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Mahmood A. Mahmood, Saleh Almuayqil, Khalaf Okab Alsalem and Karim Gasmi
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Johannes Rank, Jonas Herget, Andreas Hein and Helmut Krcmar
Big Data and primarily distributed stream processing systems (DSPSs) are growing in complexity and scale. As a result, effective performance management to ensure that these systems meet the required service level objectives (SLOs) is becoming increasingl...
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Vyas O?Neill and Ben Soh
Cloud computing has become ubiquitous in the enterprise environment as its on-demand model realizes technical and economic benefits for users. Cloud users demand a level of reliability, availability, and quality of service. Improvements to reliability ge...
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Giuseppe Granato, Alessio Martino, Andrea Baiocchi and Antonello Rizzi
Network traffic analysis, and specifically anomaly and attack detection, call for sophisticated tools relying on a large number of features. Mathematical modeling is extremely difficult, given the ample variety of traffic patterns and the subtle and vari...
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Alessio Martino, Luca Baldini and Antonello Rizzi
Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal...
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Youkou Dong, Dingtao Yan and Lan Cui
The discrete element method (DEM), a discontinuum-based method to simulate the interaction between neighbouring particles of granular materials, suffers from intensive computational workload caused by massive particle numbers, irregular particle shapes, ...
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Piotr Artiemjew
Granular computing techniques are a huge discipline in which the basic component is to operate on groups of similar objects according to a fixed similarity measure. The first references to the granular computing can be seen in the works of Zadeh in fuzzy...
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Piotr Artiemjew and Krzysztof Krzysztof Ropiak
This paper is a continuation of works based on a previously developed new granulation method?homogeneous granulation. The most important new feature of this method compared to our previous ones is that there is no need to estimate optimal parameters. App...
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Krzysztof Ropiak and Piotr Artiemjew
The set of heuristics constituting the methods of deep learning has proved very efficient in complex problems of artificial intelligence such as pattern recognition, speech recognition, etc., solving them with better accuracy than previously applied meth...
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Hongbing Liu, Xiaoyu Diao and Huaping Guo
Parametric granular computing classification algorithms lead to difficulties in terms of parameter selection, the multiple performance times of algorithms, and increased algorithm complexity in comparison with nonparametric algorithms. We present nonpara...
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