Browsing by Subject "Filtration"

Now showing 1 - 3 of 3
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    Group classification and some similarity solutions for a nonlinear filtration equation
    (Association for Scientific Research, 2002) Pakdemirli M.
    A nonlinear filtration equation in which the filter coefficient is an arbitrary function of the specific deposit is considered. Lie Group theory is applied to the coupled system of partial differential equations. Group classification is performed with respect to the arbitrary filter coefficient. Some similarity solutions are constructed using the symmetries.
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    Removal of heavy metals in wastewater by using zeolite nano-particles impregnated polysulfone membranes
    (Elsevier B.V., 2016) Yurekli Y.
    In this study, the adsorption and the filtration processes were coupled by a zeolite nanoparticle impregnated polysulfone (PSf) membrane which was used to remove the lead and the nickel cations from synthetically prepared solutions. The results obtained from X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) analysis indicated that the synthesized zeolite nanoparticles, using conventional hydrothermal method, produced a pure NaX with ultrafine and uniform particles. The performance of the hybrid membrane was determined under dynamic conditions. The results also revealed that the sorption capacity as well as the water hydraulic permeability of the membranes could both be improved by simply tuning the membrane fabricating conditions such as evaporation period of the casting film and NaX loading. The maximum sorption capacity of the hybrid membrane for the lead and nickel ions was measured as 682 and 122 mg/g respectively at the end of 60 min of filtration, under 1 bar of transmembrane pressure. The coupling process suggested that the membrane architecture could be efficiently used for treating metal solutions with low concentrations and transmembrane pressures. © 2016 Elsevier B.V.
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    A feature selection model based on genetic rank aggregation for text sentiment classification
    (SAGE Publications Ltd, 2017) Onan A.; KorukoGlu S.
    Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials. The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes it difficult to train the learning algorithms in a feasible time and degrades the classification accuracy of the built model. Hence, feature selection becomes an essential task in developing robust and efficient classification models whilst reducing the training time. In text mining applications, individual filter-based feature selection methods have been widely utilized owing to their simplicity and relatively high performance. This paper presents an ensemble approach for feature selection, which aggregates the several individual feature lists obtained by the different feature selection methods so that a more robust and efficient feature subset can be obtained. In order to aggregate the individual feature lists, a genetic algorithm has been utilized. Experimental evaluations indicated that the proposed aggregation model is an efficient method and it outperforms individual filter-based feature selection methods on sentiment classification. © The Author(s) 2015.