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Journal articles on the topic 'Deep Learning Applications'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

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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2

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

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3

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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4

Lee, Kian Yang. "Study on Deep Learning: Applications and Research Trends." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1603–11. http://dx.doi.org/10.5373/jardcs/v12sp7/20202264.

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5

Firdaus, Naina, and Madhuvan Dixit. "Deep Learning Techniques, Applications and Challenges: An Assessment." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 1710–14. http://dx.doi.org/10.31142/ijtsrd14437.

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6

Karamanji, Awab Qasim, Asia S. Ahmed, and Ali F. Fadhil. "Comparative Deep Learning Models in Applications of Steganography Detection." Journal of Image and Graphics 12, no. 3 (2024): 312–19. http://dx.doi.org/10.18178/joig.12.3.312-319.

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This paper explores the use of deep learning algorithms in steganography detection. More specifically, it examines deep learning-based binary classification to distinguish between stego and non-stego images from the three steganography algorithms, The Wavelet Obtained Weights (WOW), Spatial Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Steganography (HUGO). It also highlights the lack of research to develop a practical universal image steganography detection system using trained deep learning. The proposed farmwork combines multiple detection deep learning architecture

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7

Deng, Li. "Deep Learning: Methods and Applications." Foundations and Trends® in Signal Processing 7, no. 3-4 (2014): 197–387. http://dx.doi.org/10.1561/2000000039.

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8

Rahimy, Ehsan. "Deep learning applications in ophthalmology." Current Opinion in Ophthalmology 29, no. 3 (2018): 254–60. http://dx.doi.org/10.1097/icu.0000000000000470.

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9

Surma, Jerzy. "Deep Learning in Military Applications." Safety & Defense 10, no. 1 (2024): 1–7. https://doi.org/10.37105/sd.214.

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The latest advancements in Artificial Intelligence, especially in Deep Learning technology, accelerate innovation and development in different application domains. The development of Deep Learning technology has profoundly impacted military development trends, leading to major changes in the forms and models of war. In this paper, we overview Deep Learning's history and architecture. Then, we review related work and extensively describe Deep Learning in two primary military applications: intelligence operations and autonomous platforms. Finally, we discuss related threats, opportunities, techn

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10

Ding, Junhua, Haihua Chen, Yunhe Feng, and Tozammel Hossain. "Applications of Deep Learning Techniques." Electronics 13, no. 17 (2024): 3354. http://dx.doi.org/10.3390/electronics13173354.

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11

Ahn, SungMahn. "Deep Learning Architectures and Applications." Journal of Intelligence and Information Systems 22, no. 2 (2016): 127–42. http://dx.doi.org/10.13088/jiis.2016.22.2.127.

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12

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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13

Rana, J. AL-Sukeinee, and S. Khudeyer Raidah. "Review: Deep Learning and Fuzzy Logic Applications." Engineering and Technology Journal 9, no. 06 (2024): 4231–40. https://doi.org/10.5281/zenodo.11922700.

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The modeling and prediction field bosses a variety of   practical applications, deep learning is a powerful tool used in this field. It has been proved that deep learning is a useful technique for extracting extremely accurate predictions from complex data sources, and also Recursive neural networks have demonstrated their usefulness in language translation and caption production, but convolutional neural networks remain the dominant solution for image classification tasks. In addition, deep learning, also known as deep neural networks, involves training models with multiple layers o

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14

Pomyen, Yotsawat, Kwanjeera Wanichthanarak, Patcha Poungsombat, Johannes Fahrmann, Dmitry Grapov, and Sakda Khoomrung. "Deep metabolome: Applications of deep learning in metabolomics." Computational and Structural Biotechnology Journal 18 (2020): 2818–25. http://dx.doi.org/10.1016/j.csbj.2020.09.033.

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15

Banday, Mehroush. "Deep learning and Big Data Analysis: Challenges, Opportunities and Applications." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 2216–22. http://dx.doi.org/10.31142/ijtsrd12710.

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16

Hindi, Nezar. "Deep Learning Applications in Energy Consumption Optimization: A Comprehensive Analysis." International Journal of Research Publication and Reviews 6, no. 6 (2025): 4450–60. https://doi.org/10.55248/gengpi.6.0125.0625.

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17

Nariya, Sunrut, and Milan Gohel. "Deep Learning Applications in Personalized International Education: A Comprehensive Review." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 769–71. https://doi.org/10.21275/sr25407222616.

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18

Dipak, Raghunath Patil, and Purohit Rajesh. "Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 608–12. https://doi.org/10.35940/ijeat.A9961.129219.

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Memory management is very essential task for large-scale storage systems; in mobile platform generate storage errors due to insufficient memory as well as additional task overhead. Many existing systems have illustrated different solution for such issues, like load balancing and load rebalancing. Different unusable applications which are already installed in mobile platform user never access frequently but it allocates some memory space on hard device storage. In the proposed research work we describe dynamic resource allocation for mobile platforms using deep learning approach. In Real world

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19

Sharma, Neha, Reecha Sharma, and Neeru Jindal. "Machine Learning and Deep Learning Applications-A Vision." Global Transitions Proceedings 2, no. 1 (2021): 24–28. http://dx.doi.org/10.1016/j.gltp.2021.01.004.

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20

NAKASHIMA, Tomoharu. "Machine Learning and Deep Learning: Introduction and Applications." Journal of the Society of Materials Science, Japan 69, no. 9 (2020): 633–39. http://dx.doi.org/10.2472/jsms.69.633.

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21

Yan, Peiyi, Yaojia Liu, Yuran Jia, and Tianyi Zhao. "Deep Learning and Machine Learning Applications in Biomedicine." Applied Sciences 14, no. 1 (2023): 307. http://dx.doi.org/10.3390/app14010307.

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22

Park, Hyo Jung, Bumwoo Park, and Seung Soo Lee. "Radiomics and Deep Learning: Hepatic Applications." Korean Journal of Radiology 21, no. 4 (2020): 387. http://dx.doi.org/10.3348/kjr.2019.0752.

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23

Chen, Ming. "Applications of Deep Learning: A Review." International Journal of Computer Science and Information Technology for Education 4, no. 2 (2019): 17–24. http://dx.doi.org/10.21742/ijcsite.2019.4.2.03.

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24

Chen, Chi-Hua, Hsu-Yang Kung, and Feng-Jang Hwang. "Deep Learning Techniques for Agronomy Applications." Agronomy 9, no. 3 (2019): 142. http://dx.doi.org/10.3390/agronomy9030142.

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This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by

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25

Mathew, Alex. "Deep Reinforcement Learning for Cybersecurity Applications." International Journal of Computer Science and Mobile Computing 10, no. 12 (2021): 32–38. http://dx.doi.org/10.47760/ijcsmc.2021.v10i12.005.

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There has been a rapid growth of the devices connected to the internet in the last decade for the various internet (IoT) of things applications. The increase of these smart devices has posed a great security concern in the internet of things ecosystem. The internet of things ecosystem must be protected from these threats. Reinforcement learning has been proposed by the cybersecurity professionals to provide the needed security tools for securing the IoT system since it is able to interact with the environment and learn how to detect the threats. This paper presents a comprehensive research on

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26

Khan, Adnan A., Hamza Ibad, Kaleem Sohail Ahmed, Zahra Hoodbhoy, and Shahzad M. Shamim. "Deep learning applications in neuro-oncology." Surgical Neurology International 12 (August 30, 2021): 435. http://dx.doi.org/10.25259/sni_433_2021.

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Deep learning (DL) is a relatively newer subdomain of machine learning (ML) with incredible potential for certain applications in the medical field. Given recent advances in its use in neuro-oncology, its role in diagnosing, prognosticating, and managing the care of cancer patients has been the subject of many research studies. The gamut of studies has shown that the landscape of algorithmic methods is constantly improving with each iteration from its inception. With the increase in the availability of high-quality data, more training sets will allow for higher fidelity models. However, logist

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27

Christin, Sylvain, Éric Hervet, and Nicolas Lecomte. "Applications for deep learning in ecology." Methods in Ecology and Evolution 10, no. 10 (2019): 1632–44. http://dx.doi.org/10.1111/2041-210x.13256.

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28

Zhong, Zhiqiao. "Deep Learning Applications in Business Activities." American Journal of Management Science and Engineering 3, no. 5 (2018): 38. http://dx.doi.org/10.11648/j.ajmse.20180305.11.

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29

Zhong, Mingrui. "Applications of Deep Learning in Medicine." Applied and Computational Engineering 113, no. 1 (2024): 20–23. https://doi.org/10.54254/2755-2721/2024.melb18312.

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In the current era, with the development of artificial intelligence, deep learning is an essential branch of it. This paper aims to discuss the development history, application status and development prospect of DL model based on deep learning in medical field. Firstly, the advantages of deep learning are powerful data processing capabilities, improved disease prediction accuracy, and the ability to predict disease risk, which contribute to the development of personalized medicine. Deep learning is an attempt to simulate the working principle of the human brain. By training a large amount of d

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30

Mamoshina, Polina, Armando Vieira, Evgeny Putin, and Alex Zhavoronkov. "Applications of Deep Learning in Biomedicine." Molecular Pharmaceutics 13, no. 5 (2016): 1445–54. http://dx.doi.org/10.1021/acs.molpharmaceut.5b00982.

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31

Palmer, Chris. "Neuromorphic Computing Advances Deep-Learning Applications." Engineering 6, no. 8 (2020): 854–56. http://dx.doi.org/10.1016/j.eng.2020.06.010.

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32

Liu, Yingchen. "Applications of deep reinforcement learning Alphago." Applied and Computational Engineering 5, no. 1 (2023): 637–41. http://dx.doi.org/10.54254/2755-2721/5/20230668.

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With the progress of the times, the field of artificial intelligence (AI) has become one of the hottest fields in the 21st century. Currently, artificial intelligence is successfully used in the retail, financial, and medical industries. Especially in 2016, Google's DeepMind used deep reinforcement learning to train AlphaGo and defeated Lee Sedol, which propelled the field into the public eye. Most people are aware of artificial intelligence, but few understand it. This article will focus on analyzing the literature "Mastering the game of Go with deep neural networks and tree search" and other

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33

Alqahtani, Norah Dhafer, Bander Alzahrani, and Muhammad Sher Ramzan. "Deep Learning Applications for Dyslexia Prediction." Applied Sciences 13, no. 5 (2023): 2804. http://dx.doi.org/10.3390/app13052804.

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Dyslexia is a neurological problem that leads to obstacles and difficulties in the learning process, especially in reading. Generally, people with dyslexia suffer from weak reading, writing, spelling, and fluency abilities. However, these difficulties are not related to their intelligence. An early diagnosis of this disorder will help dyslexic children improve their abilities using appropriate tools and specialized software. Machine learning and deep learning methods have been implemented to recognize dyslexia with various datasets related to dyslexia acquired from medical and educational orga

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34

Koseoglu, Neslihan Dilruba, and TY Alvin Liu. "Predictive Deep Learning Applications in Ophthalmology." touchREVIEWS in Ophthalmology 17, no. 2 (2023): 4. http://dx.doi.org/10.17925/usor.2023.17.2.4.

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Deep learning is a state-of-the-art machine learning technique used in medical image analysis. In recent years, there has been a growing interest in applying deep learning methods to perform prediction and prognostication tasks. Broadly speaking, these applications can be characterized as structure-structure prediction, structure-function prediction, disease onset/progression prediction and treatment response prediction. This review aims to summarize the most recent studies in this area, with a particular focus on age-related macular degeneration, diabetic retinopathy and glaucoma.

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35

Matinyan, Senik, Pavel Filipcik, and Jan Pieter Abrahams. "Deep learning applications in protein crystallography." Acta Crystallographica Section A Foundations and Advances 80, no. 1 (2024): 1–17. http://dx.doi.org/10.1107/s2053273323009300.

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Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This field has some significant challenges, in particular producing high-quality and well ordered protein crystals. Additionally, collecting diffraction data with high completeness and quality, and determining and refining protein structures can be problematic. Protein crystallographic data are often high-dimensional, noisy and incomplete. Deep learning

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36

Patel, Hardik, Theodoros Zanos, and D. Brock Hewitt. "Deep Learning Applications in Pancreatic Cancer." Cancers 16, no. 2 (2024): 436. http://dx.doi.org/10.3390/cancers16020436.

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Pancreatic cancer is one of the most lethal gastrointestinal malignancies. Despite advances in cross-sectional imaging, chemotherapy, radiation therapy, and surgical techniques, the 5-year overall survival is only 12%. With the advent and rapid adoption of AI across all industries, we present a review of applications of DL in the care of patients diagnosed with PC. A review of different DL techniques with applications across diagnosis, management, and monitoring is presented across the different pathological subtypes of pancreatic cancer. This systematic review highlights AI as an emerging tec

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37

Hu, Shengkun. "Deep Learning in Healthcare." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 279–85. http://dx.doi.org/10.54097/hset.v57i.10014.

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This article aims to discuss and demonstrate deep learning techniques used in healthcare. After introducing the feasibility of deep learning in the medical field, the article discussed the opportunities and challenges of deep learning in healthcare from different perspectives. Then, the article showed the current implementations and applications of deep learning in the medical healthcare system. Finally, the article summarizes deep learning techniques and applications in healthcare.

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38

Abdulwahhab, Ali H., Noof T. Mahmood, Ali Abdulwahhab Mohammed, Indrit Myderrizi, and Mustafa Hamid Al-Jumaili. "A Review on Medical Image Applications Based on Deep Learning Techniques." Journal of Image and Graphics 12, no. 3 (2024): 215–27. http://dx.doi.org/10.18178/joig.12.3.215-227.

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The integration of deep learning in medical image analysis is a transformative leap in healthcare, impacting diagnosis and treatment significantly. This scholarly review explores deep learning’s applications, revealing limitations in traditional methods while showcasing its potential. It delves into tasks like segmentation, classification, and enhancement, highlighting the pivotal roles of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Specific applications, like brain tumor segmentation and COVID-19 diagnosis, are deeply analyzed using datasets like NIH Clini

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39

Kremena, Arsova-Borisova Veselin Hristov. "DEEP NEURAL NETWORKS APPLICATIONS IN THE STUDY OF A GEOLOGICAL INDICATOR." "Sustainable Extraction and Processing of Raw Materials" Journal (SEPRM) 2 (October 24, 2021): 25–27. https://doi.org/10.5281/zenodo.5594817.

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The differences between shallow neural networks and deep neural networks are considered. Data from operational exploration of an open pit mine are used to train different types of deep neural networks to predict a useful indicator. The results of deep network training are compared both with each other and with previous results obtained from the training of shallow neural networks. The errors in training and testing of the models according to the number of layers and nodes in the networks are analysed.

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40

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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41

Samrat Medavarapu, Sachin. "Harnessing Deep Learning for Computer Vision: Current Applications and Future Directions." International Journal of Science and Research (IJSR) 11, no. 12 (2022): 1372–76. http://dx.doi.org/10.21275/sr24810074919.

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42

Rao Sangarsu, Raghavendra. "Advances in Deep Learning for Image Processing: Techniques, Challenges, and Applications." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 1960–63. http://dx.doi.org/10.21275/sr24213025624.

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43

Hindi, Nezar. "Deep Learning Applications in Drug-Target Interaction Prediction: A Systematic Review." International Journal of Research Publication and Reviews 6, no. 6 (2025): 4433–49. https://doi.org/10.55248/gengpi.6.0125.0626.

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44

Thete, Prof Sharda, Siddheshwar Midgule, Nikesh Konde, and Suraj Kale. "Malware Detection Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1942–45. http://dx.doi.org/10.22214/ijraset.2022.47682.

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Abstract: Android application security is based on permission-based mechanisms that restrict third-party Android applications' access to critical resources on an Android device. The user must accept a set of permissions required by the application before proceeding with the installation. This process is intended to inform users about the risks of installing and using applications on their devices. However, most of the time, even with a well-understood permission system, users are not fully aware of endangered threats, relying on application stores or the popularity of applications and relying

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Midgule, Siddheshwar. "Malware Detection Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4755–58. http://dx.doi.org/10.22214/ijraset.2023.52704.

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Abstract: Android application security is based on permission-based mechanisms that restrict third-party Android applications’ access to critical resources on an Android device. The user must accept a set of permissions required by the application before proceeding with the installation. This process is intended to inform users about the risks of installing and using applications on their devices. However, most of the time, even with a well-understood permission system, users are not fully aware of endangered threats, relying on application stores or the popularity of applications and relying

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46

Altabeiri, Raed, Moath Alsafasfeh, and Mohanad Alhasanat. "Image compression approach for improving deep learning applications." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5607–16. https://doi.org/10.11591/ijece.v13i5.pp5607-5616.

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In deep learning, dataset plays a main role in training and getting accurate results of detection and recognition objects in an image. Any training model needs a large size of dataset to be more accurate, where improving the dataset size is one of the most research problems that needs enhancement. In this paper, an image compression approach was developed to reduce the dataset size and improve classification accuracy for the trained model using a convolutional neural network (CNN), and speeds up the machine learning process, while maintaining image quality. The results revealed that the best s

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47

Dubey, A., and A. Rasool. "Usage of deep learning in recent applications." Archives of Materials Science and Engineering 115, no. 2 (2022): 49–57. http://dx.doi.org/10.5604/01.3001.0016.0752.

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Deep learning is a predominant branch in machine learning, which is inspired by the operation of the human biological brain in processing information and capturing insights. Machine learning evolved to deep learning, which helps to reduce the involvement of an expert. In machine learning, the performance depends on what the expert extracts manner features, but deep neural networks are self-capable for extracting features. Deep learning performs well with a large amount of data than traditional machine learning algorithms, and also deep neural networks can give better results with different kin

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48

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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Mr., Dinesh D.V. "Review Of Deep Learning: Concepts, Approaches, Applications & Future Directions." IJAPR, UGC Care Listed Journal 7, no. 1 (2022): 87–92. https://doi.org/10.5281/zenodo.8118695.

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More i

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50

Orhani, Senad. "Deep Learning in Math Education." International Journal of Research and Innovation in Social Science VIII, no. IV (2024): 270–78. http://dx.doi.org/10.47772/ijriss.2024.804022.

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This scientific paper aims to present information about the use of Deep Learning in the field of mathematical education. In the review of the literature, a series of studies and applications of advanced technologies in the teaching of mathematics were identified. Several studies have shown that the use of deep models can identify specific difficulties of students and provide personalized feedback to improve learning outcomes. In addition, the use of innovative applications has shown the potential to make mathematics learning more engaging and interesting for students. In conclusion, the litera

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