Skip to main content
SpringerLink
Log in

Machine Learning–Enhanced Decision-Making

  • Reference work entry
  • First Online:
Handbook of Smart Materials, Technologies, and Devices

  • 135 Accesses

Abstract

Machine learning comprises a broad range of analysis methods that can be applied to artefacts such as data, images, and sound recordings to produce insights or findings. This chapter describes what machine learning is and how it works. It then provides an example of real-world application of machine learning to address industry problem. The chapter concludes by discussing important considerations when using machine learning methods to ensure that quality results are produced, and these results are validly interpreted.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 899.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,399.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alanis AY, Arana-Daniel N, Lopez-Franco C (2019) Artificial neural networks for engineering applications. Academic Press, Cambridge, Massachusetts

    Google Scholar 

  • Alioua N, Amine A, Rziza M (2014) Driver’s fatigue detection based on yawning extraction. Int J Vehicular Technol, vol. 2014, Article ID 678786, pp. 7

    Google Scholar 

  • Al-Shayea Q, El-Refae G, Yaseen S (2013) Artificial neural networks for medical diagnosis using biomedical dataset. Int J Behav Healthcare Res 4(1):45–63

    Article  Google Scholar 

  • Amato F, Lopez A, Pena-Mendez EM, Vanhara P, Hampl A, Havel J (2013) Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, Elsevier. 11(2):47–58

    Google Scholar 

  • Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. Paper presented at the Proceedings of ICML workshop on unsupervised and transfer learning

    Google Scholar 

  • Barlow HBJN c. (1989) Unsupervised learning. 1(3):295–311

    Google Scholar 

  • Bekey GA, Goldberg KY (2012) Neural networks in robotics, vol 202. Springer Science & Business Media. New York

    Google Scholar 

  • Bence JRJE (1995) Analysis of short time series: correcting for autocorrelation. Journal of Ecological Society of America, Washington, 76(2):628–639

    Google Scholar 

  • Bergasa LM, Buenaposada JM, Nuevo J, Jimenez P, Baumela L (2008) Analysing driver's attention level using computer vision. Paper presented at the Intelligent Transportation Systems, 2008. 11th international IEEE conference on ITSC 2008

    Google Scholar 

  • Bibri SEJJ (2019) On the sustainability of smart and smarter cities in the era of big data: an interdisciplinary and transdisciplinary literature review. 6(1):25

    Google Scholar 

  • Branch MAI (2016) Report on the investigation into the listing, flooding and grounding of Hoegh Osaka–Bramble Bank, the solent, UK on 3 January 2015. Marine Accident Investigation Branch, Southampton

    Google Scholar 

  • Caldwell JA, Caldwell JL, Thompson LA, Lieberman HR (2018) Fatigue and its management in the workplace. Neurosci Biobehav Rev. 96:272–289

    Google Scholar 

  • Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. Paper presented at the Proceedings of the 23rd international conference on machine learning

    Google Scholar 

  • Chatfield C, Xing H (2019) The analysis of time series: an introduction with R. Chapman and Hall/CRC Press, Boca Raton, Florida

    Google Scholar 

  • Cichocki A, Unbehauen R (1993) Neural networks for optimization and signal processing. Wiley, Chichester. Google Scholar Digital Library

    MATH  Google Scholar 

  • Contreras J, Espinola R, Nogales FJ, Conejo AJJI (2003) ARIMA models to predict next-day electricity prices. 18(3):1014–1020

    Google Scholar 

  • Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Paper presented at the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05)

    Google Scholar 

  • Damper R (1997) A biocybernetic simulation of speech perception by humans and animals. Paper presented at the 1997 IEEE international conference on systems, man, and cybernetics. Computational cybernetics and simulation

    Google Scholar 

  • Dwivedi K, Biswaranjan K, Sethi A (2014) Drowsy driver detection using representation learning. Paper presented at the 2014 IEEE international advance computing conference (IACC)

    Google Scholar 

  • Eoh HJ, Chung MK, Kim S-H (2005) Electroencephalographic study of drowsiness in simulated driving with sleep deprivation. Int J Ind Ergon 35(4):307–320

    Article  Google Scholar 

  • Fan X, Yin B-C, Sun Y-F (2008) Dynamic human fatigue detection using feature-level fusion. Paper presented at the international conference on image and signal processing

    Google Scholar 

  • Fernández-Delgado M, Cernadas E, Barro S, Amorim DJT (2014) Do we need hundreds of classifiers to solve real world classification problems? 15(1):3133–3181

    Google Scholar 

  • Gander PH, Purnell HM, Garden A, Woodward A (2007) Work patterns and fatigue-related risk among junior doctors. Occup Environ Med. 64(11):733–738

    Google Scholar 

  • Ghahramani Z (2003) Unsupervised learning. Paper presented at the summer school on machine learning

    Google Scholar 

  • Ghoddoosian R, Galib M, Athitsos V (2019) A realistic dataset and baseline temporal model for early drowsiness detection. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition workshops

    Google Scholar 

  • Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. Paper presented at the Proceedings of the fourteenth international conference on artificial intelligence and statistics

    Google Scholar 

  • Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT Press, Cambridge

    MATH  Google Scholar 

  • Grace R, Byrne VE, Bierman DM, Legrand J-M, Gricourt D, Davis B, ... Carnahan B (1998) A drowsy driver detection system for heavy vehicles. Paper presented at the Digital avionics systems conference, 1998. Proceedings, 17th DASC. The AIAA/IEEE/SAE

    Google Scholar 

  • Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall PTR, Hamilton, Ontario, Canada

    Google Scholar 

  • Hertz J, Krogh A, Palmer RG, Horner H (1991) Introduction to the theory of neural computation. Phys Today 44:70

    Article  Google Scholar 

  • Hillmer SC, Tiao GC (1982). An ARIMA-model-based approach to seasonal adjustment. 77(377):63–70

    Google Scholar 

  • Hong K, Satchell SJQF (2015) Time series momentum trading strategy and autocorrelation amplification. 15(9):1471–1487

    Google Scholar 

  • Huang C, Li Y, Loy CC, Tang X (2016) Learning deep representation for imbalanced classification. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition

    Google Scholar 

  • Huynh X-P, Park S-M, Kim Y-G (2016) Detection of driver drowsiness using 3D deep neural network and semi-supervised gradient boosting machine. Paper presented at the Asian Conference on Computer Vision

    Google Scholar 

  • Jain LC, Vemuri VR (1998) Industrial applications of neural networks, vol 3. CRC Press, Boca Raton, Florida

    Google Scholar 

  • Jepsen JR, Zhao Z, van Leeuwen WM (2015) Seafarer fatigue: a review of risk factors, consequences for seafarers’ health and safety and options for mitigation. Int Marit Health 66(2):106–117

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Paper presented at the advances in neural information processing systems

    Google Scholar 

  • Krueger GP (1989) Sustained work, fatigue, sleep loss and performance: a review of the issues. Work Stress 3(2):129–141

    Article  Google Scholar 

  • Land KC, McCall PL, Nagin DSJSM (1996) A comparison of Poisson, negative binomial, and semiparametric mixed Poisson regression models: with empirical applications to criminal careers data. 24(4):387–442, Research

    Google Scholar 

  • Lee JD, Young KL, Regan MAJD (2008) Defining driver distraction. Theory, effects, & mitigation. 13(4):31–40

    Google Scholar 

  • Liu Y, Zhao T, Ju W, Shi SJJ (2017) Materials discovery and design using machine learning. 3(3):159–177

    Google Scholar 

  • Lloyd SP (1982) Least squares quantization in PCM 28(2):129–137

    Google Scholar 

  • Lu Y, Wang Z (2007) Detecting driver yawning in successive images. Paper presented at the Bioinformatics and Biomedical Engineering, 2007. The 1st international conference on ICBBE 2007

    Google Scholar 

  • Mining, CGJCGMReJ (2007) Viewpoint perspectives on modern mining 21. Mining industry by the Caterpillar Global Mining. http://www.viewpointmining.com/st/mine-features

  • Morris MB, Wiedbusch MD, Gunzelmann G (2018) Fatigue incident antecedents, consequences, and aviation operational risk management resources. Aerosp Med Human Perform 89(8):708–716

    Article  Google Scholar 

  • Nixon M, Aguado A (2019) Feature extraction and image processing for computer vision. Academic Press, Cambridge, Massachusetts

    Google Scholar 

  • Park S, Pan F, Kang S, Yoo CD (2016) Driver drowsiness detection system based on feature representation learning using various deep networks. Paper presented at the Asian conference on computer vision

    Google Scholar 

  • Pedrycz W, Gomide F (1998) An introduction to fuzzy sets: analysis and design. MIT Press, Cambridge, Massachusetts

    Google Scholar 

  • Qiu X, Zhang L, Ren Y, Suganthan PN, Amaratunga G (2014) Ensemble deep learning for regression and time series forecasting. Paper presented at the 2014 IEEE symposium on computational intelligence in ensemble learning (CIEL)

    Google Scholar 

  • Raghupathi W, Raghupathi VJH (2014) Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2(1):3

    Google Scholar 

  • Raju CJJ (1982) Products and compositions with the Dirac delta function. J Phys A Math Gen 15(2):381

    Google Scholar 

  • Rangesh A, Deo N, Yuen K, Pirozhenko K, Gunaratne P, Toyoda H, Trivedi MM (2018) Exploring the situational awareness of humans inside autonomous vehicles. Paper presented at the 2018 21st international conference on intelligent transportation systems (ITSC)

    Google Scholar 

  • Reed R, Marks RJ II (1999) Neural smithing: supervised learning in feedforward artificial neural networks. MIT Press, Cambridge, Massachusetts

    Google Scholar 

  • Rezzoug N, Gorce P (2003) A biocybernetic method to learn hand grasping posture. Kybernetes, 32(4):478–490

    Google Scholar 

  • Ribarie S, Lovrenete J, Paveene N (2010) A neural-network-based system for monitoring driver fatigue. Paper presented at the MELECON 2010-2010 15th IEEE Mediterranean electrotechnical conference

    Google Scholar 

  • Rybak N (2020) Technical considerations for the application of deep learning methods for multimodal emotion recognition. School of Information Technology and Electrical Engineering, The University of Queensland, Australia, pp. 223. https://doi.org/10.14264/bf3427d

  • Rybak N, Angus DJ (2021) Tracking conflict and emotions with a computational qualitative discourse analytic support approach. PLoS One. https://doi.org/10.1371/journal.pone.0251186

  • Rybak N, Hassall M, Parsa K, Angus DJ (2017a) New real-time methods for operator situational awareness retrieval and higher process safety in the control room. Paper presented at the 2017 IEEE international systems engineering symposium (ISSE)

    Google Scholar 

  • Rybak N, Hassall M, Parsa K, Angus DJ (2017b) New real-time methods for operator situational awareness retrieval and higher process safety in the control room. Paper presented at the 2017 IEEE international systems engineering symposium (ISSE)

    Google Scholar 

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  • Shannon CEJT (1948) A mathematical theory of communication. 27(3):379–423

    Google Scholar 

  • Szeliski R (2010) Computer vision: algorithms and applications. Springer Science & Business Media, New York

    Google Scholar 

  • Tang Y, Zhang Y-Q, Chawla NV, Krasser SJIT (2008) SVMs modeling for highly imbalanced classification. IEEE Trans Syst Man Cybern 39(1):281–288

    Article  Google Scholar 

  • Tang X, Zhou P, Wang P (2016) Real-time image-based driver fatigue detection and monitoring system for monitoring driver vigilance. Paper presented at the 2016 35th Chinese Control Conference (CCC)

    Google Scholar 

  • Tock D, Craw I (1996) Tracking and measuring drivers’ eyes. Image Vis Comput 14(8):541–547

    Article  Google Scholar 

  • Veeraraghavan H, Papanikolopoulos NP (2001) Detecting driver fatigue through the use of advanced face monitoring techniques. ITS Institute Center for Transportation Studies, University of Minnesota, Minneapolis. pp. 31

    Google Scholar 

  • Vora S, Rangesh A, Trivedi MM (2017) On generalizing driver gaze zone estimation using convolutional neural networks. Paper presented at the 2017 IEEE Intelligent Vehicles Symposium (IV)

    Google Scholar 

  • Vora S, Rangesh A, Trivedi MM (2018) Driver gaze zone estimation using convolutional neural networks: a general framework and ablative analysis. arXiv preprint arXiv:1802.02690

    Google Scholar 

  • Ward JHJJ Jr (1963) Hierarchical grouping to optimize an objective function. 58(301):236–244

    Google Scholar 

  • Wasserman PD (1993) Advanced methods in neural computing. Wiley, Hoboken, New Jersey

    Google Scholar 

  • Weisstein EWJ (2002) Bernoulli distribution. From MathWorld–A Wolfram Web Resource. https://mathworld.wolfram.com/BernoulliDistribution.html

  • Yan C, Jiang H, Zhang B, Coenen F (2015) Recognizing driver inattention by convolutional neural networks. Paper presented at the 2015 8th international congress on image and signal processing (CISP)

    Google Scholar 

  • Yuen K, Martin S, Trivedi MM (2016a) Looking at faces in a vehicle: a deep CNN based approach and evaluation. Paper presented at the 2016 IEEE 19th international conference on intelligent transportation systems (ITSC)

    Google Scholar 

  • Yuen K, Martin S, Trivedi MM (2016b) On looking at faces in an automobile: issues, algorithms and evaluation on naturalistic driving dataset. Paper presented at the 2016 23rd international conference on pattern recognition (ICPR)

    Google Scholar 

  • Zhang GPJN (2003) Time series forecasting using a hybrid ARIMA and neural network model. 50:159–175

    Google Scholar 

  • Zhang Y, Hua C (2015) Driver fatigue recognition based on facial expression analysis using local binary patterns. Optik-Int J Light Electron Opt 126(23):4501–4505

    Article  Google Scholar 

  • Zhang W, Murphey YL, Wang T, Xu Q (2015) Driver yawning detection based on deep convolutional neural learning and robust nose tracking. Paper presented at the 2015 international joint conference on neural networks (IJCNN)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Chemical Engineering, The University of Queensland, St Lucia, QLD, Australia

    Nikodem Rybak

  2. School of Chemical Engineering, The University of Queensland, Brisbane, QLD, Australia

    Maureen Hassall

Authors
  1. Nikodem Rybak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maureen Hassall
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikodem Rybak .

Editor information

Editors and Affiliations

  1. Department of Chemistry and Environmental Science, New Jersey Institute of Technology, Newark, NJ, USA

    Chaudhery Mustansar Hussain

  2. School of Science, University of Padova, Padova, Italy

    Paolo Di Sia

Glossary

Glossary

ARIMA:

Autoregressive Integrated Moving Average – class of algorithms for forecasting a time series. As the model’s name suggests that it is a combination of auto-regression (AR) model with moving average (MA) model.

Algorithmics:

Subfield of computer science. Study of design and efficiency of algorithms.

Centroid:

Machine learning method that has its origins in geometric decomposition; it is a representative of a given cluster or, in other terms, the center of a given group.

Entropy:

In information theory, average amount of information attributable to a single message from information source.

Extrapolation:

It is the use of the same machine learning model to calculate output variable value for input variable values falling outside training data values.

Labelled data:

In machine learning, data that have already been categorized.

Prediction:

In machine learning, calculating the value of output variable for input variable values falling within training data values.

Probability Distribution:

Function used to compute the likelihoods of occurrence of various observation outcomes.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Rybak, N., Hassall, M. (2022). Machine Learning–Enhanced Decision-Making. In: Hussain, C.M., Di Sia, P. (eds) Handbook of Smart Materials, Technologies, and Devices. Springer, Cham. https://doi.org/10.1007/978-3-030-84205-5_20

Download citation

Publish with us

Policies and ethics

Search

Navigation