Unveiling the Mathematical Essence of Machine Learning: A Comprehensive Exploration

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guiding the dynamic area of machine learning—where models thrive and algorithms change over time—are rooted in an innate mathematical link. This study explores the fundamental ideas that drive the development of intelligent systems, providing light on the mutually beneficial link between mathematics and machine learning.</p> <iframe src="https://publications.waset.org/10013634.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Machine%20Learning" title="Machine Learning">Machine Learning</a>, <a href="https://publications.waset.org/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/search?q=Neural%20Network" title=" Neural Network"> Neural Network</a>, <a href="https://publications.waset.org/search?q=optimization." title=" optimization."> optimization.</a> </p> <a href="https://publications.waset.org/10013634/unveiling-the-mathematical-essence-of-machine-learning-a-comprehensive-exploration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013634/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013634/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013634/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013634/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013634/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013634/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013634/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013634/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013634/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013634/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013634.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">165</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Ali, Abdulalem, Shukor Abd Razak, Siti Hajar Othman, Taiseer Abdalla Elfadil Eisa, Arafat Al-Dhaqm, Maged Nasser, Tusneem Elhassan, Hashim Elshafie, and Abdu Saif. 2022. Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review. Applied Sciences 12: 9637 <br>[2] Aven, Terje. 2016. Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research 253: 1–13. <br>[3] Beyerer, Jurgen, Alexander Maier, and Oliver Niggemann. 2017. Machine Learning for Cyber-Physical Systems Selected papers from the International Conference ML4CPS. Berlin/Heidelberg: Springer. <br>[4] Bryman, Alan. 2012. Social Research Methods, 4th ed. Oxford: Oxford University Press. <br>[5] Buchanan, Bonnie, and Danika Wright. 2021. The impact of machine learning on UK financial services. Oxford Review Economic Policy 37: 537–63. <br>[6] Burrell, Jenna. 2016. How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society 3: 1–12. <br>[7] Cath, Corinne, Sandra Wachter, Brent Mittelstadt, Mariarosaria Taddeo, and Luciano Floridi. 2018. Artificial intelligence and the ‘good society’: The US, EU, and UK approach. Science and Engineering Ethics 24: 505–28. <br>[8] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Cambridge: MIT Press. <br>[9] Jordan, Michael. 2019. Artificial Intelligence—The Revolution Hasn’t Happened Yet. Harvard Data Science Review 6: 15–29. <br>[10] Kelleher, John D., and Brendan Tierney. 2018. Data Science. Cambridge: MIT Press. <br>[11] OECD. 2021. Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. Available online: https://www.oecd.org/finance/artificial-intelligence-machine-learningbig-data-in-finance.htm <br>[12] Wilinski, Antony, Mateusz Sochanowski, and Wojciech Nowicki. 2022. An investment strategy based on the first derivative of the moving averages difference with parameters adapted by machine learning. Data Science in Finance and Economics 2: 96–116. <br>[13] Zhang, Xiaoqiang, and Ying Chen. 2017. An artificial intelligence application in portfolio management. Advances in Economics, Business and Management Research 37: 86–100. <br>[14] Z.M. Fadlullah, F. Tang, B. Mao, N. Kato, O. Akashi, T. Inoue, K Mizutani State-of-the-art deep learning: evolving machine intelligence toward tomorrow's intelligent network traffic control systems IEEE Commun. Surv. Tutor., 19 (4) (2017), pp. 2432-2455 <br>[15] C.S. Wickramasinghe, K. Amarasinghe, D.L. Marino, C. Rieger, M. Manic Explainable unsupervised machine learning for cyber-physical systems IEEE Access, 9 (2021), pp. 131824-131843 <br>[16] G. Chen, Z. Shen, A. Iyer, U.F. Ghumman, S. Tang, J. Bi, Y Li Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges Polym. (Basel), 12 (1) (2020), p. 163 <br>[17] M. Stern, D. Hexner, J.W. Rocks, A.J. Liu Supervised learning in physical networks: from machine learning to learning machines Phys. Rev. X, 11 (2) (2021) <br>[18] I.H. Sarker Machine learning: algorithms, real-world applications and research directions SN Comput. Sci., 2 (3) (2021), p. 160 <br>[19] J. Hegde, B Rokseth Applications of machine learning methods for engineering risk assessment – a review Saf. Sci., 122 (2020), Article 104492 <br>[20] V. Kulkarni, M. Kulkarni, A. Pant Quantum computing methods for supervised learning Quant. Mach. Intell., 3 (2) (2021), p. 23 <br>[21] S. Shetty, S. Shetty, C.V. Singh, A. Rao Supervised machine learning: algorithms and applications Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications (2022), pp. 1-16 <br>[22] J Dhar, RS Baghel, AK Sharma, Role of instant nutrient replenishment on plankton dynamics with diffusion in a closed system: a pattern formation, Applied Mathematics and Computation, 218, 17, 2012, pp 8925-8936 <br>[23] J Dhar, RS Baghel, Role of dissolved oxygen on the plankton dynamics in the spatiotemporal domain, Modeling Earth Systems and Environment 2 (1), 2016, pp 1-6 <br>[24] RS Baghel, J Dhar, R Jain, Bifurcation and spatial pattern formation in spreading of disease with incubation period in a phytoplankton dynamics, Electronic Journal of Differential Equations 2012 (21), 2012, pp1-12 <br>[25] RS Baghel, J Dhar, Pattern formation in three species food web model in spatiotemporal domain with Beddington–DeAngelis functional response, Nonlinear Analysis: Modelling and Control 19 (2), 2014, pp 155-171 <br>[26] RS Baghel, J Dhar, R Jain, Chaos and spatial pattern formation in phytoplankton dynamics, Elixir Applied Mathematics 45, 2012, pp 8023-8026 <br>[27] RS Baghel, J Dhar, R Jain, Analysis of a spatiotemporal phytoplankton dynamics: Higher order stability and pattern formation, World Academy of Science, Engineering, and Technology 60, 2011, pp1406-1412 <br>[28] RS Baghel, J Dhar, R Jain, Higher order stability analysis of a spatial phytoplankton dynamics: bifurcation, chaos and pattern formation, Int J Math Model Simul Appl 5, 2012, pp113-127 <br>[29] RS Baghel, Dynamical Behaviour Changes in Response to Various Functional Responses: Temporal and Spatial Plankton System, Iranian Journal of Science, 47, 2023, pp1-11 <br>[30] J.Dhar, M. Chaudhary, R.S. Baghel and A.C. Pandey, 2015 “Mathematical Modelling and Estimation of Seasonal Variation of Mosquito Population: A Real Case Study,” Bol. Soc. Paran. Mat., vol. 33 2 (2015): 165–176. <br>[31] O.P. Misra, R. S. Baghel, M. Chaudhary and J.Dhar, 2015 “Spatiotemporal based predator-prey harvesting model for fishery with Beddington-Deangelis type functional response and tax as the control entity,” Dynamics of Continuous, Discrete and Impulsive Systems Series A., vol. 26 2 (2019): 113--135. <br>[32] S. Pareek, RS Baghel, Modelling and Analysis of Prey-Predator Interaction on Spatio-temporal Dynamics: A Systematic, 4th International Conference On Emerging Trends in Multi-Disciplinary Research “ETMDR-2023”,77 <br>[33] Kaushik P, Baghel RS, Khandelwal S, (2023) The Impact of Seasonality on Rainfall Patterns: A Case Study, International Journal of Mathematical and Computational Sciences Vol 17 (10), pp 138-143 <br>[34] Baghel RS, Sharma GS, (2023) An Ecological Model for Three Species with Crowley–Martin Functional Response, International Journal of Mathematical and Computational Sciences Vol 17 (10), pp 138-143 <br>[35] Sharma, G., Baghel, R. (2023), 'Artificial Neural Network Approach for Inventory Management Problem', International Journal of Mathematical and Computational Sciences, 17(11), 160 - 164. <br>[36] Agarwal, K., Baghel, R.S., Parmar, A., Dadheech, A. (2024) Jeffery Slip Fluid Flow with the Magnetic Dipole Effect Over a Melting or Permeable Linearly Stretching Sheet. International Journal of Applied and Computational Mathematics10 (1), 1-17. <br>[37] Krishnamurthy, V. and Shukla, J., 2008, “Seasonal persistence and propagation of intra-seasonal patterns over the Indian summer monsoon region”, Climate Dynamics, 30, 353-369. <br>[38] Y. Kim, Y. Kim, C. Yang, K. Park, G.X. Gu, S. Ryu, Deep learning framework for material design space exploration using active transfer learning and data augmentation, npj Comput. Mater. 7 (1) (2021), <br>[39] P. Piotrowski, D. Baczy´nski, M. Kopyt, T Gulczy´nski, Advanced ensemble methods using machine learning and deep learning for One-Day-Ahead forecasts of electric energy production in wind farms, Energies 15 (4) (2022) <br>[40] Baghel, R., Sahu, G.. "Rainfall Seasonality Changes over India Based on Changes in the Climate". International Journal of Geological and Environmental Engineering, (2024), 18(1), 14 - 20. <br>[41] Walsh, R.P.D. and Lawler, D.M. (1981) Rainfall seasonality: Description, spatial patterns and change through time. Weather, 36(7), 201–208. <br>[42] Kaushik P, Baghel RS, Khandelwal S, (2023) An investigation of the Variation in Seasonal Rainfall Patterns Over the Years, arXiv preprint arXiv:2311.06247 <br>[43] S. Dong, P. Wang, K. Abbas, A survey on deep learning and its applications, Comput. Sci. Rev. 40 (2021), 100379 <br>[44] F. Martínez-Gil, M. Lozano, F. Fernandez, Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models, Simul. Modell.Pract. Theory 74 (2017) 117–133 <br>[45] Pareek, S., Baghel, R.S. A Complex Dynamical Study of Spatiotemporal Plankton-Fish Interaction with Effects of Harvesting. Iran J Sci (2023). <br>[46] Z. Sun, S. Zhao, J. Zhang, Short-Term Wind Power Forecasting on Multiple Scales Using VMD Decomposition, K-Means Clustering and LSTM Principal Computing, IEEE Access 7 (2019) 166917–166929, <br>[47] https://hkrtrainings.com/classifications-in-machine-learning </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

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Unveiling the Mathematical Essence of Machine Learning: A Comprehensive Exploration