The Delphi method to analyze the expert views on possible futures of the smart city adoption and development in developing countries: the case of Jordan.

Smart cities are characterized by the integration of various technologies and the use of data to achieve several objectives. These objectives include the creation of efficiencies, boosting economic development, expanding sustainability, and improving the overall quality of life for individuals residing and working within the urban environment. The aim of this study is to analyze the future of smart cities with respect to developing countries, specifically Jordan as the case. This analysis is based on the opinions and feedback from the field experts. In this study, we are tapping into multiple domains of smart cities such as smart governance, education, healthcare, communication, transportation, security, energy, and sustainability. The field experts' consensus was developed with the Delphi method. The Delphi survey comprises eight questions to assess the views about smart city adoption and development with respect to Jordan. The results and findings of this study revealed specific challenges and opportunities in smart city adoption with respect to Jordan. The experts' opinions have validated the study of the 2023 Smart City Index report. They have offered crucial input and future guidance for the adoption of smart cities in Jordan. Additionally, they have indicated which domains of smart cities should be prioritized during the implementation in Jordan.

Intelligent tumor tissue classification for Hybrid Health Care Units.

Introduction: In the evolving healthcare landscape, we aim to integrate hyperspectral imaging into Hybrid Health Care Units to advance the diagnosis of medical diseases through the effective fusion of cutting-edge technology. The scarcity of medical hyperspectral data limits the use of hyperspectral imaging in disease classification. Methods: Our study innovatively integrates hyperspectral imaging to characterize tumor tissues across diverse body locations, employing the Sharpened Cosine Similarity framework for tumor classification and subsequent healthcare recommendation. The efficiency of the proposed model is evaluated using Cohen's kappa, overall accuracy, and f1-score metrics. Results: The proposed model demonstrates remarkable efficiency, with kappa of 91.76%, an overall accuracy of 95.60%, and an f1-score of 96%. These metrics indicate superior performance of our proposed model over existing state-of-the-art methods, even in limited training data. Conclusion: This study marks a milestone in hybrid healthcare informatics, improving personalized care and advancing disease classification and recommendations.