scholarly journals Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

2019 ◽  
Vol 6 (5) ◽  
pp. 190001 ◽  
Author(s):  
Katherine E. Klug ◽  
Christian M. Jennings ◽  
Nicholas Lytal ◽  
Lingling An ◽  
Jeong-Yeol Yoon
Keyword(s):  
Heavy Metal ◽  
Support Vector Machine ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Mie Scattering ◽  
Training Data ◽  
Scattering Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Analysis

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

scholarly journals Application of ANN to the Sorption Equilibrium Modelling of Heavy Metal Ions on Clinoptilolite

2012 ◽  
Vol 19 (2) ◽  
pp. 227-237 ◽  
Author(s):  
Elwira Tomczak ◽  
Wladyslaw Kaminski
Keyword(s):  
Heavy Metal ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Sorption Isotherms ◽  
Initial Step ◽  
Training Data ◽  
Sorption Equilibrium ◽  
Network Training ◽  
Mineral Sorbent ◽  
Number Of Publications

Application of ANN to the Sorption Equilibrium Modelling of Heavy Metal Ions on Clinoptilolite The latest achievements in the field of mathematical modelling include the application of artificial neural networks (ANN). A growing interest in the ANN is confirmed by the number of publications devoted to the applicability of ANN in chemical, process and environmental engineering. A recent dynamic development of ANN provided an efficient and universal tool that is used to solve many tasks, including modelling, approximation and identification of objects. The initial step of applying the network to a given process consists in the determination of weights of the proposed neural network structure. This is performed on the basis of training data. A network that is properly trained allows correct information to be obtained on the basis of other data which have not been used in the network training. In most cases the network training is performed on the basis of a known mathematical model. However, the training of a network can be also performed using experimental data. In this paper, the sorption isotherms were predicted by means of a multilayer perceptron (MLP). Calculations were made using a training program written in Matlab, which took advantage of the Lavenberg-Marquardt procedure. In the last decade a growing interest is observed in inexpensive and very cheap adsorbents to remove heavy metal ions. Clinoptilolite is the mineral sorbent extracted in Poland used to remove heavy metal ions from diluted solutions. Equilibrium experiments were carried out to estimate sorptivity of a clinoptilolite and its selectivity towards Cu(II), Zn(II) and Ni(II) ions for multicomponent solution. Calculations with the use of MLP enabled description of sorption isotherms for one, two and three ions which were present at the same time in the solution. The network also enabled an analysis of sorption of the single ion, taking into account the effect of its concentration.

scholarly journals Classification with imperfect training labels

Biometrika ◽  
2020 ◽  
Vol 107 (2) ◽  
pp. 311-330 ◽  
Author(s):  
Timothy I Cannings ◽  
Yingying Fan ◽  
Richard J Samworth
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
General Setting ◽  
Excess Risk ◽  
Training Data ◽  
Training Dataset ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Linear Discriminant

Summary We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we derive detailed asymptotic properties for the popular $k$-nearest neighbour, support vector machine and linear discriminant analysis classifiers. One consequence of these results is that the $k$-nearest neighbour and support vector machine classifiers are robust to imperfect training labels, in the sense that the rate of convergence of the excess risk of these classifiers remains unchanged; in fact, our theoretical and empirical results even show that in some cases, imperfect labels may improve the performance of these methods. The linear discriminant analysis classifier is shown to be typically inconsistent in the presence of label noise unless the prior probabilities of the classes are equal. Our theoretical results are supported by a simulation study.

Klasifikasi Anjing dan Kucing menggunakan Algoritma Linear Discriminant Analysis dan Support Vector Machine

2019 ◽  
Vol 11 (1) ◽  
pp. 46-51
Author(s):  
Alethea Suryadibrata ◽  
Suryadi Darmawan Salim
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Systems ◽  
Training Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Medical Needs ◽  
Testing Data

One of the factors driving technological development is the increase in computers ability to complete various jobs. One of them is doing image processing, which is widely used in our daily life, such as the use of fingerprints, face/iris recognition barcodes, medical needs, and various other uses. Classification is one of the applications of image processing that is used the most. One algorithm that can be used for the development of image classification systems is Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). LDA is a feature extraction algorithm to find a subspace that separates classes well. SVM is a classification algorithm, based on the idea of finding a hyperplane that best divides a dataset into classes. In this study, LDA and SVM algorithms were tested on the dog and cat classification system, with the highest F- score calculation results being 0.69 with 200 training data and 50 testing data for cats and 0.64 with 200 training data and 30 testing data for dogs.

Bioadsorption of Multiple Heavy Metal Ions by Rhizophora Apiculate sp. and Elaesis Guineensis sp.

2017 ◽  
Vol 14 (1) ◽  
pp. 15
Author(s):  
M.B. Nicodemus Ujih ◽  
Mohammad Isa Mohamadin ◽  
Milla-Armila Asli ◽  
Bebe Norlita Mohammed
Keyword(s):  
Heavy Metal ◽  
Metal Ions ◽  
Removal Efficiency ◽  
Heavy Metal Ions ◽  
Agricultural Waste ◽  
Living Organism ◽  
Heavy Metal Concentration ◽  
Industrial Area ◽  
Electrochemical Treatment ◽  
Chemical Oxidation

Heavy metal ions contamination has become more serious which is caused by the releasing of toxic water from industrial area and landfill that are very harmful to all living organism especially human and can even cause death if contaminated in small amount of heavy metal concentration. Currently, peoples are using classic method namely electrochemical treatment, chemical oxidation/reduction, chemical precipitation and reverse osmosis to eliminate the metal ions from toxic water. Unfortunately, these methods are costly and not environmentally friendly as compared to bioadsorption method, where agricultural waste is used as biosorbent to remove heavy metals. Two types of agricultural waste used in this research namely oil palm mesocarp fiber (Elaesis guineensis sp.) (OPMF) and mangrove bark (Rhizophora apiculate sp.) (MB) biomass. Through chemical treatment, the removal efficiency was found to improve. The removal efficiency is examined based on four specification namely dosage, of biosorbent to adsorb four types of metals ion explicitly nickel, lead, copper, and chromium. The research has found that the removal efficiency of MB was lower than OPMF; whereas, the multiple metals ions removal efficiency decreased in the order of Pb2+ > Cu2+ > Ni2+ > Cr2+.

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