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.

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 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.

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.