Applications of Machine Learning and Data Analytics Models in Maritime Transportation PDF Download
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Author: Ran Yan
Publisher: Transportation
ISBN: 9781839535598
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
This book explores the principles of analysing maritime-transportation related practical problems using data-driven models, with a particular focus on machine learning. Coverage includes data-enabled methodologies, technologies, applications and case studies.
Author: Ran Yan
Publisher: Transportation
ISBN: 9781839535598
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
This book explores the principles of analysing maritime-transportation related practical problems using data-driven models, with a particular focus on machine learning. Coverage includes data-enabled methodologies, technologies, applications and case studies.
Author: Rakesh Kumar
Publisher: Independently Published
ISBN:
Category : Transportation
Languages : en
Pages : 0
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Book Description
The shipping industry, a vital component of global trade and commerce, faces a myriad of challenges ranging from operational inefficiencies to safety concerns and environmental impact. In an era defined by rapid technological advancement, the integration of Artificial Intelligence (AI) presents unprecedented opportunities to revolutionize the way maritime operations are conducted. The book "AI for Shipping Industry" explores the intersection of AI and the shipping sector, offering insights into how AI technologies can address the industry's most pressing issues while unlocking new levels of efficiency, safety, and sustainability. From automated vessel navigation to predictive maintenance, from cargo optimization to risk assessment, AI-driven solutions have the potential to reshape every aspect of maritime operations. This comprehensive guide delves into the evolution of the shipping industry, providing historical context and examining the current landscape of challenges and opportunities. It navigates through the fundamentals of AI, including Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, and Reinforcement Learning, elucidating their relevance and applicability within the maritime domain. Furthermore, the book explores real-world applications of AI in shipping, showcasing case studies, successful implementations, and best practices from leading industry players. It discusses the technologies driving AI adoption in the maritime sector, such as Big Data Analytics, Internet of Things (IoT), and Blockchain, and elucidates their transformative impact on operational workflows and decision-making processes. Moreover, the ethical implications, regulatory considerations, and data privacy concerns surrounding AI adoption in the shipping industry are thoroughly examined, highlighting the importance of responsible AI governance and compliance. Whether you are a maritime professional seeking to stay ahead of industry trends, a technology enthusiast intrigued by the potential of AI in shipping, or an academic researcher exploring the frontiers of maritime innovation, this book serves as a comprehensive guide and a roadmap for navigating the AI-powered future of the shipping industry. Embark on a journey to discover how AI is reshaping the seascape of maritime operations and charting a course towards a smarter, safer, and more sustainable shipping industry.
Author: Xinqiang Chen
Publisher:
ISBN: 9783725806553
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
Maritime transportation assumes a large number of cargo-delivering tasks in world trade. It is noted that maritime traffic safety and efficiency may be affected by varied factors such as weather, ship crew proficiency, etc. The topic Reprint focuses on the use of artificial intelligence techniques to enhance maritime transportation efficiency. More specifically, the Reprint unveils cutting-edge machine learning-supported studies, including autonomous guide vehicle path optimization, ship arrival and departure time estimation from insufficient/biased maritime data, anomaly ship kinematic data cleansing, ship collision avoidance, etc.
Author: Dimitrios Ziakkas
Publisher: CRC Press
ISBN: 1040126243
Category : Technology & Engineering
Languages : en
Pages : 148
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Book Description
Artificial Intelligence (AI) is a major technological advancement in the 21st century. With its influence spreading to all aspects of our lives and the engineering sector, establishing well-defined objectives is crucial for successfully integrating AI in the field of transportation. This book presents different ways of adopting emerging technologies in transportation operations, including security, safety, online training, and autonomous vehicle operations on land, sea, and air. This guide is a dynamic resource for senior management and decision-makers, with essential practical advice distilled from the expertise of specialists in the field. It addresses the most critical issues facing transportation service providers in adopting AI and investigates the relationship between the human operator and the technology to navigate what is and is not feasible or impossible. Case studies of actual implementation provide context to common scenarios in the transportation sector. This book will serve the reader as the starting point for practical questions regarding the deployment and safety assurance of new and emergent technologies in the transportation domains. Artificial Intelligence and Human Performance in Transportation is a beneficial read for professionals in the fields of Human Factors, Engineering (Aviation, Maritime and Land), Logistics, Manufacturing, Accident Investigation and Safety, Cybersecurity and Human Resources.
Author: Alexander Artikis
Publisher: Springer Nature
ISBN: 3030618528
Category : Computers
Languages : en
Pages : 336
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Book Description
In the last 25 years, information systems have had a disruptive effect on society and business. Up until recently though, the majority of passengers and goods were transported by sea in many ways similar to the way they were at the turn of the previous century. Gradually, advanced information technologies are being introduced, in an attempt to make shipping safer, greener, more efficient, and transparent. The emerging field of Maritime Informatics studies the application of information technology and information systems to maritime transportation. Maritime Informatics can be considered as both a field of study and domain of application. As an application domain, it is the outlet of innovations originating from data science and artificial intelligence; as a field of study, it is positioned between computer science and marine engineering. This new field’s complexity lies within this duality because it is faced with disciplinary barriers yet demands a systemic, transdisciplinary approach. At present, there is a growing body of knowledge that remains undocumented in a single source or textbook designed to assist students and practitioners. This highly useful textbook/reference starts by introducing required knowledge, algorithmic approaches, and technical details, before presenting real-world applications. The aim is to present interested audiences with an overview of the main technological innovations having a disruptive effect on the maritime industry, as well as to discuss principal ideas, methods of operation and applications, and future developments. The material in this unique volume provides requisite core knowledge for undergraduate or postgraduate students, employing an analytical approach with numerous real-world examples and case studies.
Author: Mashrur Chowdhury
Publisher: Elsevier
ISBN: 0128098511
Category : Business & Economics
Languages : en
Pages : 346
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Book Description
Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. - Includes case studies in each chapter that illustrate the application of concepts covered - Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies - Contains contributors from both leading academic and commercial researchers - Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications
Author: Guanghui Zhao
Publisher: World Scientific
ISBN: 9811236011
Category : Technology & Engineering
Languages : en
Pages : 352
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Book Description
This book is designed as a popular science book on big data analytics in intelligent transportation systems. It aims to provide an introduction to big-data transportation starting from an overview on the development of big data transportation in various countries. This is followed by a discussion on the blueprint strategies of big data transportation which include innovative models, planning, transportation logistics, and application case studies. Finally, the book discusses applications of big data transportation platforms.
Author: John D. Kelleher
Publisher: MIT Press
ISBN: 0262361108
Category : Computers
Languages : en
Pages : 853
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Book Description
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Author: Zhiyuan Liu
Publisher: Elsevier
ISBN: 0443338922
Category : Transportation
Languages : en
Pages : 0
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Book Description
Transportation Big Data: Theory and Methods is centered around big data theory and methods. As big data is now a key topic in transport because the volume of data has increased exponentially due to the growth in the amount of traffic (all modes) and detectors, this book provides a structured analysis of commonly used methods for handling transportation big data. It is supported by a wealth of transportation engineering examples with codes. The book offers a concise, yet comprehensive description key techniques and important tools in transportation big data analysis. - Covers big data applications in transportation engineering in real-world scenarios - Shows how to select different machine learning algorithms for processing, analyzing, and modeling transportation data - Provides an overview of the fundamental concepts of machine learning and how classical algorithms can be applied to transportation-related problems - Provides an overview of Python's basic syntax and commonly used modules, enabling practical data analysis and modeling tasks using Python
Author: Acharjya, Pinaki Pratim
Publisher: IGI Global
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 351
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Book Description
As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.
