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Impermeable base layers that are made of materials with low permeability, such as clay soil, are necessary to prevent leachate in landfills from harming the environment. However, over time, the permeability of the clay soil changes. Therefore, to reduce and minimize the risk, the permeability-related characteristics of the base layers must be improved. Thus, this study aims to serve this purpose by experimentally investigating the effects of nanomaterial addition (aluminum oxide, iron oxide) into kaolin samples. The obtained samples are prepared by applying standard compaction, and the permeability of the soil sample is experimentally investigated by passing leachate from the reactors, in which these samples are placed. Therefore, Flow Resistance (FR) analysis is conducted and the obtained results show that the Al additives are more successful than the Fe additive in reducing leachate permeability. Besides, the concentration values of some polluting parameters (Chemical Oxygen Demand (COD), Total Kjeldahl Nitrogen (TKN), and Total Phosphorus (TP)) at the inlet and outlet of the reactors are analyzed. Three different models (Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), Support Vector Machine (SVM)) are applied to the data obtained from the experimental study. The results have shown that polluting parameters produce high FR regression similarity rates (>75%), TKN, TP, and COD features are highly correlated with the FR value (>60%) and the most successful method is found to be the SVM model.

It is important to protect the soil and groundwater from the pollution originating from leachate. Compacted clay soils is a favorable and economic method to protect groundwater and soil against contamination. In this study, compaction tests of leachate was done by using Modified Proctor method. The effects of microbial activity on the permeability of compacted clay soils were analyzed and the obtained data were applied to k-Nearest Neighbors (k-NN) method to predict the permeability of soils in landfill sites. k-NN method, which is a non-parametric distance-based machine learning method and widely used in classification and regression problems was applied to model the relationship between the microorganisms and the permeability. By using k-NN classification method, total heterotrophic bacteria and fungi microorganisms correctly classified the permeability variance as 78.59% and 77.31% success rate, respectively. Also, k-NN modelling was set on regression mode to predict permeability value and produced similar success rates in regression similarity with the actual value. Although, fecal coliforms and fecal streptococci microorganisms had neutral or negative contribution on analyses. For prediction accuracy and regression analysis, the k-NN method was considered for modeling the data. The results of the k-Nearest Neighbors method proved that it is a promising tool for predicting permeability of compacted clay by using microbial activity.

In recent years, there has been increasing interest in the development of telediagnosis and telemonitoring systems for Parkinson's disease (PD) based on measuring the motor system disorders caused by the disease. As approximately 90% percent of PD patients exhibit some form of vocal disorders in the earlier stages of the disease, the recent PD telediagnosis studies focus on the detection of the vocal impairments from sustained vowel phonations or running speech of the subjects. In these studies, various speech signal processing algorithms have been used to extract clinically useful information for PD assessment, and the calculated features were fed to learning algorithms to construct reliable decision support systems. In this study, we apply, to the best of our knowledge for the first time, the tunable Q-factor wavelet transform (TQWT) to the voice signals of PD patients for feature extraction, which has higher frequency resolution than the classical discrete wavelet transform. We compare the effectiveness of TQWT with the state-ofthe-art feature extraction methods used in diagnosis of PD from vocal disorders. For this purpose, we have collected the voice recordings of 252 subjects in the context of this study and extracted multiple feature subsets from the voice recordings. The feature subsets are fed to multiple classifiers and the predictions of the classifiers are combined with ensemble learning approaches. The results show that TQWT performs better or comparable to the state-of-the-art speech signal processing techniques used in PD classification. We also find that Mel-frequency cepstral and the tunable-Q wavelet coefficients, which give the highest accuracies, contain complementary information in PD classification problem resulting in an improved system when combined using a filter feature selection technique. (C) 2018 Elsevier B.V. All rights reserved.

This study aims at measuring the image representation capacity of Layer-4, one of the visual processing steps in the cerebral cortex and at comparing it with pixel-like representations. Visual information that comes into Layer-4 as input is transformed into a nonlinear representation that is capable of detecting certain visual patterns, similar to an RBF-network, and then is passed to neighboring and upper layers. This internal representation, analogous to the kernel concept in the field of machine learning, specifies which frequent patterns exist in the given image patch and is also capable of easily learning and predicting its variations such as its spatial transformations. In this paper, using a Layer-4 simulation model [1], the sensitivity of Layer-4 representation to the spatial transformations of natural images is analyzed. Using multikernel support vector machine (SVM) approach using a limited number of support vectors, it has been shown that Layer-4 representation is more accurate in the targeted learning tasks.