar Ahmad, Abed Alzubi
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Milen, Mehmet
This book explores the pivotal role of explainable artificial intelligence (XAI) in driving digital transformation within the healthcare sector, providing comprehensive insights into its applications, ethical and legal considerations, technological requirements, and future trends. ©The authors ©CRC Press.
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Digital Transformation and XAI in Healthcare
(CRC Press, 2025)
Uysal, Ilhan
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Abed Alzubi, Jafar Ahmad
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Bilen, Mehmet
This book explores the pivotal role of explainable artificial intelligence (XAI) in driving digital transformation within the healthcare sector, providing comprehensive insights into its applications, ethical and legal considerations, technological requirements, and future trends. Digital Transformation and XAI in Healthcare delves into the fundamental role of XAI in transforming healthcare, addressing critical issues such as data security, ethical considerations, and the integration of XAI into existing healthcare infrastructures. By offering a comprehensive overview of technological tools, infrastructure requirements, and legal frameworks, this book equips healthcare professionals with the knowledge to navigate the complexities of XAI applications. The book explores the future of healthcare education and the pivotal role of XAI in training the next generation of healthcare professionals. It discusses how XAI can enhance learning experiences and provide more personalized education, ensuring that future clinicians are well equipped to utilize advanced AI technologies. It also delves into the technological tools and infrastructure required for implementing XAI, as well as data management and privacy concerns. The exploration of global collaborations and innovative projects highlights the book's unique perspective on the international impact of XAI in healthcare. Intended for healthcare professionals, researchers, and students, this book will provide valuable insights into the future of healthcare technology. Readers will be equipped with the knowledge to harness the power of XAI, ensuring that AI systems are not only accurate but also transparent, trustworthy, and ethically sound. ©The authors ©CRC Press.
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Leveraging mHealth and Artificial Intelligence for Enhanced Health Indicators, A TwiMV Framework Proposal
(Springer Nature Switzerland, 2025)
Domínguez-Miranda, Sergio Arturo
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Technological advances and artificial intelligence (AI) are transforming healthcare by improving prevention, diagnosis, and remote health monitoring. This paper explores the current landscape of the potential application of digital technologies to the healthcare sector. The design of the TwiMV system is proposed to integrate diverse health data for patient diagnosis and monitoring, in a comprehensive framework, encompassing biochemical studies, genetic analysis, medical images, biometric data, dietary and lifestyle information, and wearable data. The proposal involves real-time processing through cloud-based platforms, as well as the integration of artificial intelligence algorithms and digital technologies. The proposed system aims to improve health management through personalized interventions, with specialized modules addressing priority conditions such as stroke, cardiovascular disease, oncology, diabetes, and neurological disorders. ©The authors ©Springer Nature.
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Modeling the Relation Between Non-Communicable Diseases and the Health Habits of the Mexican Working Population: A Hybrid Modeling Approach
(MDPI AG, 2025)
Domínguez-Miranda, Sergio Arturo
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The impact that Non-Communicable Diseases (NCDs) have on the health status of the population has generated the need for an in-depth analysis of health habits and NCDs. In addition to its significant impact on population health, this phenomenon also translates into substantial economic consequences for countries. This study delves into the analysis of the relationship between health habits and NCDs among the economically active population of Mexico. Through a hybrid approach that integrates the use of machine learning (ML) models and a structural equation model (SEM), we seek to quantify the direct and indirect causal effects between health habits and NCDs. For this study, information from the 2022 National Health and Nutrition Survey carried out in Mexico for the working-age population is used. According to the results obtained in the first stage of analysis using ML, the most relevant variables (health habits) that impact the probability of individuals presenting with NCDs were identified (random forest precision of 78.66% and Lasso with 71.27%). The second stage of analysis through SEM using the most relevant variables, which were selected through ML, allowed us to measure the direct and indirect causal effect of health habits on NCDs. The SEM model was statistically significant (Chi-square: 449.186; p-value = 0.0000) and revealed that negative health habits, such as a poor diet, physical inactivity, smoking and alcohol consumption, significantly increase the risk of NCDs in the working-age population in Mexico (0.23), while vigorous physical activity and salary has a negative impact (−0.17 and −0.23, respectively) on the presence of NCDs. This study highlights the ability of machine learning and SEM approaches to model the impact of health habits on NCDs for the economically active population in Mexico. ©The authors © Mathematics ©MDPI.
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Modeling responsible technologies using multiagent system for climate crisis and sustainability
(Elsevier, 2025)
Nanda, Pragyan
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Sahoo, Sipra
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Marmolejo-Saucedo, José Antonio
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Behera, Itishree
This chapter delves into the dynamic landscape of responsible technologies and their crucial role in addressing the climate crisis and advancing sustainability. It highlights the need to understand the intricate connections between technology adoption, environmental impact, and societal behavior across various dimensions of sustainability, including environmental, social, economic, cultural, technological, political, ethical, health, educational, and adaptive aspects. The chapter introduces the concept of responsible technologies and their key principles, emphasizing real-world experimentation limitations and simulation advantages, particularly through a multiagent system (MAS). It explores different modeling approaches, focusing on the suitability of MAS for studying responsible technologies, along with considerations in its implementation. A step-by-step guide is provided for constructing a tailored MAS model for responsible technologies, incorporating real-world data and environmental factors. Findings and insights from MAS simulations are analyzed, shedding light on the implications of responsible technology adoption across these sustainability dimensions. The chapter also underscores the pivotal role of a MAS in comprehensively modeling responsible technologies and invites further research exploration in this expansive domain, serving as a comprehensive guide for researchers, policymakers, and stakeholders committed to leveraging responsible technologies for climate change mitigation and sustainability advancement. ©The authors ©Elsevier.
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Sales predictive analysis for improving supply chain drug sample
(Elsevier BV, 2025)
Téllez-Ballesteros, Susana Casy
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Torres-Mendoza, Ricardo
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Marmolejo-Saucedo, José Antonio
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The delivery of drug samples allows increasing sales of pharmaceutical products [6]. However, we discovered some problems that can be improved in the supply chain that delivers drug samples (used for the treatment of excess glucose). Databases were integrated; then we apply data extraction and transformation; and finally we apply multiple regression analysis to explain drug sales. The first analysis evaluates the integration of regional data and the second analysis refers to data dis-aggregated by region. We identify the region with the greatest impact on sales and the impact of the delivery of drug samples in the Mexican market. ©The authors ©Elsevier ©Procedia Computer Science.
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Dimensions Related to NCD in Developing Countries During Working Age Using Ridge, Lasso, and Elastic Net Regressions
(2024)
Domínguez-Miranda Sergio Arturo
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4
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Digitalization of Healthcare Supply Chain Design
(2024)
Jose Antonio Marmolejo-saucedo
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Liliana Marmolejo-Saucedo
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Miriam Rodriguez-Aguilar
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