Titlebook: Data Science and Big Data: An Environment of Computational Intelligence; Witold Pedrycz,Shyi-Ming Chen Book 2017 Springer International Pu

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Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analyticsd neural network) mapped on smart-gateway networks. Scalability and robustness are considered to perform real-time data analytics. Furthermore, as the success of system depends on the trust of users, network intrusion detection for smart gateway has also been developed to provide system security. Ex

arcane

Predicting Spatiotemporal Impacts of Weather on Power Systems Using Big Data Sciencelarge amount of data needs to be performed. The problem addressed in this chapter is how such Big Data can be integrated, spatio-temporally correlated, and analyzed in real-time, in order to improve capabilities of modern electricity network in dealing with weather caused emergencies.

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Osmud Rahman,Benjamin C. M. Fungtational time from cubic to linear. In the second part, a distributed clustering approach with fixed computational budget is illustrated. This method extends the k-means algorithm by applying regularization at the level of prototype vectors. An optimal stochastic gradient descent scheme for learning

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Svetlana Karelskaia,Ekaterina Zugals for the upper and lower boundaries for data objects with respect to each cluster. BFPM facilitates algorithms to converge and also inherits the abilities of conventional fuzzy and possibilistic methods. In Big Data applications knowing the exact type of data objects and selecting the most accurat

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MELON

Anita Sadeghpour MD, FACC, FASEon detection approaches, such as rule-based, signature-based and computer intelligence based approaches were developed. Out of these, computational intelligence based anomaly detection mechanisms show the ability to handle hitherto unknown intrusions and attacks. However, these approaches suffer fro

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Anita Sadeghpour MD, FACC, FASEd on supervised features following conventional data mining principle. However, the classification of majority or positive class is over-sampled by taking each minority class sample. Definitely, significant computationally intelligent methodologies have been introduced. Following the philosophy of d

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Andreas Vogel,Oktay Ergünay,Müfiz Alpmenachieve effective selection of data pre-processing techniques towards effective selection of relevant attributes, sampling of representative training and test data, and appropriate dealing with missing values and noise. More importantly, this framework allows the employment of suitable machine learn

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Mirage

N. Bayülke,E. Inan,A. Dogan,A. Hürataause of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of