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Journal articles on the topic 'Hyperbox'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Liu, Hongbing, Xiaoyu Diao, and Huaping Guo. "Nonparametric Hyperbox Granular Computing Classification Algorithms." Information 10, no. 2 (February 24, 2019): 76. http://dx.doi.org/10.3390/info10020076.

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Parametric granular computing classification algorithms lead to difficulties in terms of parameter selection, the multiple performance times of algorithms, and increased algorithm complexity in comparison with nonparametric algorithms. We present nonparametric hyperbox granular computing classification algorithms (NPHBGrCs). Firstly, the granule has a hyperbox form, with the beginning point and the endpoint induced by any two vectors in N-dimensional (N-D) space. Secondly, the novel distance between the atomic hyperbox and the hyperbox granule is defined to determine the joining process between the atomic hyperbox and the hyperbox. Thirdly, classification problems are used to verify the designed NPHBGrC. The feasibility and superiority of NPHBGrC are demonstrated by the benchmark datasets compared with parametric algorithms such as HBGrC.

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Ramos, Guilherme N., Fangyan Dong, and Kaoru Hirota. "HACO2 Method for Evolving Hyperbox Classifiers with Ant Colony Optimization." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (May 20, 2009): 338–46. http://dx.doi.org/10.20965/jaciii.2009.p0338.

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A method, called HACO2 (Hyperbox classifier with Ant Colony Optimization - type 2), is proposed for evolving a hyperbox classifier using the ant colony meta-heuristic. It reshapes the hyperboxes in a near-optimal way to better fit the data, improving the accuracy and possibly indicating its most discriminative features. HACO2 is validated using artificial 2D data showing over 90% accuracy. It is also applied to the benchmark iris data set (4 features), providing results with over 93% accuracy, and to the MIS data set (11 features), with almost 85% accuracy. For these sets, the two most discriminative features obtained from the method are used in simplified classifiers which result in accuracies of 100% for the iris and 83% for the MIS data sets. Further modifications (automatic parameter setting), extensions (initialization short comings) and applications are discussed.

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Liu, Hongbing, Fan Zhang, Ran Li, and Chang-an Wu. "Isolation-based hyperbox granular classification computing." Journal of Algorithms & Computational Technology 11, no. 2 (November 29, 2016): 110–25. http://dx.doi.org/10.1177/1748301816676818.

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Bottom-up and top-down are two main computing models in granular computing by which the granule set including granules with different granularities. The top-down hyperbox granular computing classification algorithm based on isolation, or IHBGrC for short, is proposed in the framework of top-down computing model. Algorithm IHBGrC defines a novel function to measure the distance between two hyperbox hgranules, which is used to judge the inclusion relation between two hyperbox granules, the meet operation is used to isolate the ith class data from the other class data, and the hyperbox granule is partitioned into some hyperbox granules which include the ith class data. We compare the performance of IHBGrC with support vector machines and HBGrC, for a number of two-class problems and multiclass problems. Our computational experiments showed that IHBGrC can both speed up training and achieve comparable generalization performance.

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Chen, Yue, Jian Shi, and Xiao-jian Yi. "A Globally Optimal Robust Design Method for Complex Systems." Complexity 2020 (May 11, 2020): 1–25. http://dx.doi.org/10.1155/2020/3697161.

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The uncertainty of the engineering system increases with the growing complexity of the engineering system; therefore, the tolerance to the uncertainty is essential. In the design phase, the output performance should reach the design criterion, even under large variations of design parameters. The tolerance to design parameter variations may be measured by the size of a solution space in which the output performance is guaranteed to deliver the required performance. In order to decouple dimensions, a maximum solution hyperbox, expressed by intervals with respect to each design parameter, is sought. The proposed approach combines the metaheuristic algorithm with the DIRECT algorithm where the former is used to seek the maximum size of hyperbox, and the latter is used as a checking technique that guarantees the obtained hyperbox is indeed a solution hyperbox. There are three advantages of the proposed approach. First, it is a global search and has a considerable high possibility to produce the globally maximum solution hyperbox. Second, it can be used for both analytically known and black-box performance functions. Third, it guarantees that any point selected within the obtained hyperbox satisfies the performance criterion as long as the performance function is continuous. Furthermore, the proposed approach is illustrated by numerical examples and real examples of complex systems. Results show that the proposed approach outperforms the GHZ and CES-IA methods in the literature.

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Bortolan, Giovanni, and Witold Pedrycz. "Hyperbox classifiers for arrhythmia classification." Kybernetes 36, no. 3/4 (April 17, 2007): 531–47. http://dx.doi.org/10.1108/03684920710747129.

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Bargiela, Andrzej, Witold Pedrycz, and Masahiro Tanaka. "An inclusion/exclusion fuzzy hyperbox classifier." International Journal of Knowledge-based and Intelligent Engineering Systems 8, no. 2 (August 24, 2004): 91–98. http://dx.doi.org/10.3233/kes-2004-8204.

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7

Janairo, Jose Isagani B., Kathleen B. Aviso, Michael Angelo B. Promentilla, and Raymond R. Tan. "Enhanced Hyperbox Classifier Model for Nanomaterial Discovery." AI 1, no. 2 (June 17, 2020): 299–311. http://dx.doi.org/10.3390/ai1020020.

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Machine learning tools can be applied to peptide-mediated biomineralization, which is an emerging biomimetic technique of creating functional nanomaterials. In particular, they can be used for the discovery of biomineralization peptides, which currently relies on combinatorial enumeration approaches. In this work, an enhanced hyperbox classifier is developed which can predict if a given peptide sequence has a strong or weak binding affinity towards a gold surface. A mixed-integer linear program is formulated to generate the rule-based classification model. The classifier is optimized to account for false positives and false negatives, and clearly articulates how the classification decision is made. This feature makes the decision-making process transparent, and the results easy to interpret for decision support. The method developed can help accelerate the discovery of more biomineralization peptide sequences, which may expand the utility of peptide-mediated biomineralization as a means for nanomaterial synthesis.

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8

Liu, Hongbing, Huaping Guo, and Chang-an Wu. "Hyperbox Granular Computing Based on Distance Measure." International Journal of Control and Automation 9, no. 1 (January 31, 2016): 1–10. http://dx.doi.org/10.14257/ijca.2016.9.1.01.

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9

Liu, Jinhai, Zhibo Yu, and Dazhong Ma. "An Adaptive Fuzzy Min-Max Neural Network Classifier Based on Principle Component Analysis and Adaptive Genetic Algorithm." Mathematical Problems in Engineering 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/483535.

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A novel adaptive fuzzy min-max neural network classifier called AFMN is proposed in this paper. Combined with principle component analysis and adaptive genetic algorithm, this integrated system can serve as a supervised and real-time classification technique. Considering the loophole in the expansion-contraction process of FMNN and GFMN and the overcomplex network architecture of FMCN, AFMN maintains the simple architecture of FMNN for fast learning and testing while rewriting the membership function, the expansion and contraction rules for hyperbox generation to solve the confusion problems in the hyperbox overlap region. Meanwhile, principle component analysis is adopted to finish dataset dimensionality reduction for increasing learning efficiency. After training, the confidence coefficient of each hyperbox is calculated based on the distribution of samples. During classifying procedure, utilizing adaptive genetic algorithm to complete parameter optimization for AFMN can also fasten the entire procedure than traversal method. For conditions where training samples are insufficient, data core weight updating is indispensible to enhance the robustness of classifier and the modified membership function can adjust itself according to the input varieties. The paper demonstrates the performance of AFMN through substantial examples in terms of classification accuracy and operating speed by comparing it with FMNN, GFMN, and FMCN.

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10

Eastwood, Mark, and Chrisina Jayne. "Evaluation of hyperbox neural network learning for classification." Neurocomputing 133 (June 2014): 249–57. http://dx.doi.org/10.1016/j.neucom.2013.11.011.

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11

Grzegorek, Paulina, and Janusz Januszewski. "Drawer algorithms for 1-space bounded multidimensional hyperbox packing." Journal of Combinatorial Optimization 37, no. 3 (August 17, 2018): 1011–44. http://dx.doi.org/10.1007/s10878-018-0338-y.

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12

Tan, Raymond R., Kathleen B. Aviso, Jose Isagani B. Janairo, and Michael Angelo B. Promentilla. "A hyperbox classifier model for identifying secure carbon dioxide reservoirs." Journal of Cleaner Production 272 (November 2020): 122181. http://dx.doi.org/10.1016/j.jclepro.2020.122181.

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13

Kahraman, Pinar, and Metin Turkay. "Classification of 1,4-Dihydropyridine Calcium Channel Antagonists Using the Hyperbox Approach." Industrial & Engineering Chemistry Research 46, no. 14 (July 2007): 4921–29. http://dx.doi.org/10.1021/ie0614327.

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14

Leyva, Horacio, Julio Solís-Daun, and Rodolfo Suárez. "Global CLF Stabilization of Systems with Control Inputs Constrained to a Hyperbox." SIAM Journal on Control and Optimization 51, no. 1 (January 2013): 745–66. http://dx.doi.org/10.1137/100809398.

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15

Xu, Jie, Barry L. Nelson, and L. Jeff Hong. "An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems." INFORMS Journal on Computing 25, no. 1 (February 2013): 133–46. http://dx.doi.org/10.1287/ijoc.1110.0481.

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16

BOLLT, ERIK M. "CONTROLLING CHAOS AND THE INVERSE FROBENIUS–PERRON PROBLEM: GLOBAL STABILIZATION OF ARBITRARY INVARIANT MEASURES." International Journal of Bifurcation and Chaos 10, no. 05 (May 2000): 1033–50. http://dx.doi.org/10.1142/s0218127400000736.

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The inverse Frobenius–Perron problem (IFPP) is a global open-loop strategy to control chaos. The goal of our IFPP is to design a dynamical system in ℜn which is: (1) nearby the original dynamical system, and (2) has a desired invariant density. We reduce the question of stabilizing an arbitrary invariant measure, to the question of a hyperplane intersecting a unit hyperbox; several controllability theorems follow. We present a generalization of Baker maps with an arbitrary grammar and whose FP operator is the required stochastic matrix.

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17

RAHUL, SALADI, PROSENJIT GUPTA, and K. S. RAJAN. "DATA STRUCTURES FOR RANGE-AGGREGATION OVER CATEGORIES." International Journal of Foundations of Computer Science 22, no. 07 (November 2011): 1707–28. http://dx.doi.org/10.1142/s0129054111008982.

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We solve instances of a general class of problems defined as follows: Preprocess a set S of possibly weighted colored geometric objects (e.g. points/orthogonal segments/rectangles) in IR d, d ≥ 1 such that given a query orthogonal range q (e.g. hyperbox/point), we can report efficiently for each distinct color c of the points in S ∩ q, the tuple [Formula: see text] where [Formula: see text] is a function (e.g. weighted sum, bounding box etc.) of the objects of color c in q.

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18

Lu, Wei, Dan Shan, Witold Pedrycz, Liyong Zhang, Jianhua Yang, and Xiaodong Liu. "Granular Fuzzy Modeling for Multidimensional Numeric Data: A Layered Approach Based on Hyperbox." IEEE Transactions on Fuzzy Systems 27, no. 4 (April 2019): 775–89. http://dx.doi.org/10.1109/tfuzz.2018.2870050.

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19

Chen, Chia‐Chong. "A hybrid SVM and supervised learning approach to fuzzy min‐max hyperbox classifiers." Journal of the Chinese Institute of Engineers 29, no. 5 (July 2006): 791–99. http://dx.doi.org/10.1080/02533839.2006.9671178.

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20

Chen, Chia-Chong. "DESIGN OF A FUZZY MIN-MAX HYPERBOX CLASSIFIER USING A SUPERVISED LEARNING METHOD." Cybernetics and Systems 37, no. 4 (July 2006): 329–46. http://dx.doi.org/10.1080/01969720600626337.

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21

Liu, Hongbing, Chunhua Liu, and Chang-an Wu. "Granular Computing Classification Algorithms Based on Distance Measures between Granules from the View of Set." Computational Intelligence and Neuroscience 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/656790.

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Granular computing classification algorithms are proposed based on distance measures between two granules from the view of set. Firstly, granules are represented as the forms of hyperdiamond, hypersphere, hypercube, and hyperbox. Secondly, the distance measure between two granules is defined from the view of set, and the union operator between two granules is formed to obtain the granule set including the granules with different granularity. Thirdly the threshold of granularity determines the union between two granules and is used to form the granular computing classification algorithms based on distance measures (DGrC). The benchmark datasets in UCI Machine Learning Repository are used to verify the performance of DGrC, and experimental results show that DGrC improved the testing accuracies.

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22

ABE, Shigeo, and Ruck THAWONMAS. "Improvement of Generalization Ability of a Fuzzy Classifier with Hyperbox Regions by Quadratic Membership Functions." Transactions of the Institute of Systems, Control and Information Engineers 13, no. 8 (2000): 377–85. http://dx.doi.org/10.5687/iscie.13.8_377.

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23

Borzooei, R. A., W. A. Dudek, and N. Koohestani. "On hyperBCC-algebras." International Journal of Mathematics and Mathematical Sciences 2006 (2006): 1–18. http://dx.doi.org/10.1155/ijmms/2006/49703.

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We study hyperBCC-algebras which are a common generalization ofBCC-algebras and hyperBCK-algebras. In particular, we investigate different types of hyperBCC-ideals and describe the relationship among them. Next, we calculate all nonisomorphic22hyperBCC-algebras of order3of which only three are not hyperBCK-algebras.

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24

Khuat, Thanh Tung, and Bogdan Gabrys. "Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule." Information Sciences 547 (February 2021): 887–909. http://dx.doi.org/10.1016/j.ins.2020.08.046.

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25

Ramos, G. N., Y. Hatakeyama, F. Dong, and K. Hirota. "Hyperbox clustering with Ant Colony Optimization (HACO) method and its application to medical risk profile recognition." Applied Soft Computing 9, no. 2 (March 2009): 632–40. http://dx.doi.org/10.1016/j.asoc.2008.09.004.

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26

Mohammed, Mohammed Falah, and Chee Peng Lim. "A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min–max neural network." Neural Networks 86 (February 2017): 69–79. http://dx.doi.org/10.1016/j.neunet.2016.10.012.

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27

Tardu, Mehmet, Fatih Rahim, I. Halil Kavakli, and Metin Turkay. "Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6." RAIRO - Operations Research 50, no. 2 (March 28, 2016): 387–400. http://dx.doi.org/10.1051/ro/2015042.

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28

Mohammed, Mohammed Falah, and Chee Peng Lim. "Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification." Applied Soft Computing 52 (March 2017): 135–45. http://dx.doi.org/10.1016/j.asoc.2016.12.001.

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29

WACHS, JUAN, HELMAN STERN, and MARK LAST. "COLOR FACE SEGMENTATION USING A FUZZY MIN-MAX NEURAL NETWORK." International Journal of Image and Graphics 02, no. 04 (October 2002): 587–601. http://dx.doi.org/10.1142/s021946780200086x.

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This work presents an automated method of segmentation of faces in color images with complex backgrounds. Segmentation of the face from the background in an image is performed by using face color feature information. Skin regions are determined by sampling the skin colors of the face in a Hue Saturation Value (HSV) color model, and then training a fuzzy min-max neural network (FMMNN) to automatically segment these skin colors. This work appears to be the first application of Simpson's FMMNN algorithm to the problem of face segmentation. Results on several test cases showed recognition rates of both face and background pixels to be above 93%, except for the case of a small face embedded in a large background. Suggestions for dealing with this difficult case are proffered. The image pixel classifier is linear of order O(Nh) where N is the number of pixels in the image and h is the number of fuzzy hyperbox sets determined by training the FMMNN.

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Feruza, Abdurakhimova. "ON THE METHODOLOGICAL CHARACTERISTICS OF SOME STYLISTIC MEANS." INTERNATIONAL JOURNAL OF WORD ART 6, no. 3 (June 30, 2020): 68–75. http://dx.doi.org/10.26739/2181-9297-2020-6-10.

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This article defines the terms hyperbola and the stylistic trope anti-hyperbola as opposed to it, and illustrates the differences in English and Uzbek with examples. The linguistic field of the conceptual semantics of anti-hyperbole "understatement" is illustrated by comparison in Uzbek and English with the help of examples of verbal means.

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31

ASUQUO, B. O., and L. E. UDOH. "REPRODUCTIVE PERFORMANCE OF HARCO, SHAVER STARCROSS AND HYPECO WHITE UNDER THE HUMID ENVIRONMENT." Nigerian Journal of Animal Production 18 (January 12, 2021): 98–100. http://dx.doi.org/10.51791/njap.v18i.2000.

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Reproductive performance measured by fertility and hatchability of eggs, was studied in three commercial breeds of chicken; Harco, Hyperco White and Shaver StarCross. Day-old parent stock chicks were obtained from three different sources, raised and stocked in a sex ratio of 1 cock: 7 hens for monitoring of the two traits under the sub-humid environment. No significant difference (P<0.05) was obtained for percent fertility of eggs, mean values of which were 84.67, 80.89 and 81.17% for Harco, Hyperco White and shaver StarCross respectively. Average hatchability of fertile eggs was significantly higher for Shaver StarCross (89.96%) than for Harco (83.11%) and Hyperko White (79.02%).

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32

Molter, G. P., S. Solt�sz, R. Larsen, S. Baumann-Noss, A. Biedler, and M. Silomon. "H�modynamische Reaktionen nach pr�operativer hypervol�mischer H�modilution mit hyperton-hyperonkotischen Kolloiden bei Koronarbypassoperationen." Der Anaesthesist 52, no. 10 (October 1, 2003): 905–18. http://dx.doi.org/10.1007/s00101-003-0568-x.

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33

Clarke, Malcolm. "Hyperbook." Economic Journal 101, no. 407 (July 1991): 1027. http://dx.doi.org/10.2307/2233900.

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34

Escriva, Robert, Bernard Wong, and Emin Gün Sirer. "HyperDex." ACM SIGCOMM Computer Communication Review 42, no. 4 (September 24, 2012): 25–36. http://dx.doi.org/10.1145/2377677.2377681.

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35

Buratti, Bonnie J. "Hyperion." Nature Astronomy 1, no. 9 (September 2017): 574. http://dx.doi.org/10.1038/s41550-017-0243-9.

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36

Rohaniyah, Jaftiyatur, and Saimatul Fadilah. "The Analysis of Hyperbole and Synecdoche in Jokowi’s Political Speeches On 2014 and 2015 live on Metro TV." Wacana Didaktika 6, no. 01 (June 30, 2018): 76–85. http://dx.doi.org/10.31102/wacanadidaktika.6.01.76-85.

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Speeches became an important media for a president like Joko Widodo (Jokowi) to deliver messages, persuade people, influence audience. The speech making process involved a long discussion between Jokowi and a particular team since they should consider some language aspects such as lexical choices and sentence structure in order to achieve certain goals. With regard to this, languages in speeches became important to be analyzed. There are two objects that will be analyzed. These are: kind of hyperbole and synecdoche expression are used by Jokowi in his two political speeches and How is the utterance of hyperbole and synecdoche expression used in the two of Jokowi’s political speeches. The research data consisted of two selected speeches delivered by Jokowi in APEC CEO summit 2014 forum held in November 10, 2014 and the speech delivered in Asian-African Conference Commemoration (AACC) held from 19-21 April 2015. While the research method was a descriptive qualitative research. The data in this research were speech videos downloaded from www.youtube.com. The videos were then transcribed and analyzed. The main research instrument was the researcher himself supported by the data analysis sheet. The data analysis was performed by categorizing the data based on Figurative language categorization which hyperbole and synecdoche. The finding of this research, the resercher found 18 expression in the two of Jokowi’s speeches (APEC CEO SUMMIT 2014 and Asian-Affrican Conference Commemoration 2015). In 2014 Jokowi use 11 figurative expressions from 5 expression in hyperbole and 6 expressions in synecdoche. In 2015, jokowi less use figurative expression. The language that he used was mostly natural language. The figurative language he used in his political speech at 2015 only found 7 expressions; 3 expression of hyperbole and 4 expression of synecdoche. Keywords: Hyperbole, Synecdoche, and Political Speech Abstract: Sebuah Pidato merupakan media yang penting bagi seorang President seperti Joko Widodo dalam menyampaikan pesan, mengajak seseorang dan dapat memberi pengaruh terhadap audien. Sebuah pidato kepresidenan melewati proses yang panjang antara Jokowi dan team tertentu karena harus mempertimbangkan beberapa aspek kebahasaan seperti pemilihan kata secara leksikal dan susunan kalimat agar dapat menyampaikan beberapa tujuan. Sehingga, sehubungan dengan ini, bahasa dalam sebuah pidato menjadi penting untuk dianalisa. Ada dua focus penelitian (1) apa saja ungkapan hyperbola dan synecdoche yang disampaikan Jokowi dalam pidato politiknya? (2) apa makna sesungguhnya dari beberapa ungkapan hiperbola dan synecdoche dalam pidato politik Jokowi?. Dalam hal ini, ada dua pidato terpilih untuk dianalisa yaitu pidato yang disampaikan Jokowi di acara APEC CEO summit 2014 forum dan di Asian-African Conference Commemoration (AACC) 2015. Metode penelitian ini adalah metode deskriptif kualitatif. Data dalam penelitian ini adalah bersumber dari video yang didapat dari www.youtube.com yang kemuadian di transcripkan kedalam bentuk tulisan untuk dianalisa. Instrumen utama penelitian ini adalah peneliti sendiri yang didukung oleh lembaran analisis. Hasil dari penelitian ini adalah peneliti menemukan 18 ungkapan dalam 2 pidato Jokowi (APEC CEO SUMMIT 2014 dan Asian-Affrican Conference Commemoration 2015). Pada tahun 2014 Jokowi menggunakan 11 ungkapan figuratif yaitu ada 5 kalimat hiperbola dan 6 ungkapan sinekdos. Pada tahun 2015, Jokowi kurang menggunakan ungkapan figuratif. Bahasa yang digunakan cendrung pada bahasa natural. Di tahun tersebut, ungkapan figuratif hanya ditemukan 7 ungkapan: 3 ungkapan hiperbola dan 4 ungkapan sinecdoc.

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Nguyen, Thuan Van, and Cuong Van Anh Le. "Energy Difference Of High Frequency Electromagnetic Waves Amplitude In Detecting Buried Objects At Ho Chi Minh City, Vietnam." Science and Technology Development Journal - Natural Sciences 5, no. 1 (December 27, 2020): first. http://dx.doi.org/10.32508/stdjns.v5i1.923.

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Ground penetrating radar method (GPR) is common place in imaging buried objects, such as: supply water and drainage systems, electric and telecom cables. For shallow geology, GPR method has provided high-resolution sections containing subsurface information. In processing data, the velocity of GPR wave is the most important parameter. It helps to exactly specify the structure of a geology sections. Defining a wrong velocity may lead to an erroneous result. In this research, we are going to present two processing steps to define the electromagnetic wave velocity and the position of object from GPR data that was acquired by using a shield antenna machine prototype. The first step is application of Kirchhoff migration and energy difference graphs to calculate the electromagnetic wave velocity. In the second step, the result of migrated sections consisting of hyperbole diffraction was evaluated. If migrated using the right velocity, the diffracted hyperbola will focus on its peak and have the maximum energy. Finally, we used the workflow to define the velocity, the position and the size of the object from real GPR data collected at Go Vap district, Ho Chi Minh City, Vietnam.

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38

Roche, Mark, and Howard Gaskill. "Holderlin's "Hyperion"." German Quarterly 59, no. 4 (1986): 650. http://dx.doi.org/10.2307/406538.

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39

Harrison, Robin, and Howard Gaskill. "Holderlin's Hyperion." Modern Language Review 82, no. 3 (July 1987): 780. http://dx.doi.org/10.2307/3730504.

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40

Aljadaan, Noura. "Understanding Hyperbole." Arab World English Journal, no. 212 (October 15, 2018): 1–31. http://dx.doi.org/10.24093/awej/th.212.

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41

Joshua R. Ritter. "Recovering Hyperbole:." Philosophy & Rhetoric 45, no. 4 (2012): 406. http://dx.doi.org/10.5325/philrhet.45.4.0406.

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42

Bode, Christoph. "Hyperion, "The Fall of Hyperion", and Keats's Poetics." Wordsworth Circle 31, no. 1 (January 2000): 31–37. http://dx.doi.org/10.1086/twc24044192.

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43

Kojoyan, Ani. "Stylistic Peculiarities of Contextual Hyperbole in Sherwood Anderson’s “The Book of the Grotesque” and “Godliness”." Armenian Folia Anglistika 6, no. 1-2 (7) (October 15, 2010): 95–101. http://dx.doi.org/10.46991/afa/2010.6.1-2.095.

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The article aims to investigate the stylistic characteristics of hyperbole in Sherwood Anderson’s short stories. The examination of the theoretical part of the given material and the analysis of the examples make it possible to distinguish three major types of contextual hyperbole in the short stories – mixed hyperbole, non-mixed hyperbole and biblical allusive hyperbole.

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44

Seshadri, Kalpana. "Hyperbole and Ellipses." Environmental Philosophy 16, no. 1 (2019): 89–113. http://dx.doi.org/10.5840/envirophil201842667.

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45

Wasser, Audrey. "Hyperbole in Proust." MLN 129, no. 4 (2014): 829–54. http://dx.doi.org/10.1353/mln.2014.0088.

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46

Elmer, Jonathan. "Torture and Hyperbole." Law, Culture and the Humanities 3, no. 1 (January 17, 2007): 18–34. http://dx.doi.org/10.1177/1743872107073235.

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47

McKinnon, William B. "The Hyperion hypothesis." Nature 346, no. 6283 (August 1990): 414–15. http://dx.doi.org/10.1038/346414a0.

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48

DUXBURY, NEIL. "History as Hyperbole." Oxford Journal of Legal Studies 15, no. 3 (1995): 477–88. http://dx.doi.org/10.1093/ojls/15.3.477.

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Black, G. "Hyperion: Rotational Dynamics." Icarus 117, no. 1 (September 1995): 149–61. http://dx.doi.org/10.1006/icar.1995.1148.

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Frantz, Marc. "Hyperbola: Under Construction!" College Mathematics Journal 45, no. 5 (November 2014): 388–90. http://dx.doi.org/10.4169/college.math.j.45.5.388.

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