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This article delves into the critical integration of domain knowledge into AI/ML systems across various industries, highlighting its importance in developing ethically responsible, effective, and contextually relevant solutions. Through detailed case studies from the healthcare and manufacturing sectors, we explore the challenges, strategies, and successes of this integration. We discuss the evolving role of domain experts and the emerging tools and technologies that facilitate the incorporation of human expertise into AI/ML models. The article forecasts future trends, predicting a more seamless and strategic collaboration between AI/ML and domain expertise. It emphasizes the necessity of this synergy for fostering innovation, ensuring ethical practices, and aligning technological advancements with human values and real-world complexities. [ABSTRACT FROM AUTHOR]

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä's Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI's potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations. [ABSTRACT FROM AUTHOR]

The maritime industry, responsible for moving approximately 90% of the world's goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry's environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry. [ABSTRACT FROM AUTHOR]

Water-quality monitoring in urban lakes is of paramount importance due to the direct implications for ecosystem health and human well-being. This study presents a novel approach to predicting the Water Quality Index (WQI) in an urban lake over a span of two decades. Leveraging the power of Machine Learning (ML) algorithms, we developed models that not only predict, but also provide insights into, the intricate relationships between various water-quality parameters. Our findings indicate a significant potential in using ML techniques, especially when dealing with complex environmental datasets. The ML methods employed in this study are grounded in both statistical and computational principles, ensuring robustness and reliability in their predictions. The significance of our research lies in its ability to provide timely and accurate forecasts, aiding in proactive water-management strategies. Furthermore, we delve into the potential explanations behind the success of our ML models, emphasizing their capability to capture non-linear relationships and intricate patterns in the data, which traditional models might overlook. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is significantly transforming the maritime industry, enabling the generation of vast amounts of data that can drive operational efficiency, safety, and sustainability. This review explores the role and potential of data analysis in maritime IoT applications. Through a series of case studies, it demonstrates the real-world impact of data analysis, from predictive maintenance to efficient port operations, improved navigation safety, and environmental compliance. The review also discusses the benefits and limitations of data analysis and highlights emerging trends and future directions in the field, including the growing application of AI and Machine Learning techniques. Despite the promising opportunities, several challenges, including data quality, complexity, security, cost, and interoperability, need to be addressed to fully harness the potential of data analysis in maritime IoT. As the industry continues to embrace IoT and data analysis, it becomes critical to focus on overcoming these challenges and capitalizing on the opportunities to improve maritime operations. [ABSTRACT FROM AUTHOR]

This review article explores the applications and impacts of Machine Learning (ML) techniques in marine traffic management and prediction within complex maritime systems. It provides an overview of ML techniques, delves into their practical applications in the maritime sector, and presents an in-depth analysis of their benefits and limitations. Real-world case studies are highlighted to illustrate the transformational impact of ML in this field. The article further provides a comparative analysis of different ML techniques and discusses the future directions and opportunities that lie ahead. Despite the challenges, ML's potential to revolutionize marine traffic management and prediction, driving safer, more efficient, and more sustainable operations, is substantial. This review article serves as a comprehensive resource for researchers, industry professionals, and policymakers interested in the interplay between ML and maritime systems. [ABSTRACT FROM AUTHOR]

The article describes a study of ship delays on the Świnoujście-Szczecin waterway observed by the VTS operator. The research has led to an understanding of the factors that affect delays of ships calling at the ports of Szczecin and Police, as well as the possibilities of predicting and preventing these delays. This article presents the results of the study on the traffic intensity on the investigated waterway and the process of identifying the port regulation that causes the most frequent delays. Based on the obtained results from the statistical analysis and from using multiple regression, a statistical model has been developed that has the ability to estimate expected delays. Additionally, the model has been expanded to calculate financial losses resulting from delays, taking into account the daily cost of maintaining the studied ships. The study took place during ongoing project "Modernization of the Świnoujście-Szczecin waterway to a depth of 12.5 m" but does not include delays resulting from this project. [ABSTRACT FROM AUTHOR]