13. References#
Martín Abadi. Tensorflow: learning functions at scale. In Proceedings of the 21st ACM SIGPLAN international conference on functional programming, 1–1. 2016. doi:10.1145/2951913.2976746.
C.C. Aggarwal. Neural Networks and Deep Learning: A Textbook. Springer International Publishing, 2023. ISBN 9783031296420. URL: https://books.google.ca/books?id=0-rIEAAAQBAJ.
Usman Ahmed, Gautam Srivastava, Unil Yun, and Jerry Chun-Wei Lin. Eandc: an explainable attention network based deep adaptive clustering model for mental health treatment. Future Generation Computer Systems, 130:106–113, 2022. doi:10.1016/j.future.2021.12.008.
Muhammad Shamsul Alam, Farhan Bin Mohamed, Ali Selamat, and Akm Bellal Hossain. A review of recurrent neural network based camera localization for indoor environments. IEEE Access, 11():43985–44009, 2023. doi:10.1109/ACCESS.2023.3272479.
E. Alpaydin. Introduction to Machine Learning, fourth edition. Adaptive Computation and Machine Learning series. MIT Press, 2020. ISBN 9780262043793. URL: https://books.google.ca/books?id=tZnSDwAAQBAJ.
Laith Alzubaidi, Jinglan Zhang, Amjad J Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, José Santamaría, Mohammed A Fadhel, Muthana Al-Amidie, and Laith Farhan. Review of deep learning: concepts, cnn architectures, challenges, applications, future directions. Journal of big Data, 8(1):53, 2021. doi:10.1186/s40537-021-00444-8.
S. Ansari. Building Computer Vision Applications Using Artificial Neural Networks: With Examples in OpenCV and TensorFlow with Python. Apress, 2023. ISBN 9781484298657. URL: https://books.google.ca/books?id=KE4Z0AEACAAJ.
A.B. Badiru and O. Asaolu. Handbook of Mathematical and Digital Engineering Foundations for Artificial Intelligence: A Systems Methodology. Systems Innovation Book Series. CRC Press, 2023. ISBN 9781000899672. URL: https://books.google.ca/books?id=T-a9EAAAQBAJ.
L.L. Beck. System Software: An Introduction to Systems Programming. Addison-Wesley, 1997. ISBN 9780201423006. URL: https://books.google.ca/books?id=eaZ-zwEACAAJ.
Y. Bengio. Learning Deep Architectures for AI. Foundations and trends in machine learning. Now, 2009. ISBN 9781601982940. URL: https://books.google.ca/books?id=cq5ewg7FniMC.
Moinak Bhattacharya, Shubham Jain, and Prateek Prasanna. Radiotransformer: a cascaded global-focal transformer for visual attention–guided disease classification. In European Conference on Computer Vision, 679–698. Springer, 2022. doi:10.1186/s40537-021-00444-8.
C.M. Bishop. Neural Networks for Pattern Recognition. Advanced Texts in Econometrics. Clarendon Press, 1995. ISBN 9780198538646. URL: https://books.google.ca/books?id=T0S0BgAAQBAJ.
C.M. Bishop. Pattern Recognition and Machine Learning. Information Science and Statistics. Springer New York, 2016. ISBN 9781493938438. URL: https://books.google.ca/books?id=kOXDtAEACAAJ.
Sushman Biswas. Advantages of deep learning, plus use cases and examples. https://www.width.ai/post/advantages-of-deep-learning, 2023. [Online; accessed 01-August-2023].
G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.
L. Breiman. Classification and Regression Trees. CRC Press, 2017. ISBN 9781351460491. URL: https://books.google.ca/books?id=MGlQDwAAQBAJ.
Leo Breiman. Random forests. Machine Learning, 45(1):5–32, Oct 2001. doi:10.1023/A:1010933404324.
Saga Briggs. Deeper learning: what is it and why is it so effective? https://www.mathworks.com/discovery/deep-learning.html, 2023. [Online; accessed 01-August-2023].
J. Brownlee. Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python. Machine Learning Mastery, 2020. URL: https://books.google.ca/books?id=uAPuDwAAQBAJ.
T. Tony Cai and Rong Ma. Theoretical foundations of t-sne for visualizing high-dimensional clustered data. J. Mach. Learn. Res., jan 2022.
John Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6):679–698, 1986. doi:10.1109/TPAMI.1986.4767851.
Carla Cardinali. Observation Influence Diagnostic of a Data Assimilation System, pages 89–110. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013. doi:10.1007/978-3-642-35088-7_4.
Emilio Carrizosa, Cristina Molero-Río, and Dolores Romero Morales. Mathematical optimization in classification and regression trees. TOP, 29(1):5–33, Apr 2021. doi:10.1007/s11750-021-00594-1.
S. Chakraborty, S.H. Islam, and D. Samanta. Data Classification and Incremental Clustering in Data Mining and Machine Learning. EAI/Springer Innovations in Communication and Computing. Springer International Publishing, 2022. ISBN 9783030930882. URL: https://books.google.ca/books?id=xSFvEAAAQBAJ.
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357, 2002. doi:10.1613/jair.953.
Gal Chechik, Varun Sharma, Uri Shalit, and Samy Bengio. Large scale online learning of image similarity through ranking. Journal of Machine Learning Research, 2010.
Kathleen M Chen, Evan M Cofer, Jian Zhou, and Olga G Troyanskaya. Selene: a pytorch-based deep learning library for sequence data. Nature methods, 16(4):315–318, 2019. doi:10.1038/s41592-019-0360-8.
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2014. doi:10.3115/v1/d14-1179.
F. Chollet and F. Chollet. Deep Learning with Python, Second Edition. Manning, 2021. ISBN 9781617296864. URL: https://books.google.ca/books?id=XHpKEAAAQBAJ.
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289, 2015.
R. Dennis Cook. Detection of influential observation in linear regression. Technometrics, 42(1):65–68, 2000. doi:10.1080/00401706.2000.10485981.
Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine Learning, 20(3):273–297, Sep 1995. doi:10.1007/BF00994018.
T. Cover and P. Hart. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1):21–27, 1967. doi:10.1109/TIT.1967.1053964.
T.M. Cover and J.A. Thomas. Elements of Information Theory. Wiley, 2012. ISBN 9781118585771. URL: https://books.google.ca/books?id=VWq5GG6ycxMC.
Hulin Dai, Xuan Peng, Xuanhua Shi, Ligang He, Qian Xiong, and Hai Jin. Reveal training performance mystery between tensorflow and pytorch in the single gpu environment. Science China Information Sciences, 65(1):112103, 2021. doi:10.1007/s11432-020-3182-1.
P. Dangeti. Statistics for Machine Learning. Packt Publishing, 2017. ISBN 9781788291224. URL: https://books.google.ca/books?id=C-dDDwAAQBAJ.
Hatef Dastour and Quazi K. Hassan. A comparison of deep transfer learning methods for land use and land cover classification. Sustainability, 15(10):7854, 2023. doi:10.3390/su15107854.
M.I.T.C. Data. Secondary Analysis of Electronic Health Records. Springer International Publishing, 2016. ISBN 9783319437422. URL: https://books.google.ca/books?id=qtlCDwAAQBAJ.
M.P. Deisenroth, A.A. Faisal, and C.S. Ong. Mathematics for Machine Learning. Cambridge University Press, 2020. ISBN 9781108470049. URL: https://books.google.ca/books?id=pFjPDwAAQBAJ.
Li Deng, Xiaodong He, and Jianfeng Gao. Deep stacking networks for information retrieval. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, volume, 3153–3157. 2013. doi:10.1109/ICASSP.2013.6638239.
Rahul Dey and Fathi M. Salem. Gate-variants of gated recurrent unit (gru) neural networks. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), volume, 1597–1600. 2017. doi:10.1109/MWSCAS.2017.8053243.
Van Hoan Do and Stefan Canzar. A generalization of t-sne and umap to single-cell multimodal omics. Genome Biology, 22(1):130, May 2021. doi:10.1186/s13059-021-02356-5.
Y. Dodge. The Concise Encyclopedia of Statistics. The Concise Encyclopedia of Statistics. Springer New York, 2008. ISBN 9780387317427. URL: https://books.google.ca/books?id=k2zklGOBRDwC.
J. Doornik and H. Hansen. An Omnibus Test for Univariate and Multivariate Normality. SSRN, 2008. URL: https://books.google.ca/books?id=kJ3gzwEACAAJ.
A.B. Downey. Think Python: How to Think Like a Computer Scientist. O'Reilly Media, 2015. ISBN 9781491939413. URL: https://books.google.ca/books?id=mZwbCwAAQBAJ.
Shiv Ram Dubey, Satish Kumar Singh, and Bidyut Baran Chaudhuri. Activation functions in deep learning: a comprehensive survey and benchmark. Neurocomputing, 503:92–108, 2022. doi:10.1016/j.neucom.2022.06.111.
J.M. Dufour and M.G. Dagenais. Durbin-Watson Tests for Serial Correlation in Regressions with Missing Observations. Cahier (Université de Montréal. Département de sciences économiques). Université de Montréal, 1983. URL: https://books.google.ca/books?id=Y2PuAAAAMAAJ.
B. Efron and T. Hastie. Computer Age Statistical Inference, Student Edition: Algorithms, Evidence, and Data Science. IMS monographs. Cambridge University Press, 2021. ISBN 9781108823418. URL: https://books.google.ca/books?id=q1ctEAAAQBAJ.
Charles Elkan. The foundations of cost-sensitive learning. In International joint conference on artificial intelligence, volume 17, 973–978. Lawrence Erlbaum Associates Ltd, 2001.
Christina Ellis. When to use t-sne. https://crunchingthedata.com/when-to-use-t-sne/, 2023. [Online; accessed 01-August-2023].
Okan Erkaymaz. Resilient back-propagation approach in small-world feed-forward neural network topology based on newman–watts algorithm. Neural Computing and Applications, 32(20):16279–16289, Oct 2020. doi:10.1007/s00521-020-05161-6.
Soroor Malekmohamadi Faradonbe and Faramarz Safi-Esfahani. A classifier task based on neural turing machine and particle swarm algorithm. Neurocomputing, 396:133–152, 2020. doi:10.1016/j.neucom.2018.07.097.
C. Fehily. Python. Visual quickstart guide. Peachpit Press, 2002. ISBN 9780201748840. URL: https://books.google.ca/books?id=carqdIdfVlYC.
T.L. Fine. Feedforward Neural Network Methodology. Information Science and Statistics. Springer New York, 2006. ISBN 9780387226491. URL: https://books.google.ca/books?id=s-PlBwAAQBAJ.
A. Fred, M. De Marsico, and M. Figueiredo. Pattern Recognition: Applications and Methods: 4th International Conference, ICPRAM 2015, Lisbon, Portugal, January 10-12, 2015, Revised Selected Papers. Lecture Notes in Computer Science. Springer International Publishing, 2016. ISBN 9783319276779. URL: https://books.google.ca/books?id=Bm9aCwAAQBAJ.
J. Frost. Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models. Statistics By Jim Publishing, 2020. ISBN 9781735431185. URL: https://books.google.ca/books?id=1UPzzQEACAAJ.
K. Gallatin and C. Albon. Machine Learning with Python Cookbook. O'Reilly Media, 2023. ISBN 9781098135690. URL: https://books.google.ca/books?id=Wq3NEAAAQBAJ.
Ruturaj G Gavaskar and Kunal N Chaudhury. Fast adaptive bilateral filtering. IEEE Transactions on Image Processing, 28(2):779–790, 2018.
Paul Gavrikov. Visualkeras. paulgavrikov/visualkeras, 2020.
Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, 249–256. JMLR Workshop and Conference Proceedings, 2010.
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, 315–323. JMLR Workshop and Conference Proceedings, 2011.
I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. Adaptive Computation and Machine Learning series. MIT Press, 2016. ISBN 9780262337373. URL: https://books.google.ca/books?id=omivDQAAQBAJ.
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 2014.
A. Graves. Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence. Springer Berlin Heidelberg, 2012. ISBN 9783642247972. URL: https://books.google.ca/books?id=wpb-CAAAQBAJ.
Isabelle Guyon and André Elisseeff. An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157–1182, 2003.
A. Géron. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly, 2022. ISBN 9781098125974. URL: https://books.google.ca/books?id=bWD5zgEACAAJ.
Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06), volume 2, 1735–1742. IEEE, 2006.
W.L. Hamilton. Graph Representation Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2020. ISBN 9781681739649. URL: https://books.google.ca/books?id=Csj-DwAAQBAJ.
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant. Array programming with NumPy. Nature, 585(7825):357–362, September 2020. doi:https://doi.org/10.1038/s41586-020-2649-2.
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer New York, 2013. ISBN 9780387216065. URL: https://books.google.ca/books?id=yPfZBwAAQBAJ.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision, 1026–1034. 2015.
Yuming Hua, Junhai Guo, and Hua Zhao. Deep belief networks and deep learning. In Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, 1–4. IEEE, 2015.
Thomas Huang, GJTGY Yang, and Greory Tang. A fast two-dimensional median filtering algorithm. IEEE transactions on acoustics, speech, and signal processing, 27(1):13–18, 1979.
Zhizhong Huang, Jie Chen, Junping Zhang, and Hongming Shan. Learning representation for clustering via prototype scattering and positive sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. doi:10.1109/TPAMI.2022.3216454.
Peter J Huber. Robust estimation of a location parameter. In Breakthroughs in statistics: Methodology and distribution, pages 492–518. Springer, 1992.
A.J. Izenman. Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer Texts in Statistics. Springer New York, 2009. ISBN 9780387781891. URL: https://books.google.ca/books?id=1CuznRORa3EC.
A.P. James. Deep Learning Classifiers with Memristive Networks: Theory and Applications. Modeling and Optimization in Science and Technologies. Springer International Publishing, 2019. ISBN 9783030145248. URL: https://books.google.ca/books?id=yWWRDwAAQBAJ.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. An Introduction to Statistical Learning: with Applications in R. Springer Texts in Statistics. Springer Cham, 2023. ISBN 9783031391897. URL: https://link.springer.com/book/10.1007/978-3-031-38747-0.
Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li, and Liang Zhao. Latent dirichlet allocation (lda) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78:15169–15211, 2019.
P.P. Joby, V.E. Balas, and R. Palanisamy. IoT Based Control Networks and Intelligent Systems: Proceedings of 3rd ICICNIS 2022. Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. ISBN 9789811958458. URL: https://books.google.ca/books?id=__WVEAAAQBAJ.
I.T. Jolliffe. Principal Component Analysis. Springer Series in Statistics. Springer New York, 2013. ISBN 9781475719048. URL: https://books.google.ca/books?id=-ongBwAAQBAJ.
S. Kaddoura. A Primer on Generative Adversarial Networks. Springer International Publishing, 2023. ISBN 9783031326622. URL: https://books.google.ca/books?id=dJUr0AEACAAJ.
I. Kalb. Object-Oriented Python: Master OOP by Building Games and GUIs. No Starch Press, 2022. ISBN 9781718502062. URL: https://books.google.ca/books?id=CxZOEAAAQBAJ.
Bo Kang, Dario Garcia Garcia, Jefrey Lijffijt, Raúl Santos-Rodríguez, and Tijl De Bie. Conditional t-sne: more informative t-sne embeddings. Machine Learning, 110:2905–2940, 2021.
R.S. Kenett, S. Zacks, and P. Gedeck. Industrial Statistics: A Computer-Based Approach with Python. Statistics for Industry, Technology, and Engineering. Springer International Publishing, 2023. ISBN 9783031284823. URL: https://books.google.ca/books?id=4M7FEAAAQBAJ.
N. Ketkar and J. Moolayil. Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch. Apress, 2021. ISBN 9781484253632. URL: https://books.google.ca/books?id=FHJ0yAEACAAJ.
S. Khan, H. Rahmani, S.A.A. Shah, and M. Bennamoun. A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer Vision. Springer International Publishing, 2022. ISBN 9783031018213. URL: https://books.google.ca/books?id=54ZyEAAAQBAJ.
Dmitry Kobak and Philipp Berens. The art of using t-sne for single-cell transcriptomics. Nature communications, 10(1):5416, 2019.
Ron Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI'95, 1137–1143. San Francisco, CA, USA, 1995. Morgan Kaufmann Publishers Inc.
T. Kohonen. Self-Organizing Maps. Springer Series in Information Sciences. Springer Berlin Heidelberg, 2012. ISBN 9783642569272. URL: https://books.google.ca/books?id=M-zxCAAAQBAJ.
Rayan Krishnan, Pranav Rajpurkar, and Eric J Topol. Self-supervised learning in medicine and healthcare. Nature Biomedical Engineering, 6(12):1346–1352, 2022. doi:10.1038/s41551-022-00914-1.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84–90, 2017.
M. Kuhn and K. Johnson. Feature Engineering and Selection: A Practical Approach for Predictive Models. Chapman & Hall/CRC Data Science Series. CRC Press, 2019. ISBN 9781351609463. URL: https://books.google.ca/books?id=q5alDwAAQBAJ.
Solomon Kullback and Richard A Leibler. On information and sufficiency. The annals of mathematical statistics, 22(1):79–86, 1951.
M. Kutner, C. Nachtsheim, J. Neter, and W. Li. Applied Linear Statistical Models with Student CD. McGraw-Hill Companies,Incorporated, 2004. ISBN 9780073108742. URL: https://books.google.ca/books?id=0Qq-swEACAAJ.
K. Kuttler and I. Farah. A First Course in Linear Algebra. Lyryx Learning Incorporated, 2020. URL: https://books.google.ca/books?id=1jd8zgEACAAJ.
Yunseok Kwak, Won Joon Yun, Jae Pyoung Kim, Hyunhee Cho, Jihong Park, Minseok Choi, Soyi Jung, and Joongheon Kim. Quantum distributed deep learning architectures: models, discussions, and applications. ICT Express, 9(3):486–491, 2023.
Pawel Ladosz, Lilian Weng, Minwoo Kim, and Hyondong Oh. Exploration in deep reinforcement learning: a survey. Information Fusion, 85:1–22, 2022. doi:10.1016/j.inffus.2022.03.003.
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436–444, 2015.
Jia Li. Linear discriminant analysis. http://www.stat.ucla.edu/ ywu/research/documents/BOOKS/LinearDiscriminantAnalysis.pdf, 2023. [Online; accessed 01-August-2023].
S.E. Li. Reinforcement Learning for Sequential Decision and Optimal Control. Springer Nature, 2023. ISBN 9789811977862. URL: https://books.google.ca/books?id=BNYy0AEACAAJ.
Wentian Li, Jane E Cerise, Yaning Yang, and Henry Han. Application of t-sne to human genetic data. Journal of bioinformatics and computational biology, 15(04):1750017, 2017.
Xiangyu Li and Hua Wang. On mean-optimal robust linear discriminant analysis. In 2022 IEEE International Conference on Data Mining (ICDM), 1047–1052. IEEE, 2022.
Q. Liang, W. Wang, X. Liu, Z. Na, X. Li, and B. Zhang. Communications, Signal Processing, and Systems: Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2021. ISBN 9789811584114. URL: https://books.google.ca/books?id=RDYyEAAAQBAJ.
J.W.B. Lin, H. Aizenman, E.M.C. Espinel, K. Gunnerson, and J. Liu. An Introduction to Python Programming for Scientists and Engineers. Cambridge University Press, 2022. ISBN 9781108701129. URL: https://books.google.ca/books?id=w7htEAAAQBAJ.
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, 2980–2988. 2017.
Z. Lin, H. Li, and C. Fang. Accelerated Optimization for Machine Learning: First-Order Algorithms. Springer Singapore, 2020. ISBN 9789811529108. URL: https://books.google.ca/books?id=4yjoDwAAQBAJ.
R.J.A. Little and D.B. Rubin. Statistical Analysis with Missing Data. Wiley Series in Probability and Statistics. Wiley, 2019. ISBN 9780470526798. URL: https://books.google.ca/books?id=BemMDwAAQBAJ.
N. Lopes and B. Ribeiro. Machine Learning for Adaptive Many-Core Machines - A Practical Approach. Studies in Big Data. Springer International Publishing, 2014. ISBN 9783319069388. URL: https://books.google.ca/books?id=oTkqBAAAQBAJ.
Yuzhen Lu, Dong Chen, Ebenezer Olaniyi, and Yanbo Huang. Generative adversarial networks (gans) for image augmentation in agriculture: a systematic review. Computers and Electronics in Agriculture, 200:107208, 2022. doi:10.1016/j.compag.2022.107208.
Andrew L Maas, Awni Y Hannun, Andrew Y Ng, and others. Rectifier nonlinearities improve neural network acoustic models. In Proc. icml, volume 30, 3. Atlanta, GA, 2013.
Soroor Malekmohamadi Faradonbe, Faramarz Safi-Esfahani, and Morteza Karimian-Kelishadrokhi. A review on neural turing machine (ntm). SN Computer Science, 1(6):333, 2020.
A. Martelli. Python in a Nutshell. In a Nutshell (o'Reilly) Series. O'Reilly, 2003. ISBN 9780596001889. URL: https://books.google.ca/books?id=6TEcaEzA8N0C.
A. Martelli, A.M. Ravenscroft, S. Holden, and P. McGuire. Python in a Nutshell. O'Reilly Media, 2023. ISBN 9781098113513. URL: https://books.google.ca/books?id=2WSmEAAAQBAJ.
E. Matthes. Python Crash Course: A Hands-On, Project-Based Introduction to Programming. No Starch Press, 2015. ISBN 9781593276034. URL: https://books.google.ca/books?id=RXoZCwAAQBAJ.
Christian Mayer. Python __contains__() magic method. https://blog.finxter.com/python-__contains__-magic-method/, 2023. [Online; accessed 01-August-2023].
Christian Mayer. Python __len__() magic method. https://blog.finxter.com/python-__len__-magic-method/, 2023. [Online; accessed 01-August-2023].
M. Maynard. Neural Networks: Introduction to Artificial Neurons, Backpropagation and Multilayer Feedforward Neural Networks with Real-World Applications. Advanced Data Analytics. Independently Published, 2020. ISBN 9798642783528. URL: https://books.google.ca/books?id=82F5zQEACAAJ.
P. Mccaffrey. An Introduction to Healthcare Informatics: Building Data-Driven Tools. Elsevier Science, 2020. ISBN 9780128149164. URL: https://books.google.ca/books?id=U-LcDwAAQBAJ.
W. McKinney. Python for Data Analysis. O'Reilly Media, 2022. ISBN 9781098104009. URL: https://books.google.ca/books?id=EgKBEAAAQBAJ.
Wes McKinney and others. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, volume 445, 51–56. Austin, TX, 2010.
G. McPherson. Applying and Interpreting Statistics: A Comprehensive Guide. Springer Texts in Statistics. Springer, 2001. ISBN 9780387951102. URL: https://books.google.ca/books?id=FezZhTX9OgQC.
B. Mehlig. Machine Learning with Neural Networks: An Introduction for Scientists and Engineers. Cambridge University Press, 2021. ISBN 9781108494939. URL: https://books.google.ca/books?id=cE49zgEACAAJ.
Milan Meloun and Jiří Militký. Statistical Data Analysis: A Practical Guide. Woodhead Publishing India Series. Woodhead Pub. India Pvt Limited, 2011. ISBN 9780857091093. URL: https://books.google.ca/books?id=FgtEYgEACAAJ.
D. Mertz. Text Processing in Python. Addison-Wesley, 2003. ISBN 9780321112545. URL: https://books.google.ca/books?id=GxKWdn7u4w8C.
Agnieszka Mikołajczyk and Michał Grochowski. Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW), 117–122. IEEE, 2018. doi:https://doi.org/10.1109/IIPHDW.2018.8388338.
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV), 565–571. Ieee, 2016.
P. Mishra. PyTorch Recipes: A Problem-Solution Approach. Apress, 2019. ISBN 9781484242582. URL: https://books.google.ca/books?id=X5OFDwAAQBAJ.
K.K. Mohbey and B. Bakariya. An Introduction to Python Programming: A Practical Approach: Using Python to Solve Complex Problems with a Burst of Machine Learning (English Edition). BPB Publications, 2021. ISBN 9789391392062. URL: https://books.google.ca/books?id=tVc\_EAAAQBAJ.
S. Molin and K. Jee. Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization. Packt Publishing, 2021. ISBN 9781800565913. URL: https://books.google.ca/books?id=Eh4sEAAAQBAJ.
L. Mou and Z. Jin. Tree-Based Convolutional Neural Networks: Principles and Applications. SpringerBriefs in Computer Science. Springer Nature Singapore, 2018. ISBN 9789811318702. URL: https://books.google.ca/books?id=eQlxDwAAQBAJ.
D. Nagakura. Normality Tests with Robust Scale Estimators. SSRN, 2022. URL: https://books.google.ca/books?id=-DDfzwEACAAJ.
Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), 807–814. 2010.
Raoof Naushad, Tarunpreet Kaur, and Ebrahim Ghaderpour. Deep transfer learning for land use and land cover classification: a comparative study. Sensors, 21(23):8083, 2021.
R. Nayak and N. Gupta. Python for Engineers and Scientists: Concepts and Applications. CRC Press, 2022. ISBN 9781000802160. URL: https://books.google.ca/books?id=\_PmXEAAAQBAJ.
Varsha Nemade, Sunil Pathak, and Ashutosh Kumar Dubey. A systematic literature review of breast cancer diagnosis using machine intelligence techniques. Archives of Computational Methods in Engineering, 29(6):4401–4430, 2022.
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng. Multimodal deep learning. In Proceedings of the 28th international conference on machine learning (ICML-11), 689–696. 2011.
G.R. Norman and D.L. Streiner. Biostatistics: The Bare Essentials. B.C. Decker, 2008. ISBN 9781550094008. URL: https://books.google.ca/books?id=8rkqWafdpuoC.
Ovidiu-Constantin Novac, Mihai Cristian Chirodea, Cornelia Mihaela Novac, Nicu Bizon, Mihai Oproescu, Ovidiu Petru Stan, and Cornelia Emilia Gordan. Analysis of the application efficiency of tensorflow and pytorch in convolutional neural network. Sensors, 22(22):8872, 2022.
Foivos Ntelemis, Yaochu Jin, and Spencer A Thomas. Information maximization clustering via multi-view self-labelling. Knowledge-Based Systems, 250:109042, 2022. doi:10.1016/j.knosys.2022.109042.
A. Pajankar. Hands-on Matplotlib: Learn Plotting and Visualizations with Python 3. Apress, 2021. ISBN 9781484274095. URL: https://books.google.ca/books?id=kUCZzgEACAAJ.
Pankaj and Andrea Anderson. How to use the __str__() and __repr__() methods in python. https://www.digitalocean.com/community/tutorials/python-str-repr-functions, 2023. [Online; accessed 01-August-2023].
Kitsuchart Pasupa and Wisuwat Sunhem. A comparison between shallow and deep architecture classifiers on small dataset. In 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE), 1–6. IEEE, 2016.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.
Tony Phillips. Principal component analysis. https://mathvoices.ams.org/featurecolumn/2021/08/01/principal-component-analysis/, 2023. [Online; accessed 01-August-2023].
J.O.P. Pinto, M.L.M. Kimpara, R.R. Reis, T. Seecharan, B.R. Upadhyaya, and J. Amadi-Echendu. 15th WCEAM Proceedings. Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. ISBN 9783030967949. URL: https://books.google.ca/books?id=fN5mEAAAQBAJ.
Alexander Platzer. Visualization of snps with t-sne. PloS one, 8(2):e56883, 2013.
David Powers. Evaluation: from precision, recall and f-measure to ROC, informedness, markedness & correlation. Journal of Machine Learning Technologies, 2(1):37–63, 2011.
K.B. Prakash and G.R. Kanagachidambaresan. Programming with TensorFlow: Solution for Edge Computing Applications. EAI/Springer Innovations in Communication and Computing. Springer International Publishing, 2021. ISBN 9783030570774. URL: https://books.google.ca/books?id=VcUWEAAAQBAJ.
K.B. Prakash, R. Kannan, S.A. Alexander, and G.R. Kanagachidambaresan. Advanced Deep Learning for Engineers and Scientists: A Practical Approach. EAI/Springer Innovations in Communication and Computing. Springer International Publishing, 2021. ISBN 9783030665180. URL: https://books.google.ca/books?id=MDsNzgEACAAJ.
K.L. Priddy and P.E. Keller. Artificial Neural Networks: An Introduction. SPIE tutorial texts. SPIE Press, 2005. ISBN 9780819459879. URL: https://books.google.ca/books?id=BrnHR7esWmkC.
Shangran Qiu, Matthew I Miller, Prajakta S Joshi, Joyce C Lee, Chonghua Xue, Yunruo Ni, Yuwei Wang, Ileana De Anda-Duran, Phillip H Hwang, Justin A Cramer, and others. Multimodal deep learning for alzheimer’s disease dementia assessment. Nature communications, 13(1):3404, 2022. doi:10.1038/s41467-022-31037-5.
L. Ramalho. Fluent Python. O'Reilly Media, 2022. ISBN 9781492056324. URL: https://books.google.ca/books?id=ICdnEAAAQBAJ.
Leodanis Pozo Ramos. Python's .__call__() method: creating callable instances. hhttps://realpython.com/python-callable-instances/, 2023. [Online; accessed 01-August-2023].
B. Ramsundar and R.B. Zadeh. TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning. O'Reilly Media, 2018. ISBN 9781491980408. URL: https://books.google.ca/books?id=GZ1ODwAAQBAJ.
R. Razavi-Far, B. Wang, M.E. Taylor, and Q. Yang. Federated and Transfer Learning. Adaptation, Learning, and Optimization. Springer International Publishing, 2022. ISBN 9783031117480. URL: https://books.google.ca/books?id=0M2REAAAQBAJ.
Payam Refaeilzadeh, Lei Tang, and Huan Liu. Cross-Validation, pages 532–538. Springer US, Boston, MA, 2009. doi:10.1007/978-0-387-39940-9_565.
Nicholas Rini. Benefits of object-oriented programming in java. https://www.developer.com/java/oop-benefits/, 2023. [Online; accessed 01-August-2023].
J. Rioux. Data Analysis with Python and PySpark. Manning, 2022. ISBN 9781617297205. URL: https://books.google.ca/books?id=4ytfEAAAQBAJ.
P. Roback and J. Legler. Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R. Chapman & Hall/CRC Texts in Statistical Science. CRC Press, 2021. ISBN 9781439885406. URL: https://books.google.ca/books?id=pYAUEAAAQBAJ.
Javier A Romero, Paulina Putko, Mateusz Urbańczyk, Krzysztof Kazimierczuk, and Anna Zawadzka-Kazimierczuk. Linear discriminant analysis reveals hidden patterns in nmr chemical shifts of intrinsically disordered proteins. PLoS computational biology, 18(10):e1010258, 2022.
Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto, Michele Piana, and Alessandro Verri. Are loss functions all the same? Neural computation, 16(5):1063–1076, 2004.
Sebastian Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016.
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. nature, 323(6088):533–536, 1986.
F. Sabry. Restricted Boltzmann Machine: Fundamentals and Applications for Unlocking the Hidden Layers of Artificial Intelligence. Artificial Intelligence. One Billion Knowledgeable, 2023. URL: https://books.google.ca/books?id=uQzHEAAAQBAJ.
F.M. Salem. Recurrent Neural Networks: From Simple to Gated Architectures. Springer International Publishing, 2022. ISBN 9783030899295. URL: https://books.google.ca/books?id=bJpXEAAAQBAJ.
Batuhan Sariturk, Dursun Zafer Seker, Ozan Ozturk, and Bulent Bayram. Performance evaluation of shallow and deep cnn architectures on building segmentation from high-resolution images. Earth Science Informatics, 15(3):1801–1823, 2022.
Iqbal H Sarker. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. sn computer science; 2. epub ahead of print 2021. 2021.
Alexander Schindler, Thomas Lidy, and Andreas Rauber. Comparing shallow versus deep neural network architectures for automatic music genre classification. In FMT, 17–21. 2016.
B. Scholkopf and A.J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Adaptive Computation and Machine Learning series. MIT Press, 2018. ISBN 9780262536578. URL: https://books.google.ca/books?id=7r34DwAAQBAJ.
Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: a unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, 815–823. 2015.
David W Scott. Scott's rule. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4):497–502, 2010.
Skipper Seabold and Josef Perktold. Statsmodels: econometric and statistical modeling with python. In 9th Python in Science Conference. 2010.
R.K. Sevakula and N.K. Verma. Improving Classifier Generalization: Real-Time Machine Learning based Applications. Studies in Computational Intelligence. Springer Nature Singapore, 2022. ISBN 9789811950735. URL: https://books.google.ca/books?id=0JCREAAAQBAJ.
Alok Sharma and Kuldip K Paliwal. Linear discriminant analysis for the small sample size problem: an overview. International Journal of Machine Learning and Cybernetics, 6:443–454, 2015.
Connor Shorten and Taghi M Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of big data, 6(1):1–48, 2019. doi:https://doi.org/10.1186/s40537-019-0197-0.
P. Singh, D. Singh, V. Tiwari, and S. Misra. Machine Learning and Computational Intelligence Techniques for Data Engineering: Proceedings of the 4th International Conference MISP 2022, Volume 2. Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. ISBN 9789819900473. URL: https://books.google.ca/books?id=5BW\_EAAAQBAJ.
B. Slatkin. Effective Python: 90 Specific Ways to Write Better Python. Effective Software Development Series. Pearson Education, 2019. ISBN 9780134854595. URL: https://books.google.ca/books?id=9kG4DwAAQBAJ.
R.S. Sutton and A.G. Barto. Reinforcement Learning, second edition: An Introduction. Adaptive Computation and Machine Learning series. MIT Press, 2018. ISBN 9780262352703. URL: https://books.google.ca/books?id=uWV0DwAAQBAJ.
Masahiro Suzuki and Yutaka Matsuo. A survey of multimodal deep generative models. Advanced Robotics, 36(5-6):261–278, 2022. doi:10.1080/01691864.2022.2035253.
A. Sweigart. Beyond the Basic Stuff with Python: Best Practices for Writing Clean Code. No Starch Press, 2020. ISBN 9781593279660. URL: https://books.google.ca/books?id=7GUKEAAAQBAJ.
Qiaoying Teng, Zhe Liu, Yuqing Song, Kai Han, and Yang Lu. A survey on the interpretability of deep learning in medical diagnosis. Multimedia Systems, 28(6):2335–2355, 2022. doi:10.1007/s00530-022-00960-4.
Juan Terven, Diana M Cordova-Esparza, Alfonzo Ramirez-Pedraza, and Edgar A Chavez-Urbiola. Loss functions and metrics in deep learning. a review. arXiv preprint arXiv:2307.02694, 2023.
Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, and Prayag Tiwari. A survey on few-shot class-incremental learning. Neural Networks, 169:307–324, 2024. doi:10.1016/j.neunet.2023.10.039.
Kurt Hornik Torsten Hothorn and Achim Zeileis. Unbiased recursive partitioning: a conditional inference framework. Journal of Computational and Graphical Statistics, 15(3):651–674, 2006. doi:10.1198/106186006X133933.
Matthew Urwin. 20 deep learning applications you should know. https://builtin.com/artificial-intelligence/deep-learning-applications, 2023. [Online; accessed 01-August-2023].
E. Van Baar. Digital Engineer, Traditional Engineer. Amazon Digital Services LLC - KDP Print US, 2022. ISBN 9798403670685. URL: https://books.google.ca/books?id=en_wzgEACAAJ.
Gido M van de Ven, Tinne Tuytelaars, and Andreas S Tolias. Three types of incremental learning. Nature Machine Intelligence, 4(12):1185–1197, 2022. doi:10.1038/s42256-022-00568-3.
Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 2008.
R. van Hattem. Mastering Python: Write powerful and efficient code using the full range of Python's capabilities. Packt Publishing, 2022. ISBN 9781800202108. URL: https://books.google.ca/books?id=jehvEAAAQBAJ.
J. VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media, 2023. ISBN 9781098121228. URL: https://books.google.ca/books?id=h_UtzwEACAAJ.
R.A. Vannatta, K.N. LaVenia, P. Atkinson, S. Delamont, A. Cernat, J.W. Sakshaug, and R.A. Williams. Linear Discriminant Analysis. SAGE Publications Limited, 2020. ISBN 9781529749090. URL: https://books.google.ca/books?id=gAb7zQEACAAJ.
A. Vellido, K. Gibert, C. Angulo, and J.D.M. Guerrero. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization: Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019. Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. ISBN 9783030196424. URL: https://books.google.ca/books?id=moOVDwAAQBAJ.
J. Wang and Y. Chen. Introduction to Transfer Learning: Algorithms and Practice. Machine Learning: Foundations, Methodologies, and Applications. Springer Nature Singapore, 2023. ISBN 9789811975844. URL: https://books.google.ca/books?id=GOW2EAAAQBAJ.
Michael L. Waskom. Seaborn: statistical data visualization. Journal of Open Source Software, 6(60):3021, 2021. URL: https://doi.org/10.21105/joss.03021, doi:10.21105/joss.03021.
Martin Wattenberg, Fernanda Viégas, and Ian Johnson. How to use t-sne effectively. Distill, 2016. URL: http://distill.pub/2016/misread-tsne, doi:10.23915/distill.00002.
Kilian Weinberger. Lecture 2: k-nearest neighbors. https://www.cs.cornell.edu/courses/cs4780/2017sp/lectures/lecturenote02_kNN.html, 2022. [Online; accessed 01-September-2023].
B. Wilson. Machine Learning Engineering in Action. Manning, 2022. ISBN 9781617298714. URL: https://books.google.ca/books?id=gitnEAAAQBAJ.
R.S. Witte and J.S. Witte. Statistics. Wiley, 2017. ISBN 9781119254515. URL: https://books.google.ca/books?id=KcxjDwAAQBAJ.
L. Wu, P. Cui, J. Pei, and L. Zhao. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer Nature Singapore, 2022. ISBN 9789811660542. URL: https://books.google.ca/books?id=XplXEAAAQBAJ.
Yong Xia, Yueqi Xiong, and Kuanquan Wang. A transformer model blended with cnn and denoising autoencoder for inter-patient ecg arrhythmia classification. Biomedical Signal Processing and Control, 86:105271, 2023.
Yan Xiong, Guo Xinya, and Junjie Xu. Cnn-transformer: a deep learning method for automatically identifying learning engagement. Education and Information Technologies, pages 1–20, 2023.
Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li. Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853, 2015.
Hemant Yadav, Jalpesh Vasa, and Rudra Patel. Gan (generative adversarial network)-based image super-resolution: a technical perspective. In International Conference on Information and Communication Technology for Intelligent Systems, 283–293. Springer, 2023.
W.Q. Yan. Computational Methods for Deep Learning: Theoretic, Practice and Applications. Texts in Computer Science. Springer International Publishing, 2020. ISBN 9783030610814. URL: https://books.google.ca/books?id=AP4MEAAAQBAJ.
J.C. Ye. Geometry of Deep Learning: A Signal Processing Perspective. Mathematics in Industry. Springer Nature Singapore, 2022. ISBN 9789811660467. URL: https://books.google.ca/books?id=fd5XEAAAQBAJ.
Xinghuo Yu, M Onder Efe, and Okyay Kaynak. A general backpropagation algorithm for feedforward neural networks learning. IEEE transactions on neural networks, 13(1):251–254, 2002.
D. Zhang, F. Song, Y. Xu, and Z. Liang. Advanced Pattern Recognition Technologies with Applications to Biometrics. Advances in Information and Communication Technology Education. Medical Information Science Reference, 2009. ISBN 9781605662015. URL: https://books.google.ca/books?id=InQK2wXcpF0C.
H. Zhao, Z. Lai, H. Leung, and X. Zhang. Feature Learning and Understanding: Algorithms and Applications. Information Fusion and Data Science. Springer International Publishing, 2020. ISBN 9783030407940. URL: https://books.google.ca/books?id=eBLbDwAAQBAJ.
Google Developers. Machine learning glossary. https://developers.google.com/machine-learning/glossary, 2023. [Online; accessed 01-October-2023].
MATLAB Developers. Cook’s distance. https://se.mathworks.com/help/stats/cooks-distance.html, 2023. [Online; accessed 01-August-2023].
MATLAB Developers. What is deep learning? 3 things you need to know. https://www.mathworks.com/discovery/deep-learning.html, 2023. [Online; accessed 01-August-2023].
Matplotlib Developers. Matplotlib 3.7.2 documentation. https://matplotlib.org/stable/index.html, 2023. [Online; accessed 01-August-2023].
Numba Developers. Numba documentation. https://numba.readthedocs.io/en/stable/index.html, 2023. [Online; accessed 01-August-2023].
NumPy Developers. Numpy documentation. https://numpy.org/doc/stable/index.html, 2023. [Online; accessed 01-August-2023].
OpenCV Developers. Opencv documentation. https://docs.opencv.org/4.x/index.html, 2023. [Online; accessed 01-August-2023].
Pandas Developers. Pandas documentation. https://pandas.pydata.org/docs/, 2023. [Online; accessed 01-August-2023].
Python Software Foundation. Python 3.11.4 documentation. https://docs.python.org/, 2023. [Online; accessed 01-August-2023].
PyTorch Developers. Pytorch documentation. https://pytorch.org/docs/stable/index.html, 2023. [Online; accessed 01-October-2023].
RPubs by RStudio. Cook’s distance. https://rpubs.com/DragonflyStats/Cooks-Distance, 2023. [Online; accessed 01-August-2023].
scikit-learn Developers. Scikit-learn user guide. https://scikit-learn.org/stable/user_guide.html, 2023. [Online; accessed 01-August-2023].
Statsmodels Developers. Statsmodels api reference. https://www.statsmodels.org/stable/api.html, 2023. [Online; accessed 01-August-2023].
TensorFlow Developers. Tensorflow documentation. https://www.tensorflow.org/guide, 2023. [Online; accessed 01-August-2023].
xgboost developers. Xgboost documentation. https://xgboost.readthedocs.io/en/stable/, 2023. [Online; accessed 01-August-2023].