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Article | 17-July-2017

The Achievements of Students at the Stages of Education from the Second to Fourth Using Functional Principal Component Analysis

research covers the average exam results received on graduation from the second, third and fourth stage of education. Functional principal component analysis, which is based on functional data, will be applied in the study. This method allows an analysis of dynamic data.

Mirosława Sztemberg-Lewandowska

Statistics in Transition New Series, Volume 18 , ISSUE 1, 139–150

Article | 30-November-2018

Hazard Grading Model of Terrorist Attack Based on Machine Learning

and summing the 14 indicators of each event to obtain the score of the event. For each hazard level, finding the average score for all events is at that level. 4)Sorting by the average scores of the five hazard levels, We divide them into one to five grades from high to low. The higher score means the greater damage. A. Using the PCA algorithm for dimensional reduction Principal Component Analysis (PCA) extracts M-dimensional feature matrices from N-dimensional matrices. First, we calculates

Jun Yu, Tong Xian, Zhiyi Hu, Yutong Liu

International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 2, 81–85

Article | 01-September-2016

A NOVEL HYBRID LOCALIZATION METHOD FOR WIRELESS SENSOR NETWORK

Wireless sensor network is a kind of brand-new information acquisition platform, which is realized by the introduction of self-organizing and auto-configuration mechanisms. Node localization technology represents a crucial component of wireless sensor network. In this paper, a localization method based on kernel principal component analysis and particle swarm optimization back propagation algorithm is carefully discussed. First of all, taking KPCA as the front-end system to extract the main

Wang Jun, Zhang Fu, Ren Tiansi, Chen Xun, Liu Gang

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 3, 1323–1340

Article | 02-November-2017

Normalization techniques for gas sensor array as applied to classification for black tea

tea-taster like scores. It has been observed that pre-processing of gas sensor data improves the classification accuracy and in this paper, a comparative study of different normalization techniques is presented for black tea application using electronic nose. For this study black tea samples were collected from different tea gardens in India. At first Principal Component Analysis (PCA) is used to investigate the presence of clusters in the sensors responses in multidimensional space. Then

Bipan Tudu, Bikram Kow, Nabarun Bhattacharyya, Rajib Bandyopadhyay

International Journal on Smart Sensing and Intelligent Systems, Volume 2 , ISSUE 1, 176–189

Article | 01-September-2015

FRAGRANCE MEASUREMENT OF SCENTED RICE USING ELECTRONIC NOSE

into a sensor chamber; a water bath module for preparing rice sample, said water bath module including a heater attachment to facilitate cooking; a computing module to quantify the aroma data acquired by sensors; data acquisition module etc. Principal Component Analysis (PCA) implemented for clustering the data sets acquired from sensor array. Also data generated from sensor array was fed to Probabilistic Neural Network (PNN), Back-propagation Multilayer Perceptron (BPMLP) and Linear Discriminant

Arun Jana, Nabarun Bhattacharyya, Rajib Bandyopadhyay, Bipan Tudu, Subhankar Mukherjee, Devdulal Ghosh, Jayanta Kumar Roy

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1730–1747

Research Article | 05-September-2018

Multi-algorithmic Palmprint Authentication System Based on Score Level Fusion

Fusion of multiple algorithms utilizes as much information as possible from each algorithm for enhancing the performance of the biometric authentication system. It is a big challenge to formulate a single algorithm for any biometric authentication system to addresses the problem of illumination, orientations and pose variations. The palmprint features are extracted using two feature extraction algorithms namely contourlet transform with principal component analysis and dual-tree complex wavelet

C. Murukesh, G. Arul Elango

International Journal on Smart Sensing and Intelligent Systems, Volume 11 , ISSUE 1, 1–11

Article | 24-July-2017

Numerical Taxonomy Helps Identification of Merliniidae and Telotylenchidae (Nematoda: Tylenchoidea) from Iran

REZA GHADERI, HABIBALLAH HAMZEHZARGHANI, AKBAR KAREGAR

Journal of Nematology, Volume 49 , ISSUE 2, 207–222

research-article | 30-November-2018

New data on known species of Hirschmanniella and Pratylenchus (Rhabditida, Pratylenchidae) from Iran and South Africa

. Scanning electron microscopy (SEM) Specimens preserved in glycerine were selected for observation under SEM according to Abolafia (2015). The nematodes were hydrated in distilled water, dehydrated in a graded ethanol-acetone series, critical point dried, coated with gold, and observed with a Zeiss Merlin microscope (5 kV) (Zeiss, Oberkochen, Germany). Statistical analysis Principal component analysis and the correlation of morphometric data using the Pearson method was done by XLSTAT (Addinsoft, 2007

Ebrahim Shokoohi, Joaquín Abolafia, Phatu William Mashela, Nafiseh Divsalar

Journal of Nematology, Volume 51 , 1–26

Research Article | 15-February-2020

Thin Film Coated QCM-Sensors and Pattern Recognition Methods for Discrimination of VOCs

gas sensors with different chemical affinity towards VOC molecules. The sensitivity of the elaborated QCMbased sensors was evaluated by monitoring the frequency shifts of the quartz exposed to different concentrations of volatile organic compounds, such as; ethanol, benzene and chloroform. The sensors responses data have been used for the identification and quantification of VOCs. The principal component analysis (PCA) and the neural-network (NNs) pattern recognition analysis were used for the

Omar C. Lezzar, A. Bellel, M. Boutamine, S. Sahli, Y. Segui, P. Raynaud

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6

Article | 14-October-2020

A Comparative Study of Face Recognition Classification Algorithms

analysis PCA data dimensionality reduction The data set contains a total of 400 photos. We use the machine learning library Scikit-learn provided by python to process the data, and display part of the data set pictures as shown in Figure 1. Figure 1. ORL partial face image The experimental data has 4096 features per picture. Since the number of features is much greater than the number of samples, it is easy to regenerate overfitting during training. Therefore, a principal component analysis

Changyuan Wang, Guang Li, Pengxiang Xue, Qiyou Wu

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 23–29

Article | 21-July-2017

First Report of Heterorhabditis amazonensis from Venezuela and Characterization of Three Populations

During a survey in western Venezuela in 2011, three new populations of Heterorhabditis amazonensis (LPV081, LPV156, and LPV498) were isolated. Some differences were found in terms of morphometry compared with the original description; however, the distance from the anterior end to the excretory pore is the most variable character; significantly shorter in all infective juveniles and in other developmental stages depending on the population. According to a Principal Component Analysis, LPV498

NAIYULIN MORALES, PATRICIA MORALES-MONTERO, VLADIMIR PUZA, ERNESTO SAN-BLAS

Journal of Nematology, Volume 48 , ISSUE 3, 139–147

Article | 05-September-2013

AN INVESTIGATION OF DECISION ANALYTIC METHODOLOGIES FOR STRESS IDENTIFICATION

In modern society, more and more people are suffering from some type of stress. Monitoring and timely detecting of stress level will be very valuable for the person to take counter measures. In this paper, we investigate the use of decision analytics methodologies to detect stress. We present a new feature selection method based on the principal component analysis (PCA), compare three feature selection methods, and evaluate five information fusion methods for stress detection. A driving stress

Yong Deng, Chao-Hsien Chu, Huayou Si, Qixun Zhang, Zhonghai Wu

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1675–1699

Research Article | 17-October-2018

NMR Analysis Reveals a Wealth of Metabolites in Root-Knot Nematode Resistant Roots of Citrullus amarus Watermelon Plants

) technology and principal component analysis (PCA) were used to analyze and compare metabolic profiles of seven CA accessions resistant to RKN along with two RKN-susceptible watermelon cultivars (Charleston Gray and Crimson Sweet). Calculation of the Mahalanobis distance revealed that the CA United States Plant Introduction (PI) 189225 (Line number 1832) and PI 482324 (1849) have the most distinct metabolic profiles compared with the watermelon cultivars Charleston Gray and Crimson Sweet, respectively

Mihail Kantor, Amnon Levi, Judith Thies, Nihat Guner, Camelia Kantor, Stuart Parnham, Arezue Boroujerdi

Journal of Nematology, Volume 50 , ISSUE 3, 303–316

Article | 01-December-2014

FILM THICKNESS MEASUREMENT OF MECHANICAL SEAL BASED ON CASCADED ARTIFICIAL NEURAL NETWORK RECOGNITION MODEL

, wavelet packet and kernel principal component analysis are used to extract the data features. Then cascaded decision is presented to improve the recognition rate of artificial neural network, by which the film thickness can be estimated accurately. With a set of tests, the results demonstrate that the method is effective. It can be widely used to take measurement of the film thickness in industrial field.

Erqing Zhang, Pan Fu, Kesi LI, Xiaohui Li, Zhongrong Zhou

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1870–1889

original-paper | 11-March-2020

Oligotrophic Nitrification and Denitrification Bacterial Communities in a Constructed Sewage Treatment Ecosystem and Nitrogen Removal of Delftia tsuruhatensis NF4

effluent water samples eluate from OAD medium Fig. 2. 2a) The rarefaction curve representing bacterial diversity of IC, I.NF, I.DNF, CRIC, CRI.NF, CRI.DNF, EC, E.NF, and E.DNF; 2b) The phylum community abundance of nine samples, including IC, I.NF, I.DNF, CRIC, CRI.NF, CRI.DNF, EC, E.NF, and E.DNF; 2c) The genera community abundance of nine samples, including IC, I.NF, I.DNF, CRIC, CRI.NF, CRI.DNF, EC, E.NF, and E.DNF; 2d) Principal component analysis (PCA) of the bacterial community using an

RUILAN YANG, JING LI, LUYAO WEI-XIE, LIN SHAO

Polish Journal of Microbiology, Volume 69 , ISSUE 1, 99–108

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