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Relevant bibliographies by topics / Deep learning / Journal articles

Journal articles on the topic 'Deep learning'

To see the other types of publications on this topic, follow the link: Deep learning.

Author: Grafiati

Published: 4 June 2021

Last updated: 14 September 2024

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1

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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2

Chagas, Edgar Thiago De Oliveira. "Deep Learning e suas aplicações na atualidade." Revista Científica Multidisciplinar Núcleo do Conhecimento 04, no. 05 (May 8, 2019): 05–26. http://dx.doi.org/10.32749/nucleodoconhecimento.com.br/administracao/deep-learning.

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3

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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4

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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5

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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6

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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7

P C, Haris, and Dr Srikanth V. "Smart Eye Using Deep Learning." International Journal of Research Publication and Reviews 5, no. 3 (March 2, 2024): 467–70. http://dx.doi.org/10.55248/gengpi.5.0324.0615.

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8

Chagas, Edgar Thiago De Oliveira. "Deep Learning and its applications today." Revista Científica Multidisciplinar Núcleo do Conhecimento 04, no. 05 (May 8, 2019): 05–26. http://dx.doi.org/10.32749/nucleodoconhecimento.com.br/business-administration/deep-learning-2.

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9

Zitar, Raed Abu, Ammar EL-Hassan, and Oraib AL-Sahlee. "Deep Learning Recommendation System for Course Learning Outcomes Assessment." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1491–78. http://dx.doi.org/10.5373/jardcs/v11sp10/20192993.

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10

Akgül, İsmail, and Yıldız Aydın. "OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS." NWSA Academic Journals 17, no. 4 (October 29, 2022): 54–61. http://dx.doi.org/10.12739/nwsa.2022.17.4.2a0189.

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11

Alla, Sri Sai Meghana, and Kavitha Athota. "Brain Tumor Detection Using Transfer Learning in Deep Learning." Indian Journal Of Science And Technology 15, no. 40 (October 27, 2022): 2093–102. http://dx.doi.org/10.17485/ijst/v15i40.1307.

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12

Kumar Jitender Kumar, Yogesh. "Facemask Detection using Deep Learning Algorithm." International Journal of Science and Research (IJSR) 12, no. 5 (May 5, 2023): 1520–24. http://dx.doi.org/10.21275/sr23518151522.

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13

Nurmuhammet, Abdullayev. "DEEP REINFORCEMENT LEARNING ON STOCK DATA." Alatoo Academic Studies 23, no. 2 (June 30, 2023): 505–18. http://dx.doi.org/10.17015/aas.2023.232.49.

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This study proposes using Deep Reinforcement Learning (DRL) for stock trading decisions and prediction. DRL is a machine learning technique that enables agents to learn optimal strategies by interacting with their environment. The proposed model surpasses traditional models and can make informed trading decisions in real-time. The study highlights the feasibility of applying DRL in financial markets and its advantages in strategic decision- making. The model's ability to learn from market dynamics makes it a promising approach for stock market forecasting. Overall, this paper provides valuable insights into the use of DRL for stock trading decisions and prediction, establishing a strong case for its adoption in financial markets. Keywords: reinforcement learning, stock market, deep reinforcement learning.

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14

Pawar, Rutika. "Comparative Analysis of Deep Learning Techniques." International Journal of Science and Research (IJSR) 13, no. 1 (January 5, 2024): 1740–45. http://dx.doi.org/10.21275/sr24127165416.

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15

Sainath, J., M. Saketh Reddy, S. Saketh, Ch Sakshitha, K. Akshay, Thayyaba Khatoon Mohammed, and Prof A. kalyani. "Color Detection using Deep Learning Techniques." International Journal of Research Publication and Reviews 5, no. 5 (May 17, 2024): 9529–32. http://dx.doi.org/10.55248/gengpi.5.0524.1358.

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16

Patil, Nikita, Krishna Kadam, and Rahul Patil. "Deep Learning." IJARCCE 7, no. 8 (August 30, 2018): 99–101. http://dx.doi.org/10.17148/ijarcce.2018.7820.

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17

Rusk, Nicole. "Deep learning." Nature Methods 13, no. 1 (December 30, 2015): 35. http://dx.doi.org/10.1038/nmeth.3707.

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18

Hao, Xing, Guigang Zhang, and Shang Ma. "Deep Learning." International Journal of Semantic Computing 10, no. 03 (September 2016): 417–39. http://dx.doi.org/10.1142/s1793351x16500045.

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Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been applied to various fields. This paper provides an overview of deep learning in neural networks including popular architecture models and training algorithms.

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19

Okatani, Takayuki. "Deep Learning." Journal of the Institute of Image Information and Television Engineers 68, no. 6 (2014): 466–71. http://dx.doi.org/10.3169/itej.68.466.

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20

LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (May 2015): 436–44. http://dx.doi.org/10.1038/nature14539.

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21

Akleman, Ergun. "Deep Learning." Computer 53, no. 9 (September 2020): 17. http://dx.doi.org/10.1109/mc.2020.3004171.

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22

Wick, Christoph. "Deep Learning." Informatik-Spektrum 40, no. 1 (December 6, 2016): 103–7. http://dx.doi.org/10.1007/s00287-016-1013-2.

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23

Schulz, Hannes, and Sven Behnke. "Deep Learning." KI - Künstliche Intelligenz 26, no. 4 (May 17, 2012): 357–63. http://dx.doi.org/10.1007/s13218-012-0198-z.

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24

Schmidhuber, Juergen. "Deep Learning." Scholarpedia 10, no. 11 (2015): 32832. http://dx.doi.org/10.4249/scholarpedia.32832.

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25

Naylor, Amanda, and Janet Gibbs. "Deep Learning." International Journal of Mobile and Blended Learning 10, no. 1 (January 2018): 62–77. http://dx.doi.org/10.4018/ijmbl.2018010105.

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This article presents results from an international collaboration between college students and pre-service teachers in Norway and the UK. This research is part of a large, international project exploring and developing the interrelationship between mobile technology and teachers' perceptions of teaching and learning. Data was collected for this study through an on-line survey of 37 pre-service teachers followed by six semi-structured, in-depth interviews. The data analysis revealed the themes of collaboration, authenticity and professional learning through the use of mobile technology in the data. The collaboration enabled the use of the affordances of mobile technology to enhance the pre-service teachers' professional learning and the data suggested that this enhanced their emergent conceptions of teaching and learning.

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26

Hao, Xing, and Guigang Zhang. "Deep learning." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630018. http://dx.doi.org/10.1142/s2425038416300184.

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Artificial intelligence is one of the most beautiful dreams of mankind. Although computer technology has made considerable progress, so far, there is no computer showing intelligence like human beings. The emergence of deep learning gives people a glimmer of hope. So, what is learning deep? Why is it so important? How does it work? And what are the existing achievements and difficulties? This paper provides an overview of deep learning which will answer these questions.

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27

Rousseau, Axel-Jan, Melvin Geubbelmans, Tomasz Burzykowski, and Dirk Valkenborg. "Deep learning." American Journal of Orthodontics and Dentofacial Orthopedics 165, no. 3 (March 2024): 369–71. http://dx.doi.org/10.1016/j.ajodo.2023.12.003.

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28

Xuan, Junyu, Jie Lu, Zheng Yan, and Guangquan Zhang. "Bayesian Deep Reinforcement Learning via Deep Kernel Learning." International Journal of Computational Intelligence Systems 12, no. 1 (2018): 164. http://dx.doi.org/10.2991/ijcis.2018.25905189.

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29

Evseenko, Alla, and Dmitrii Romannikov. "Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1-2 (August 26, 2020): 7–25. http://dx.doi.org/10.17212/2307-6879-2020-1-2-7-25.

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Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is mainly carried out to solve problems in Atari 2600 games or in other similar ones. In this article, reinforcement training will be applied to one of the dynamic objects – an inverted pendulum. As a model of this object, we consider a model of an inverted pendulum on a cart taken from the Gym library, which contains many models that are used to test and analyze reinforcement learning algorithms. The article describes the implementation and study of two algorithms from this approach, Deep Q-learning and Double Deep Q-learning. As a result, training, testing and training time graphs for each algorithm are presented, on the basis of which it is concluded that it is desirable to use the Double Deep Q-learning algorithm, because the training time is approximately 2 minutes and provides the best control for the model of an inverted pendulum on a cart.

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30

Jain, Migul. "Machine Learning and Deep Learning Approaches for Cybersecurity: A Review." International Journal of Science and Research (IJSR) 12, no. 10 (October 5, 2023): 1706–10. http://dx.doi.org/10.21275/sr231023115126.

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31

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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32

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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33

Madhavappa Bachala Sathyanarayana, T. "A Review on Fraud Detection Using Machine Learning and Deep Learning." International Journal of Science and Research (IJSR) 13, no. 2 (February 5, 2024): 438–43. http://dx.doi.org/10.21275/sr24114141555.

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34

Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING." NATURE AND SCIENCE 03, no. 04 (October 27, 2020): 7–13. http://dx.doi.org/10.36719/2707-1146/04/7-13.

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Medical imaging technology plays an important role in the detection, diagnosis and treatment of diseases. Due to the instability of human expert experience, machine learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and reduce the imbalance of medical resources. This article systematically summarizes some methods of deep learning technology, introduces the application research of deep learning technology in medical imaging, and discusses the limitations of deep learning technology in medical imaging. Key words: Artificial Intelligence, Deep Learning, Medical Imaging, big data

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35

Kim, T., Y. Yoon, K. Lee, K. Y. Kwahk, and N. Kim. "Application of Deep Learning in Art Therapy." International Journal of Machine Learning and Computing 11, no. 6 (November 2021): 407–12. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1069.

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36

Jiang, Zhengfen, Boyi Li, Tho N. H. T. Tran, Jiehui Jiang, Xin Liu, and Dean Ta. "Fluo-Fluo translation based on deep learning." Chinese Optics Letters 20, no. 3 (2022): 031701. http://dx.doi.org/10.3788/col202220.031701.

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37

Tolentino, Lean Karlo S., Ronnie O. Serfa Juan, August C. Thio-ac, Maria Abigail B. Pamahoy, Joni Rose R. Forteza, and Xavier Jet O. Garcia. "Static Sign Language Recognition Using Deep Learning." International Journal of Machine Learning and Computing 9, no. 6 (December 2019): 821–27. http://dx.doi.org/10.18178/ijmlc.2019.9.6.879.

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38

Manasa, P. Venkata Sai, J. Jeevitha, M. Lakshmi Chandana, M. Jeevana Sravanthi, and M. Ali Shaik. "Brain Tumor Radiogenomic Classification Using Deep Learning." International Journal of Research Publication and Reviews 4, no. 3 (March 17, 2023): 1830–36. http://dx.doi.org/10.55248/gengpi.2023.4.33058.

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39

Annapurna, Prof V., H. Janardhan Reddy, G. Akhil, M. A. Samuel, and P. Vamshi. "Diagnosis of Alzheimer Disease Using Deep Learning." International Journal of Research Publication and Reviews 4, no. 4 (April 2023): 3805–13. http://dx.doi.org/10.55248/gengpi.4.423.37434.

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40

Bhat Kannagi Rajkhowa, Puran. "Deep Learning Model to Revive Indian Manuscripts." International Journal of Science and Research (IJSR) 12, no. 4 (April 5, 2023): 1365–68. http://dx.doi.org/10.21275/sr23422084622.

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41

Shetty D S, Radhika. "Multi-Modal Fusion Techniques in Deep Learning." International Journal of Science and Research (IJSR) 12, no. 9 (September 5, 2023): 526–32. http://dx.doi.org/10.21275/sr23905100554.

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42

Agrawal, Madhav, and Arham Jain. "Deep Learning Techniques in Brain Cancer Detection." International Journal of Science and Research (IJSR) 12, no. 11 (November 5, 2023): 41–49. http://dx.doi.org/10.21275/sr231029151256.

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43

Shin, Hong-Im. "Learning strategies and deep learning." Korean Medical Education Review 11, no. 1 (June 30, 2009): 35–43. http://dx.doi.org/10.17496/kmer.2009.11.1.35.

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Learning strategies are defined as behaviors and thoughts that a learner engages in during learning and that are intended to influence the learner’s encoding process. Today, demands for teaching how to learn increase, because there is a lot of complex material which is delivered to students. But learning strategies shouldn be identified as tricks of students for achieving high scores in exams. Cognitive researchers and theorists assume that learning strategies are related to two types of learning processing, which are described as ‘surface learning’ and ‘deep learning’. In addition learning strategies are associated with learning motivation. Students with ‘meaning orientation’ who struggle for deep learning, are intrinsically motivated, whereas students with ‘reproduction orientation’ or ‘achieving orientation’ are extrinsically motivated. Therefore, to foster active learning and intrinsic motivation of students, it isn’t enough to just teach how to learn. Changes of curriculum and assessment methods, that stimulate deep learning and curiosity of students are needed with educators and learners working cooperatively

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44

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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45

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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46

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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47

Wang, Shiqiang. "Efficient deep learning." Nature Computational Science 1, no. 3 (March 2021): 181–82. http://dx.doi.org/10.1038/s43588-021-00042-x.

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48

Wiebe, Nathan, Ashish Kapoor, and Krysta M. Svore. "Quantum deep learning." Quantum Information and Computation 16, no. 7&8 (May 2016): 541–87. http://dx.doi.org/10.26421/qic16.7-8-1.

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In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of multilayer and fully connected models.

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49

Cao, Longbing. "Deep Learning Applications." IEEE Intelligent Systems 37, no. 3 (May 1, 2022): 3–5. http://dx.doi.org/10.1109/mis.2022.3184260.

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50

El Ghaoui, Laurent, Fangda Gu, Bertrand Travacca, Armin Askari, and Alicia Tsai. "Implicit Deep Learning." SIAM Journal on Mathematics of Data Science 3, no. 3 (January 2021): 930–58. http://dx.doi.org/10.1137/20m1358517.

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