Application of Optimization Methods for Solving Clustering and Classification Problems

Cluster and classification analysis are very interesting data mining topics that can be applied in many fields. Clustering includes the identification of subsets of the data that are similar. Intuitively, samples within a valid cluster are more similar to each other than they are to a sample belonging to a different cluster. Samples in the same cluster have the same label. The aim of data classification is to set up rules for the classification of some observations that the classes of data are supposed to be known. Here, there is a collection of classes with labels and the problem is to label a new observation or data point belonging to one or more classes of data. The focus of this thesis is on solvingclustering and classification problems. Specifically, we will focus on new optimization methods for solving clustering and classification problems. First we briefly give some data analysis background. Then a review of different methods currently available that can be used to solve clustering and classification problems is also given. Clustering problem is discussed as a problem of non-smooth, non-convex optimization and a new method for solving this optimization problem is developed. This optimization problem has a number of characteristics that make it challenging: it has many local minimum, the optimization variables can be either continuous or categorical, and there are no exact analytical derivatives. In this study we show how to apply a particular class of optimization methods known as pattern search methods to address these challenges. This method does not explicitly use derivatives, and is particularly appropriate when functions are non-smooth. Also a new algorithm for finding the initial point is proposed. We have established that our proposed method can produce excellent results compared to those previously known methods. Results of computational experiments on real data sets present the robustness and advantage of the new method. Next the problem of data classification is studied as a problem of global, non-smooth and non-convex optimization; this approach consists of describing clusters for the given training sets. The data vectors are assigned to the closest cluster and correspondingly to the set, which contains this cluster and an algorithm based on a derivative-free method is applied to the solution of this problem. The proposed method has been tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithm

A New Method for Solving Supervised Data Classification Problems

Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms

Application of Artificial Neural Network (ANN) for prediction diameter of silver nanoparticles biosynthesized in Curcuma longa extract

In this study silver nanoparticles (Ag-NPs) are biosynthesized from silver nitrate aqueous solution through a simple and eco-friendly route using Curcuma longa (C. longa) tuber powder extracts which acted as a reductant and stabilizer simultaneously. Characterizations of nanoparticles are done using X-ray diffraction (XRD) and transmission electron microscopy (TEM). We present an artificial neural network (ANN) approach is used to model the size of Ag-NPs as a function of the volume of C. Longa extraction, temperature of reaction, stirring time and volume of AgNO3. The suitable ANN model is found to be a network with two layers that first layer has 10 neurons and second layer has 1 neuron. This model is capable for predicting the size of Ag-NPs synthesized by green method for a wide range of conditions with a mean absolute error of less than 0.01 and a regression of about 0.99. Based on the presented model it is possible to design an effective green method for obtain Ag-NPs, while minimum received materials are used and minimum size of Ag-NPs will be obtained. Also simulation of the process is performed using ANN media. According to the model’s results, the volume of C. Longa extraction, temperature of reaction, and volume of AgNO3 about 18 mL, 30 °C and 2 mL are chosen as the optimum size of Ag-NPs, respectively. Results obtained reveal the reliability and good predicatively of neural network model for the prediction of the size of Ag-NPs in green method

Artificial intelligence in numerical modeling of silver nanoparticles prepared in montmorillonite interlayer space

Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3 concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT) d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3 concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO 3 of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm

Application of artificial neural network(ANN) for the prediction of size of silver nanoparticles prepared by green method

The artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. Determination of particle size is one of the critical parameters in nanotechnology.TheAg-NPs have attracted significant attention for chemical, physical and clinical applications due to their exceptional properties.The nanosilver crystals were prepared in the biopolymer mediated without any aggregation by using green chemical reduction method. The method has an advantage of size control which is essential in nano-metal synthesis. The resulting of silver nanoparticles (Ag-NPs) characterized by using of X-ray diffraction (XRD) and transmission electron microscopy (TEM) technique.XRD patterns confirmed that Ag-NPs crystallographic planes were face centered cubic (fcc) type. TEM results showed that mean diameters of Ag-NPs for four different amounts of variables were less than 40 nm. This method with comparison to other methods is green, high yield, speedy and easy to use.This paper presents an ANN model for the predictionsize of Ag-NPs by green method. Themodel accounts for the effect of NaOH volumes, temperature, stabilizer, and AgNO3 concentration on the size of nanoparticle.The best model presented a trustworthy agreement in predicting experimental data. The characteristic parameters of the presented ANN models are fully reported in the paper

Synthesis and characterization of silver/montmorillonite/chitosan bionanocomposites by chemical reduction method and their antibacterial activity

Silver nanoparticles (AgNPs) of a small size were successfully synthesized using the wet chemical reduction method into the lamellar space layer of montmorillonite/chitosan (MMT/Cts) as an organomodified mineral solid support in the absence of any heat treatment. AgNO3, MMT, Cts, and NaBH4 were used as the silver precursor, the solid support, the natural polymeric stabilizer, and the chemical reduction agent, respectively. MMT was suspended in aqueous AgNO3/Cts solution. The interlamellar space limits were changed (d-spacing = 1.24–1.54 nm); therefore, AgNPs formed on the interlayer and external surface of MMT/Cts with d-average = 6.28–9.84 nm diameter. Characterizations were done using different methods, ie, ultraviolet-visible spectroscopy, powder X-ray diffraction, transmission electron microscopy, scanning electron microscopy, energy dispersive X-ray fluorescence spectrometry, and Fourier transform infrared spectroscopy. Silver/montmorillonite/chitosan bionanocomposite (Ag/MMT/Cts BNC) systems were examined. The antibacterial activity of AgNPs in MMT/Cts was investigated against Gram-positive bacteria, ie, Staphylococcus aureus and methicillin-resistant S. aureus and Gram-negative bacteria, ie, Escherichia coli, E. coli O157:H7, and Pseudomonas aeruginosa by the disc diffusion method using Mueller Hinton agar at different sizes of AgNPs. All of the synthesized Ag/MMT/Cts BNCs were found to have high antibacterial activity. These results show that Ag/MMT/Cts BNCs can be useful in different biological research and biomedical applications, including surgical devices and drug delivery vehicles

Synthesis of nickel doped cobalt ferrite in presence of SDS with different heat treatment by co-precipitation method

Structural properties of nickel doped cobalt ferrite were synthesized by co-precipitation method with sodium dodecyl sulfate (SDS) as a surfactant at different temperatures. The particle size was estimated by the full width half maximum (FWHM) of the strongest X-ray diffraction (XRD) peak. The average particle size was in the range of 21-36 nm. The particles size was controlled via controlling calcination temperature which was in the range of 600 to 900°C. The morphology of nickel doped cobalt ferrite was investigated. The results showed that a well crystalline single cubic structure of nickel doped CoFe2O4 phase was formed through precipitation precursors at pH value of 11. The pH was adjusted by the use of ammonium hydroxide solution

Fabrication of aluminum matrix composites reinforced with Al2ZrO5 nano particulates synthesized by sol-gel auto-combustion method

Nanocrystalline Al2ZrO5 with particle size of about 38 nm was directly synthesized by combination of sol−gel auto-combustion and ultrasonic irradiation techniques from metal nitrates and glycine as precursors. The overall process involves formation of homogeneous sol, formation of dried gel and combustion process of the dried gel. Aluminum alloy matrix composites reinforced with 0.75%, 1.5% and 2.5% Al2ZrO5 nanoparticles were fabricated via stir casting method and the fabrication was performed at various casting temperatures. The resulting composites were tested for their nanostructure and present phases by SEM and XRD analysis. Optimum amount of reinforcement and casting temperature were determined by evaluating the density, hardness and compression strength of the composites. Al matrix alloy reinforced by Al2ZrO5 nanoparticles improves the hardness and compressive strength of the alloy to maximum values of BHN 61 and 900 MPa, respectively. The most improved mechanical properties are obtained with the specimen including 1.5% Al2ZrO5 produced at 850 °C

Effect of Curcuma longa tuber powder extract on size of silver nanoparticles prepared by green method

Biosynthesis of noble metal nanoparticles is a vast developing area of research. In the present study, silver nanoparticles (Ag-NPs) were synthesized from aqueous silver nitrate through a simple and biosynthetic route using water extract of Curcuma longa (C. longa) tuber powder, which acted simultaneousl as a reductant and stabilizery. The as-prepared samples are characterized using UV–Visible, XRD, TEM, SEM, EDXF, and FT-IR techniques. The formation of Ag-NPs is evidenced by the appearance of the signatory brown color of the solution and UV–vis spectra. Formation of Ag/C. longa was determined by UV–Vis spectroscopy where surface plasmon absorption maxima can be observed at 457–415 nm from the UV–Vis spectrum. The XRD analysis shows that the Ag-NPs are of a face-centered cubic structure. Well-dispersed Ag-NPs with anisotropic and isotropic morphology for 5, 10, and 20 mL of C. longa water extract having a size less than 10 nm are seen in TEM images. The optimum volume extraction to synthesize smallest particle size was 20 mL with mean diameter and standard division 4.90 ± 1.42 nm. FT-IR spectrum indicates the presence of different functional groups in capping the nanoparticles with C. longa. The zeta potential analysis results indicated that the charge of C. longa was negative and increased in Ag/C. longa emulsion with increasing of volumes of extract used (10–20 mL). The most needed outcome of this work will be the development of value-added products from C. longa for biomedical and nanotechnology-based industries

An Efficient Optimization Method for Solving Unsupervised Data Classification Problems

Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification