Data Mining Practical Machine Learning Tools and Techniques

https://andreashellander.se/wp-content/uploads/2016/01/1-s2.0-S0306437914001288-main.pdf

The rise of big data on cloud computing

http://wpage.unina.it/valerio.persico/pubs/CloudIoT_FGCS.pdf

Integration of Cloud computing and Internet of Things

http://extev.syros.aegean.gr/papers/J50.pdf

Addressing cloud computing security issues

THE NEED FOR QUALITY The first thing that we need to consider, in any organization, is that quality is the most important thing. The quality of your work defines you.  Whoever you are,  Whatever you do,  I can find the same products and services cheaper somewhere else. But your quality is your signature. Developing and delivering high quality products and services means that you are doing things correctly from the beginning. As a consequence, you are reducing the need for additional services, from verification to warranty.

Total quality management.

http://extev.syros.aegean.gr/papers/J42.pdf

Securing e-Government and e-Voting with an open cloud computing architecture

We train an Artificial Neural Network (ANN) to predict future vessel's behavior.We study if an ANN is capable of inferring the unique behaviour of a vessel.We design, train and implement a proof of concept ANN as a cloud based web app.The derived ANN has the ability to predict short and long term vessel behaviour. Progressively huge amounts of data, tracking vessels during their voyages across the seas, are becoming available, mostly due to the automatic identification system (AIS) that vessels of specific categories are required to carry. These datasets provide detailed insights into the patterns vessels follow, while safely navigating across the globe, under various conditions. In this paper, we develop an Artificial Neural Network (ANN) capable of predicting a vessels future behaviour (position, speed and course), based on events that occur in a predictable pattern, across large map areas. The main concept of this study is to determine if an ANN is capable of inferring the unique behavioural patterns that each vessel follows and successively use this as a means for predicting multiple vessel behaviour into a future point in time. We design, train and implement a proof of concept ANN, as a cloud based web application, with the ability of overlaying predicted short and long term vessel behaviour on an interactive map. Our proposed approach could potentially assist in busy port scheduling, vessel route planning, anomaly detection and increasing overall Maritime Domain Awareness.

/pdf/a-cloud-based-architecture-capable-of-perceiving-and-2tmpej5ln1.pdf

A cloud based architecture capable of perceiving and predicting multiple vessel behaviour

Vessel traffic management systems (VTMS) and vessel traffic monitoring information systems (VTMIS) have been available for a number of years now. These systems have significantly contributed to increasing the efficiency and safety of operations at sea. However, nowadays, risks at sea are once again on the rise, thus demanding an evolution in VTMS and VTMIS, such that they can support a human operator’s better understanding of the complex reality at sea and enhance his or her decision-making in light of danger. A critical requirement of such systems, is that they exhibit the ability to for-see unfolding cautious and potentially hazardous situations, so as to propose measures of danger avoidance. In this study, we employ machine learning, and specifically artificial neural networks, as a tool to add predictive capacity to VTMIS. The main objective of this study is to implement a publicly accessible, web-based system capable of real time learning and accurately predicting any vessels future behavior in low computational time. This work describes our approach, design choices, implementation and evaluation details, while we present a proof of concept prototype system. Our proposal can potentially be used as the predictive foundation for various intelligent systems, including vessel collision prevention, vessel route planning, operation efficiency estimation and even anomaly detection systems.

Real-time vessel behavior prediction

Raising awareness and providing guidance to on-line data protection is undoubtedly a crucial issue worldwide. Equally important is the issue of applying privacy-related legislation in a coherent and coordinated way. Both these topics gain extra attention when referring to medical environments and, thus, to the protection of patients' privacy and medical data. Electronic medical transactions require the transmission of personal and medical information over insecure communication channels like the Internet. It is, therefore, a rather straightforward task to capture the electronic medical behavior of a patient, thus constructing "patient profiles," or reveal sensitive information related to a patient's medical history. The consequence is clearly a potential violation of the patient's privacy. We performed a risk analysis study for a Greek shared care environment for the treatment of patients suffering from beta-thalassemia, an empirically embedded scenario that is representative of many other electronic medical environments; we capitalized on its results to provide an assessment of the associated risks, focusing on the description of countermeasures, in the form of technical guidelines that can be employed in such medical environments for protecting the privacy of personal and medical information.

Dimitrios Lekkas

Papers

Addressing cloud computing security issues

Securing e-Government and e-Voting with an open cloud computing architecture

A cloud based architecture capable of perceiving and predicting multiple vessel behaviour

Real-time vessel behavior prediction

Technical guidelines for enhancing privacy and data protection in modern electronic medical environments