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Timely diagnosis is crucial for the successful treatment of a serious medical condition like brain hemorrhage. Deep learning algorithms have shown great promise in applications for medical image analysis, like the identification of brain hemorrhages. The goal of this study is to assess how well various deep learning algorithms can detect brain hemorrhages. Using a suitable dataset, the study evaluates the computational efficiency, accuracy, sensitivity, and specificity of the selected algorithms. The results demonstrate the potential of deep learning models to assist physicians in identifying this potentially fatal condition and demonstrate how well they can identify brain hemorrhages. The study's findings improve automated brain hemorrhage detection technology, improving patient outcomes and the efficiency of healthcare delivery. EfficientNetB3 typically achieves higher accuracy due to its increased model complexity. Despite its increased complexity, EfficientNetB3 is still more parameter-efficient and computationally efficient than many alternative architectures. EfficientNetB3's strong performance and feature extraction capabilities make it a good choice for transfer learning tasks. Out of all the models implemented in this paper, the proposed model with EfficientNetB3 gave the best accuracy for training as well as validation i.e. 99.95% and 93.29% respectively followed by EfficientNetB2, ResNet, SEResNext and ResNext. [ABSTRACT FROM AUTHOR]

For over a decade, mobile applications are frequently used to make people's daily lives more accessible in all areas, such as commerce, tourism, education, medical care, and others. For implementing these applications, several frameworks exist that can accomplish this task, such as Google's Flutter, Microsoft's Xamarin, Facebook's React Native, Apple's Swift UI, Ionic, Django..etc. However, choosing the best framework to develop an application is not a simple enough decision for developers. This paper presents a detailed analysis of the four most widely used frameworks for implementing mobile applications: React Native, Swift UI, Flutter, and Xamarin. Furthermore, this study also aims to assist developers in choosing the most suitable framework by proposing an application that can recommend the best framework according to the needs entered by answering the questions, which are generally related to the relevant criteria resulting from the analysis of these frameworks. [ABSTRACT FROM AUTHOR]

This paper presents a global approach for implementing Digital Predictive Maintenance (DPM), based on the principles of the digital industry. The proposed approach is applicable at both academic and professional scales, across all sectors and all sizes of organisations. This approach consists of four main phases: the identification of the system or work environment, the identification of the specific targets that justify the adoption of digital maintenance, the choice of appropriate digital technologies for predictive maintenance and the implementation of maintenance X.0 at all levels (i.e., strategic, tactical and/or operational). To validate this approach, the healthcare sector was chosen as a field of application. The uninterrupted operation of facilities and equipment in this sector is paramount. The adoption of maintenance X.0, which has already demonstrated significant benefits in other sectors, would be of great value in the healthcare sector. By applying our approach to the Pneumatic Tube System (PTS) at the Ibn Sina University Hospital Center (actually) in Rabat, Morocco, we have succeeded in introducing a structured and streamlined implementation of DPM in the healthcare sector. [ABSTRACT FROM AUTHOR]

In the complex landscape of healthcare supply chains, challenges extend well beyond internal operational concerns, necessitating innovative solutions that align with the evolving technological landscape. The metaverse has emerged as a compelling solution to address these challenges by offering the potential for significant enhancements in efficiency and traceability. This paper focuses on both the theoretical underpinnings and practical applications of integrating the metaverse into healthcare supply chain management. Employing the Cyber-Physical-Social System framework, the study comprehensively explores the pivotal roles played by emerging information technologies in constructing the metaverse and overseeing various stages of the healthcare supply chain. The practical application is exemplified through the introduction of the TraceLink example, illustrating the successful healthcare supply chain digitalization. Furthermore, the paper introduces a barrier framework based on the Technology-Organization-Environment framework, addressing obstacles such as obsolete management technologies, data security, organizational structure, and societal acceptance. The examination and comparison of individual barriers using the Bayesian best-worst method further provide a nuanced understanding of the challenges. This comprehensive exploration not only equips research scholars with profound insights for addressing these challenges but also lays the foundation for future endeavors. The envisioned improvements in healthcare service delivery hold substantial benefits for patients and the industry, making this research a pioneering exploration of the metaverse's potential applications within the realm of the healthcare supply chain. [ABSTRACT FROM AUTHOR]

The cognitive load experienced by humans is an important factor affecting their performance. Cognitive overload or underload may result in suboptimal human performance and may compromise safety in emerging human-in-the-loop systems. In driving, cognitive overload, due to various secondary tasks, such as texting, results in driver distraction. On the other hand, cognitive underload may result in fatigue. In automated manufacturing systems, a distracted operator may be prone to muscle injuries. Similar outcomes are possible in many other fields of human performance such as aviation, healthcare, and learning environments. The challenge with such human-centred applications is that the cognitive load is not directly measurable. Only the change in cognitive load is measured indirectly through various physiological, behavioural, performance-based and subjective means. A method to objectively assess the performance of such diverse measures of cognitive load is lacking in the literature. In this paper, a performance metric for the comparison of different measures to determine the cognitive workload is proposed in terms of the signal-to-noise ratio. Using this performance metric, several measures of cognitive load, that fall under the four broad groups were compared on the same scale for their ability to measure changes in cognitive load. Using the proposed metrics, the cognitive load measures were compared based on data collected from 28 participants while they underwent n -back tasks of varying difficulty. The results show that the proposed performance evaluation method can be useful to individually assess different measures of cognitive load. [ABSTRACT FROM AUTHOR]

Personal health records (PHR) offer significant benefit for patients, such as reducing medical cost and improving the quality of medical care. Majority of the current schemes lack provisions for tracking and revoking malicious doctors. The explicit access policies are prone to leaking patient private information. What is more, owning to the uneven distribution of medical supplies, shocking computational overhead during decryption is a burden that cannot be ignored for busy medical workers. This paper proposed a patient-centric medical service matching scheme that supports policy hiding, attribute matching, fine-grained access control, and user dynamic management. The scheme uses ciphertext policy-based attribute encryption (CP-ABE) to achieve fine-grained access control and supports policy hiding. It utilizes white-box tracking technology and binary tree structure to achieve malicious doctor tracking. Revocation information is ciphertext to achieve dynamic management of doctors. From the experimental results, it can be concluded that our protocol achieves both patient-centric security and performance advantages. • A secure patient-centric medical service matching scheme with policy hiding. • Achieve fine-grained access control and withdraw untrustworthy doctors. • Low computational and communication overhead. [ABSTRACT FROM AUTHOR]

Supplier selection is critical to the healthcare industry, especially in emergencies. Accurate and timely supply of healthcare resources can reduce economic cost and ensure the safety of people's lives. However, the healthcare supplier market is enormous and complex; making accurate evaluations of different suppliers and helping managers choose the most suitable one when considering many factors is becoming an important task. Many multicriteria decision-making (MCDM) methods have been applied to supplier selection in existing literature. However, the research on healthcare supplier selection still needs to be completed. In this paper, to deal with healthcare supplier selection problems when there is no sufficient time and information to decide with uncertainty, the stochastic multicriteria acceptability analysis (SMAA) method and multi-attributive border approximation area comparison (MABAC) method are first combined by taking both the advantages of SMAA and MABAC methods when the assessments are expressed by belief distributions (BDs). In the SMAA-MABAC method, the global ignorance in BDs and the uncertain criteria weights are considered by judging the feasible space to derive the final rank of healthcare suppliers with the most possible. The more straightforward calculation process of SMAA-MABAC makes it faster to make an accurate choice among various suppliers. Moreover, it is flexible to generate different recommended suggestions when diverse preference information is added in SMAA-MABAC. Finally, a case study of healthcare supplier selection is examined for managers based on distributed reviews, and targeted selection advice is given concerning different demands. Besides, the sensitive analysis is provided to better understand the influence of the criteria weights on the proposed model. The results of the comparative analysis also show the effectiveness and rationality of the proposed method. [ABSTRACT FROM AUTHOR]

Medical waste management (MWM) is a challenging issue for medical facility managers owing to its potential to prevent environmental and health risks. Many treatment technologies (TTs) have been used for MWM, and selecting appropriate TTs is a complex multi-criteria decision-making (MCDM) problem. In this paper, we propose a novel integrated MCDM method based on the best-worst method (BWM) and the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method under the Fermatean fuzzy environment to evaluate, rank, and select treatment technologies for medical waste management. First, novel distance measure and entropy measure for Fermatean fuzzy sets are developed, and their properties are examined. Second, different treatment technologies are evaluated by experts using Fermatean fuzzy sets. Third, the experts' weights are determined using a novel method based on the entropy measure, and the criteria weights are calculated by a novel hybrid criteria weight calculation method. Finally, the ranking of different treatment technologies is determined by the proposed method. To validate the proposed method, a real case study in Jinan, China is presented, where the proposed method is applied to determine the optimal health care waste (HCW) treatment technologies from five TTs with eight criteria. The results are compared to other MCDM methods, which shows the effectiveness and reliability of the proposed method. Moreover, sensitivity analysis is also carried out to show the robustness of the proposed method. From the results, it can be found that the proposed method could provide reliable and robust results for obtaining the optimal health care waste treatment technology. • Proposes a novel decision-making method under Fermatean fuzzy set called FF-BWM-VIKOR. • The FF-BWM-VIKOR method is proposed to select health care waste treatment technology. • Novel distance measure and entropy measure for Fermatean fuzzy sets are developed. • A hybrid criteria weight calculation method is proposed to determine weights of criteria. • A real case of health care waste treatment technology selection is presented. [ABSTRACT FROM AUTHOR]

The efficient distribution and timely vaccine delivery to the masses during any pandemic situation require reliable and efficient vaccine cold chain management (VCCM). Therefore, the present study is an effort to explore the factors of VCCM to develop a sustainable and resilient healthcare delivery system in any health outbreak. The factors of VCCM are scrutinized using Fuzzy-Delphi method and then prioritized adopting Fuzzy-Analytical Hierarchy Process (F-AHP) approach while considering the stakeholders' inputs. Finally, factors are categorized into clusters by implementing a machine learning tool ' k -means clustering' algorithm. The results of the study are further validated by conducting extended robustness tests. The findings of the study highlight the factors, namely, cold chain infrastructure, vaccine storage guidelines and availability of skilled professionals as the most important for VCCM. The ′ k -means clustering' algorithm analysis classifies all the 16 factors into three clusters: continuous process improvement (including eight factors), stakeholders' coordination (five factors) and maximum resource utility (three factors). The study can be significantly useful to VCCM decision-makers to implement the prioritized factors by ensuring the advanced cold chain infrastructure and issuing special storage and distribution guidelines to fight against health outbreaks like COVID-19. This is one of the few papers to discuss the factors of VCCM for developing a sustainable and resilient healthcare delivery system in the context of pandemics and stakeholders can address the factors in different clusters at a time. [Display omitted] • Vaccine cold chain management (VCCM) to develop a resilient healthcare delivery system during health outbreaks. • Prioritization of factors of VCCM using Fuzzy-Analytic Hierarchy Process approach. • Categorization of factors of VCCM by implementing a machine learning tool of K − m e a n s clustering algorithm. • Storage and distribution guidelines to fight against the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

The smart healthcare system plays a vital role in modern healthcare, facilitating the exchange of Electronic Patient Records (EPR), and improving medical care. Nevertheless, safeguarding the inherent security and confidentiality of EPR data persists as a formidable challenge. This issue demanding rigorous attention and innovative solutions. Digital watermarking safeguards the genuineness and integrity of digital images, and is widely employed. In medical imaging, it is vital to guarantee the confidentiality and security of patient data. In this paper, we propose an Encryption-Then-Embedding-Based data hiding scheme for medical images that combines cryptography and watermarking techniques. The proposed technique first encrypts the patient information using Advanced Encryption Standard-Galois/Counter Mode (AES-GCM) before embedding. The intensity-based image segmentation method is then used to select the Region of Non-interest (RONI) for embedding the encrypted patient information and the watermark using a Fused Transform-Based Method (FTBM). The experiments conducted in this study utilize a large dataset of medical data, The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD). The findings of this study demonstrate the efficacy of the proposed technique in safeguarding patient data while simultaneously preserving the quality of medical images. In comparison to existing techniques, the proposed approach demonstrates superior performance in terms of security, authenticity, and integrity. [ABSTRACT FROM AUTHOR]