References
- Theobald O. Machine learning for absolute beginners. 3rd ed. eBook; 2021. ISBN: 9798488251090
- Awad M, Khanna R. Efficient learning machines: theories, concepts, and applications for engineers and system designers. 1st ed. Springer Nature Customer Service Centre LLC; 2014. ISBN: 9781430259893
- Dsouza J. What is a GPU and do you need one in deep learning? Towards data science. Data Sci J;2020. [online]. Available from: https://towardsdatascience.com/what-is-a-gpu-and-do-you-need-one-in-deep-learning-718b9597aa0d
- Wang S, Hongyuan H, Rulin L, Weiwen H, Chunyu L, Nengbin C. Classification modeling method for hyperspectral stamp-pad ink data based on one-dimensional convolutional neural network. J Forensic Sci. 2021;67(2):550–561. doi:10.1111/1556-4029.14909.
- Bewes J, Low A, Morphett A, Pate D, Henneberg M. Artificial intelligence for sex determination of skeletal remains: application of a deep learning artificial neural network to human skulls. J Forensic Leg Med. 2019;62:40–43. doi:10.1016/j.jflm.2019.01.004.
- Dobay A, Ford J, Decker S, Ampanozi G, Franckenberg S, Affolter R, Sieberth T, Ebert LC. Potential use of deep learning techniques for postmortem imaging. Forensic Sci Med Pathol. 2020;16(4):671–679. doi:10.1007/s12024-020-00307-3.
- Airlie M, Robertson J, Krosch MN, Brooks E. Contemporary issues in forensic science – worldwide survey results. Forensic Sci Int. 2021;320:110704. doi:10.1016/j.forsciint.2021.110704.
- National Academy of Sciences. Strengthening Forensic Science in the United States. Washington (DC): The National Academies Press; 2009.
- President’s Council of Advisors on Science and Technology. Forensic science in the criminal courts: ensuring scientific validity of feature-comparison methods. 2016
- Alberts B, Johnson A, Lewis J, Morgan D, Raff M, Roberts K, Walter P. Molecular biology of the cell. 6th ed. eBook; 2020. ISBN: 978-0393870947
- Kamali K. Deep learning (Part 1) – feedforward neural networks (FNN). Galaxy Training [online];2021. Available from: https://training.galaxyproject.org/training-material/topics/statistics/tutorials/FNN/tutorial.html#activation-functions
- Koech EE. Cross-entropy loss function. Towards Data Science. Data Sci J. 2020. [online]. Available from: https://towardsdatascience.com/cross-entropy-loss-function-f38c4ec8643e
- Neo B. Building an image classification model from scratch using Pytorch. Bitgrit Data Science Publication. [online]; 2021. Available from: https://medium.com/bitgrit-data-science-publication/building-an-image-classification-model-with-pytorch-from-scratch-f10452073212
- Ajit A, Acharya K, Samanta A. A review of convolutional neural networks. International Conference on Emerging Trends in Information Technology and Engineering; 2020, pp. 1–5. doi: 10.1109/ic-ETITE47903.2020.049
- Balaji S. Binary image classifier CNN using tensorflow. Techiepedia. [online]; 2020. Available from: https://medium.com/techiepedia/binary-image-classifier-cnn-using-tensorflow-a3f5d6746697
- Shahinfar S, Meek PD, Falzon G. “How many images do I need?” understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecol Inform. 2020;57(1):101085. doi:10.1016/j.ecoinf.2020.101085.
- Yamashita R, Nishio M, Do R, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9:611–629. doi:10.1007/s13244-018-0639-9.
- Maruzani R. Are you unwittingly helping to train google’s AI models? Towards data science. Data Sci J. [online]. Available from: https://towardsdatascience.com/are-you-unwittingly-helping-to-train-googles-ai-models-f318dea53aee
- Wu A. A beginner’s tutorial on building and AI image classifier using Pytorch. Towards Data Science. Data Science Journal. 2019. [online]. Available from: https://towardsdatascience.com/a-beginners-tutorial-on-building-an-ai-image-classifier-using-pytorch-6f85cb69cba7
- He K, Zhang X, Shaoqing R, Sun J. Deep residual learning for image recognition. Proc IEEE Conf Comput Vision Pattern Recognit. 2015;770–778. Available from: https://arxiv.org/abs/1512.03385
- Robertson J, Brooks E. A practical guide to the forensic examination of hair: from crime scene to court. CRC Press; 2022. ISBN-13: 978–1138628618
- Rogers GE. Structural and biochemical features of the hair follicle. In: Montagna W, Lobitz WC, editors. The epidermis. New York: Academic Press; 1964.
- Robertson JR. Forensic examination of hair. London: Taylor and Francis; 1999.
- Brooks EM, Cullen M, Sztydna T, Walsh J. Nuclear staining of telogen hair roots contributes to successful forensic nDNA analysis. Aust J Forensic Sci. 2010;42(2):115–122. doi:10.1080/00450610903258136.
- Airlie M, Robertson J, Brooks E. Forensic hair analysis – worldwide survey results. Forensic Sci Int. 2021;327:110966. doi:10.1016/j.forsciint.2021.110966.
- Pytorch. Available from: https://pytorch.org/
- Visual Studio. Available from: https://visualstudio.microsoft.com
- Airlie M. Machine learning forensic application. GitHub. Available from: https://github.com/airliem/Machine-Learning-Forensic-Application
- Jordan J. Evaluating a machine learning model. Jeremy Jordan Data Sci. 2017. [online]. Available from: https://www.jeremyjordan.me/evaluating-a-machine-learning-model/
- Mishra A. Ways to improve the accuracy of machine learning models. Data Science Foundation; 2020. [online]. Available from: https://datascience.foundation/datatalk/ways-to-improve-the-accuracy-of-machine-learning-models
- GIMP. Available from: https://www.gimp.org
- Brownlee J. Understanding the impact of learning rate on neural network performance. Deep Learn Perform. 2020. [online]. Available from: https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/
- Brownlee J. How to configure the learning rate when training deep learning neural networks. Deep Learn Perform. 2019. [online]. Available from: https://machinelearningmastery.com/learning-rate-for-deep-learning-neural-networks/
- Li K. How to choose a learning rate scheduler for neural networks. Neptune Blog. [online]; 2021. Available from: https://neptune.ai/blog/how-to-choose-a-learning-rate-scheduler
- Brownlee J. How to use learning curves to diagnose machine learning model performance. Deep Learn Perform. 2019. [online]. Available from: https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/
- Radiuk PM. Impact of training set batch size on the performance of convolutional neural networks for diverse datasets. Inf Technol Manage Sci. 2017;20:20–24. doi:10.1515/itms-2017-0003.
- Afaq S, Rao S. Significance of epochs on training a neural network. Int J Sci Technol Res. 2020;9:485–488. Available from: https://www.ijstr.org/final-print/jun2020/Significance-Of-Epochs-On-Training-A-Neural-Network.pdf
- Kafadar K. The need for objective measures in forensic evidence. Significance. 2019;16:16–20. doi:10.1111/j.1740-9713.2019.01249.x.
- Earwaker H, Nakhaeizadeh S, Smith NM, Morgan RM. A cultural change to enable improved decision-making in forensic science: a six phased approach. Sci Justice. 2020;60(1):9–19. doi:10.1016/j.scijus.2019.08.006.