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Deep Learning: An Application Perspective

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Cyber Intelligence and Information Retrieval

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 291))

Abstract

Nowadays, machine learning and deep learning have proven to be an encouraging field after artificial intelligence. Deep learning being operative, supervised, time and cost-effective technique evolving has its myriad applications not just in the field of pattern recognition and prediction but also can be utilized for addressing some important issues in the field of data science and data analytics. Deep learning came into existence with the availability of the exabyte of data collected with time. This subset technique of machine learning acquires the instructive, differential, and distinct features of data. Deep learning is a subset of machine learning that has made a noteworthy breakthrough as it is inspired by the working of the brain and its cells called neurons now coming up with significant performance, efficient evaluation and remarkable outcomes in a huge range of applications, and deep learning techniques have set their way with useful security tools and are now widely used in the field of statistics, neuroscience, data analytics, pattern recognition, image sensing, and the list goes on. This paper covers the basics of deep learning, its several commonly used algorithms and techniques, their architectures along with several application areas where these techniques have shown prominent differences and revolutionary change with the time. We have also tried to generalize the characteristics of different used methods of deep learning. At last, we present the challenges for the growth of these methods, concluding with our perspectives and future scope.

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References

  1. Moor J (2003) The turing test: the elusive standard of artificial intelligence, 30th ed. Springer Science & Business Media, Berlin, Germany

    Google Scholar 

  2. Adeli H (1988) Expert systems in construction and structural engineering. Chapman & Hall, United Kingdom

    Book  Google Scholar 

  3. Kim W, Oh JM (2017) Deep learning: from chemoinformatics to precision medicine. J Pharm Investig 47(4):317–323. https://doi.org/10.1007/s40005-017-0332-x

    Article  Google Scholar 

  4. Safdar S, Zafar S, Zafar N, Khan NF (2018) Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Artif Intell Rev 50(4):597–623. https://doi.org/10.1007/s10462-017-9552-8

    Article  Google Scholar 

  5. Kukreja V, Dhiman P (2020) A deep neural network based disease detection scheme for citrus fruits. In: 2020 international conference on smart electronics and communication (ICOSEC), pp. 97–101

    Google Scholar 

  6. Dargan S, Kumar M, Ayyagari MR, Kumar G (2019) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng 1–22. https://doi.org/10.1007/s11831-019-09344-w

  7. Kukreja V, Kumar D, Kaur A et al (2020) GAN-based synthetic data augmentation for increased CNN performance in vehicle number plate recognition. In: 2020 4th international conference on electronics, communication and aerospace technology (ICECA), pp 1190–1195

    Google Scholar 

  8. Mohamed A-R, Dahl G, Hinton G (2009) Deep belief networks for phone recognition . Nips Work Deep Learn Speech Recognit Relat Appl 1(9):31–39. https://doi.org/10.4249/scholarpedia.5947

    Article  Google Scholar 

  9. Santana D, Queiroz LM, Santos RM, Matos LN, Macedo HT (2018) Deep neural networks for acoustic modeling in the presence of noise. IEEE Lat Am Trans 16(3):918–925. https://doi.org/10.1109/msp.2007.4317458

    Article  Google Scholar 

  10. Luckow, Cook M, Ashcraft N, Weill E, Djerekarov E, Vorster B (2016) Deep learning in the automotive industry: applications and tools. In Proceedings—IEEE international conference on big data, Big Data 2016, pp 3759–3768. https://doi.org/10.1109/BigData.2016.7841045.

  11. Chen XW, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525. https://doi.org/10.1109/ACCESS.2014.2325029

    Article  Google Scholar 

  12. Gheisari M, Wang G, Bhuiyan MZA (2017) A survey on deep learning in big data. In: Proceedings—IEEE international conference on computational science and engineering (CSE) and IEEE/IFIP international conference on embedded and ubiquitous computing EUC ) pp 173–180. https://doi.org/10.1109/CSE-EUC.2017.215

  13. Wu Z, Swietojanski P, Veaux C, Renals S, King S (2015) A study of speaker adaptation for DNN-based speech synthesis. In: Proceedings of the annual conference of the international speech communication association, INTERSPEECH, pp 879–883

    Google Scholar 

  14. Ashiquzzaman A, Tushar AK (2017) Handwritten Arabic numeral recognition using deep learning neural networks. In: 2017 IEEE international conference on imaging, vision and pattern recognition, icIVPR, pp 3–6. https://doi.org/10.1109/ICIVPR.2017.7890866

  15. Ignatov A Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl Soft Comput J 62:915–922. https://doi.org/10.1016/j.asoc.2017.09.027

  16. Nweke HF, Teh YW, Al-garadi MA, Alo UR (2018) Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Syst Appl 105:233–261. https://doi.org/10.1016/j.eswa.2018.03.056

    Article  Google Scholar 

  17. Loh CS, Then PHH (2017) Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions. MHealth 3:45–45. https://doi.org/10.21037/mhealth.2017.09.01

    Article  Google Scholar 

  18. Dai Y, Wang G (2018) A deep inference learning framework for healthcare. Pattern Recognit Lett 1–12. https://doi.org/10.1016/j.patrec.2018.02.009

  19. Ciregan UM, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 3642–3649. https://doi.org/10.1109/CVPR.2012.6248110

  20. Liu PH, Su SF, Chen MC, Hsiao CC (2015) Deep learning and its application to general image classification. In: Proceedings international conference on informative and cybernetics for computational social systems (ICCSS), pp 7–10. https://doi.org/10.1109/ICCSS.2015.7281139

  21. Zhou X, Gong W, Fu W, Du F (F) Application of deep learning in object detection. In: Proceedings 16th IEEE/ACIS international conference on computer and information science (ICIS), pp 631–634. https://doi.org/10.1109/ICIS.2017.7960069

  22. López ER, Gualdron O (2019) Primary user characterization for cognitive radio wireless networks using a neural system based on deep learning. Artif Intell Rev 7(1):1–27. https://doi.org/10.1177/1550147718811828

  23. Yu X, Wu X, Luo C, Ren P (2017) Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GIScience Remote Sens 54(5):741–758. https://doi.org/10.1080/15481603.2017.1323377

  24. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848. https://doi.org/10.1109/TPAMI.2017.2699184

    Article  Google Scholar 

  25. Wang L, Sng D (2015) Deep learning algorithms with applications to video analytics for a smart city: a survey. ArXiv Prepr. arXiv1512.03131, pp 1–8

    Google Scholar 

  26. Bhardwaj V, Kukreja V (2021) Effect of pitch enhancement in Punjabi children’s speech recognition system under disparate acoustic conditions. Appl Acoust 177:107918

    Google Scholar 

  27. Arevalo A, Nino J, Hernandez G, Sandoval J (2016) High-frequency trading strategy based on deep neural networks. In: Proceedings of the international conference on intelligent computing, pp 424–436. https://doi.org/10.1007/978-3-319-42297-8

  28. Chong E, Han C, Park F (2017) Deep learning networks for stock market analysis and prediction. Expert Syst Appl 83:187–205. https://doi.org/10.1016/j.eswa.2017.04.030

  29. Yonel EM, Yazici B (2018) Deep learning for passive synthetic aperture radar. IEEE J Sel Top Signal Process 12(1):90–103. https://doi.org/10.1109/JSTSP.2017.2784181

  30. Thomas S, Chatelain C, Heutte L, Paquet T, Kessentini Y (2015) A deep HMM model for multiple keywords spotting in handwritten documents. Pattern Anal Appl 18(4):1003–1015. https://doi.org/10.1007/s10044-014-0433-3

    Article  MathSciNet  Google Scholar 

  31. Wicht B, Fischer A, Hennebert J (2016) Deep learning features for handwritten keyword spotting. In: IEEE proceedings of international conference on pattern recognition (ICPR), pp 3434–3439. https://doi.org/10.1109/ICPR.2016.7900165

  32. Chu J, Srihari S (2014) Writer identification using a deep neural network. In Proceedings of the 2014 Indian conference on computer vision graphics and image processing, pp 1–7. https://doi.org/10.1145/2683483.2683514

  33. Dhieb T, Ouarda W, Boubaker H, Alimi AM (1870) Deep neural network for online writer identification using Beta-elliptic model. In: Proceedings of the international joint conference on neural networks, pp 1863–1870. https://doi.org/10.1109/IJCNN.2016.7727426

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Author information

Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Sakshi & Vinay Kukreja

  2. Chitkara University School of Computer Applications, Chitkara University, Solan, Himachal Pradesh, India

    Prasenjit Das

  3. Chitkara University Institute of Engineering and Technology, Chitkara University, Solan, Himachal Pradesh, India

    Shaily Jain

  4. Chitkara University, Solan, Himachal Pradesh, India

    Chetan Sharma

Authors
  1. Sakshi
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  2. Prasenjit Das
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  3. Shaily Jain
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  4. Chetan Sharma
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  5. Vinay Kukreja
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Corresponding author

Correspondence to Chetan Sharma .

Editor information

Editors and Affiliations

  1. Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, India

    Paramartha Dutta

  3. Institute of Engineering and Management, Kolkata, India

    Soumi Dutta

  4. Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India

    Debabrata Samanta

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sakshi, Das, P., Jain, S., Sharma, C., Kukreja, V. (2022). Deep Learning: An Application Perspective. In: Tavares, J.M.R.S., Dutta, P., Dutta, S., Samanta, D. (eds) Cyber Intelligence and Information Retrieval. Lecture Notes in Networks and Systems, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-16-4284-5_28

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