Accumulation of plastic waste during COVID-19 Adyel, Tanveer M
Science (American Association for the Advancement of Science),
2020-Sep-11, 2020-09-11, Letnik:
369, Številka:
6509
Journal Article
•Electroencephalogram signal classification is performed using universum learning.•Support vector machine classifier uses prior information from interictal signals.•Many feature extraction techniques ...are used for comparing the algorithms.•Universum support vector machine is used first time for seizure classification.
Support vector machine (SVM) has been used widely for classification of electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as epilepsy and sleep disorders. SVM shows good generalization performance for high dimensional data due to its convex optimization problem. The incorporation of prior knowledge about the data leads to a better optimized classifier. Different types of EEG signals provide information about the distribution of EEG data. To include prior information in the classification of EEG signals, we propose a novel machine learning approach based on universum support vector machine (USVM) for classification. In our approach, the universum data points are generated by selecting universum from the EEG dataset itself which are the interictal EEG signals. This removes the effect of outliers on the generation of universum data. Further, to reduce the computation time, we use our approach of universum selection with universum twin support vector machine (UTSVM) which has less computational cost in comparison to traditional SVM. For checking the validity of our proposed methods, we use various feature extraction techniques for different datasets consisting of healthy and seizure signals. Several numerical experiments are performed on the generated datasets and the results of our proposed approach are compared with other baseline methods. Our proposed USVM and proposed UTSVM show better generalization performance compared to SVM, USVM, Twin SVM (TWSVM) and UTSVM. The proposed UTSVM has achieved highest classification accuracy of 99% for the healthy and seizure EEG signals.
In this paper, we propose a new linear programming formulation of exact 1-norm twin support vector machine (TWSVM) for classification whose solution is obtained by solving a pair of dual exterior ...penalty problems as unconstrained minimization problems using Newton–Armijo algorithm. The idea of our formulation is to reformulate TWSVM as a strongly convex problem by incorporated regularization techniques and then derive an exact 1-norm linear programming formulation for TWSVM to improve robustness and sparsity. The solution of two modified unconstrained minimization problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems in TWSVM and TBSVM, which leads to extremely simple and fast algorithm. One significant advantage of our proposed method is the implementation of structural risk minimization principle. However, only empirical risk is considered in the primal problems of TWSVM due to its complex structure and thus may incur overfitting and suboptimal in some cases. Our approach has the advantage that a pair of matrix equation of order equals to the number of input examples is solved at each iteration of the algorithm. The algorithm converges from any starting point that can be easily implemented in MATLAB without using any optimization packages. Computational comparisons of our proposed method against original TWSVM, GEPSVM and SVM have been made on both synthetic and benchmark datasets. Experimental results show that our method is better or comparable in both computation time and classification accuracy.
In this paper, a new unconstrained minimization problem formulation is proposed for linear programming twin support vector machine (TWSVM) classifiers. The proposed formulation leads to two ...smaller-sized unconstrained minimization problems having their objective functions piecewise differentiable. However, since their objective functions contain the non-smooth “plus” function, two new smoothing approaches are assumed to solve the proposed formulation, and then apply Newton-Armijo algorithm. The idea of our formulation is to reformulate TWSVM as a strongly convex problem by incorporated regularization techniques and then derive smooth 1-norm linear programming formulation for TWSVM to improve robustness. One significant advantage of our proposed algorithm over TWSVM is that the structural risk minimization principle is implemented in the primal problems which embodies the marrow of statistical learning theory. In addition, the solution of two modified unconstrained minimization problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems in TWSVM and TBSVM, which leads to extremely simple and fast algorithm. Our approach has the advantage that a pair of matrix equation of order equals to the number of input examples is solved at each iteration of the algorithm. The algorithm converges from any starting point that can be easily implemented in MATLAB without using any optimization packages. The performance of our proposed method is verified experimentally on several benchmark and synthetic datasets. Experimental results show the effectiveness of our methods in both training time and classification accuracy.
Abstract
In this paper we explain the temperature dependence of excitonic effective mass and charge carrier conduction mechanism occurs in CH
3
NH
3
PbI
3−x
Cl
x
thin films prepared by chemical dip ...coating (CDC), spray pyrolysis (Spray) and repeated dipping-withdrawing (Dipping). Hall Effect study confirmed that prepared CH
3
NH
3
PbI
3−x
Cl
x
samples are p-type semiconductor having carrier concentration of the order of ~ 10
16
cm
−3
. The charge carrier mobility, mean free path and mean free life time were found to decrease with increasing temperature due to polaronic effect. The excitonic effective mass is estimated to (0.090–0.196)m
e
and excitonic binding energy (15–33) meV, well consistent with Wannier-Mott hydrogenic model and the nature of exciton is likely to be Mott-Wannier type. From electrical measurement, it was observed that charge carrier conduction in CH
3
NH
3
PbI
3−x
Cl
x
is governed by migration of
$${\mathrm{I}}^{-}$$
I
-
and CH
3
N
$${\mathrm{H}}_{3}^{+}$$
H
3
+
vacancies and vacancy-assisted diffusion processes depending on temperature.
Microplastics (MPs) have frequently been detected in freshwater environments, and there is growing concern about their ecological effects, especially the influence of the “plastisphere” on the ...freshwater ecosystems. The colonization of microbes on MPs would significantly alter their transport behavior, i.e., buoyancy, in fresh water. In this research, we studied the effects of biofilm colonization on the sinking and floating of three MPs, i.e., polyethylene terephthalate (PET), polypropylene (PP), and polyvinyl chloride (PVC), after 44 days of incubation in three freshwater systems (the Niushoushan River, the Qinhuai River, and East Lake) in China. The results showed that the biofilms attached to the three MPs contained different biomass and chlorophyll-a levels were related to water environmental conditions and physicochemical properties of MPs, based on redundancy analysis. Generally, PET and PVC sinking, with density higher than water, tended to increase after biofilm formation. Thereafter, the settling velocity of biofouled PET and PVC squares became faster than that of the virgin ones. In summary, our study suggested that biofouling does affect the sinking of MPs in fresh water and consequently influences the transport behavior and the distribution characteristics of MPs in freshwater environments, and this issue deserves more scientific attention.
Display omitted
•Effects of biofilm colonization on the transport behaviors of MPs were studied.•Distinct biomasses of biofilms were found on various MPs in the three fresh waters.•Biofouling increased PET and PVC density.•Biofouling favored the sinking of MPs with original density higher than water.
Coronavirus (COVID-19) and its new strain resulted in massive damage to society and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and MRI offers excellent early ...diagnosis potential to augment the traditional healthcare strategy to fight against COVID-19. However, the identification of COVID infected lungs X-rays is challenging due to the high variation in infection characteristics and low-intensity contrast between normal tissues and infections. To identify the infected area, in this work, we present a novel depth-wise dense network that uniformly scales all dimensions and performs multilevel feature embedding, resulting in increased feature representations. The inclusion of depth wise component and squeeze-and-excitation results in better performance by capturing a more receptive field than the traditional convolutional layer; however, the parameters are almost the same. To improve the performance and training set, we have combined three large scale datasets. The extensive experiments on the benchmark X-rays datasets demonstrate the effectiveness of the proposed framework by achieving 96.17% in comparison to cutting-edge methods primarily based on transfer learning.
•An efficient projection based clustering algorithm is presented.•Proposed technique is an alternative to plane based clustering algorithms.•Concave-convex procedure is utilized to solve the ...optimization problem.•Comparison on clustering performance is presented on large scale datasets.•Better generalization performance is achieved on real world applications.
Clustering is a prominent unsupervised learning technique. In the literature, many plane based clustering algorithms are proposed, such as the twin support vector clustering (TWSVC) algorithm. In this work, we propose an alternative algorithm based on projection axes termed as least squares projection twin support vector clustering (LSPTSVC). The proposed LSPTSVC finds projection axis for every cluster in a manner that minimizes the within class scatter, and keeps the clusters of other classes far away. To solve the optimization problem, the concave-convex procedure (CCCP) is utilized in the proposed method. Moreover, the solution of proposed LSPTSVC involves a set of linear equations leading to very less training time. To verify the performance of the proposed algorithm, several experiments are performed on synthetic and real world benchmark datasets. Experimental results and statistical analysis show that the proposed LSPTSVC performs better than existing algorithms w.r.t. clustering accuracy as well as training time. Moreover, a comparison of the proposed method with existing algorithms is presented on biometric and biomedical applications. Better generalization performance is achieved by proposed LSPTSVC on clustering of facial images, and Alzheimer’s disease data.
Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not ...been comprehensively evaluated. Here, we sought to assess the efficiency of different regression algorithms on brain age estimation. To this end, we built a brain age estimation framework based on a large set of cognitively healthy (CH) individuals (<inline-formula><tex-math notation="LaTeX">N = 788</tex-math></inline-formula>) as a training set followed by different regression algorithms (22 different algorithms in total). We then quantified each regression-algorithm on independent test sets composed of 88 CH individuals, 70 mild cognitive impairment patients as well as 30 Alzheimer's disease patients. The prediction accuracy in the independent test set (i.e., CH set) varied in regression algorithms mean absolute error (MAE) from 4.63 to 7.14 yrs, <inline-formula><tex-math notation="LaTeX">R^2</tex-math></inline-formula> from 0.76 to 0.88. The highest and lowest prediction accuracies were achieved by Quadratic Support Vector Regression algorithm (MAE <inline-formula><tex-math notation="LaTeX">= 4.63</tex-math></inline-formula> yrs, <inline-formula><tex-math notation="LaTeX">R^2 = 0.88, 95\%</tex-math></inline-formula> CI <inline-formula><tex-math notation="LaTeX">= -1.26, 1.42</tex-math></inline-formula>) and Binary Decision Tree algorithm (MAE <inline-formula><tex-math notation="LaTeX">= 7.14</tex-math></inline-formula> yrs, <inline-formula><tex-math notation="LaTeX">R^2 = 0.76, 95\%</tex-math></inline-formula> CI <inline-formula><tex-math notation="LaTeX">= -1.50, 2.62</tex-math></inline-formula>), respectively. Our experimental results demonstrate that the prediction accuracy in brain age frameworks is affected by regression algorithms, indicating that advanced machine learning algorithms can lead to more accurate brain age predictions in clinical settings.
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