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Journal articles on the topic 'Kernel testing'

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Published: 5 March 2024

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1

Chen, Zhengpu, Carl Wassgren, and Kingsly Ambrose. "A Review of Grain Kernel Damage: Mechanisms, Modeling, and Testing Procedures." Transactions of the ASABE 63, no. 2 (2020): 455–75. http://dx.doi.org/10.13031/trans.13643.

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HighlightsPublished literature on grain kernel damage during handling is reviewed.Types and sources of grain kernel damage are discussed.Factors affecting the level of grain kernel damage are outlined.Models to predict grain kernel damage and corresponding test devices are summarized.Abstract. Grain kernel damage during harvest and handling continues to be a challenge in grain postharvest operations. This damage causes physical and physiological changes to grain, which reduces the grain quality and leads to significant yield loss. During harvesting and handling, grain kernels are subject to complex loading conditions consisting of a combination of impact, shear, and compression forces. The main damage mechanisms include impact, which causes external and internal cracks or even fragmentation of the kernel; attrition, which generates fine material; jamming, which deforms and breaks kernels due to high compressive forces; and fatigue, which produces broken kernels and fine material via repeatedly applied loads. Grain kernel damage accumulates as the grain moves through harvesting and handling operations. Harvesting is the major cause of cracks and breakage, while conveying after drying produces fine material. This article provides a comprehensive review of the types of grain kernel damage, sources of grain kernel damage, factors affecting damage, predictive damage models, and the experimental methods used to assess the damage. This review shows that although there is considerable empirical data focused on kernel damage, there is a lack of generalizable mechanics-based predictive models. Mechanics-based models are desirable because they would be useful for providing guidance on designing and operating grain handling processes to minimize kernel damage and thus improve grain quality. In addition, several damage models developed for non-grain particulate materials based on fracture mechanics are reviewed. With some modifications and detailed property analysis, there is potential for adapting the models developed for inorganic materials to predict grain kernel damage. Keywords: Grain kernel damage, Grain harvesting and handling, Breakage susceptibility, Grain damage prediction.
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Wu, Michael C., Arnab Maity, Seunggeun Lee, Elizabeth M. Simmons, Quaker E. Harmon, Xinyi Lin, Stephanie M. Engel, Jeffrey J. Molldrem, and Paul M. Armistead. "Kernel Machine SNP-Set Testing Under Multiple Candidate Kernels." Genetic Epidemiology 37, no. 3 (March 7, 2013): 267–75. http://dx.doi.org/10.1002/gepi.21715.

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3

Kiefer, Nicholas M., and Timothy J. Vogelsang. "HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTING USING BANDWIDTH EQUAL TO SAMPLE SIZE." Econometric Theory 18, no. 6 (September 24, 2002): 1350–66. http://dx.doi.org/10.1017/s026646660218604x.

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Asymptotic theory for heteroskedasticity autocorrelation consistent (HAC) covariance matrix estimators requires the truncation lag, or bandwidth, to increase more slowly than the sample size. This paper considers an alternative approach covering the case with the asymptotic covariance matrix estimated by kernel methods with truncation lag equal to sample size. Although such estimators are inconsistent, valid tests (asymptotically pivotal) for regression parameters can be constructed. The limiting distributions explicitly capture the truncation lag and choice of kernel. A local asymptotic power analysis shows that the Bartlett kernel delivers the highest power within a group of popular kernels. Finite sample simulations suggest that, regardless of the kernel chosen, the null asymptotic approximation of the new tests is often more accurate than that for conventional HAC estimators and asymptotics. Finite sample results on power show that the new approach is competitive.
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Ahmad, Ibrahim, and A. R. Mugdadi. "Testing normality using kernel methods." Journal of Nonparametric Statistics 15, no. 3 (June 2003): 273–88. http://dx.doi.org/10.1080/1048525021000049649.

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Martinez, Kara, Arnab Maity, Robert H. Yolken, Patrick F. Sullivan, and Jung‐Ying Tzeng. "Robust kernel association testing (RobKAT)." Genetic Epidemiology 44, no. 3 (January 14, 2020): 272–82. http://dx.doi.org/10.1002/gepi.22280.

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TiaraSari, Arum, and Emy Haryatmi. "Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 2 (April 28, 2021): 265–71. http://dx.doi.org/10.29207/resti.v5i2.3040.

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Corn kernels detection can be implemented in industry area. This can be implemented in the selection and packaging the corn kernels before it is distributed. This technique can be implemented in the selection and packaging machine to detect corn kernels accurately. Corn kernel images was used before it is implemented in real-time. The objective of this research was corn kernel detection using Convolutional Neural Network (CNN) deep learning. This technique consists of 3 main stages, the first preprocessing or normalizing the input of corn kernels image data by wrapping and cropping, both modeling and training the system, and testing. The experiment used CNN method to classify images of dry corn kernels and to determine the accuracy value. This research used 20 dry corn kernels images as testing from 80 dry corn kernels images which used in training dataset. The accuracy of detection was dependent from the size of image and position when the image was taken. The accuracy is around 80% - 100% by using 7 convolutional layers and the average of accuracy for testing data was 0,90296. The convolutional layer which implemented in CNN has the strength to detect features in the input image.
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Bruggink, H., H. L. Kraak, M. H. G. E. Dijkema, and J. Bekendam. "Some factors influencing electrolyte leakage from maize (Zea mays L.) kernels." Seed Science Research 1, no. 1 (March 1991): 15–20. http://dx.doi.org/10.1017/s0960258500000581.

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AbstractEven though the embryo of a maize (Zea mays L.) kernel contributes relatively little to total kernel weight, it is a main source of electrolytes which leach from the kernel during imbibition. Ageing of maize kernels for 18 days at 40°C and a moisture content of about 15% results in an increase of electrolyte leakage which almost exclusively originates from the embryo. The effect of ageing is most apparent after prolonged periods of imbibition. Mechanical damage increases leakage early during imbibition, the effect of damage being considerably larger for aged than for unaged kernels. The large amount of electrolytes measured after the first hour of imbibition of undamaged kernels comes mainly from the pericarp. The electrolyte content of the pericarp is variety dependent and may interfere with quality testing by conductivity measurements.
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Hidayatullah, Martin Sulung, Tamrin Tamrin, Oktafri Oktafri, and Warji Warji. "Rancang Bangun dan Uji Kinerja Alat Pemisah Kernel Sawit dari Cangkangnya dengan Menggunakan Larutan Garam." Jurnal Agricultural Biosystem Engineering 2, no. 2 (June 22, 2023): 281. http://dx.doi.org/10.23960/jabe.v2i2.7482.

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. Palm oil has an important role to play in improving the country's foreign exchange. The largest selling point in palm oil is palm kernel oil (PKO), this palm kernel oil processing process involves a mixture of kernels and shells that will later be separated. This research aims to design the build and produce a prototype kernel separator from its shell by using a saline solution to minimize excess costs and able to separate the kernel and shell >80%. Methods carried out in this study include designing, manufacturing and testing. After that, prototype kernel separator with palm shell using salt solution with tool dimensions on container length 53 cm, width 40 cm, height 42 cm, water receiver body length 22 cm, width 16 cm, height 28. The kernel separator with palm shell using this saline solution is able to separate the kernel and shell mixture by 82%. Keywords: Design, Kernel,Palm Oil, Shell, Separation.
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Pan, Shuang, Jianguo Wei, and Hao Pan. "Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine." Scientific Programming 2020 (May 8, 2020): 1–7. http://dx.doi.org/10.1155/2020/4561834.

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Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid convergence. Empirical testing based on cross-sectional data from Chinese P2P lending market demonstrates the superiority of the improved hybrid kernel SVM model. The classification accuracy of credit risk level and operation quality is higher than the single kernel SVM model as well as the hybrid kernel model with empirical parameter values.
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Gao, Jiti, and Irène Gijbels. "Bandwidth Selection in Nonparametric Kernel Testing." Journal of the American Statistical Association 103, no. 484 (December 2008): 1584–94. http://dx.doi.org/10.1198/016214508000000968.

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11

Ahmad, Ibrahim A., and Qi Li. "Testing independence by nonparametric kernel method." Statistics & Probability Letters 34, no. 2 (June 1997): 201–10. http://dx.doi.org/10.1016/s0167-7152(96)00183-6.

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Al Rivan, Muhammad Ezar, Molavi Arman, Hafiz Irsyad, and Reynald Dwika Prameswara. "Klasifikasi Hewan Mamalia Berdasarkan Bentuk Wajah Menggunakan Fitur Histogram of Oriented dan Metode Support Vector Machine." Jurnal Sisfokom (Sistem Informasi dan Komputer) 11, no. 1 (April 12, 2022): 93–99. http://dx.doi.org/10.32736/sisfokom.v11i1.1205.

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Mammals have several characteristics that can be distinguished, such as footprints, voice, and face shape. Mammals can be recognized. To classify the face shape of mammals, the Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) methods can be used. This study uses the LHI-Animal-Faces dataset which is taken as many as 15 species of mammals, where each type of mammal is selected 60 images and resized to 150x150 pixels. The image is converted into a grayscale image for the HOG feature extraction process. Furthermore, the classification process uses SVM. The kernels used are Linear, Polynomial, and Gaussian kernels. The testing process uses K-Fold Cross Validation. The folds used are 3-fold, 4-fold, 5-fold, 6-fold, and 10-fold. The performance of the HOG feature and the SVM method that gives the best results is the Linear kernel using 10-fold with an accuracy value of 96.55%, precision of 77.92%, and recall of 74.11%. The sequence of kernels that give the best results in this test is the Linear kernel, Polynomial kernel, and Gaussian kernel.
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Kanchana, M., and P. Varalakshmi. "Computer aided system for breast cancer in digitized mammogram using shearlet band features with LS-SVM classifier." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 03 (May 2016): 1650017. http://dx.doi.org/10.1142/s021969131650017x.

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Breast cancer is life threatening and dangerous diseases among the women across the world. In this paper, mammogram image classification performed using LS-SVM with various kernels functions namely, Gaussian Radial Basis Function (GRBF) kernel, Polynomial kernel, Quadratic kernel, Linear kernel and MLP kernel. Shearlet transform is a multidimensional version of the composite dilation wavelet transform, and is especially designed to address anisotropic and directional information at various scales and directions, which is used to decompose the regions of interest (ROI) image after preprocessing stage. Initially, mammogram images are transformed into different resolution levels from 2 levels to 4 levels with various directions varying from 2 to 64. The evaluation of the system is carried out on the Mammography Image Analysis Society (MIAS) database. From the experimental analysis, based on classification accuracy and Receiver Operating Characteristics (ROC), it is concluded that LS-SVM with Gaussian RBF kernel function outperforms than Quadratic, polynomial, linear and MLP kernel functions. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes.
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Febrian Sengkey, Daniel, Agustinus Jacobus, and Fabian Johanes Manoppo. "Effects of kernels and the proportion of training data on the accuracy of SVM sentiment analysis in lecturer evaluation." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 734. http://dx.doi.org/10.11591/ijai.v9.i4.pp734-743.

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Support vector machine (SVM) is a known method for supervised learning in sentiment analysis and there are many studies about the use of SVM in classifying the sentiments in lecturer evaluation. SVM has various parameters that can be tuned and kernels that can be chosen to improve the classifier accuracy. However, not all options have been explored. Therefore, in this study we compared the four SVM kernels: radial, linear, polynomial, and sigmoid, to discover how each kernel influences the accuracy of the classifier. To make a proper assessment, we used our labeled dataset of students’ evaluations toward the lecturer. The dataset was split, one for training the classifier, and another one for testing the model. As an addition, we also used several different ratios of the training:testing dataset. The split ratios are 0.5 to 0.95, with the increment factor of 0.05. The dataset was split randomly, hence the splitting-training-testing processes were repeated 1,000 times for each kernel and splitting ratio. Therefore, at the end of the experiment, we got 40,000 accuracy data. Later, we applied statistical methods to see whether the differences are significant. Based on the statistical test, we found that in this particular case, the linear kernel significantly has higher accuracy compared to the other kernels. However, there is a tradeoff, where the results are getting more varied with a higher proportion of data used for training.
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Shirmohammadi, Maryam. "Investigation of Different Post-Harvest Treatments on the Quality of Almond Kernels during Ambient Storage." World Journal of Food and Nutrition (WJFN 1, no. 1 (October 22, 2021): 1–7. http://dx.doi.org/10.54026/wjfn/1003.

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Maintaining almond kernel quality during and after harvest is crucial in producing premium kernel product for Australian and international markets. In this study, we investigated the effects of different post-harvest treatments on changes in moisture content, texture, colour, nutritional profile and flavour of almond kernels over a storage period of 9 months under ambient conditions. Post-harvest treatments included steam pasteurization, volumetric heating pasteurization, oven roasting and dry roasting, which were compared with raw kernels. Moisture and texture analysis revealed that the average values within each treatment group did not change significantly over 9 months, although the breaking force required to create an initial crack in the kernel structure were markedly lower for Steam Pasteurized (SP) and Oven Roasted (OR) samples after 9 months. Sensory analysis conducted by a trained panel of experts revealed that the chewiness of raw samples increased over time, and both toasted and roasted characteristics were low. For OR and Dry Roasted (DR) samples the chewiness was low and roasted and toasted properties were higher. Average overall enjoyment score given to samples as a part of sensory testing was higher for Volumetric Heating Pasteurization (VHP) and DR at start of the storage (control) and stayed higher than others after 6 and 9 month of storage. Testing of nutritional content of samples showed changes in alpha tocopherol content in roasted samples. However, DR samples had higher content in comparison with OR samples. Volumetric heating treatment didn’t diminish tocopherol content of samples in comparison with raw samples while the average alpha tocopherol content of SP over first 3 month of storage was lower. Both SP and OR samples showed lower fat percentage in comparison with raw, VHP and DR. A reduction was observed in Lightness (L*) values for all samples tested. Among the tested treatments OR samples had darker kernels and Raw and VHP samples had lightest colours. The testing results showed the potential of volumetric heating pasteurization and roasting in maintaining quality of kernel over bulk storage period.
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Handayani, Meli, Rika Rosnelly, and Hartono Hartono. "Classification of Basurek Batik Using Pre-Trained VGG-16 and Support Vector Machine." International Conference on Information Science and Technology Innovation (ICoSTEC) 2, no. 1 (March 5, 2023): 40–44. http://dx.doi.org/10.35842/icostec.v2i1.34.

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By introducing Indonesian batik motifs, we know that the island of Sumatra, especially Bengkulu and Jambi provinces, has a distinctive batik called Basurek batik. This research aims to classify the two batik motifs using the Support Vector Machine (SVM) algorithm. First, we extract the image of the batik motif with a pre trained VGG-16 model and then use them as a dataset for the SVM classification process. The classification process itself uses linear, polynomial, and sigmoid kernels. We divided the data 90:10 and used 10-fold cross-validation to analyze each training and testing data classification result. The results of this study are the highest values of accuracy, precision, and recall of 76.4%, 76.5%, and 76.4% produced by the linear kernel for the training data classification. For the testing data classification, both the linear and polynomial kernels generate the best accuracy, precision, and recall values of 87.5%, 90%, and 85.5%. On average, incorporating the training and testing classification results, we found that the linear kernel is the best function for classifying the Basurek batik motif using the collected images from the internet.
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Mohie El-Din, M. M., S. E. Abu-Youssef, and M. KH Hassan. "Testing Exponentiality Against UBAC Using Kernel Methods." Journal of Statistical Theory and Applications 13, no. 2 (2014): 111. http://dx.doi.org/10.2991/jsta.2014.13.2.2.

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Barone-Adesi, Giovanni, and Carlo Sala. "Testing market efficiency with the pricing kernel." European Journal of Finance 25, no. 13 (February 27, 2019): 1166–93. http://dx.doi.org/10.1080/1351847x.2019.1581638.

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Zhang, Qinyi, Sarah Filippi, Arthur Gretton, and Dino Sejdinovic. "Large-scale kernel methods for independence testing." Statistics and Computing 28, no. 1 (January 24, 2017): 113–30. http://dx.doi.org/10.1007/s11222-016-9721-7.

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Sun, Yixiao, and Jingjing Yang. "Testing-optimal kernel choice in HAR inference." Journal of Econometrics 219, no. 1 (November 2020): 123–36. http://dx.doi.org/10.1016/j.jeconom.2020.06.007.

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Rojo-Suárez, Javier, and Ana Belén Alonso-Conde. "Relative Entropy and Minimum-Variance Pricing Kernel in Asset Pricing Model Evaluation." Entropy 22, no. 7 (June 30, 2020): 721. http://dx.doi.org/10.3390/e22070721.

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Recent literature shows that many testing procedures used to evaluate asset pricing models result in spurious rejection probabilities. Model misspecification, the strong factor structure of test assets, or skewed test statistics largely explain this. In this paper we use the relative entropy of pricing kernels to provide an alternative framework for testing asset pricing models. Building on the fact that the law of one price guarantees the existence of a valid pricing kernel, we study the relationship between the mean-variance efficiency of a model’s factor-mimicking portfolio, as measured by the cross-sectional generalized least squares (GLS) R 2 statistic, and the relative entropy of the pricing kernel, as determined by the Kullback–Leibler divergence. In this regard, we suggest an entropy-based decomposition that accurately captures the divergence between the factor-mimicking portfolio and the minimum-variance pricing kernel resulting from the Hansen-Jagannathan bound. Our results show that, although GLS R 2 statistics and relative entropy are strongly correlated, the relative entropy approach allows us to explicitly decompose the explanatory power of the model into two components, namely, the relative entropy of the pricing kernel and that corresponding to its correlation with asset returns. This makes the relative entropy a versatile tool for designing robust tests in asset pricing.
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Friedrichs, Stefanie, Juliane Manitz, Patricia Burger, Christopher I. Amos, Angela Risch, Jenny Chang-Claude, Heinz-Erich Wichmann, Thomas Kneib, Heike Bickeböller, and Benjamin Hofner. "Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies." Computational and Mathematical Methods in Medicine 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/6742763.

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The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.
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Vujanovic, Vladimir. "Tremellomycetes Yeasts in Kernel Ecological Niche: Early Indicators of Enhanced Competitiveness of Endophytic and Mycoparasitic Symbionts against Wheat Pathobiota." Plants 10, no. 5 (April 30, 2021): 905. http://dx.doi.org/10.3390/plants10050905.

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Tremellomycetes rDNA sequences previously detected in wheat kernels by MiSeq were not reliably assigned to a genus or clade. From comparisons of ribosomal internal transcribed spacer region (ITS) and subsequent phylogenetic analyses, the following three basidiomycetous yeasts were resolved and identified: Vishniacozymavictoriae, V. tephrensis, and an undescribed Vishniacozyma rDNA variant. The Vishniacozyma variant’s clade is evolutionarily close to, but phylogenetically distinct from, the V. carnescens clade. These three yeasts were discovered in wheat kernel samples from the Canadian prairies. Variations in relative Vishniacozyma species abundances coincided with altered wheat kernel weight, as well as host resistance to chemibiotrophic Tilletia (Common bunt—CB) and necrotrophic Fusarium (Fusarium head blight—FHB) pathogens. Wheat kernel weight was influenced by the coexistence of Vishniacozyma with endophytic plant growth-promoting and mycoparasitic biocontrol fungi that were acquired by plants. Kernels were coated with beneficial Penicillium endophyte and Sphaerodes mycoparasite, each of which had different influences on the wild yeast population. Its integral role in the kernel microbiome renders Vishniacozyma a measurable indicator of the microbiome–plant interaction. The ability of NGS technology to detect specific endophytic DNA variants and early changes in dynamics among symbionts within the kernel ecological niche enables the prediction of crop disease emergence, suggesting that advanced microbiological testing may be a potentially useful tool for both phytoprotection and more efficient wheat breeding programs.
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Husni, Arif Muntasa, and Mochamad Dani Hartanto. "Classification of Public Opinion on Online Learning Policies using Various Support Vector Machine’s Kernel." Technium: Romanian Journal of Applied Sciences and Technology 17 (November 1, 2023): 427–34. http://dx.doi.org/10.47577/technium.v17i.10119.

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The COVID-19 pandemic has resulted in significant changes in the education sector. The government issued a policy so that learning must be carried out online from home. This policy became a polemic for teachers and students so that pro and con opinions emerged on social media, especially Twitter. Sentiment analysis of public opinion is an interesting study. Standard classification algorithms such as k-Nearest neighbours, naïve bayes, decision tree, random forest, and support vector machine (SVM) can categorize these opinions in a short time with good accuracy. Many studies show that SVM is more accurate than all other classification methods. SVM works using kernels, including Linear, Polynomial and Radial Basis Functions (RBF) where each kernel requires different parameters. The linear kernel only requires one parameter, namely c (Cost). The RBF kernel requires 2 parameters, c and ɣ (gamma) while the Polynomial kernel uses 2 parameters, c and degrees. SVM does not have default values for these parameters and are based on experience and experimentation. The wider the range of parameters, the more likely the classifier obtains the optimal values. This study tries some parameters values of SVM kernels for text classification based on sentiment. Testing using 5-fold cross validation and confusion matrix show that SVM with a linear kernel provides the best performance with an accuracy of above 84%.
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Andono, Pulung Nurtantio, and Eko Hari Rachmawanto. "Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (February 13, 2021): 1–9. http://dx.doi.org/10.29207/resti.v5i1.2615.

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Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature for recognizing batik textures and LBP was chosen to do spot pattern recognition. In the experiment, we have used 160 images of batik motifs which are divided into two, namely 128 training data and 32 testing data. The accuracy results obtained from the SVM, GLCM and LBP algorithms produce 100% accuracy in polyniomial, linear and gaussian kernels with distances at GLCM 1, 3, and 5, where at a distance of 1 linear kernel is 78.1%, gaussian 93.7%. At a distance of 3 linear kernels 75%, gaussian 87.5% and at a distance of 5 linear kernels 84.3%, gaussian 87.5%. In the SVM and GLCM algorithms the resulting accuracy is at a distance of 1 with a polynomial kernel 96.8%, linear 68.7%, and gaussian 75%. At distance 3, the polynomial kernel is 100%, linear 71.8%, and gaussian 78.1%, while for distance 5, the polynomial kernel is 87.5%, linear 75%, and gaussian 81.2%.
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Kabutey, Abraham, David Herak, Rostislav Choteborsky, Čestmír Mizera, Riswanti Sigalingging, and Olaosebikan Layi Akangbe. "Oil point and mechanical behaviour of oil palm kernels in linear compression." International Agrophysics 31, no. 3 (July 1, 2017): 351–56. http://dx.doi.org/10.1515/intag-2016-0055.

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AbstractThe study described the oil point and mechanical properties of roasted and unroasted bulk oil palm kernels under compression loading. The literature information available is very limited. A universal compression testing machine and vessel diameter of 60 mm with a plunger were used by applying maximum force of 100 kN and speed ranging from 5 to 25 mm min−1. The initial pressing height of the bulk kernels was measured at 40 mm. The oil point was determined by a litmus test for each deformation level of 5, 10, 15, 20, and 25 mm at a minimum speed of 5 mmmin−1. The measured parameters were the deformation, deformation energy, oil yield, oil point strain and oil point pressure. Clearly, the roasted bulk kernels required less deformation energy compared to the unroasted kernels for recovering the kernel oil. However, both kernels were not permanently deformed. The average oil point strain was determined at 0.57. The study is an essential contribution to pursuing innovative methods for processing palm kernel oil in rural areas of developing countries.
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Kumar, M., N. K. Tiwari, and S. Ranjan. "Kernel function based regression approaches for estimating the oxygen transfer performance of plunging hollow jet aerator." Journal of Achievements in Materials and Manufacturing Engineering 2, no. 95 (August 1, 2019): 74–84. http://dx.doi.org/10.5604/01.3001.0013.7917.

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Purpose: To evaluate the capability of various kernels employed with support vector regression (SVR) and Gaussian process regression (GPR) techniques in estimating the volumetric oxygen transfer coefficient of plunging hollow jets. Design/methodology/approach: In this study, a data set of 81 observations is acquired from laboratory experiments of hollow jets plunging on the surface of water in the tank. The jet variables: jet velocity, jet thickness, jet length, and water depth are varied accordingly and the values of volumetric oxygen transfer coefficient is computed. An empirical relationship expressing the oxygenation performance of plunging hollow jet aerator in terms of jet variables is formulated using multiple nonlinear regression. The performance of this nonlinear relationship is compared with various kernel function based SVR and GPR models. Models developed with the training data set (51 observations) are checked on testing data set (24 observations) for performance comparison. Sensitivity analysis is carried out to examine the influence of jet variables in effecting the oxygen transfer capabilities of plunging hollow jet aerator. Findings: The overall comparison of kernels yielded good estimation performance of Radial Basis Function kernel (RBF) and Pearson VII Function kernel (PUK) using the SVR technique which is followed by nonlinear regression, and other kernel function based regression models. Research limitations/implications: The results of the study pertaining to the performance of kernels are based on the current experimental conditions and the estimation potential of the regression models may fluctuate beyond the selection of current data range due to datadependant learning of the soft computing models. Practical implications: Volumetric oxygen transfer coefficient of plunging hollow jets can be predicted precisely using SVR model by employing RBF as kernel function as compared to empirical correlation and other kernel function based regression models. Originality/value: The comparative analysis of kernel functions is conducted in this study. In previous studies, the predictive modelling approaches are implemented in simulating the aeration properties of cylindrical solid jets only, while this paper simulates the volumetric oxygen transfer coefficient of diverging hollow jets with the jet variables by utilizing polynomial, normalized polynomial, PUK, and RBF kernels in SVR and GPR.
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Boueshagh, M., and M. Hasanlou. "ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 219–26. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-219-2019.

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Abstract. Lakes play a pivotal role in the development of cities and have major impacts on the ecosystem balancing of the area. Remote sensing techniques and advanced modeling methods make it possible to monitor natural phenomena, such as lakes’ water level. The ecosystem of Urmia Lake is one of the most momentous ecosystems in Iran, which is almost close-ended and has become a global environmental issue in recent years. One of the parameters affecting this lake water level is snowfall, which has a key role in the fluctuations of its water level and water resources management. Hence, the purpose of this paper is the Urmia Lake water level estimation during 2000–2006 using observed water level, snow cover, direct precipitation, and evaporation. For this purpose, Support Vector Regression (SVR), which is the most outstanding kernel method (with various kernel types), has been used. Furthermore, four scenarios are considered with different variables as inputs, and the output of all scenarios is the water level of the lake. The results of training and testing data indicate the substantial impact of snow on retrieving the water level of the Urmia Lake at the desired period, and due to the complexity of the data relationships, the Gaussian kernel generally had better results. On the other hand, Quadratic and Cubic kernels did not work well. The fourth scenario, with RBF kernel has the best results [Training: R2 = 97% and RMSE = 0.09 m, Testing: R2 = 96.97% and RMSE = 0.08 m].
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Tsyvarev, A., and A. Khoroshilov. "Using fault injection for testing Linux kernel components." Proceedings of the Institute for System Programming of RAS 27, no. 5 (2015): 157–74. http://dx.doi.org/10.15514/ispras-2015-27(5)-9.

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Monforti, Fabio, Lina Vitali, Gianni Pagnini, Rita Lorenzini, Luca Delle Monache, and Gabriele Zanini. "Testing kernel density reconstruction for Lagrangian photochemical modelling." Atmospheric Environment 40, no. 40 (December 2006): 7770–85. http://dx.doi.org/10.1016/j.atmosenv.2006.07.046.

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Tsai, Chen-An, and James J. Chen. "Kernel estimation for adjusted -values in multiple testing." Computational Statistics & Data Analysis 51, no. 8 (May 2007): 3885–97. http://dx.doi.org/10.1016/j.csda.2006.03.007.

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Pope, Benjamin, Peter Tuthill, Sasha Hinkley, Michael J. Ireland, Alexandra Greenbaum, Alexey Latyshev, John D. Monnier, and Frantz Martinache. "The Palomar kernel-phase experiment: testing kernel phase interferometry for ground-based astronomical observations." Monthly Notices of the Royal Astronomical Society 455, no. 2 (November 17, 2015): 1647–53. http://dx.doi.org/10.1093/mnras/stv2442.

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Pasynkov, A. V., and E. N. Pasynkova. "EFFICIENCY OF RAW GLUTEN CONTENT PREDICTION IN WHEAT KERNELS." Grain Economy of Russia, no. 4 (September 5, 2019): 19–26. http://dx.doi.org/10.31367/2079-8725-2019-64-4-19-26.

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The conducted regression analysis allowed us to obtain the equation of multiple nonlinear regression, which reflects the dependence of the raw gluten content in wheat kernels (Y, %) on the protein content (X1 = Ntotal · 5.7, %) and 1000-kernel weight (X2, g): Y = -41.928 + 0.081Х1 2 + 2.548Х2 - 0.028Х2 2. In the presented equation, all quality indicators are given at 12% humidity. If protein content and/or 1000-kernel weight are determined for absolutely dry matter (a.d.m.), the developed equation to predict raw gluten content in wheat kernels is recalculated with the use of coefficients of 0.88 and 1.136, respectively. The purpose of the research is to identify the effectiveness of raw gluten content prediction in wheat kernels using the developed regression equation, which reflects its dependence on protein content and 1000-kernel weight. There have been developed and presented an algorithm and results of testing the predictive capabilities of the equation based on independent data. That is, using experimental data on protein and gluten content, and 1000-kernel weight obtained by other researchers in the experiments with different wheat varieties and in other soil and climatic conditions. The summarized experimental data of 124 Soviet, Russian and foreign literary references with a total number of observations n = 2485 on more than a hundred wheat varieties grown from 1959 to 2019 in various soil and climatic zones of the USSR, Russia and abroad have shown that the number of values beyond the limits regulated by GOST R 54478 - 2011 (± 2%) was 462 or 18.6% of the total number of observations. The accuracy of the raw gluten content prediction in wheat kernels was 81.4%. The developed equation can be used to predict raw gluten content in kernels of various winter and spring soft and durum wheat varieties.
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Zeng, Bo, An Hua Chen, and Ling Li Jiang. "Fault Diagnosis of Rolling Bearing Based on Kernel Independent Component Analysis by Using Mixed Kernel Function." Applied Mechanics and Materials 312 (February 2013): 593–96. http://dx.doi.org/10.4028/www.scientific.net/amm.312.593.

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Studies have shown that the type of kernel function and parameters have a very important impact on the performance of the kernel method. Aiming at the requirement of rolling bearing fault diagnosis, this paper presents a mixed kernel function of kernel independent component and studies on the optimization of its kernel parameters. The mixed kernel function is constructed based on the weighted fusion method, and the kernel parameters are optimized by using the genetic algorithm. The improved kernel independent component method is used for fault diagnosis of rolling bearing, and the testing results demonstrate that it is an effective method.
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Zia, Huma, Hafiza Sundus Fatima, Muhammad Khurram, Imtiaz Ul Hassan, and Mohammed Ghazal. "Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection." Foods 11, no. 18 (September 6, 2022): 2723. http://dx.doi.org/10.3390/foods11182723.

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The assessment of food quality is of significant importance as it allows control over important features, such as ensuring adherence to food standards, longer shelf life, and consistency and quality of taste. Rice is the predominant dietary source of half the world’s population, and Pakistan contributes around 80% of the rice trade worldwide and is among the top three of the largest exporters. Hitherto, the rice industry has depended on antiquated methods of rice quality assessment through manual inspection, which is time consuming and prone to errors. In this study, an efficient desktop-application-based rice quality evaluation system, ‘National Grain Tech’, based on computer vision and machine learning, is presented. The analysis is based on seven main features, including grain length, width, weight, yellowness, broken, chalky, and/or damaged kernels for six different types of rice: IRRI-6, PK386, 1121 white and Selah, Super kernel basmati brown, and white rice. The system was tested in rice factories for 3 months and demonstrated 99% accuracy in determining the size, weight, color, and chalkiness of rice kernels. An accuracy of 98.8% was achieved for the classification of damaged and undamaged kernels, 98% for determining broken kernels, and 100% for paddy kernels. The results are significant because the developed system improves the local rice quality testing capacity through a faster, more accurate, and less expensive mechanism in comparison to previous research studies, which only evaluated four features of the singular rice type, rather than the seven features achieved in this study for six rice types.
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Samir, Mutmainnah, Purnawansyah, Herdianti Darwis, and Fitriyani Umar. "Fourier Descriptor Pada Klasifikasi Daun Herbal Menggunakan Support Vector Machine Dan Naive Bayes." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 6 (December 30, 2023): 1205–12. http://dx.doi.org/10.25126/jtiik.1067309.

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Daun herbal bermanfaat sebagai obat alternatif karena kandungan alaminya dapat menyembuhkan berbagai penyakit dan menjaga kesehatan tubuh. Klasifikasi citra daun herbal digunakan untuk membedakan jenis tanaman herbal berdasarkan bentuk daun. Penelitian ini Penelitian menggunakan Fourier Descriptor (FD) untuk mengekstraksi fitur pada daun herbal dan mengklasifikasikannya menggunakan metode Support Vector Machine (SVM) dan Naive Bayes (NB). SVM diimplementasikan dengan empat kernel yaitu Linear, polynomial, Radial Basis Function (RBF), dan sigmoid sementara Naive bayes diaplikasikan dengan tiga jenis kernel yaitu Gaussian, Multinomial, Bernoulli. Evaluasi kinerja menggunakan Precision, accuracy F1-Score dan Recall. Citra daun herbal terdiri dari daun katuk (Sauropus Androgynus) dan daun kelor (Moringa) dengan total 480 citra. Data tersebut dibagi menjadi 80% untuk training dan 20% untuk testing. Terdapat dua skenario pencahayaan yaitu kondisi gelap dan terang. Hasil penelitian menunjukkan bahwa perbandingan metode SVM dengan ekstraksi FD dimana kernel Linear mencapai akurasi sebesar 98% pada skenario gelap, sementara kernel Sigmoid memberikan akurasi terendah sebesar 44% pada scenario gelap maupun terang. Adapun hasil dari metode Naive bayes dengan ekstraksi FD pada kernel multinomial menghasilkan akurasi tertinggi sebesar 83% pada terang, sedangkan kernel Bernoulli memberikan akurasi terendah sebesar 46% pada skenario gelap dan terang. Berdasarkan perbandingan hasil klasifikasi dari kedua metode, disarankan bahwa metode SVM pada ekstraksi FD lebih direkomendasikan dalam proses klasifikasi daun herbal. Penelitian ini dapat memberikan rekomendasu pengembang sistem untuk menetapkan metode yang tepat dalam klasifikasi citra daun herbal. Abstract Herbal leaves are beneficial as alternative medicine because their natural content can cure various diseases and maintain a healthy body. The classification of herbal leaf images is used to differentiate types of herbal plants based on leaf shapes. This study utilizes Fourier Descriptor (FD) to extract features from herbal leaves and classify them using the Support Vector Machine (SVM) and Naive Bayes (NB) methods. SVM is implemented with four kernels namely linear, polynomial, Radial Basis Function (RBF), and Sigmoid while Naive bayes is applied with three types of kernels namely Gaussian, multinomial, Bernoulli. Performance evaluation includes precision, accuracy, F1- score and recall. Herbal leaf images consist of leaves (Sauropus Androgynus) and moringa leaves with a total of 480 images. The data is divided into 80% for training and 20 % for testing. There are two lighting scenarios, namely dark and light conditions. The result of this study shows a comparison of the SVM method with FD extraction where the Linear kernel achieves the highest accuracy of 98% in dark scenarios, while the Sigmoid kernel provides the lowest accuracy of 44% in both dark and light scenarios. The result of the naïve bayes method with FD extraction on the Multinomial kernel yield the highest accuracy of 83% in light scenarios while the Bernoulli kernel provides the lowest accuracy 46% in both dark and light scenarios. Based on the comparison of the classification result of the two methods, it is suggested that the SVM method for FD extraction is more recommended in the herbal leaf classification process. This research can provide recommendation for system developers to determine the appropriate method for classifying herbal leaf images.
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Fu, Zhenyu, Yong Yang, Isabella J. Van Rooyen, Subhashish Meher, and Boopathy Kombaiah. "Microstructural and Micro-Chemical Evolutions in Irradiated UCO Fuel Kernels of AGR-1 and AGR-2 TRISO Fuel Particles." Journal of Physics: Conference Series 2048, no. 1 (October 1, 2021): 012006. http://dx.doi.org/10.1088/1742-6596/2048/1/012006.

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Abstract AGR-1 and AGR-2 tristructural-isotropic (TRISO) fuel particles were fabricated using slightly different fuel kernel chemical compositions, modified fabrication processes, different fuel kernel diameters, and changed 235U enrichments. Extensive microstructural and analytical characterizations were conducted to correlate those differences with the fuel kernels’ responses to neutron irradiations in terms of irradiated fuel microstructure, fission products’ chemical and physical states, and fission gas bubble evolutions. The studies used state-of-the-art transmission electron microscopy (TEM) equipped with energy-dispersive x-ray spectroscopy (EDS) via four silicon solid-state detectors with super sensitivity and rapid speed. The TEM specimens were prepared from selected AGR-1 and AGR-2 irradiated fuel kernels exposed to safety testing after irradiation. The particles were chosen in order to create representative irradiation conditions with fuel burnup in the range of 10.8 to 18.6% fissions per initial metal atom (FIMA) and time-average volume-average temperatures varying from 1070 to 1287°C. The 235U enrichment was 19.74 wt.% and 14.03 wt.% for the AGR-1 and AGR-2 fuel kernels, respectively. The TEM results showed significant microstructural reconstructions in the irradiated fuel kernels from both the AGR-1 and AGR-2 fuels. There are four major phases: fuel matrix of UO2 and UC, U2RuC2, and UMoC2—in the irradiated AGR-2 fuel kernel. Zr and Nd form a solid solution in the UC phase. The UMoC2 phase often features a detectable concentration of Tc. Pd was mainly found to be located in the buffer layer or associated with fission gas bubbles within the UMoC2 phase. EDS maps qualitatively show that rare-earth fission products (Nd et al.) preferentially reside in the UO2 phase. In contrast, in the irradiated AGR-1 fuel kernel, no U2RuC2 or UMoC2 precipitates were positively identified. Instead, there was a high number of rod-shaped precipitates enriched with Ru, Tc, Rh, and Pd observed in the fuel kernel center and edge zone. The differences in irradiated fuel kernel microstructural and micro-chemical evolution when comparing AGR-1 and AGR-2 TRISO fuel particles may result from a combination of irradiation temperature, fuel geometry, and chemical composition. However, irradiation temperature probably plays a more deterministic role. Limited electron energy-loss spectroscopy (EELS) characterizations of the AGR-2 fuel kernel show almost no carbon in the UO2 phase, but a small fraction of oxygen was detected in the UC/UMoC2 phase.
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Kabutey, Abraham, David Herak, Cestmir Mizera, and Petr Hrabe. "Compressive loading experiment of non-roasted bulk oil palm kernels at varying pressing factors." International Agrophysics 32, no. 3 (July 1, 2018): 357–63. http://dx.doi.org/10.1515/intag-2017-0020.

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Abstract Compression testing of non-roasted bulk oil palm kernels at different processing factors was performed using a universal compression testing machine and a pressing vessel witha plunger. The purpose of the research was to describe regression models of deformation, deformation energy and percentage kernel oil depending on force, speed and vessel diameter. The tested compression forces were 100, 125, 150, 175 and 200 kN, while the speeds were 5, 10, 15, 20 and 25 mm min−1. Three pressing vessels of diameter 60, 80 and 100 mm were used. Samples were compressed at an initial height of 60 mm. For varying forces and vessel diameters at a constant speed of 5 mm min−1, the values of deformation, deformation energy and percentage kernel oil ranged from 28.47±0.89 to 37.45±0.11 mm, 796±0.82 to 1795±49.01 J and 7.33±0.26 to 25.67±0.39%. At a constant force of 200 kN for different speeds and vessel diameters; the aforementioned determined parameters also ranged from 31.91±1.61 to 37.63±1.21 mm, 1012±26.76 to 1795±49.01 J and 14.66±0.42 to 24.98±1.37%. The results were statistically significant (p<0.05) or (F-ratio>F-critical), with high coefficients of determination between 0.74 and 0.99. Thus, higher force at specific speed may be needed to maximally recover kernel oil.
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Abdurrahman, Ginanjar. "Klasifikasi Kanker Payudara Menggunakan Algoritma SVM dengan Kernel RBF, Linier, dan Sigmoid." JUSTIFY : Jurnal Sistem Informasi Ibrahimy 2, no. 1 (July 20, 2023): 74–80. http://dx.doi.org/10.35316/justify.v2i1.3370.

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Breast cancer ranks first in both the gender category and the death rate. Late treatment is often found in cases of breast cancer which causes an increase in the risk factors for this cancer. For this reason, early detection of breast cancer is needed, so that treatment can be done in a timely manner, so that the death rate due to breast cancer can be reduced. For this reason, this article offers early detection of breast cancer using classification. The dataset in this study used the Wisconsin breast cancer dataset taken from Kaggle. Initially the dataset has a missing value, besides that the categorical data is not yet in numerical form, so it is necessary to do preprocessing with the missing value imputing technique and encoding to convert categorical data into numeric data. The dataset is divided into two proportions, namely 80% as training data and 20% as testing data. In the classification process, datasets that have been preprocessed are classified using SVM with three different kernels, namely the linear kernel, the RBF kernel, and the Sigmoid kernel. Based on the research results that have been obtained, the linear kernel shows the best classification results when applied to the SVM classification, namely with an accuracy value of up to 99%, followed by RBF kernel performance with an accuracy rate of 92%, and finally the sigmoid kernel with an accuracy value of 41%
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Ayan, Sezgin, Erkan Ünalan, Oytun Sakici, Esra Yer, Fulvio Ducci, Vasilije Isajev, and Halil Ozel. "Preliminary results of Turkish hazelnut (Corylus colurna L.) populations for testing the nut characteristics." Genetika 50, no. 2 (2018): 669–86. http://dx.doi.org/10.2298/gensr1802669a.

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This paper aims to identify the hazelnut characteristics of four different populations (A?l?-Tunuslar, A?l?-M?sellimler, Ara?-G?zl?k and Tosya-K???ksekiler) in the North Western Black Sea Region of Turkey, one of the most important areas of economic interest for this species. There, the Turkish hazel (Corylus colurna L.) grows in its optimal conditions and reveals relatively high inter-population and intra-population variation in terms of nut characteristics. With the purpose of assessing variation, measurements were performed in four populations in Kastamonu district on 14 different nut characteristics (number of nuts per cluster, nut length (mm), nut width (mm), nut thickness (mm), shell thickness (mm), nut size (mm), nut shape, compression index, nut weight (g), kernel length (mm), kernel width (mm), kernel thickness (mm), kernel weight (g) and kernel ratio (%) of representative samples of the populations. Significant differences were found out among populations with regard to all of nut characteristics (p<0.05). The four populations have created two groups, population of A?l?-Tunuslar and the others, according to cluster analysis. The closest populations have been Tosya-K???ksekiler and Ara?-G?zl?k in terms of nut characteristics. According to the results obtained either on population basis or without population discrimination; significant correlations were determined between the majorities of the nut characters. The Ara?-G?zl?k population showed nuts the biggest among those examined and it is the population which took the highest values in terms of nut size traits while the Tosya-K???ksekiler provenance showed the highest values with the average values of 5, 15.92 mm, 1.32 and 11.75 mm respectively for nuts per cluster, nut width, compression index and kernel width. The A?l?-Tunuslar population showed the highest kernel ratio with 38.2%.
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McIntyre, Lauren M., and B. S. Weir. "Hardy-Weinberg Testing for Continuous Data." Genetics 147, no. 4 (December 1, 1997): 1965–75. http://dx.doi.org/10.1093/genetics/147.4.1965.

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Abstract Estimation of allelic and genotypic distributions for continuous data using kernel density estimation is discussed and illustrated for some variable number of tandem repeat data. These kernel density estimates provide a useful representation of data when only some of the many variants at a locus are present in a sample. Two Hardy-Weinberg test procedures are introduced for continuous data: a continuous chi-square test with test statistic TCCS and a test based on Hellinger's distance with test statistic TCCS. Simulations are used to compare the powers of these tests to each other and to the powers of a test of intraclass correlation TIC, as well as to the power of Fisher's exact test TFET applied to discretized data. Results indicate that the power of TCCS is better than that of THD but neither is as powerful as TFET. The intraclass correlation test does not perform as well as the other tests examined in this article.
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Kiro, Ogiana, Herman Mawengkang, and Elviawaty Muisa Zamzami. "Customer Profile Prediction model based on classification through approach Support Vector Machine (SVM)." SinkrOn 7, no. 3 (July 31, 2022): 1035–43. http://dx.doi.org/10.33395/sinkron.v7i3.11608.

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Nowadays the market is characterized globally, products and services are almost identical and there are many suppliers. The most important aspect in classifying data in data mining is classification. Classification techniques have been widely used in many problems in research. The purpose of this research is to build a model that can predict behavior based on the information of each customer. This research was conducted by making a Prediction Model of Customer Profile Based on Classification Through the Support Vector Machine Approach which aims to obtain a package prediction accuracy value that is suitable for WO (Wedding Organizer) customers in classifying based on the profile of prospective customers. In the optimization results on the SVM model kernel function, the linear and polynomial kernels get the same accuracy value on the training data of 99.29% and the testing data of 94.92%. The lowest accuracy value was obtained in the RBF kernel function of 97.16% on training data and 96.61% on testing data. the best precision class value in the data testing was obtained in the basic package at 100%. The total value of the appropriate prediction on the training data was obtained by 56 samples from a total of 59 samples, and 3 samples that did not match the prediction with an accuracy of 94.92% on the data testing
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Zeng, Jie, Panayiotis C. Roussis, Ahmed Salih Mohammed, Chrysanthos Maraveas, Seyed Alireza Fatemi, Danial Jahed Armaghani, and Panagiotis G. Asteris. "Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels." Applied Sciences 11, no. 8 (April 20, 2021): 3705. http://dx.doi.org/10.3390/app11083705.

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This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.
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Mortuza, Fahad Bin. "Kernel-Coefficient-Based Feature Method for Face Detection." International Journal of Image and Graphics 19, no. 02 (April 2019): 1950009. http://dx.doi.org/10.1142/s0219467819500098.

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A kernel-coefficient-based feature method is proposed to detect faces. The proposed method uses a mathematical expression and 26 different arrangements of kernel-coefficients of a kernel (testing region). The method manipulates the symmetric appearance of a face with respect to a rigid-kernel (fixed region). The expression, which is used to generate feature values, responds to pixels on edges of the image-objects only. For each distinct arrangement of kernel-coefficients, a feature-value is generated. The objective of the proposed kernel-coefficient-based feature method is to reduce the number of feature values required for face detection.
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Harchaoui, Zaid, Francis Bach, Olivier Cappe, and Eric Moulines. "Kernel-Based Methods for Hypothesis Testing: A Unified View." IEEE Signal Processing Magazine 30, no. 4 (July 2013): 87–97. http://dx.doi.org/10.1109/msp.2013.2253631.

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Gheorghe, Marian, Rodica Ceterchi, Florentin Ipate, Savas Konur, and Raluca Lefticaru. "Kernel P systems: From modelling to verification and testing." Theoretical Computer Science 724 (May 2018): 45–60. http://dx.doi.org/10.1016/j.tcs.2017.12.010.

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Puspitasari, Chasandra, Nur Rokhman, and Wahyono. "PREDICTION OF OZONE (O3) VALUES USING SUPPORT VECTOR REGRESSION METHOD." Jurnal Informatika Polinema 7, no. 4 (August 31, 2021): 81–88. http://dx.doi.org/10.33795/jip.v7i4.777.

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A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.
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Zivanovic, Tomislav, Gordana Brankovic, and Slavko Radanovic. "Combining abilities of maize inbred lines for grain yield and yield components." Genetika 42, no. 3 (2010): 565–74. http://dx.doi.org/10.2298/gensr1003565z.

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Diallel mating design experiment with reciprocal crosses was used to determine combining abilities of five maize inbred lines and their hybrid combinations for grain yield, ear length, ear diameter, number of kernel rows per ear, number of kernels per row in 2005. and 2006. year. GCA and SCA significant values were observed for all traits under study in both years. GCA/SCA relation showed that dominant gene effect had prevalent influence in the inheritance of grain yield, ear length and ear diameter. Additive gene effect had larger importance in the inheritance of number of kernel rows per ear. NS-1445 inbred line showed best GCA effect for grain yield, ear length and number of kernels per row, but worst GCA effect for number of kernel rows per ear. Best GCA effect for ear diameter achieved inbred line F-7R. Line BL-47 showed best GCA effect for number of kernel rows per ear in both years, but also the worst GCA effect for grain yield and number of kernels per row. Hybrid combination NS-1445 x BL-47 showed largest SCA effect for grain yield in both years and also showed, like hybrid combination F-7R x NS-1445, significant SCA effects for all other traits, except ear diameter. This cross also proved that hybrid combinations that include one parent with good GCA effect and the other parent with bad GCA effect can have very successful performance. It will be useful during selection material testing, to keep also genotypes which show bad GCA effect, but have phenotypic favorable trait values. Reciprocity effect was significant for SCA effects of all traits but ear diameter. It is the conformation of involvement of plasmagenes in maize quantitative traits inheritance. The largest reciprocity effect for grain yield achieved F-7R x BL-47 in both years. Significantly higher grain yield in this hybrid combination was achieved when line F-7R was used as a female parent and significantly higher number of kernel rows per ear was achieved when line BL-47 was used as a female parent.
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49

Lavergne, Pascal, and Quang Vuong. "NONPARAMETRIC SIGNIFICANCE TESTING." Econometric Theory 16, no. 4 (August 2000): 576–601. http://dx.doi.org/10.1017/s0266466600164059.

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Abstract:
A procedure for testing the significance of a subset of explanatory variables in a nonparametric regression is proposed. Our test statistic uses the kernel method. Under the null hypothesis of no effect of the variables under test, we show that our test statistic has an nhp2/2 standard normal limiting distribution, where p2 is the dimension of the complete set of regressors. Our test is one-sided, consistent against all alternatives and detects local alternatives approaching the null at rate slower than n−1/2h−p2/4. Our Monte-Carlo experiments indicate that it outperforms the test proposed by Fan and Li (1996, Econometrica 64, 865–890).
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50

Briggs, K. G., and Zhang Hongli. "Winter increases and kernel hardness testing in a spring wheat breeding program." Canadian Journal of Plant Science 72, no. 1 (January 1, 1992): 247–49. http://dx.doi.org/10.4141/cjps92-028.

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Abstract:
Kernel hardness measurements were performed on lines from a Canada Prairie Spring wheat cross. High correlations were found between performance of F2-derived F5 bulks from a winter increase nursery (latitude 33°N) with performance of the related F4 and F6 bulks grown in Edmonton (latitude 54°N). It is proposed that hardness testing of samples increased in winter nurseries may allow greater efficiency in a spring wheat breeding program.Key words: Wheat (spring), kernel hardness, breeding
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