Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Deep Learning and Edge Computing Solution for High-Performance Computing Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Heterogenous Applications of Deep Learning Techniques in Diverse Domains: A Review

Authors : Desai Karanam Sreekantha, R. V. Kulkarni

Published in: Deep Learning and Edge Computing Solutions for High Performance Computing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Deep learning (DL) techniques have recently emerged as the most significant techniques for processing big multimedia data. DL networks autonomously extract advanced and inherent features from the big data sets using systematic learning methods. The real-world problem-solving using DL techniques demands large parallel computing infrastructure facilities for achieving high efficiency. Recent developments in deep learning techniques have demonstrated that it could outperform humans in some tasks such as classifying and tracking multimedia data. The deep learning networks can have about 150 hidden layers. The increase in the output performance of deep learning networks is directly proportional to input sample data size. This paper reviewed the literature on applications on deep learning from diverse application domains. Authors have also carried out a comparative study of various DL methods used and highlighted their results.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Applying Blockchain in Agriculture: A Study on Blockchain Technology, Benefits, and Challenges
next chapter Healthcare Informatics to Analyze Patient Health Records, for Enabling Better Clinical Decision-Making and Improved Healthcare Outcomes
Literature
1.
S. Zhong, and Y. Xianmin, The design, and application of a software: Promoting deep-level reading in the web-based classroom in Chinese primary school, Second international symposium on intelligent information technology applications, pp. 923–927, (2008)
2.
T. Wei-Keong, M.E. Vethamani, F. Hassan, S.-L. Wong, Reflection in the reading of literary texts in weblogs, 2nd international conference on education technology, and computer (ICETC). Pre-service Teachers (3), pp. 453–456, (2010)
3.
M. Archana, A blended learning model to achieve academic excellence in preparing postgraduate engineering students to become University teachers, IEEE 3rd International Conference on MOOCs, Innovation, and Technology in Education (MITE), Amritsar, pp. 9–14 (2019)
4.
L. Xiangfeng, L. Lei, L. Weidongm, Z. Ju, and X. Lingyu, Deep textual semantics acquisition based on the activation of domain knowledge, International conference of soft computing, and pattern recognition (SoCPaR), pp. 284–294 (2011)
5.
H.S. Chiranjeevi, K. Manjulam Shenoy, S. Prabhu, and S. Sundhar, DSSM with text hashing technique for text document retrieval in next-generation search engine for big data, and data analytics, IEEE International Conference on Engineering, and Technology (ICE-TECH), Coimbatore, pp.395–399, (2016)
6.
S. Feng, L. Xiong, and C. Yi, Text classification dimension reduction algorithm for Chinese webpage based on deep learning, pp. 451–456 (2013)
7.
Soniya, S. Paul, and L. Singh, A review on advances in deep learning, IEEE Workshop on Computational Intelligence: Theories, Applications, and Future Directions (WCI), Kanpur, pp. 1–6, (2015)
8.
Ding, Lili, Guo, Yanlu, Houl, Extreme learning machine with kernel model based on deep learning. Neural Comput. Appl. 28, pp. 1975–1984 (2017)CrossRef
9.
D.H.J.M. Dolmans, S.M.M. Loyens, H. Marcq, D. Gijbels, Deep and surface learning in problem-based learning: A review of the literature. Adv Health Sci Educ 21, pp. 1087–1112 (2016)CrossRef
10.
L. Yu, Z. Yang, L. Tang, A novel multistage deep belief network-based extreme learning machine ensemble learning paradigm for credit risk assessment. Flex. Serv. Manuf. J. (2015)
11.
Z. Qingchen, L.T. Yang, Z. Chen, Deep computation model for unsupervised feature learning on big data. IEEE Trans. Serv. Comput. 9(1) (2016)
12.
J. Su, B.W.K. Ling, Q. Dai, J. Xiao, and K.F. Tsang, Mobile-based big data design patent image retrieval system via LP norm deep learning approach, IECON, 41st Annual Conference of the IEEE Industrial Electronics Society (2015)
13.
S. Sahar, W. Dingding, P. Anna, T.M. Khoshgoftaar, Big Data: Deep Learning for financial sentiment analysis. J. Big Data 5, 3 (2018)CrossRef
14.
M.A. Ahad, G. Tripathi, P. Agarwal, Learning analytics for IoE based educational model using deep learning techniques: Architecture, challenges, and applications. Smart Learn. Environ. 5, 7 (2018)CrossRef
15.
G. Nguyen, S. Dlugolinsky, M. Bobák, V. Tran, Á.L. García, I. Heredia, P. Malík, L. Hluchý, Machine learning, and deep learning frameworks, and libraries for large-scale data mining: A survey. Artif. Intell. Rev. 52(1), pp. 77–124 (2019)CrossRef
16.
M.M. Najafabadi, F. Villanustre, T.M. Khoshgoftaar, N. Seliya, R. Wald, E. Muharemagic, Deep learning applications, and challenges in big data analytics. J. Big Data 2, 1 (2015)CrossRef
17.
C. Sungjoon, K. Eunwoo, and O. Songhwai, Human behaviour prediction for smart homes using deep learning, The 22nd IEEE international symposium on a robot, and human interactive communication, Gyeongju, Korea (2013)
18.
R. Law, G. Li, D.K.C. Fong, X. Han, Tourism demand forecasting: A deep learning approach. Ann. Tour. Res.., Elsevier 75, pp. 410–423 (2019)CrossRef
19.
J. Ruoxi, J. Ming, S. Kaiyu, H. Tianzhen, S. Costas, Advanced Building Control via Deep Reinforcement Learning, 10th International Conference on Applied Energy (ICAE2018), 22–25 August (2018), Hong Kong, China, Energy Procedia, 158: pp. 6158–6106 (2018)
20.
Y. Mei, L. Menglin, H. Hongwen, and P. Jiankun, Deep Learning for Vehicle Speed Prediction, Low carbon cities, and urban energy systems, Energy Procedia, pp. 618–623, CUE, (2018)
21.
I. Lille, L. Cosys, V. d’Ascq, G. Laurent, Deep neural networks for automatic detection of screams, and shouted speech in Subway trains. J. Mach. Learn. Res. 15, pp. 3133–3181 (2016)
22.
V.B. Semwal, K. Mondal, G.C. Nandi, Robust, and accurate feature selection for humanoid push recovery, and classification: Deep learning approach. Neural Comput. Appl. 28(3), pp. 565–574 (2017)CrossRef
23.
P.M. Shakeel, S. Baskar, V.R.S. Dhulipala, Maintaining security, and privacy in the health care system using learning-based deep-Q-networks. J. Med. Syst. 42(186), pp. 1–10 (2018)
24.
L. Weifeng, and C. Hsinchun, Identifying top sellers. In the underground economy using deep learning-based sentiment analysis, IEEE Joint intelligence, and security informatics conference, pp. 64–67, (2014)
25.
D. Enjie, Z. Zongwei, and Z. Duan, Terminal replacement prediction based on deep belief networks, International Conference on Network, and Information Systems for Computers, pp. 255–258 (2015)
26.
A. Grigory, B. Moez, B. Sid-Ahmed, and D. Jean-Luc, Apparent Age Estimation from Face Images Combining General, and children-Specialized Deep Learning Models, IEEE International Conference on Computer Vision Workshop (ICCVW) (2015)
27.
L. Ziwei, L. Xiaoxiao, L. Ping, C.C. Loy, and T. Xiaoou, Semantic Image Segmentation via Deep Parsing Network, Computer Vision, and Pattern Recognition, arXiv:1509.02634v2 (2015)
28.
K. Oscar, Human language technology & pattern recognition, IEEE International Conference on Computer Vision Workshops Deep Learning of Mouth Shapes for Sign Language, pp. 447–483, (2015)
29.
H. Dan, Xi’an, and X. Yu, Study on deep learning, and its application in visual tracking, IEEE 10th International Conference on Broadband, and Wireless Computing, Communication, and Application, pp. 240–246 (2015)
30.
K. Koki, and Y. Tomohiro, Visualizing extracted feature by deep learning in p300 discrimination task, Seventh International Conference of Soft Computing, and Pattern Recognition, pp. 149–154 (2015)
31.
Q. Yanmin, T. Tian, Y. Dong, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 24(12) (2016)
32.
H. Ryan, G. Luke, and M. Jason, Trelliscope: A system for detailed visualization in the deep analysis of large complex data, October 13–14, IEEE Symposium on large data analysis, and visualization, pp. 106–115, (2013)
33.
A. Ahmad, S. Al Mohsen, and A. Rashwan, Self-learning machines using deep networks, Proc. 10th International conference on cognitive informatics, and Cognitive, pp. 21–26 (2018)
34.
W. Ragheb, and L. Ali, Hand-written digit recognition using sparse deep architectures, 9th International Conference on Intelligent Systems: Theories and Applications, (SITA-14), pp. 1–6, (2014)
35.
S.L. Jian., J.F. Jiang, K. Lu., and Y.P. Zhang, Seu-tolerant restricted Boltzmann machine learning on DSP-based fault detection, Proceedings Restricted Boltzmann Machine (RBM), pp. 1503–1506 (2014)
36.
C. Sizhe, and W. Haipeng, SAR target recognition based on deep learning, International Conference on Data Science, and Advanced Analytics (DSAA) (2014)
37.
S.H. Khan, M. Bennamoun, F. Sohel, R. Togneri, Automatic shadow detection and removal from a single image. J. Latex Class Files 6, 1 (2015)
38.
A. Sajid, H. Kyuyeon, and S. Wonyong, Fixed point optimization of deep convolutional neural networks for object recognition, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2015)
39.
G. Yanhe, W. Shuang, G. Chenqiong, S. Dandong, Z. Donghui, and H. Biao, Wishart, RBM based DBN for polarimetric synthetic radar data classification, pp. 1841–1844 (2015)
40.
B. Pablo, B. Emilia, and W. Stefan, A deep neural model of emotion appraisal, Neural, and Evolutionary Computing, arXiv:1808.00252v (2018)
41.
Y. Xueyi, C. Xueting, C. Huahua, G. Yafeng, and L. Qiuyun. The deep learning network for face detection, Proceedings of ICCT (2015)
42.
H. Guosheng, IEEE International Conference on Computer Vision Workshop, pp. 384–392 (2015)
43.
B. Sourav, and N.D. Lane, From smart to deep: Robust activity recognition on smart-watches using deep learning, IEEE International Conference on Pervasive Computing, and Communication Workshops (PerCom Workshops) (2016)
44.
Q. Jun, and T. Javier. Deep multi-view representation learning for multi-modal features of the Schizophrenia, and Schizo-affective Disorder, pp. 952–956 (2016)
45.
46.
L. Xiaoxiang, S. Lingxiao, X. Wu, and T. Tan, Transferring deep representation for NIR-VIS heterogeneous face recognition, International Conference on Biometrics (ICB); Halmstad, pp. 1–8 (2016)
47.
Z. Hui, G. Maoguo, Z. Puzhao, S. Linzhi, S. Jiao, Feature-level change detection using deep representation, and feature change analysis for multi-spectral imagery. IEEE Geosci. Remote Sens. Lett. 13(11) (2016)
48.
S. Youyi, T. Ee-Leng, J. Xudong, C. Jie-Zhi, N. Dong, C. Siping, L. Baiying, and W. Tianfu. Accurate cervical cell segmentation from overlapping clumps in pap smear images, IEEE Trans. Med. Imaging 36 288 (2016)
49.
M.R. Alam, A joint deep Boltzmann machine (jDBM) model for person identification using Mobile phone data. IEEE Trans. Multimedia 19 (2016)
50.
E. Nasr-Esfahani, S. Samavi, N. Karimi, S.M.R. Soroushmehr, K. Ward, M.H. Jafari, B. Felfeliyan, B. Nallamothu, K. Najarian, Vessel extraction in X-ray angiograms using deep learning. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, pp. 643–646 (2016)
51.
L. Yang, L. Wei, Z. Yin, A. Haibo, J. Tan, Automatic lumbar vertebrae detection based on feature fusion deep learning for partial occluded C-arm X-ray images. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, 647 (2016)
52.
C. Chensi, L. Feng, T. Hai, D. Song, S. Wenji, L. Weizhong, Z. Yiming, B. Xiaochen, X. Zhi, Deep learning, and its applications in biomedicine. Genomics Proteomics Bioinformatics 16(1), pp. 17–32 (2018)CrossRef
53.
S. Yashvardhan, G. Sahil, Deep learning approaches for question answering system, international conference on computational intelligence, and data science (ICCIDS 2018). Procedia Comput. Sci. 132, pp. 785–794 (2019)
54.
A.R. Pathaka, M. Pandeya, S. Rautaraya, Application of deep learning for object detection, international conference on computational intelligence, and data science (ICCIDS 2018). Procedia Comput. Sci. 132, pp.1706–1717 (2018)
55.
H. Maha, T. Marwan, E.-M. Nagwa, Sentiment analysis of Arabic tweets using deep learning, 4th International Conference on Arabic Computational Linguistics (AC Ling 2018), November 17-19, 2018, Dubai, United Arab Emirates. Procedia Comput. Sci. 142, pp. 114–122 (2018)CrossRef
56.
M. Heba, Classification using deep learning neural networks for brain tumours. Future Comput. Inf. J., pp. 68–71 (2018)
57.
G. Swapna, R. Vinayakumar, K.P. Soman, Diabetes detection using deep learning algorithms. ICT Express 4(4), 243–246 (2018)CrossRef
58.
J. Yankang, B. Yuemin, H. Ziheng, W. Lirong, X. Xiang-Qun, Correction to deep learning for drug design: An artificial intelligence, the paradigm for drug discovery in the big data era. AAPS J. 20(4), 79 (2018)CrossRef
59.
L. Hong, Y. Long, T. Shengwei, L. Li, W. Mei, L. Xueyuan, Deep learning in pharmacy: The prediction of aqueous solubility based on deep belief network. Autom. Control. Comput. Sci. 51, pp. 97–107 (2017)CrossRef
60.
S. Li, Z.Q. Liu, Chan, Heterogeneous multi-task learning for human pose estimation with deep convolutional neural network. Int. J. Comput. Vis. 113, 19 (2015)MathSciNetCrossRef
61.
Y. Zhen-Jie, B. Jie, C. Yi-Xin, Applying deep learning to the individual, and community health monitoring data: A survey. Int. J. Autom. Comput. 15(6), pp. 643–655 (2018)
Metadata
Title
Heterogenous Applications of Deep Learning Techniques in Diverse Domains: A Review
Authors
Desai Karanam Sreekantha
R. V. Kulkarni
Copyright Year
2021
Book
Deep Learning and Edge Computing Solutions for High Performance Computing

Print ISBN: 978-3-030-60264-2

Electronic ISBN: 978-3-030-60265-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-60265-9_12