14,859 research outputs found
Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities
Critical Infrastructures (CIs), such as smart power grids, transport systems,
and financial infrastructures, are more and more vulnerable to cyber threats,
due to the adoption of commodity computing facilities. Despite the use of
several monitoring tools, recent attacks have proven that current defensive
mechanisms for CIs are not effective enough against most advanced threats. In
this paper we explore the idea of a framework leveraging multiple data sources
to improve protection capabilities of CIs. Challenges and opportunities are
discussed along three main research directions: i) use of distinct and
heterogeneous data sources, ii) monitoring with adaptive granularity, and iii)
attack modeling and runtime combination of multiple data analysis techniques.Comment: EDCC-2014, BIG4CIP-201
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionâmaking process as well as strategic planning. In this study, a prototype asymmetricâbased neuroâfuzzy network (AGFINN) architecture has been implemented for shortâterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellâestablished learningâbased models
Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning
Decision support is a probabilistic and quantitative method designed for
modeling problems in situations with ambiguity. Computer technology can be
employed to provide clinical decision support and treatment recommendations.
The problem of natural language applications is that they lack formality and
the interpretation is not consistent. Conversely, ontologies can capture the
intended meaning and specify modeling primitives. Disease Ontology (DO) that
pertains to cancer's clinical stages and their corresponding information
components is utilized to improve the reasoning ability of a decision support
system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider
disease manifestations and provides physicians with treatment solutions from
similar previous cases for reference. The proposed DSS supports natural
language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease
classification with the help of the ontology
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