Usage of deep learning in recent applications

• Autorzy: Dubey A.

Identyfikatory

DOI

10.5604/01.3001.0016.0752

• Języki publikacji: EN

Abstrakty

EN

Purpose: 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. Design/methodology/approach: 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 kinds of unstructured data. Findings: Deep learning is an inevitable approach in real-world applications such as computer vision where information from the visual world is extracted, in the field of natural language processing involving analyzing and understanding human languages in its meaningful way, in the medical area for diagnosing and detection, in the forecasting of weather and other natural processes, in field of cybersecurity to provide a continuous functioning for computer systems and network from attack or harm, in field of navigation and so on. Practical implications: Due to these advantages, deep learning algorithms are applied to a variety of complex tasks. With the help of deep learning, the tasks that had been said as unachievable can be solved. Originality/value: This paper describes the brief study of the real-world application problems domain with deep learning solutions.

Słowa kluczowe

EN

conceptual based information retrieval; ontology; semantic search

PL

wyszukiwanie informacji oparte na pojęciach; ontologia; wyszukiwanie semantyczne

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2022
• Tom: Vol. 115, nr 2
• Strony: 49--57
• Opis fizyczny: Bibliogr. 29 poz.

Twórcy

autor

Dubey A.

• Department of Computer Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India

• https://orcid.org/0000-0002-4885-0632

Bibliografia

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• [4] A. Khan, A. Sohail, U. Zahoora, A.S. Qureshi, A Survey of the Recent Architectures of Deep Convolutional Neural Networks, Artificial Intelligence Review 53 (2020) 5455-5516. DOI: https://doi.org/10.1007/s10462-020-09825-6
• [5] H. Purwins, B. Li, T. Virtanen, J. Schlüter, S.Y. Chang, T. Sainath, Deep Learning for Audio Signal Processing, IEEE Journal of Selected Topics in Signal Processing 13/2 (2019) 206-219. DOI: https://doi.org/10.1109/JSTSP.2019.2908700
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• [16] X. Bai, Text classification based on LSTM and attention, Proceedings of the 13th International Conference on Digital Information Management “ICDIM”, Berlin, 2018, 29-32. DOI: https://doi.org/10.1109/ICDIM.2018.8847061
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• [18] C.T.C. Arsene, R. Hankins, H. Yin, Deep Learning Models for Denoising ECG Signals, Proceedings of the 27th European Signal Processing Conference “EUSIPCO”, A Coruna, 2019, 1-5. DOI: https://doi.org/10.23919/EUSIPCO.2019.8902833
• [19] J.W. Pak, M.K. Kim, Convolutional Neural Network Approach for Aircraft Noise Detection, Proceedings of the International Conference on Artificial Intelligence in Information and Communication “ICAIIC”, Okinawa, 2019, 430-434. DOI: https://doi.org/10.1109/ICAIIC.2019.8669006
• [20] J. Ren, R. Ren, M. Green, X. Huang, A Deep Learning Method for Fault Detection of Autonomous Vehicles, Proceedings of the 14th International Conference on Computer Science & Education “ICCSE”, Toronto, 2019, 749-754. DOI: https://doi.org/10.1109/ICCSE.2019.8845483
• [21] N. Sanil, P.A.N. Venkat, V. Rakesh, R. Mallapur, M.R. Ahmed, Deep Learning Techniques for Obstacle Detection and Avoidance in Driverless Cars, International Conference on Artificial Intelligence and Signal Processing “AISP”, Amaravati, 2020, 1-4. DOI: https://doi.org/10.1109/AISP48273.2020.9073155
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• [24] N.K. Mudaliar, K. Hegde, A. Ramesh, V. Patil, Visual Speech Recognition: A Deep Learning Approach, Proceedings of the 5th International Conference on Communication and Electronics Systems “ICCES 2020”, 2020, Coimbatore, 1218-1221. DOI: https://doi.org/10.1109/ICCES48766.2020.9137926
• [25] M.K. Sharma, P. Kumar, A. Rasool, A. Dubey, V.K. Mahto, Classification of Actual and Fake News in Pandemic, Proceedings of the 5th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) “I-SMAC”, Palladam, 2021, 1168-1174. DOI: https://doi.org/10.1109/I-SMAC52330.2021.9640639
• [26] P. Vyas, F. Sharma, A. Rasool, A. Dubey, Supervised Multimodal Emotion Analysis of Violence on Doctors Tweets, Proceedings of the 5th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) “I-SMAC”, Palladam, 2021, 962-967. DOI: https://doi.org/10.1109/I-SMAC52330.2021.9640732
• [27] A.M. Aromal, A. Rasool, A. Dubey, B.N. Roy, Optimized Weighted Samples Based Semi- Supervised Learning, Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems “ICESC”, Coimbatore, 2021, 1311-1318. DOI: https://doi.org/10.1109/ICESC51422.2021.9532994
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• [29] A. Soni, A. Rasool, A. Dubey, N. Khare, Data mining based dimensionality reduction techniques, Proceedings of the International Conference for Advancement in Technology ICONAT”, Goa, 2022, 1-8. DOI: https://doi.org/10.1109/ICONAT53423.2022.9725846

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).