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BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7. Results from SVM, ANN, SVMANN, and ANNSVM that used WLHT

From the results of ANNSVM_WL and ANNSVM_HT, we found that wavelet coefficients had a larger impact on classification than the Hough transformation data because the results from our proposed method applied to WL were more accurate than those of HT. The wavelet coefficients can capture the dominant characteristics from the graphs better than the Hough transformation. The one-dimensional image represented in the frequency domain had oscillations with different amplitudes depending on the graph types. For example, a dominant part of a pie chart should be in the low-frequency domain, because there is a large island of concatenated pixels in a onedimensional image, and it has only a few changes. Conversely, since the scatter plot contains many widely spread points, its dominant part should be located in the high-frequency domain. Performing the wavelet transformation, if a mother wavelet and a part of the wavelet function have a close match, the wavelet coefficient will be large. Assuming we use a suitable wavelet family with the example pie chart case, the wavelet coefficients in the low-frequency domain should be large as compared to other parts of the domain.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 9. Illustration of three different wavelets with three waves that have high amplitude values, as indicated by the dashed red circles

The mother wavelet of Coiflet 5 contained triple-high oscillation amplitude (i.e., Figure 9a). We considered that this mother wavelet was inappropriate for our data because overall our data possibly contained only a few matches with the mother wavelet of Coiflet 5. Moreover, the Symlet 10 (i.e., Figure 9b) and 20 (i.e., Figure 9c) also provided supportive results that were lower than others in ANNSVM_WLHT because their mother wavelets also had a similar shape as that of Coiflet 5. For similar reasons, the Haar wavelet was not proper because it is a step function.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 6. Results from ANNSVN that used WL and HT

To identify which features of data influentially impacted data separability, we conducted experiments for ANNSVM with WL and HT (i.e., Figure 6). The WL contained only wavelet coefficients, whereas HT included only results of the Hough transformation. We found that, again, results obtained via the linear kernel were not significant; however, using the RBF kernel, accuracy for WL was higher than that of HT, indicating that wavelet coefficients provide influential features that make data separable.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 3. Demonstrating the process of classification by applying the ANN, then the SVM

Essentially, if the number of nodes in the hidden layers increases, processing time increases, and the resultant ANN will suffer from over-fitting. Conversely, too small of a number of hidden layers will cause under-fitting for the ANN. In our setting, the number of hidden layers and the number of nodes in each hidden layer were fixed at five. Concerning the learning rate and momentum settings, these impact sensitive training performances are set to optimal values obtained via a grid search technique. The number of nodes in the output layer was three because there are three different class labels (i.e., 2Dchart, bar, and pie) in our datasets. We used the ANN here because our datasets have nonlinear separation, and the ANN is also highly applicable to nonlinear modeling. Thus the ANN with multiple hidden layers was an optimal candidate; however, since the ANN is a black box learning approach, it is difficult to interpret implicit relationships between inputs and outputs.

RAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 4. Processes of all experiments:

In this study, accuracy values of each dataset showed the performance of each method. These values represent are the proportion of the total number of predictions that were correctly classified. Initially, we classified training instances into three classes, with approximately 300 images per class. The graphs had been selectively gathered from the Web. We manually normalized the collected images by eliminating unused areas, such as unnecessary text. Moreover, we evaluated the experiments with 10 folds cross-validation because such an approach can mitigate the problem of over-fitting.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 5. Results from CNN and ANNSVM that used 1Dimg and 2Dimg

We compared the results of CNN_1Dimg, CNN_2Dimg, ANNSVM_1Dimg, and ANNSVM_2Dimg to confirm the validity of ANNSVM when applied to images. The 1Dimg represented the dataset of one-dimensional images, while 2Dimg represented the dataset of twodimensional images. Results are shown in Figure 5.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT

First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 1. Example displaying two scatter plots with different characteristics and patterns

In addition to this introductory section, the remainder of this paper is organized as follows. In Section 2, we present previous work related to our present study. In Section 3, we describe details regarding the methodology used in this study. In Section 4, we describe our experiments and results, then discuss our findings. Finally, we summarize the key content of our study in Section 5.

opencc-by-4.0Feb 2018View details →

Wood preservatives utilizing low-value olive oil production by-products: Analysis

The objective of this study was to develop and assess the efficacy of two experimental methodologies for the maleinisation of lampante oil to be used for wood protection. Two maleinisation techniques were used to chemically modify low-value lampante oil in an attempt to limit leaching, increase hydrophobicity, and impart some level of antimicrobial performance when impregnated in wood. Pine and beech wood specimens were treated with the modified oils and underwent leaching, accelerated weathering, and fungi tests. The following analysis assessed the efficacy of the modified oil treatments in improving these characteristics.

opencc-by-4.0Jul 2019View details →

Data used in "Utilizing the Heliophysics/Geospace System Observatory to Understand Particle Injections: Their Scale Sizes and Propagation Directions"

These are the raw data files for the data used in the paper, "Utilizing the Heliophysics/Geospace System Observatory to Understand Particle Injections: Their Scale Sizes and Propagation Directions" by Gabrielse et al. They can be read using the SPEDAS software found here: http://themis.ssl.berkeley.edu/software.shtml. Tplot variables, which are specifically read and plotted by SPEDAS, are included for pertinent THEMIS data.

Fig. 5 in Description of Sarcocystis scandentiborneensis sp. nov. from treeshrews (Tupaia minor, T. tana) in northern Borneo with annotations on the utility of COI and 18S rDNA sequences for species delineation

Fig. 5. Phylogenetic tree based on analysis of mitochondrial COI sequences of the Sarcocystidae including the new Sarcocystis sp. examined in this study (black symbols). Other taxa of the Apicomplexa served as root. Evolutionary history was inferred by the Maximum Likelihood (ML) method based on the TamuraNei model, whereby 619 positions were included in the final data set. All positions with less than 95% site coverage were eliminated; that is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. Bootstrap percentages (1000 iterations) are shown next to branches. COI sequences E357-13 and E120-13 (not shown in the tree) are available at GenBank (MN732561 and MN732562, respectively).

Fig. 2 in Description of Sarcocystis scandentiborneensis sp. nov. from treeshrews (Tupaia minor, T. tana) in northern Borneo with annotations on the utility of COI and 18S rDNA sequences for species delineation

Fig. 2. Ultrastructure of S. scandentiborneensis sp. nov. Note, due to ethanol-fixation some ultrastructural details are poorly resolved (e.g. membranes). A) Longitudinal section through the same sample as in Fig. 1C, showing a gross view of the sarcocyst and its villous protrusions (VP) that are sectioned in different orientations. The inset shows a cross section through various VP that reveals the arrangement of microtubules in their inner core; while in this case 16 microtubules are visible (asterisks), sections through more apical portions of the VP showed lower numbers. B) Longitudinal section through the fingerlike VPs that appear to be anchored in the ground substance (arrow) by microtubules (asterisks) that extend into each protrusion; note the electron-dense, U-shaped structure at each tip of the protrusions (arrowheads) and the apparently serrated surface of the VP (flat arrowheads). The inset shows a higher magnification of the apical part of a single VP with the typical U-shaped apex (asterisk), which appears to be connected with the host cell through an electronlucent contact zone (white arrowheads); interestingly, the protrusion appears fenestrated (also visible in the main image) possessing thorn-like structures (black arrows; the white arrow indicates a crosssectional view) that could be responsible for the serration visible at lower magnification. CZ, cystozoites; HC, host cell; VP, villous protrusion.

Fig. 1 in Description of Sarcocystis scandentiborneensis sp. nov. from treeshrews (Tupaia minor, T. tana) in northern Borneo with annotations on the utility of COI and 18S rDNA sequences for species delineation

Fig. 1. Light microscopy of Sarcocystis scandentiborneensis sp. nov. A and B, Haematoxylin & Eosinstained histological sections of striated musculature; C and D, Richardson's dye-stained 1.0 μm thin sections of sarcocysts. A) Tissue section of laryngeal muscle with various sarcocysts in cross section (asterisks), indicating a relatively high density of cysts in this part of musculature. B) Longitudinal section through a sarcocyst, showing a cigar-shaped appearance; however, isolated native sarcocysts, which were not available, may look different. C) Part of a longitudinal section through the tip of a sarcocyst, note the very thin ground substance (arrows) and the fine septae extending into the interior of the cyst (arrowheads); cystozoites (CZ) were loosely scattered within chambers while metrocytes were rarely seen, indicating maturity of the cyst; bars indicate the variable thickness of the cyst wall: the wall was thinner in regions where the villous protrusions were bent (right bar); note that the intense staining at the interface between host cell (HC) and parasite is part of the host cell. D) Cross-section through a sarcocyst showing cystozoites and the cyst wall (bar) including its thin ground substance (arrows).

Fig. 4 in Description of Sarcocystis scandentiborneensis sp. nov. from treeshrews (Tupaia minor, T. tana) in northern Borneo with annotations on the utility of COI and 18S rDNA sequences for species delineation

Fig. 4. Mapping (to the Toxoplasma gondii reference molecule M97703) of frequencies (%) of base pair changes observed in sequence comparisons of nu clear 18S rDNA within the new Sarcocystis sp. from treeshrews (intraspecific variation: isolates E364–13 versus E357–13) and between the new species and Sarcocystis zuoi and/or S. clethrionomyelaphis (interspecific variation: E364–13 versus S. zuoi/clethrionomyelaphis). Results were combined for the two latter species to simplify the graph. Here, 87.2% of 2118 alignment positions showed moderate to high levels of consistency, while sections of ambiguous alignment did not relate to the species under investigation. Due to gaps in the alignment, not all of the observed nt changes could be mapped to a homologous position of the reference molecule (i.e., 7 out of 24 bp changes in intraspecific comparison; 33 out of 74 bp changes in interspecific comparison), in which case the position of each nt relative to the helix was inferred from neighboring nt for which such position was known. Gaps were mainly due to insertions in helices V2, V4, and V9 rendering E357-13/E364-13 longer than the sequence of T. gondii. The percentage of parsimony-informative (pi) bp changes per helix is shown for helices V1, V2, V4, V7, and V9 above each column. Also shown is the ratio of transitions versus transversions (Ti/Tv) for selected helices.

Fig. 2 in Untapped potential: The utility of drylands for testing eco-evolutionary relationships between hosts and parasites

Fig. 2. Worldwide endemic and imported cases of (A) cutaneous leishmaniasis (CL) and (B) viceral leishmaniasis (VL) as of 2018. Warmer colors indicate a higher number of cases reported that year. Source: World Health Organization (2019).

opencc-by-4.0Apr 2017View details →

Fig. 3 in A new occurrence of Dakotasuchus kingi from the Late Cretaceous of Utah, USA, and the diagnostic utility of postcranial characters in Crocodyliformes

Fig. 3. Comparative morphology of coracoids (A–D) and dorsal scutes E–H) in coelognathosuchians from the medial Cretaceous of North America. Right coracoids in lateral view and right dorsal scutes in ventral view. A, E. Dakotasuchus kingi Mehl, 1941, OMNH 34500, Mussentuchit Member of the Cedar Mountain Formation (Cenomanian), Utah, USA. B, F. Dakotasuchus kingi Mehl, 1941, KWU uncatalogued (holotype), Dakota Formation (Cenomanian), Kansas, USA. C, G. Woodbinesuchus byersmauricei Lee, 1997, SMU 74626 (holotype), Woodbine Formation Cenomanian), Texas, USA. D, H. Terminonaris robusta Wu, Russell, and Cumbaa, 2001, SMNH P2411.1 (coracoid is inverted), Keld Member of the Favel Formation (Turonian), Saskatchewan, Canada. Images modified from Mehl (1941), Lee (1997), and Wu et al. (2001). Images are not to scale.

opencc-by-4.0Apr 2017View details →

Fig. 2. Representative elements ofgoniopholidid crocodyliform Dakotasuchus kingi Mehl, 1941, OMNH 34500 from OMNH locality V828 in A new occurrence of Dakotasuchus kingi from the Late Cretaceous of Utah, USA, and the diagnostic utility of postcranial characters in Crocodyliformes

Fig. 2. Representative elements ofgoniopholidid crocodyliform Dakotasuchus kingi Mehl, 1941, OMNH 34500 from OMNH locality V828, Mussentuchit Member, Cedar Mountain Formation, Cenomanian. A. Right cervical rib in ventral (A1) and dorsal (A2) views. B. Right coracoid in lateral (B1), caudal (B2), and medial (B3) views. C. Dorsal vertebra in cranial (C1), caudal (C2), lateral (C3), and dorsal (C4) views. D. Right radius in medial (D1) and lateral (D2) views. E. Dorsal scute in dorsal (E1) and ventral (E2) views. F. Ventral scute in dorsal (F1) and ventral (F2) views. G. Close-up views of neural canal in dorsal vertebrae, illustrating distinctive heart shape (white arrows); G1, OMNH 34500 vertebra in caudal view; G2, D. kingi holotype vertebra mold in cranial view. H. Tooth in labiolingual (H1), basal (H2), and mesiodistal (H3) views.

opencc-by-4.0Apr 2017View details →

Fig. 1 in A new occurrence of Dakotasuchus kingi from the Late Cretaceous of Utah, USA, and the diagnostic utility of postcranial characters in Crocodyliformes

Fig. 1. Map of the western United States (A) with the approximate locations of the holotype in Salina, Kansas (KWU uncatalogued; circle) and referred specimen in Emery County, Utah (OMNH 34500; star) and map of Emery County (B) with the approximate location of V868 (star) and the distribution of the Mussentuchit Member (grey area) (modified from Cifelli et al. 1999).

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Taxonomic synthesis of the eastern North American millipede genus Pseudopolydesmus (Diplopoda: Polydesmida: Polydesmidae), utilizing high-detail ultraviolet fluorescence imaging.

Natural history specimen data linked to collectors and determiners held within, "Taxonomic synthesis of the eastern North American millipede genus Pseudopolydesmus (Diplopoda: Polydesmida: Polydesmidae), utilizing high-detail ultraviolet fluorescence imaging". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7226548c-af21-4648-85ea-733acdfda22e">https://bionomia.net/dataset/7226548c-af21-4648-85ea-733acdfda22e</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7226548c-af21-4648-85ea-733acdfda22e">https://gbif.org/dataset/7226548c-af21-4648-85ea-733acdfda22e</a>. Formatted as a Frictionless Data package.