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Machine learning and data analytics can be used to inform technical, commercial and financial decisions in the maritime industry. Applications of Machine Learning and Data Analytics Models in Maritime Transportation explores the fundamental principles of analysing maritime transportation related practical problems using data-driven models, with a particular focus on machine learning and operations research models.

Machine learning algorithms are increasingly finding applications in the healthcare sector. Whether assisting a clinician to process an individual patient's data or helping administrators view hospital bed turnover, the volume and complexity of healthcare data is a compelling reason for the development of machine learning based tools to aid in its interpretation and use.

Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data. Applications of machine learning in wireless communications have been receiving a lot of attention, especially in the era of big data and IoT, where data mining and data analysis technologies are effective approaches to solving wireless system evaluation and design issues.

Computation has now been reconfigured by machine learning: those technical processes and operations that yoke together statistics and computer science to create artificial intelligence (AI) by furnishing vast datasets to learn tasks and predict outcomes. In DeepAesthetics, Anna Munster examines the range of more-than-human experiences this transformation has engendered and considers how those experiences can be qualitative as well as quantitative. Drawing on process philosophy, Munster approaches computational experience through its relations and operations. She combines deep learning—the subfield of machine learning that uses neural network architectures—and aesthetics to offer a way to understand the insensible and frequently imperceptible forms of nonlinear and continuously modulating statistical function. Attending to the domains and operations of image production, statistical racialization, AI conversational agents, and critical AI art, Munster analyzes how machine learning is operationally entangled with racialized, neurotypical, and cognitivist modes of producing knowledge and experience. She approaches machine learning as events through which a different sensibility registers, one in which AI is populated by oddness, disjunctions, and surprises, and where artful engagement with machine learning fosters indeterminate futures.

We are living in an AI and data-driven era. A huge volume of data is generated from all sectors including smart cities, intelligent transportation systems, smart healthcare, education, smart agriculture and smart industrial processes. This information comprises multiple modalities such as textual data, images, sound, gestures and genetic sequences, which are generated through various sources.

Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment.

Internet of Things (IoTs) are now being integrated at a large scale in fast-developing applications such as healthcare, transportation, education, finance, insurance and retail. The next generation of automated applications will command machines to do tasks better and more efficiently. Both industry and academic researchers are looking at transforming applications using machine learning and deep learning to build better models and by taking advantage of the decentralized nature of Blockchain. But the advent of these new technologies also brings very high expectations to industries, organisations and users. The decrease of computing costs, the improvement of data integrity in Blockchain, and the verification of transactions using Machine Learning are becoming essential goals.

Medical imaging informatics play an important role in the effectiveness of present-day healthcare systems. Advancement of artificial intelligence, big data analytics, and internet of things technologies contribute greatly to various healthcare applications. Artificial intelligence techniques are contributing to improvements with traditionally human-based systems and ensuring that the accuracy of prediction and diagnosis is being continually enhanced. The development of reliable and accurate healthcare models is becoming ever more possible with the help of machine learning and deep learning technologies. Artificial intelligence has the power to solve many complex problems in medical imaging and is a technology that will help to design the future of many healthcare systems.

As digitized collections grow, Navigating Artificial Intelligence assesses how AI is reshaping the accessibility and ethical challenges of cultural records.

As artificial intelligence reshapes the cultural heritage sector, libraries, archives, and museums face novel opportunities and pressing ethical challenges. Navigating Artificial Intelligence for Cultural Heritage Organisations brings together leading experts from digital humanities, computer science, and information science to explore how AI-driven technologies transform the management and interpretation of digitized and born-digital records.

Through an examination of real-world case studies from the UK, US, and beyond, this volume provides significant insights into AI’s role in record management and text and data mining, while addressing issues of trust and risk in automated systems. Designed for heritage professionals, researchers, and information specialists, this book offers a timely guide to leveraging AI while also safeguarding the integrity and accessibility of cultural archives for the future.
 

A vivid, fast-paced inside look at financial markets, the people who work on them, and how technology is changing their world (and ours).
 
Markets are messy, and no one knows this better than traders who work tirelessly to predict what they will do next. In Whoosh Goes the Market, Daniel Scott Souleles takes us into the day-to-day experiences of a team at a large trading firm, revealing what it’s actually like to make and lose money on contemporary capital markets.
 
The traders Souleles shadows have mostly moved out of the pits and now work with automated, glitch-prone computer systems. They remember the days of trading manually, and they are suspicious of algorithmically driven machine-learning systems. Openly musing about their own potential extinction, they spend their time expressing fear and frustration in profanity-laced language. With Souleles as our guide, we learn about everything from betting strategies to inflated valuations, trading swings, and market manipulation. This crash course in contemporary finance vividly reveals the existential anxiety at the evolving front lines of American capitalism.