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

SWOT Analysis and Developing Strategies for the Realisation of Urban Livability in Tehran

The present study aims to analyse the strengths, weaknesses, opportunities and threats (SWOT) of managing the Tehran Metropolitan to help comprehend the status quo as well as the challenges to the ..

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

Abdominal Symptoms and Incident Gallstones in a Population Unaware of Gallstone Status

Introduction. Symptoms associated with newly formed gallstones have never been studied in a population unaware of their gallstones. The objective of this population-based cohort study was to determine which debut of abdominal symptoms was associated with newly formed gallstones. Materials and Methods. A cohort study was performed of a random sample from general population of Copenhagen. Participants had ultrasound examinations and answered questionnaires about abdominal symptoms at baseline and two reexaminations over 12 years. Participants were not informed of gallstone status. Inclusion criteria were no gallstones or cholecystectomy at baseline and attending a reexamination. Results. Of 3,785 participants, 2,845 fulfilled inclusion criteria. Changes in overall abdominal pain were not significantly different between incident gallstones or gallstone-free participants. Multiple adjusted logistic regression analyses showed that incident gallstones were significantly associated with debut of abdominal pain with projection, localized in the whole upper abdomen, and of longer duration. No significant associations for functional symptoms were identified. Conclusions. A new onset of abdominal pain with projection, localized in the whole upper abdomen, and of longer duration is associated with newly formed gallstones in participants unaware of gallstone status. Functional symptoms should not be the indication for surgical treatment

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

Behjatoshoara and a New Approach in Biography-writing in the Qajar Era (With the Introduction of Hadiqatoshoara)

Persian biographies are among the main sources in the history of literature. The movement of writing biographies was accelerated during Qajar era and on the verge of the Constitutional Revolution. Like other proses, the critique entered into the writing of this period, and it took the form of ironical or humorous biographies. Similar examples were also written for these critical-pleasantry works. One of the followers of this new approach was Mohammad Kazem Tabrizi (known as Asrar Ali Shah). He wrote his biography book in regard to the superficial and indiscreet thinking of the public with a humorous tone and full of anecdotes, and called it Behjatoshoara or Romouz Nojaba. Among various types of biographies, writing a biography like this full of worthless poems and unconditional praises show that the author had a special motive. But this issue has not been investigated by the researchers. This lack of clarification regarding the writer's motivation has led to some falsifications and judgments about the work and its author. In this study, Asrar Ali Shah's Behjatoshoara and his motivation to write it is investigated. Also, Hadiqatoshoara is introduced and its relationship with Behjatoshoara is clarified