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The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground-breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non-destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non-destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry., (© 2023 The Authors. Comprehensive Reviews in Food Science and Food Safety published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.)

Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively., Competing Interests: The author of this publication declares that there is no conflict of interest associated with this publication., (Copyright © 2022 M. M. Kamruzzaman et al.)

Financial markets are exposed to sharp price volatility, and defaults and errors, which results in high risks for relevant stakeholders. In such markets, success depends upon the quality and quantity of information available to assist the decision‐making. Artificial intelligence (AI), in this regard, can measure or predict systemic and systematic risk in the financial markets. This research study aims to highlight how the risks can be measured and controlled with the support and integration of modern AI and machine learning mechanisms. By performing a review‐based methodology, the study first presents an explanation of the models, which is followed by proposing a new AI‐based model. The model relies on several financial system's inputs, which include portfolio data, trade data, market data, financial reports, market condition, and sector‐wise data. The systemic and systematic risk is then assessed through a number of outputs that the AI algorithm will process, in the form of an interactive dashboard. The article further tests that model and presents benefits and implications. [ABSTRACT FROM AUTHOR]

Security management is the process of identifying a company's assets (such as people, buildings, equipment, systems, and information assets) and then developing, documenting, and implementing policies and procedures to secure those assets. Meanwhile, artificial intelligence (AI) applications are flourishing thanks to advances in deep learning and numerous hardware architecture improvements based on cloud edge computing (CEC) issues are associated with the Internet of Things (IoT), including inadequate security measures, user ignorance, and the dreaded active monitoring. Therefore, a 6G wireless communication‐assisted security management using artificial intelligence (WC‐SM‐AI) has been introduced to enhance security. The energy‐efficient 6G real‐time communication framework and the enhanced deep neural network security module are key components of this architecture. The first module optimizes network lifespan and spectral efficiency while reducing energy consumption and latency. Another module offers a more secure network connection while increasing privacy, data integrity, and access. For this reason, this article discusses how AI can strengthen the security of 6G networks while promoting strategy problems and solutions. [ABSTRACT FROM AUTHOR]

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