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Edible Bird Nest (EBN) constitutes a thriving industry in several Southeast Asian nations where its value chain encompasses several critical processes starting with nest harvesting to processing, and finally sales. However, a detailed review addressing EBN production from an intelligent system perspective is currently missing. Hence, this paper aims to document a comprehensive study of the various parts of the EBN value chain, where machine-intelligence has been incorporated into various solutions. We classified all the EBN processes into three primary segments: farming and production, quality control, and market analysis. In farming and production, two key areas emerge. First, there is process analysis which involves Failure Mode and Effect Analysis (FMEA) as well as profit, cost, and efficiency analysis, while swiftlet house monitoring pinpoints the optimal environment for swiftlets to flourish. On the other hand, works related to the second segment of quality control can be divided two primary approaches: image analysis which involves either auto-grading or automatic impurities inspection, and chemical analysis to ascertain the origin and authenticity of the EBN. The third and final domain of market analysis, covers both business strategy and customer behavior analysis. We have identified the integration of cutting-edge intelligent methods in each area while offering recommendations for future work. Our findings also unveiled intricate patterns, networks, relationships, and trends in the application of machine intelligence within the EBN value chain. These insights highlight many underexplored areas as well as several strategic aspects in this emerging industry. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest. [ABSTRACT FROM AUTHOR]