Evolutionary-modified fuzzy nearest-neighbor rule for pattern classification

Kassani, P.H.,Teoh, A.B.J.,Kim, E. Pergamon ; Elsevier Science Ltd 2017 expert systems with applications Vol.88 No.-

This paper presents an improved version of the well-established k nearest neighbor (k-NN) and fuzzy NN (FNN), termed the multi-objective genetic-algorithm-modified FNN (MOGA-MFNN). The MFNN design problem is converted into a multi-modal objective maximization problem constrained by four objective functions. Thereafter, the associated parameter set of the MFNN and the feature attributes can be determined optimally and automatically via the non-dominated sorting genetic algorithm II. We introduce two new objective functions termed the Margin-I and Margin-II, which are used to improve the generalization capability of the MFNN for the unknown data, along with two existing performance functions: the geometric mean and the area under the receiver-operated characteristic curve for the training accuracy. Moreover, we proposed a novel data-dependent weight-assignment technique for local class membership functions of the MFNN. The technique enables the MFNN to determine its local neighbors adaptively through the MOGA algorithm. To expedite the classification, the MOGA-MFNN is implemented on a graphical processing unit (GPU), which significantly increases the computation speed. Furthermore, the local class-membership function of the MFNN can be computed in advance, rather than delaying it to the classification stage. This again can improve the classification speed. The MOGA-MFNN is evaluated on 20 datasets obtained from the repository of the University of California, Irvine (UCI). The experiments with rigorous statistical significance tests demonstrate that the proposed method performs competitively with the existing methods.

Pseudoinverse Matrix Decomposition Based Incremental Extreme Learning Machine with Growth of Hidden Nodes

Kassani, Peyman Hosseinzadeh,Kim, Euntai Korean Institute of Intelligent Systems 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.2

The proposal of this study is a fast version of the conventional extreme learning machine (ELM), called pseudoinverse matrix decomposition based incremental ELM (PDI-ELM). One of the main problems in ELM is to determine the number of hidden nodes. In this study, the number of hidden nodes is automatically determined. The proposed model is an incremental version of ELM which adds neurons with the goal of minimization the error of the ELM network. To speed up the model the information of pseudoinverse from previous step is taken into account in the current iteration. To show the ability of the PDI-ELM, it is applied to few benchmark classification datasets in the University of California Irvine (UCI) repository. Compared to ELM learner and two other versions of incremental ELM, the proposed PDI-ELM is faster.

Prevalence of Cigarette Smoking and Associated Factors among Male Citizens in Tehran, Iran

Kassani, Aziz,Baghbanian, Abdolvahab,Menati, Rostam,Hassanzadeh, Jafar,Asadi-Lari, Mohsen,Menati, Walieh Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.3

Background: Cigarette smoking is as the leading cause of cancer mortality and other chronic diseases in males worldwide. The prevalence of cigarette smoking is different across and within countries by age, education level, occupation, and so on. This study aimed to determine the prevalence of cigarette smoking and its relationship with individuals' demographic factors and BMI in adolescent men living in Tehran, Iran. Materials and Methods: This study involved secondary analysis of the 'Urban Health Equity Assessment and Response Tool-2' survey conducted in Tehran, Iran, among men aged 20+, 2011-2012. Using a multistage sampling method, 45,990 men were included in the study. The cigarette smoking status, BMI and demographic factors measured through a self-administered questionnaire. Chi-square, t-test, and logistic regression model were used to examine the relationships between the independents variables and cigarette smoking behavior, using SPSS software version 21. Results: In the total of 45,990 men, the overall prevalence of cigarette smoking was 14.6% (CI 95%: 14.29-14.94). Age (OR=0.96; CI 95%:0.94-0.98), house ownership (OR=0.68; CI 95%: 0.64-0.72), job status (OR=0.60; CI 95%: 0.46-0.86), marital status (OR=0.42; CI 95%: 0.39-0.47) and educational levels (OR=0.50; CI95%: 0.45-0.54) were associated with the prevalence of cigarette smoking. However, associations with BMI, family size, residency years, and district were not statistically significant. Conclusions: Given the relatively high prevalence of cigarette smoking in the study population, policy interventions are required to address this major public health issue, with a focus on the population demographic influences.

A new sparse model for traffic sign classification using soft histogram of oriented gradients

Kassani, P.H.,Teoh, A.B.J. Elsevier Science, B.V 2017 Applied soft computing Vol.52 No.-

<P>Traffic sign recognition (TSR) is an integrated part of driver assistance systems and it remains an active research topic in computer vision today. This paper proposes a solution for TSR problem which composed of robust traffic sign image descriptor and sparse classifiers. Specifically, we outline a variant of histogram of oriented gradients (HOG), namely Soft HOG (SHOG) which exploits the symmetry shape of traffic sign images to find the optimal locations of the cell of histogram for SHOG computation. We show that our compact SHOG feature is more discriminative than HOG. Furthermore, two sparse analytical polynomial based classifiers, namely Sparse Bayesian Multivariate polynomial model and Sparse Bayesian Reduced polynomial model are introduced. The proposed sparse classifiers enable implicit feature selection and alleviate the overfitting problem. This leads to higher accuracy performance with prudent set of features. Our solution is evaluated on publicly available German Traffic Sign Recognition Benchmark (GTSRB) dataset and 16 datasets from (UCI) repository. Experiment results demonstrated that the proposed method has satisfactory result when compared to state-of-the-art methods. (C) 2016 Elsevier B.V. All rights reserved.</P>

Fast response in-line gas sensor using C-type fiber and Ge-doped ring defect photonic crystal fiber.

Kassani, Sahar Hosseinzadeh,Park, Jiyoung,Jung, Yongmin,Kobelke, Jens,Oh, Kyunghwan Optical Society of America 2013 Optics express Vol.21 No.12

<P>An in-line chemical gas sensor was proposed and experimentally demonstrated using a new C-type fiber and a Ge-doped ring defect photonic crystal fiber (PCF). The C-type fiber segment served as a compact gas inlet/outlet directly spliced to PCF, which overcame previous limitations in packaging and dynamic responses. C-type fiber was prepared by optimizing drawing process for a silica tube with an open slot. Splicing conditions for SMF/C-type fiber and PCF/C-type fiber were experimentally established to provide an all-fiber sensor unit. To enhance the sensitivity and light coupling efficiency we used a special PCF with Ge-doped ring defect to further enhance the sensitivity and gas flow rate. Sensing capability of the proposed sensor was investigated experimentally by detecting acetylene absorption lines.</P>

Enhancement of Surface Wettability by Intra-Helium Plasma for Liquid Core Fiber Lens

Kassani, Sahar Hosseinzadeh,Khazaeinezhad, Reza,Nazari, Tavakol,Wonho Choe,Kyunghwan Oh IEEE 2014 IEEE photonics technology letters Vol.26 No.20

<P>We generated an atmospheric pressure helium plasma inside a silica hollow optical fiber (HOF) and the surface wettability of the inside wall was flexibly controlled by the plasma intensity. By filling the glycerol into the HOF, we experimentally investigated the surface characteristics by contact angle measurements. Liquid-fiber optic lenses with various numerical apertures were proposed and the light intensity patterns through liquid core lens to the air were investigated numerically.</P>

Sparse pseudoinverse incremental extreme learning machine

Kassani, Peyman Hosseinzadeh,Teoh, Andrew Beng Jin,Kim, Euntai Elsevier 2018 Neurocomputing Vol.287 No.-

<P>An extreme learning machine (ELM) is a popular analytic single hidden layer feedforward neural network because of its rapid learning capacity. However, vanilla dense ELMs are affected by the overfitting problem when the number of hidden neurons is high. Further direct consequences of the density are decreases in both the training and prediction speeds. In this study, we propose an incremental method for sparsifying the ELM using a newly devised indicator driven by the condition number in the ELM design matrix, in which we call sparse pseudoinverse incremental-ELM (SPI-ELM). SPI-ELM exhibits better generalization performance and lower run-time complexity compared with ELM. However, the sparsification process may negatively affect the learning speed of SPI-ELM; thus, we introduce an iterative matrix decomposition algorithm to address this issue. We also demonstrate that there is a useful relationship between the condition number in the ELM design matrix and the number of hidden neurons. This relationship helps to understand the random weights and nonlinear activation functions in ELMs. We evaluated the SPI-ELM method based on 20 benchmark data sets from the University of California Irvine repository and three real-world databases from the computer vision domain. (C) 2018 Elsevier B.V. All rights reserved.</P>

Pseudoinverse Matrix Decomposition Based Incremental Extreme Learning Machine with Growth of Hidden Nodes

Peyman Hosseinzadeh Kassani,Euntai Kim 한국지능시스템학회 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.2

The proposal of this study is a fast version of the conventional extreme learning machine (ELM), called pseudoinverse matrix decomposition based incremental ELM (PDI-ELM). One of the main problems in ELM is to determine the number of hidden nodes. In this study, the number of hidden nodes is automatically determined. The proposed model is an incremental version of ELM which adds neurons with the goal of minimization the error of the ELM network. To speed up the model the information of pseudoinverse from previous step is taken into account in the current iteration. To show the ability of the PDI-ELM, it is applied to few benchmark classification datasets in the University of California Irvine (UCI) repository. Compared to ELM learner and two other versions of incremental ELM, the proposed PDI-ELM is faster.

Proposing a GPU based Modified Fuzzy Nearest Neighbor Rule for Traffic Sign Detection

Peyman Hosseinzadeh Kassani,Junhyuk Hyun,Euntai Kim 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10

The purpose of this study is introducing a graphical process unit (GPU) implementation of a modified fuzzy nearest neighbor rule useful for traffic sign detection (TSD). The new method tries to detect road signs using color information in order to locate regions of interest. The candidate regions of interest are obtained by color information. Afterward, candidate regions are used for making histogram of oriented gradient (HOG) feature. Finally, the features are fed into the GPU-based modified fuzzy nearest neighbor in order to detect traffic signs. The proposed rule modifies the way for fuzzification of query sample in terms of distances while the conventional fuzzy nearest neighbor (FNN) doesn’t care distance of local neighbors. The accuracy of the proposed method is compared with the state of the arts k-nearest neighbor (k-NN), FNN and support vector machine algorithms on the challenging German traffic sign detection benchmark (GTSDB) data set. Results indicate that the modified rule achieves good accuracy and is competitive compared to others.

Developing a Modified Fuzzy Nearest Neighbor Rule for Pattern Classification

Peyman Hosseinzadeh Kassani,Junhyuk Hyun,Euntai Kim 한국지능시스템학회 2015 한국지능시스템학회 학술발표 논문집 Vol.25 No.1