Momen Ortogon Legendre sebagai Suatu Fitur untuk Pengecaman Kedudukan Penumpang

2012 ◽  
Author(s):  
Choong Yeun Liong ◽  
Nor Azura Md. Ghani ◽  
Abdul Aziz Jemain ◽  
Chris Thompson
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Cross Validation ◽  
Research Work ◽  
The United States ◽  
Highway Safety ◽  
Linear Discriminant ◽  
Segmented Images ◽  
Leave One Out

Makalah ini membincangkan penggunaan momen ortogon Legendre (MOL) sebagai suatu fitur untuk pengelasan imej kedudukan penumpang yang tersegmen-sempurna. Keupayaan mengenal pasti kedudukan penumpang dalam kenderaan adalah penting misalnya sebagai input kepada suatu sistem kereta pintar yang dapat memberikan maklumat untuk pelepasan beg udara keselamatannya. Hasil kajian oleh Insurance Institute for Highway Safety di Amerika Syarikat telah menunjukkan bahawa kedudukan relatif seseorang penumpang itu kepada beg udara adalah penting demi keselamatannya. Lantaran itu kajian ini adalah suatu usaha ke arah menghasilkan satu sistem untuk pengecaman kedudukan penumpang dalam kereta. Sebanyak 1283 imej daripada sepuluh kelas kedudukan penumpang yang telah disegmen-sempurna secara manual diguna dalam kajian ini dan sembilan fitur MOL telah dijana untuk setiap imej menggunakan atur cara C++. Fitur-fitur itu seterusnya telah dimasukkan ke dalam pakej SPSS dan kajian pengelasan telah dijalankan menggunakan analisis diskriminan linear Fisher. Analisis diskriminan linear Fisher digunakan untuk memperlihatkan kepentingan dan perbezaan yang timbul di antara fitur-fitur yang diguna ke arah menerangkan kedudukan-kedudukan penumpang tersebut. Pengelasan data telah dijalankan menggunakan kaedah pengesahan-silang (pengelasan keluarkan-satu) untuk memaksimumkan penyelidikan ke atas data yang ada. Kaedah ini membolehkan kesemua data yang ada diguna untuk latihan, dan juga dalam pengesahan, di samping masih tetap menghasilkan suatu anggaran tak-bersandar akan keupayaan pengelas secara teritlak. Hasil pengelasan yang diperoleh menunjukkan bahawa kesemua kedudukan penumpang telah dikelaskan ke dalam kelas yang sepatutnya dengan kadar kejayaan 100%. Oleh itu kesimpulannya, imej yang tersegmen-sempurna itu telah didiskriminan dengan sempurna ke dalam kelas yang sepatutnya dan ini menyokong MOL sebagai suatu fitur yang boleh digunakan untuk pengecaman kedudukan penumpang yang dikaji. Kata kunci: Momen ortogon Legendre, kelas kedudukan penumpang, tersegmen–sempurna, analisis diskriminan linear Fisher In this article, the application of Legendre orthogonal moment (LOM) for the classification of perfectly hand-segmented passenger position images is discussed. The ability to identify the passenger position in a car is important for instance as an input to a smart car’s system for the deployment of its safety airbag. Reseach reports by the Insurance Institute for Highway Safety in the United States have shown that the relative position of a passenger to the airbag is important to his/her safety. Hence this research work is towards developing a system for passenger position recognition in a car. A total of 1283 images of ten passenger position classes that have been perfectly segmented by hand were used in this work, and nine LOM features have been generated for each image by using a C++ program. The features were then fed into the SPSS package for classification by using Fisher’s linear discriminant analysis. Fisher’s linear discriminant analysis was used to show the importance and the differences among the features used toward describing the passenger positions. Data classification has been performed by using cross-validation (leave-one-out classification) in order to maximise the investigation on the data available. This method enables all of the available data to be utilised for training, as well as for validation, while still generating an independent estimate of the classifier generalisation capability. The classification results produced showed that the various passenger classes have been classified into the respective classes with a 100% success rate. Therefore, it can be concluded that the perfectly segmented images has been well discriminated into the respectively classes and this supports LOM as a potential feature for the recognition of the passenger positions investigated. Kata kunci: Momen ortogon Legendre, kelas kedudukan penumpang, tersegmen–sempurna, analisis diskriminan linear Fisher

scholarly journals Fisher linear discriminant analysis for classification and prediction of genomic susceptibility to stomach and colorectal cancers based on six STR loci in a northern Chinese Han population

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7004
Author(s):  
Shuhong Hao ◽  
Ming Ren ◽  
Dong Li ◽  
Yujie Sui ◽  
Qingyu Wang ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Cancer Patients ◽  
Tumor Markers ◽  
Cross Validation ◽  
Healthy Controls ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Str Loci ◽  
Leave One Out

Objective Gastrointestinal cancer is the leading cause of cancer-related death worldwide. The aim of this study was to verify whether the genotype of six short tandem repeat (STR) loci including AR, Bat-25, D5S346, ER1, ER2, and FGA is associated with the risk of gastric cancer (GC) and colorectal cancer (CRC) and to develop a model that allows early diagnosis and prediction of inherited genomic susceptibility to GC and CRC. Methods Alleles of six STR loci were determined using the peripheral blood of six colon cancer patients, five rectal cancer patients, eight GC patients, and 30 healthy controls. Fisher linear discriminant analysis (FDA) was used to establish the discriminant formula to distinguish GC and CRC patients from healthy controls. Leave-one-out cross validation and receiver operating characteristic (ROC) curves were used to validate the accuracy of the formula. The relationship between the STR status and immunohistochemical (IHC) and tumor markers was analyzed using multiple correspondence analysis. Results D5S346 was confirmed as a GC- and CRC-related STR locus. For the first time, we established a discriminant formula on the basis of the six STR loci, which was used to estimate the risk coefficient of suffering from GC and CRC. The model was statistically significant (Wilks’ lambda = 0.471, χ2 = 30.488, df = 13, and p = 0.004). The results of leave-one-out cross validation showed that the sensitivity of the formula was 73.7% and the specificity was 76.7%. The area under the ROC curve (AUC) was 0.926, with a sensitivity of 73.7% and a specificity of 93.3%. The STR status was shown to have a certain relationship with the expression of some IHC markers and the level of some tumor markers. Conclusions The results of this study complement clinical diagnostic criteria and present markers for early prediction of GC and CRC. This approach will aid in improving risk awareness of susceptible individuals and contribute to reducing the incidence of GC and CRC by prevention and early detection.

scholarly journals A comprehensive simulation study on classification of RNA-Seq data

2017 ◽  
Author(s):  
Gokmen Zararsiz ◽  
Dinçer Göksülük ◽  
Selçuk Korkmaz ◽  
Vahap Eldem ◽  
Gözde Ertürk Zararsız ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Sample Size ◽  
Linear Discriminant Analysis ◽  
Differential Expression ◽  
Simulation Study ◽  
Rna Seq ◽  
Linear Discriminant ◽  
Number Of Genes ◽  
Expression Rate

RNA sequencing (RNA-Seq) is a powerful technique for thegene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies.Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of geneexpression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data hierarchically closer to microarrays and apply microarray-based classifiers.In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such asoverdispersion, sample size, number of genes, number of classes, differential-expression rate, andthe transformation method on model performances.A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate, and number of genes and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM clas sifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html .

scholarly journals Linear discriminant analysis based on gas chromatographic measurements for geographical prediction of USA medical domestic cannabis

2020 ◽  
Author(s):  
Ramia Z. Al Bakain ◽  
Yahya S. Al-Degs ◽  
James V. Cizdziel ◽  
Mahmoud A. Elsohly
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Components ◽  
Supervised Classification ◽  
Geographical Origin ◽  
Active Components ◽  
Linear Discriminant ◽  
Sample Extraction ◽  
Analytical Range

AbstractFifty four domestically produced cannabis samples obtained from different USA states were quantitatively assayed by GC–FID to detect 22 active components: 15 terpenoids and 7 cannabinoids. The profiles of the selected compounds were used as inputs for samples grouping to their geographical origins and for building a geographical prediction model using Linear Discriminant Analysis. The proposed sample extraction and chromatographic separation was satisfactory to select 22 active ingredients with a wide analytical range between 5.0 and 1,000 µg/mL. Analysis of GC-profiles by Principle Component Analysis retained three significant variables for grouping job (Δ9-THC, CBN, and CBC) and the modest discrimination of samples based on their geographical origin was reported. PCA was able to separate many samples of Oregon and Vermont while a mixed classification was observed for the rest of samples. By using LDA as a supervised classification method, excellent separation of cannabis samples was attained leading to a classification of new samples not being included in the model. Using two principal components and LDA with GC–FID profiles correctly predict the geographical of 100% Washington cannabis, 86% of both Oregon and Vermont samples, and finally, 71% of Ohio samples.

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