Predictive power of AIS on marine insurance : a demonstration of how activity level and operational patterns of the merchant fleet can be used to predict P&I insurance claims using machine learning

Digitalisation is making a growing appearance across all sectors, and traditional P&I insurance is no exception. Marine insurance is said to be several years behind traditional land-based insurance when it comes to digitalisation. This thesis is attempting to narrow the gap, by investigating the potential of applying machine learning on AIS-information against the extensive database on P&I insurance claims from the P&I Club Skuld. The thesis aims at investigating the potential to predict P&I insurance claims based on variables retrieved from AIS. AIS-information from 2013-2017 and Skuld's claims data for the same period was combined, and a total of five machine learning methods were tested to assess the predictive power of AIS-information. An extensive pre-processing was executed to make the data available for machine learning, and this section provides detailed information to anyone that aims at utilising AIS in their research. The research finds that AIS-information has predictive power for claims, as it links claims to activity level and operational patterns of the merchant fleet. The findings have implication for two fields in marine insurance; risk assessment/ pricing and loss prevention. In relation to loss prevention; average distance sailed, number of unique ports visited, and total distance sailed were found to have the most predictive power. Regarding risk assessment, the strongest model was able to predict 79 % of all cargo claims for Bulk & Cargo small. The research has revealed that machine learning has potential to create significant value in P&I insurance and that an extensive amount of data is ready to be applied in the pursuit of more accurate risk assessments and more precise loss prevention measures. Estimates vary between a potential yearly reduction in claims of 7-14%, in addition to increased revenue as a result of correct pricing.nhhma

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

A probabilistic model to estimate visual inspection error for metalcastings given different training and judgment types, environmental and human factors, and percent of defects

Current methods for visual inspection of cast metal surfaces are variable in both terms of repeatability and reproducibility. Because of this variation in the inspection methods, extra finishing operations are often prescribed; much of this is over processing in attempt to avoid rework or customer rejection. Additionally, defective castings may pass inspection and be delivered to the customer. Given the importance of ensuring that customers receive high-quality castings, this article analyzes and quantifies the probability of Type I and II errors, where a Type I error is a false alarm, and a Type II error misses a present defect. A probabilistic model frequently used in risk analysis, called an influence diagram, is developed to incorporate different factors impacting the chances of Type I and II errors. These factors include: training for inspectors, the type of judgment used during the inspection process, the percentage of defective castings, environmental conditions, and the inspectors’ capabilities. The model is populated with inputs based on prior experimentation and the authors’ expertise. The influence diagram calculates the probability of a Type I error at 0.35 and the probability of a Type II error at 0.40. These results are compared to a naïve Bayes model. A manufacturer can use this analysis to identify factors in its foundry that could reduce the probability of errors. Even under the best-case scenario, the probability of Type I error is 0.18 and the probability of Type II error is 0.30 for visual inspection. This indicates improvements to the inspection process for cast metal surfaces is required

Consumer Demand for Environmental, Social, and Ethical Information in Fishery and Aquaculture Product Labels

[EN] Customers' attention to sustainability labels in fishery and aquaculture products (FAPs) has been increasing in the last decades, and the industry has adapted to this growing interest by adopting fish ecolabels. However, there is a growing interest to widen the sustainability concept to include the social and ethical information of the fishery and aquaculture industry and to go further from the voluntary approach on the labeling of these aspects in FAPs. For this reason, using data from 2021 Eurobarometer and using machine learning techniques, we disentangle the characteristics of the FAP buyers that consider the importance of environmental impact, ethical, and social information appearing on FAP labeling. The results confirmed that most of the consumers who consider environmental, social, and ethical aspects when buying FAPs also think that this information should be labeled. In line with other works, young, educated, and environmentally aware consumers in high-income countries are more likely to request this information in the FAP label. One interesting finding of the study relates with the asymmetric impact of the variables and the important group of respondents who do not consider these aspects but also advocate to include them in the FAP label. The study outcomes can be beneficial for policymakers to design future public policies regarding FAP labeling, as well as to be taken into consideration in the marketing policies of fishery and aquaculture producers and retailers.This work was supported by the National Plan for Scientific and Technological Research and Innovation (Spanish Economy and Competitiveness Ministry), the Research Project PID2019105497 GB-I00, the European Fisheries Funds, opportunities for the fisheries sector through diversification, and the FLAGs management (DivPesc).Peiró Signes, A.; Miret Pastor, LG.; Antonino Galati; Segarra-Oña, M. (2022). Consumer Demand for Environmental, Social, and Ethical Information in Fishery and Aquaculture Product Labels. Frontiers in Marine Science. 9:1-14. https://doi.org/10.3389/fmars.2022.948437114

Consumer Demand for Environmental, Social, and Ethical Information in Fishery and Aquaculture Product Labels

Customers’ attention to sustainability labels in fishery and aquaculture products (FAPs) has been increasing in the last decades, and the industry has adapted to this growing interest by adopting fish ecolabels. However, there is a growing interest to widen the sustainability concept to include the social and ethical information of the fishery and aquaculture industry and to go further from the voluntary approach on the labeling of these aspects in FAPs. For this reason, using data from 2021 Eurobarometer and using machine learning techniques, we disentangle the characteristics of the FAP buyers that consider the importance of environmental impact, ethical, and social information appearing on FAP labeling. The results confirmed that most of the consumers who consider environmental, social, and ethical aspects when buying FAPs also think that this information should be labeled. In line with other works, young, educated, and environmentally aware consumers in high-income countries are more likely to request this information in the FAP label. One interesting finding of the study relates with the asymmetric impact of the variables and the important group of respondents who do not consider these aspects but also advocate to include them in the FAP label. The study outcomes can be beneficial for policymakers to design future public policies regarding FAP labeling, as well as to be taken into consideration in the marketing policies of fishery and aquaculture producers and retailers

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

Machine learning models for traffic classification in electromagnetic nano-networks

The number of nano-sensors connected to wireless electromagnetic nano-network generates different traffic volumes that have increased dramatically, enabling various applications of the Internet of nano-things. Nano-network traffic classification is more challenging nowadays to analyze different types of flows and study the overall performance of a nano-network that connects to the Internet through micro/nanogateways. There are traditional techniques to classify traffic, such as port-based technique and load-based technique, however the most promising technique used recently is machine learning. As machine learning models have a great impact on traffic classification and network performance evaluation in general, it is difficult to declare which is the best or the most suitable model to address the analysis of large volumes of traffic collected in operational nano-networks. In this paper, we study the classification problem of nano-network traffic captured by micro/nano-gateway, and then five supervised machine learning algorithms are used to analyze and classify the nano-network traffic from traditional traffic. Experimental analysis of the proposed models is evaluated and compared to show the most adequate classifier for nano-network traffic that gives very good accuracy and performance score to other classifiers.This work was supported in part by the ‘‘Agencia Estatal de Investigación’’ of ‘‘Ministerio de Ciencia e Innovación’’ of Spain under Project PID2019-108713RB-C51/MCIN/AEI/10.13039/501100011033, and in part by the ‘‘Agència de Gestió d’Ajuts Universitaris i de Recerca’’ (AGAUR) of the ‘‘Generalitat de Catalunya’’ under Grant 2021FI_B2 00091.Postprint (published version