Machine learning (ML) and deep learning (DL) as two well-known methods of artificial intelligence (AI) have emerged as powerful tools in extracting insights and patterns from vast amounts of data. In the context of knowledge-based economies and societies, these techniques have the potential to revolutionize and optimize decision-making processes, drive innovation, and enhance productivity. By analyzing and learning from diverse data sources, including digital repositories, social media, and sensor networks, ML and DL enable the creation of knowledge-driven systems that can inform policy-making, optimize resource allocation, and facilitate effective knowledge management.The intersection of these technologies presents novel solutions capable of transforming industries, optimizing efficiency, and tackling intricate challenges. Nevertheless, it also brings forth its unique set of difficulties and gives rise to significant consequences across different facets of society.This Research Topic aims to showcase research on the application of ML and DL along with optimization in various domains within knowledge-based economies and societies. We invite contributions that explore innovative methodologies, algorithms, and applications in fields such as healthcare, finance, education, agriculture, and governance. Topics of interest include but are not limited to natural language processing, sentiment analysis, recommendation systems, intelligent information retrieval, and data-driven decision-making. We encourage submissions that demonstrate the practical implications and transformative potential of these technologies in addressing real-world challenges and driving sustainable development. In addition to highlighting the applications of ML and DL, this special issue recognizes the importance of addressing ethical considerations, privacy concerns, and societal implications. As these technologies become increasingly integrated into knowledge-based economies and societies, it is crucial to examine the implications for data privacy, algorithmic fairness, transparency, and accountability.We welcome contributions that delve into the ethical frameworks, governance mechanisms, and policy guidelines essential for ensuring the responsible and inclusive implementation of ML and DL as well as optimization techniques in knowledge-based development. We invite submissions of original research, case studies, and theoretical viewpoints that contribute to the progress of knowledge-based economies and societies through the proficient application of ML, DL, and optimization techniques. Through the dissemination of top-tier research and the encouragement of interdisciplinary collaboration, this themed article collection aims to facilitate the exchange of knowledge, inspire additional research efforts, and advocate for evidence-based practices in the development of knowledge-based societies.
Machine learning (ML) and deep learning (DL) as two well-known methods of artificial intelligence (AI) have emerged as powerful tools in extracting insights and patterns from vast amounts of data. In the context of knowledge-based economies and societies, these techniques have the potential to revolutionize and optimize decision-making processes, drive innovation, and enhance productivity. By analyzing and learning from diverse data sources, including digital repositories, social media, and sensor networks, ML and DL enable the creation of knowledge-driven systems that can inform policy-making, optimize resource allocation, and facilitate effective knowledge management.The intersection of these technologies presents novel solutions capable of transforming industries, optimizing efficiency, and tackling intricate challenges. Nevertheless, it also brings forth its unique set of difficulties and gives rise to significant consequences across different facets of society.This Research Topic aims to showcase research on the application of ML and DL along with optimization in various domains within knowledge-based economies and societies. We invite contributions that explore innovative methodologies, algorithms, and applications in fields such as healthcare, finance, education, agriculture, and governance. Topics of interest include but are not limited to natural language processing, sentiment analysis, recommendation systems, intelligent information retrieval, and data-driven decision-making. We encourage submissions that demonstrate the practical implications and transformative potential of these technologies in addressing real-world challenges and driving sustainable development. In addition to highlighting the applications of ML and DL, this special issue recognizes the importance of addressing ethical considerations, privacy concerns, and societal implications. As these technologies become increasingly integrated into knowledge-based economies and societies, it is crucial to examine the implications for data privacy, algorithmic fairness, transparency, and accountability.We welcome contributions that delve into the ethical frameworks, governance mechanisms, and policy guidelines essential for ensuring the responsible and inclusive implementation of ML and DL as well as optimization techniques in knowledge-based development. We invite submissions of original research, case studies, and theoretical viewpoints that contribute to the progress of knowledge-based economies and societies through the proficient application of ML, DL, and optimization techniques. Through the dissemination of top-tier research and the encouragement of interdisciplinary collaboration, this themed article collection aims to facilitate the exchange of knowledge, inspire additional research efforts, and advocate for evidence-based practices in the development of knowledge-based societies.