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The rapid advancement and widespread adoption of artificial intelligence (AI) across diverse industries, including healthcare, finance, manufacturing, and retail, underscore the transformative potential of AI technologies. This necessitates the development of viable AI model marketplaces that facilitate the development, trading, and sharing of AI models across the pervasive industrial domains to harness and streamline their daily activities. These marketplaces act as centralized hubs, enabling stakeholders such as developers, data owners, brokers, and buyers to collaborate and exchange resources seamlessly. However, existing AI marketplaces often fail to address the demands of modern and next-generation application domains. Limitations in pricing models, standardization, and transparency hinder their efficiency, leading to a lack of scalability and user adoption. This paper aims to target researchers, industry professionals, and policymakers involved in AI development and deployment, providing actionable insights for designing robust, secure, and transparent AI marketplaces. By examining the evolving landscape of AI marketplaces, this paper identifies critical gaps in current practices, such as inadequate pricing schemes, insufficient standardization, and fragmented policy enforcement mechanisms. It further explores the AI model life-cycle, highlighting pricing, trading, tracking, security, and compliance challenges. This detailed analysis is intended for an audience with a foundational understanding of AI systems, marketplaces, and their operational ecosystems. The findings aim to inform stakeholders about the pressing need for innovation and customization in AI marketplaces while emphasizing the importance of balancing efficiency, security, and trust. This paper serves as a blueprint for the development of next-generation AI marketplaces that meet the demands of both current and future application domains, ensuring sustainable growth and widespread adoption. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of disaster damage assessments using deep learning (DL) techniques. Specifically, we propose integrating DL techniques into the Internet of Things Search Engine (IoTSE) system to carry out disaster damage assessment. Our approach is to design two scenarios, Single and Complex Event Settings, to complete performance validation using four Convolutional Neural Network (CNN) models. These two scenarios are designed with three possible network services. Our experimental results confirm that all four CNN models can learn each label during the single event setting well. Whereas, with complex event settings, the CNN models have learning difficulty because multiple events have closely related labels. [ABSTRACT FROM AUTHOR]