Abstract
The economic impact of fraud is wide and fraud can be a critical problem when the prevention procedures are not robust. In this paper we create a model to detect fraudulent transactions, and then use a classification algorithm to assess if the agent is fraud prone or not. The model (BOND) is based on the analytics of an economic network of agents of three types: individuals, businesses and financial intermediaries. From the dataset of transactions, a sliding window of rows previously aggregated per agent has been used and machine learning (classification) algorithms have been applied. Results show that it is possible to predict the behavior of agents, based on previous transactions. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Demand response (DR) is one of the most cost-effective elements of residential and small industrial building for the purpose of reducing the cost of energy. Today with broadening of the smart grid, electricity market and especially smart home, using DR can reduce cost and even make profits for consumers. On the other hand, utilizing centralized controls and have bidirectional communications Bi-directional communication between DR aggregators and consumers make many problems such as scalability and privacy violation. In this chapter, we propose a multi-agent method based on a Q-learning algorithm Q-learning algorithm for decentralized control of DR. Q-learning is a model-free reinforcement learning Reinforcement learning technique and a simple way for agents to learn how to act optimally in controlled Markovian domains. With this method, each consumer adapts its bidding and buying strategy over time according to the market outcomes. We consider energy supply for consumers such as small-scale renewable energy generators. We compare the result of the proposed method with a centralized aggregator-based approach that shows the effectiveness of the proposed decentralized DR market Decentralized DR market. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
Ordinal arrangement of objects is a common property in biomedical images. Traditional methods to deal with semantic image segmentation in this setting are ad-hoc and application specific. In this paper, we propose ordinal-aware deep learning architectures for image segmentation that enforce pixelwise consistency by construction. We validated the proposed architectures on several real-life biomedical datasets and achieved competitive results in all cases.

Abstract
Cooperative Cyber-Physical Systems (Co-CPSs) can be enabled using wireless communication technologies, which in principle should address reliability and safety challenges. Safety for Co-CPS enabled by wireless communication technologies is a crucial aspect and requires new dedicated design approaches. In this paper, we provide an overview of five Co-CPS use cases, as introduced in our SafeCOP EU project, and analyze their safety design requirements. Next, we provide a comprehensive analysis of the main existing wireless communication technologies giving details about the protocols developed within particular standardization bodies. We also investigate to what extent they address the non-functional requirements in terms of safety, security and real time, in the different application domains of each use case. Finally, we discuss general recommendations about the use of different wireless communication technologies showing their potentials in the selected real-world use cases. The discussion is provided under consideration in the 5G standardization process within 3GPP, whose current efforts are inline to current gaps in wireless communications protocols for Co-CPSs including many future use cases.

Abstract
Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.

Abstract
Long period fiber gratings (LPFGs) were coated with iron (Fe) and exposed to oxidation in air and in water having different concentrations of sodium chloride (NaCl) to detect the formation of iron oxides and hydroxides. The process was monitored in real time by measuring the characteristics of the LPFGs attenuation bands. Thin films of Fe were deposited on top of silica (SiO2) substrates, annealed in air, and exposed to water with NaCl. The composition of the oxide and hydroxide layers was analyzed by SEM/EDS and X-ray diffraction. It observed the formation of oxide phases, Fe3O4 (magnetite), and Fe2O3 (hematite) when annealing in air, and Fe-2(OH)(3) Cl (hibbingite) and FeO(OH) (lepidocrocite) when exposed to water with NaCl. Results shows that Fe-coated LPFGs can be used as sensors for real-time monitoring of corrosion in offshore and in coastal projects where metal structures made of iron alloys are in contact with sea or brackish water. In addition, LPFGs coated with hematite were characterized for sensing, leading to the conclusion that the sensitivity to the refractive index of the surrounding medium can be tuned by proper choice of hematite thickness.