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Journal articles on the topic 'Deep learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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

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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 applicat

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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 (2019): 212–17. http://dx.doi.org/10.31142/ijtsrd23641.

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4

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

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

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6

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

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7

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

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8

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

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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 (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 (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 (2022): 2093–102. http://dx.doi.org/10.17485/ijst/v15i40.1307.

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12

Pansare, Yash, Lav Parab, and Atharva Patil. "Distraction Detection for Vehicle Drivers Using Deep Learning Techniques: A Review of Advanced Deep Learning Techniques." International Journal of Research Publication and Reviews 6, no. 4 (2025): 16612–18. https://doi.org/10.55248/gengpi.6.0425.16118.

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13

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

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14

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

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15

Hao, Xing, Guigang Zhang, and Shang Ma. "Deep Learning." International Journal of Semantic Computing 10, no. 03 (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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16

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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17

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

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18

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

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19

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

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20

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

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21

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

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22

Naylor, Amanda, and Janet Gibbs. "Deep Learning." International Journal of Mobile and Blended Learning 10, no. 1 (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 d

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23

Hao, Xing, and Guigang Zhang. "Deep learning." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (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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24

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

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25

Ganesh Viswanathan, Gaurav Samdani, Yawal Dixit, and Ranjith Gopalan. "Deep Learning." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 512–27. https://doi.org/10.30574/wjaets.2025.14.3.0149.

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Deep learning has revolutionized artificial intelligence by enabling machines to learn complex patterns from vast amounts of data. This white paper explores the fundamental principles of deep learning, including neural network architectures, training methodologies, and key advancements such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. We discuss applications across various domains, including computer vision, natural language processing, healthcare, and finance, highlighting real-world use cases and breakthroughs. Additionally, we examine th

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26

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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27

Tahir, Saba. "Deep Learning Models for Sentiment Analysis." International Journal of Scientific Engineering and Research 11, no. 2 (2023): 21–23. https://doi.org/10.70729/me23213174020.

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28

V R Nandigana, Vishal. "Deep Learning and Generative, Interactive Design." International Journal of Science and Research (IJSR) 10, no. 5 (2021): 679–80. https://doi.org/10.21275/sr21516221931.

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29

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

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30

Nurmuhammet, Abdullayev. "DEEP REINFORCEMENT LEARNING ON STOCK DATA." Alatoo Academic Studies 23, no. 2 (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

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31

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

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32

Sainath, J., M. Saketh Reddy, S. Saketh, et al. "Color Detection using Deep Learning Techniques." International Journal of Research Publication and Reviews 5, no. 5 (2024): 9529–32. http://dx.doi.org/10.55248/gengpi.5.0524.1358.

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33

Das N, Hridhya, and Rinsa Rees. "Emotion Correlation Mining Through Deep Learning." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1560–64. https://doi.org/10.21275/sr25416201655.

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34

S, Sreelekshmi, and Rinsa Rees. "Road Pothole Detection Using Deep Learning." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1579–83. https://doi.org/10.21275/sr25416165603.

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35

Naziya, S. Farhin, S. Afreen, Y. Chaitanya, and Dr Manjunatha B N. "Lung Cancer Detection Using Deep Learning." International Journal of Research Publication and Reviews 6, no. 5 (2025): 14792–94. https://doi.org/10.55248/gengpi.6.0525.1958.

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36

Khatke, Shreyash, Shubham Pachpute, Rajat Shingate, and Prof kajal Khalate. "Face Spoofing Detection Using Deep Learning." International Journal of Research Publication and Reviews 6, no. 6 (2025): 2475–81. https://doi.org/10.55248/gengpi.6.0625.2051.

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37

Latha, Mrs B. Sree, Vaishnavi Vaishnavi, Yeshwanth Akhil Sai, Bhoomika Bhoomika, and Subramanium Subramanium. "Diabetic Retinopathy Detection Using Deep Learning." International Journal of Research Publication and Reviews 6, no. 6 (2025): 11839–43. https://doi.org/10.55248/gengpi.6.0625.2379.

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38

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 m

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39

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

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40

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

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41

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

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42

Rebecca, Dr B., Bathul Spandana, and Bingi Swathi. "Facial Emotion Detection using Machine Learning and Deep Learning Algorithms." International Journal of Research Publication and Reviews 6, no. 4 (2025): 14604–8. https://doi.org/10.55248/gengpi.6.0425.1663.

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43

Shivareddy, Nareddy, and Dr V. Uma Rani. "Enhancing Image Forgery Detection Using Machine Learning And Deep Learning." International Journal of Research Publication and Reviews 6, no. 6 (2025): 12129–33. https://doi.org/10.55248/gengpi.6.0625.2390.

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44

Barry, Jessie. "Applications of Deep Learning in Ornithology." Biodiversity Information Science and Standards 2 (June 6, 2018): e27251. https://doi.org/10.3897/biss.2.27251.

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Earth's ecosystems are threatened by anthropogenic change, yet relatively little is known about biodiversity across broad spatial (i.e. continent) and temporal (i.e. year-round) scales. There is a significant gap at these scales in our understanding of species distribution and abundance, which is the precursor to conservation (Hochachka et al. 2012). The cost and availability of experts to collect data does not scale to broad spatial or temporal surveys. With recent advances in artificial intelligence (AI) it is becoming possible to automate some of this data collection and analysis (Joppa 201

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45

Tirer, Tom, Raja Giryes, Se Young Chun, and Yonina C. Eldar. "Deep Internal Learning: Deep learning from a single input." IEEE Signal Processing Magazine 41, no. 4 (2024): 40–57. http://dx.doi.org/10.1109/msp.2024.3385950.

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46

Shin, Hong-Im. "Learning strategies and deep learning." Korean Medical Education Review 11, no. 1 (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 st

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47

Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING." NATURE AND SCIENCE 03, no. 04 (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 Intellige

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48

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 (2024): 438–43. http://dx.doi.org/10.21275/sr24114141555.

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49

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 (2021): 407–12. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1069.

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50

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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