Data Analysis in the Shipping Industry

This chapter identifies the critical issues that must be addressed to accelerate the digital transition in the chartering market. The maritime industry is one of the pillars of global trade, where change is a constant. Again, shipping is at the cusp of a new era—one driven by data. The authors review the state-of-the-art technology that is useful to automate chartering processes. · The Fourth Industrial Revolution (or Industry 4.0) starts to change the bulk shipping markets leveraging the data flow between industrial processes in the physical and virtual world. · The internet of things accelerates data flow from things in the real world to the virtual world and enables us to control processes in real-time. Machine-to-machine communication, together with artificial intelligence, creates autonomous systems in many areas of production and logistics. Based on the gathered elements, eShip's case study was analyzed, and future steps have been defined for the data analysis in the shipping industry.

Towards Ethical Neuromarketing 2.0 Based on Artificial Intelligence

Eighty percent of consumer behaviors and purchases rely on subconscious processes. The use of neuromarketing tools to study consumer behavior is not clear, notably regarding its practices and intentions toward consumers. This chapter aims to understand how neuromarketing can explain consumer behavior thanks to Neuromarketing 2.0 tools, how companies can manage the collected data in a responsible way and build a neuroethical charter to regulate the way companies use it. Most companies choose to not communicate about it when they use neuromarketing tools, and therefore, this chapter aims to pave the way towards solutions and recommendations and democratize its use by making Neuromarketing 2.0 more responsible and ethical.

A Software Engineering Perspective on Building Production-Ready Machine Learning Systems

This chapter discusses how to build production-ready machine learning systems. There are several challenges involved in accomplishing this, each with its specific solutions regarding practices and tool support. The chapter presents those solutions and introduces MLOps (machine learning operations, also called machine learning engineering) as an overarching and integrated approach in which data engineers, data scientists, software engineers, and operations engineers integrate their activities to implement validated machine learning applications managed from initial idea to daily operation in a production environment. This approach combines agile software engineering processes with the machine learning-specific workflow. Following the principles of MLOps is paramount in building high-quality production-ready machine learning systems. The current state of MLOps is discussed in terms of best practices and tool support. The chapter ends by describing future developments that are bound to improve and extend the tool support for implementing an MLOps approach.

Artificial Intelligences Are Subsets of Human Beings, Mainly in Fictions and Films

This chapter aims to create new knowledge regarding artificial intelligence (AI) ethics and relevant subjects while reviewing ethical relationship between human beings and AI/robotics and linking between the moral fabric or the ethical issues of AI as used in fictions and films. It carefully analyses how a human being will love robot and vice versa. Here, fictions and films are not just about technology but about their feelings and the nature of bonding between AIs and the human race. Ordinary human beings distrust and then start to like AIs. However, if the AI becomes a rogue as seen in many fictions and films, then the AI is taken down to avoid the destruction of the human beings. Scientists like Turing are champions of robot/AI's feelings. Fictional and movie AIs are developed to keenly watch and comprehend humans. These actions are so close to empathy they amount to consciousness and emotional quotient.

A Historical Review of Immersive Storytelling Technologies

This chapter is focused on describing the history and the current relevance of user experience (UX) techniques that combine data science and AI in the research field of interactive and immersive storytelling, including virtual and augmented realities. It initially presents a brief history of interactive storytelling, video games, VR and AR, AI and data science, and the user experience (UX) techniques used in those areas. Later, the chapter describes the UX techniques in depth, using AI and data science that work best and are more useful for testing interactive media products, describing examples of its applications briefly. Finally, the chapter presents conclusions in relationship with utopias and dystopias regarding the future use of UX, AI, and data science in several areas such as edutainment, social media, media arts, and business, among others.

The Effect of Population, Public Investments, and Economic Growth on Regional Renewable Energy Consumption in Turkey

Making the use of renewable energy sources widespread is of paramount importance for Turkey as for all countries. In this regard, the determiners of renewable energy consumption have been investigated. The effect of determining or factors affecting the use of renewable energy sources on a regional scale to Turkey were examined with different qualitative and quantitative research techniques. In this study, the factors of economic growth, public investments, and population are analyzed by considering regional differences on the consumption of renewable energy resources. The effect of regional economic growth, regional public investments, and regional population on the amount of regional renewable energy consumption were investigated by using panel data of 26 statistical regions of Level-2 classification in the period between 2010-2018 in Turkey. The results obtained by the dynamic panel data analysis concluded that economic growth and public investments at the regional level increased renewable energy consumption while the population growth decreased.

Time Series Forecasting in Retail Sales Using LSTM and Prophet

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

Peruvian Consumer Responses to the Use of Technologies in the Context of COVID-19

The pandemic caused by COVID-19 has led many states, in an attempt to control the spread of the virus, to decree social immobilization, which meant the introduction of restrictions on the free movement of people and the opening of shops. This led them to seek new marketing channels for purchases. Among these, ICTs have been important. This is the focus of the analysis in this document. Through surveys and interviews, information was obtained, divided into four age groups, which showed that an important part of the population has had to resort to ICTs to acquire goods and/or pay for services. This change in the way of acquiring had different particularities according to the age group analyzed, with a greater change in the oldest group (56 to 74 years old). It can be concluded that the massification of these tools has generated a change in the ways of acquiring products, and this is likely to transcend the pandemic and these channels will be maintained and strengthened in the future.

Port Dada Integration

The maritime supply chain is growing in complexity. Ports are at the crossroads of many activities, modes, and stakeholders, and are actively becoming digital hubs. Today, digital and physical connectivity go hand in hand. The port could benefit from taping the opportunities arising from digitalization and data integration since it helps to leverage external knowledge, engage stakeholders, create new decision-making anchors, lower the risk of certain investments, boost productivity and cut costs, and accelerate greening and digital transition, generating possibilities for just-in-time operations and optimizations. The chapter aims to apprehend the use of data science in the port sector. The state of the art in Brazil and Portugal are different. Even inside Brazil, there is no homogeneity of ports in the usage of digital infrastructure, cloud computing, or artificial intelligence. The existing inequalities hinder general cooperation between nations but, at the same time, reveal opportunities to approach specific nodes in the international supply chain.

AI Meaning and Applications in the Consumer Sector of Retailing, Hospitality, and Tourism

Artificial intelligence (AI) algorithms have been continuously adopted in businesses, such as retailing, hospitality, and tourism. The current chapter aims first to provide a conceptualization of evolution of AI. Second, it gives an overview of AI in business and finally points out examples of applications of AI in retailing, hospitality, and tourism. Third, the chapter offers suggestions for further research. This chapter is devoted to researchers and practitioners. The audience will benefit from an overview of AI meaning and potential research to be developed and also benefit from examples of other business that already employ such AI algorithms.