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Journal articles on the topic 'Deep'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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1

Takahashi, Satoshi. "Deep Color." Journal of The Institute of Image Information and Television Engineers 65, no. 9 (2011): 1308–11. http://dx.doi.org/10.3169/itej.65.1308.

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2

Wang, Yipu, and Stuart Perrin. "Deep Chinese Teaching and Learning Model Based on Deep Learning." International Journal of Languages, Literature and Linguistics 10, no. 1 (2024): 32–35. http://dx.doi.org/10.18178/ijlll.2024.10.1.479.

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Deep learning is a more situational and reflective way of learning that integrates complex knowledge and skills into intuitive thinking. As a language that closely combines sound, form and meaning, Chinese teaching and learning from the perspective of deep learning can help break through the limitations of the current teaching model that only focuses on certain language knowledge or cultural behaviors. This paper combines deep learning with international Chinese education, creates deep Chinese teaching and learning model including “four stages and ten steps”, and carries out practical application and teaching effect test. The results show that the deep Chinese teaching and learning model is conducive to improving students’ discourse presentation ability and comprehensive skills, cultivating the learners’ autonomous learning ability and intercultural communication competence, and strengthening the integration of language teaching and cultural teaching. At the same time, this model also has some limitations, need to be further adjusted and optimized.
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3

Yoshida, Koichiro. "Deep-seated mycosis." Medical Mycology Journal 55, no. 2 (2014): J55—J56. http://dx.doi.org/10.3314/mmj.55.j55.

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4

Honda, Hitoshi, Brian S Heist, and Ken Kikuchi. "Deep-seated mycosis." Medical Mycology Journal 56, no. 3 (2015): E21—E22. http://dx.doi.org/10.3314/mmj.56.e21.

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5

Watanabe, Akira. "Deep-seated mycosis." Medical Mycology Journal 56, no. 1 (2015): J1—J2. http://dx.doi.org/10.3314/mmj.56.j1.

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6

Honda, Hitoshi, Brian S Heist, and Ken Kikuchi. "Deep-seated mycosis." Medical Mycology Journal 56, no. 3 (2015): J105—J106. http://dx.doi.org/10.3314/mmj.56.j105.

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7

Yaguchi, Takashi. "Deep-seated mycosis." Medical Mycology Journal 56, no. 4 (2015): J137—J138. http://dx.doi.org/10.3314/mmj.56.j137.

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8

Mikamo, Hiroshige. "Deep-seated mycosis." Medical Mycology Journal 57, no. 1 (2016): J33—J34. http://dx.doi.org/10.3314/mmj.57.j33.

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9

Singh P, Barath Kumar, and Ravi Kumar Chittoria. "Deep Vein Thrombosis." Indian Journal of Medical and Health Sciences 9, no. 2 (December 15, 2022): 89–96. http://dx.doi.org/10.21088/ijmhs.2347.9981.9222.9.

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Deep vein thrombosis (DVT) and Pulmonary embolism (PE) are two symptoms of venous thromboembolism (VTE). Both in the community and in hospitals, VTE significantly increases morbidity and death. Anticoagulation is the cornerstone of DVT treatment, assuming there are no contraindications. Patients with DVT have further anticoagulation after initial anticoagulation in order to guard against subsequent recurrences, emboli, and thrombosis-related mortality. This article provides an overview of the management of lower extremity DVT.
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10

Rosenthal, Brock J. "How Deep Is Deep?" Marine Technology Society Journal 43, no. 5 (December 1, 2009): 8. http://dx.doi.org/10.4031/mtsj.43.5.2.

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11

Carper, Leslie, Ellen Gilchrist, and Mary Hood. "Deep South, Deep Roots." Women's Review of Books 2, no. 9 (June 1985): 4. http://dx.doi.org/10.2307/4019589.

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12

Grinspoon, David. "Deep Time Deep Survival." Scientific American 315, no. 3 (August 16, 2016): 76–79. http://dx.doi.org/10.1038/scientificamerican0916-76.

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13

Miller, Nigel. "Deep linking — Deep trouble." Network Security 1999, no. 12 (December 1999): 15–16. http://dx.doi.org/10.1016/s1353-4858(00)87249-9.

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14

Jaiswal, Tarun, and Sushma Jaiswal. "Deep Learning in Medicine." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 212–17. http://dx.doi.org/10.31142/ijtsrd23641.

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15

Xin-Yi Wang, Xin-Yi Wang, Hao-Ran Sun Xin-Yi Wang, Xu-Yang Yin Hao-Ran Sun, Chun-Zi Li Xu-Yang Yin, and Sheng-Yu Liu Chun-Zi Li. "Deep Collaborative Filtering System." 電腦學刊 34, no. 4 (August 2023): 255–65. http://dx.doi.org/10.53106/199115992023083404022.

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<p>Collaborative filtering-based models can use the interaction between users and products or the correlation between users and users, and between products and products. However, methods based on collaborative filtering can only grasp one type of relationship and still cannot fully fit. Various factors influencing user preferences make a lot of redundant information still not filtered out. We proposals a collaborative filtering model based on deep learning, which combines the item-item relationship learning in advance with a neural collaborative filtering network to effectually make recommendations. In the initial stage, learn low-dimensional vectors of compartments, and embed information that reflections the co-occurrence relationship between compartments. The prediction stage combines the trained embedding vector with the embedding vector of the module as a correction to the output result of the neural network. The benchmark data set MovieLens 1M is the experienced data set of this article, and the effectiveness of this method is verified on the data set. The experienced results are compared with some advanced methods on the data set. The results show that the model proposed in this paper is better than some methods based on collaborative filtering.</p> <p>&nbsp;</p>
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16

Worswick, Steven G., James A. Spencer, Gunnar Jeschke, and Ilya Kuprov. "Deep neural network processing of DEER data." Science Advances 4, no. 8 (August 2018): eaat5218. http://dx.doi.org/10.1126/sciadv.aat5218.

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17

Reyna-Martínez, Mónica, Mónica T. González-Ramírez, and Minerva T. Vanegas-Farfano. "Depresión y estrés percibido en una muestra de parejas mexicanas durante el embarazo primerizo." Pensamiento Psicológico 20 (December 15, 2022): 1–16. http://dx.doi.org/10.11144/javerianacali.ppsi20.deep.

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Objetivo. Evaluar niveles de estrés percibido y depresión, asociados a parejas con embarazo primerizo. Método. Estudio transversal correlacional con muestreo por conveniencia, que emplea el Inventario de Depresión de Beck (BDI-II) y la Escala de Estrés Percibido (PSS). Participaron 94 parejas mexicanas con embarazo primerizo de una institución de salud pública. Resultados. Diferencias significativas entre sexos, pero en ambos padres, el estrés no muestra severidad (< 28) y la depresión se encuentra en niveles de nada a mínimos (M = 4.64, DE = 3). Las correlaciones de Spearman entre grupos son positivas y significativas en estrés percibido (rs = 0.26, p = 0.001) y depresión (rs = 0.30, p = 0.001). Conclusión. Los hallazgos revelan escasa severidad, pero según los puntos de corte, las mujeres exhiben mayor prevalencia de estrés (10.6%) y depresión (24.5%), en comparación con los hombres (6.4% y 9.6% respectivamente), coincidiendo con otros informes. Las asociaciones entre la pareja muestran dependencia, por tanto, se asume una relación estrecha, donde el comportamiento de uno afecta el resultado del otro; esto representa un factor de riesgo para la díada. Se propone profundizar sobre factores de riesgo asociados a la problemática de salud pública que involucran las variables del estudio.
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18

Patel, Prachi, and Vickie Gunderson. "Deep decarbonization faces deep challenges." MRS Bulletin 42, no. 09 (September 2017): 632–33. http://dx.doi.org/10.1557/mrs.2017.191.

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19

BESS, MICHAEL D. "DEEP EVIL AND DEEP GOOD." Yale Review 94, no. 3 (July 2006): 44–69. http://dx.doi.org/10.1111/j.1467-9736.2006.00208.x.

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20

Showstack, Randy. "Deep pockets for deep seas." Eos, Transactions American Geophysical Union 80, no. 29 (1999): 318. http://dx.doi.org/10.1029/eo080i029p00318-04.

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21

White, Alexander E. "Deep learning in deep time." Proceedings of the National Academy of Sciences 117, no. 47 (November 9, 2020): 29268–70. http://dx.doi.org/10.1073/pnas.2020870117.

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22

Gross, Michael. "Deep sea in deep trouble?" Current Biology 25, no. 21 (November 2015): R1019—R1021. http://dx.doi.org/10.1016/j.cub.2015.10.030.

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23

Gunderman, Richard. "Deep Questioning and Deep Learning." Academic Radiology 19, no. 4 (April 2012): 489–90. http://dx.doi.org/10.1016/j.acra.2011.12.018.

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24

Almagro, Manuel. "Deep conflicts and deep disagreements." THEORIA. An International Journal for Theory, History and Foundations of Science 39, no. 1 (May 13, 2024): 23–42. http://dx.doi.org/10.1387/theoria.24675.

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This paper tackles the following puzzle. On the one hand, a growing body of literature suggests that most of our current political disagreements are pernicious and difficult to resolve because they are instances of deep disagreement. On the other hand, there does not seem to be anything inherent to a deep disagreement that necessarily makes it pernicious nor irresolvable. To address this issue, I distinguish two senses in which a disagreement can be deemed “deep”, and show that they are conflated in the literature. In order to separate them, I refer to these two senses as deep disagreement and deep conflict. Contrary to recent literature, I argue that most of our current pernicious political disagreements are so not because they are instances of deep disagreement, but of deep conflict. This way, the problem disappears.
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25

Cvvr, sreedhar, and sreenivas baratam. "Deep overbite—A review (Deep bite, Deep overbite, Excessive overbite)." ANNALS AND ESSENCES OF DENTISTRY I, no. 1 (June 20, 2009): 8–25. http://dx.doi.org/10.5368/aedj.2009.1.1.8-25.

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26

ARTISTE, CINDY. "Deep." Critical Quarterly 32, no. 2 (June 1990): 48–49. http://dx.doi.org/10.1111/j.1467-8705.1990.tb00587.x.

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27

Min-gyu, Park, and Andrew Krebsbach. "Deep." Azalea: Journal of Korean Literature & Culture 8, no. 1 (2015): 281–302. http://dx.doi.org/10.1353/aza.2015.0019.

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28

Altissimo, Siviero, and Terlizzese. "How Deep Are the Deep Parameters?" Annales d'Économie et de Statistique, no. 67/68 (2002): 207. http://dx.doi.org/10.2307/20076348.

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29

Weinstock, Leonard B., Albert M. Cohen, and Gershon R. Volotsky. "How Deep Should "Deep Sedation" Be?" American Journal of Gastroenterology 102, no. 4 (April 2007): 906–7. http://dx.doi.org/10.1111/j.1572-0241.2007.01090_3.x.

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30

Downes, E. "Too deep or not deep enough?" BMJ 305, no. 6849 (August 8, 1992): 365. http://dx.doi.org/10.1136/bmj.305.6849.365.

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31

Miller, Richard W. "Deep Responsibility for the Deep Future." Theological Studies 77, no. 2 (May 12, 2016): 436–65. http://dx.doi.org/10.1177/0040563916636488.

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32

Kramer, W. T. C., A. Shoshani, D. A. Agarwal, B. R. Draney, G. Jin, G. F. Butler, and J. A. Hules. "Deep scientific computing requires deep data." IBM Journal of Research and Development 48, no. 2 (March 2004): 209–32. http://dx.doi.org/10.1147/rd.482.0209.

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33

Menza, C., M. Kendall, C. Rogers, and J. Miller. "A deep reef in deep trouble." Continental Shelf Research 27, no. 17 (October 2007): 2224–30. http://dx.doi.org/10.1016/j.csr.2007.05.017.

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34

Rose, David, Jonathan Livengood, Justin Sytsma, and Edouard Machery. "Deep trouble for the deep self." Philosophical Psychology 25, no. 5 (October 2012): 629–46. http://dx.doi.org/10.1080/09515089.2011.622438.

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35

Heaton, J. B., N. G. Polson, and J. H. Witte. "Deep learning for finance: deep portfolios." Applied Stochastic Models in Business and Industry 33, no. 1 (October 7, 2016): 3–12. http://dx.doi.org/10.1002/asmb.2209.

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36

Doke, Yash. "Deep fake Detection Through Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 861–66. http://dx.doi.org/10.22214/ijraset.2023.51630.

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Abstract: Deep fake is a rapidly growing concern in society, and it has become a significant challenge to detect such manipulated media. Deep fake detection involves identifying whether a media file is authentic or generated using deep learning algorithms. In this project, we propose a deep learning-based approach for detecting deep fakes in videos. We use the Deep fake Detection Challenge dataset, which consists of real and Deep fake videos, to train and evaluate our deep learning model. We employ a Convolutional Neural Network (CNN) architecture for our implementation, which has shown great potential in previous studies. We pre-process the dataset using several techniques such as resizing, normalization, and data augmentation to enhance the quality of the input data. Our proposed model achieves high detection accuracy of 97.5% on the Deep fake Detection Challenge dataset, demonstrating the effectiveness of the proposed approach for deep fake detection. Our approach has the potential to be used in real-world scenarios to detect deep fakes, helping to mitigate the risks posed by deep fakes to individuals and society. The proposed methodology can also be extended to detect in other types of media, such as images and audio, providing a comprehensive solution for deep fake detection.
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37

K, Mr Gopi. "Deep Fake Detection using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 6, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33196.

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Deep learning is an effective method that is broadly used across a wide range of areas, i.e., computer vision, machine vision, and natural language processing. Deepfakes is an application of this technology where the images and videos of someone are manipulated in such a way that it is difficult for human beings to tell the difference between them and their true selves. Deepfakes have been the subject of several studies recently, and a number of deep learning approaches have been proposed for their detection. Here, we provide an extensive survey on deepfake generation and recognition techniques using neural networks. Additionally, a detailed study of the different technologies used in deepfake detection is provided. This will surely benefit researchers in this area because it will include new cutting-edge methods for detecting fake videos or images on social networks. Moreover, it will make it easy for us to compare what others have done in their papers by explaining how they came up with their models or what information was employed for training them. Key Words: Deep Learning, Fake Detection, Neural Networks, Social Networks
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38

FUKUSHIMA, Kunihiko. "Neocognitron: Deep Convolutional Neural Network." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 27, no. 4 (2015): 115–25. http://dx.doi.org/10.3156/jsoft.27.4_115.

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39

Ito, Makoto. "Photo Quiz : Deep-seated mycosis." Medical Mycology Journal 53, no. 3 (2012): 159–60. http://dx.doi.org/10.3314/mmj.53.159.

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40

Ito, Makoto. "Photo Quiz : Deep-seated mycosis." Medical Mycology Journal 53, no. 4 (2012): 221–22. http://dx.doi.org/10.3314/mmj.53.221.

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41

KIKUTI, Ken. "Photo Quiz : Deep-seated mycosis." Medical Mycology Journal 53, no. 2 (2012): 93–94. http://dx.doi.org/10.3314/mmj.53.93.

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42

Watanabe, Akira, and Katsuhiko Kamei. "Photo Quiz : Deep-seated mycosis." Medical Mycology Journal 54, no. 2 (2013): 103–4. http://dx.doi.org/10.3314/mmj.54.103.

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43

Watanabe, Akira, and Katsuhiko Kamei. "Photo Quiz : Deep-seated mycosis." Medical Mycology Journal 54, no. 3 (2013): 265–66. http://dx.doi.org/10.3314/mmj.54.265.

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44

Kikuchi, Ken. "Photo Quiz : Deep-seated mycosis." Medical Mycology Journal 54, no. 1 (2013): 3–4. http://dx.doi.org/10.3314/mmj.54.3.

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45

Watanabe, Akira, and Katsuhiko Kamei. "Photo Quiz: Deep-seated mycosis." Medical Mycology Journal 54, no. 4 (2013): 319–20. http://dx.doi.org/10.3314/mmj.54.319.

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46

Jaiswal, Tarun, and Sushma Jaiswal. "Deep Learning Based Pain Treatment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 193–211. http://dx.doi.org/10.31142/ijtsrd23639.

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47

Dziewonski, Adam M. "Modelling the Earth's Deep Interior." Global Tectonics and Metallogeny 4, no. 1-2 (January 1, 1991): 53–58. http://dx.doi.org/10.1127/gtm/4/1991/53.

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48

Mehmood, Rayeesa, Rumaan Bashir, and Kaiser J. Giri. "Deep Generative Models: A Review." Indian Journal Of Science And Technology 16, no. 7 (February 21, 2023): 460–67. http://dx.doi.org/10.17485/ijst/v16i7.2296.

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49

Athani Samarth Kumar, Abusufiyan. "Cryptocurrency Prediction using Deep Learning." International Journal of Science and Research (IJSR) 12, no. 3 (March 5, 2023): 1253–57. http://dx.doi.org/10.21275/sr23319215511.

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50

Bhadiyadra, Yash. "Object Detection with Deep Learning." International Journal of Science and Research (IJSR) 12, no. 7 (July 5, 2023): 1300–1304. http://dx.doi.org/10.21275/mr23717204529.

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