001461175 000__ 05574cam\a22006377i\4500
001461175 001__ 1461175
001461175 003__ OCoLC
001461175 005__ 20230502014308.0
001461175 006__ m\\\\\o\\d\\\\\\\\
001461175 007__ cr\cn\nnnunnun
001461175 008__ 230308s2023\\\\sz\\\\\\o\\\\\000\0\eng\d
001461175 019__ $$a1372196300$$a1372398363$$a1374485547
001461175 020__ $$a9783031185526$$qelectronic book
001461175 020__ $$a3031185528$$qelectronic book
001461175 020__ $$z303118551X
001461175 020__ $$z9783031185519
001461175 0247_ $$a10.1007/978-3-031-18552-6$$2doi
001461175 035__ $$aSP(OCoLC)1371687255
001461175 040__ $$aYDX$$beng$$erda$$cYDX$$dGW5XE$$dYDX$$dSFB$$dEBLCP$$dUKAHL
001461175 049__ $$aISEA
001461175 050_4 $$aHG173$$b.N68 2023
001461175 08204 $$a332.0285631$$223/eng/20230308
001461175 24500 $$aNovel financial applications of machine learning and deep learning :$$balgorithms, product modeling, and applications /$$cMohammad Zoynul Abedin, Petr Hajek, editors.
001461175 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2023]
001461175 300__ $$a1 online resource
001461175 336__ $$atext$$btxt$$2rdacontent
001461175 337__ $$acomputer$$bc$$2rdamedia
001461175 338__ $$aonline resource$$bcr$$2rdacarrier
001461175 4901_ $$aInternational Series in Operations Research & Management Science,$$x2214-7934 ;$$vv. 336
001461175 5050_ $$aPart 1: Recent Developments in FinTech -- 1. FinTech Risk Management and Monitoring -- 2. Digital Transformation of Supply Chain with Supportive Culture in Blockchain Environment -- 3. Integration of Artificial Intelligence Technology in Management Accounting Information System - An Empirical Study -- 4. The Impact of Big Data on Accounting Practices: Empirical Evidence from Africa -- Part 2: Financial Risk Prediction using Machine Learning -- 5. Using Outlier Modification Rule for Improvement of the Performance of Classification Algorithms in the Case of Financial Data -- 6. Default Risk Prediction Based on Support Vector Machine and Logit Support Vector Machine -- 7. Predicting Corporate Failure using Ensemble Extreme Learning Machine -- 8. Assessing and Predicting Small Enterprises Credit Ratings: A Multicriteria Approach -- Part 3: Financial Time-Series Forecasting -- 9. An Ensemble LGBM (Light Gradient Boosting Machine) Approach for Crude Oil Price Prediction -- 10. Model Development for Predicting the Crude Oil Price: Comparative Evaluation of Ensemble and Machine Learning Methods -- part 4: Emerging Technologies in Financial Education and Healthcare -- 11. Discovering the Role of M-learning among Finance Students: The Future of Online Education -- 12. Exploring the Role of Mobile Technologies in Higher Education: The Impact of Online Teaching on Traditional Learning.-13. Knowledge Mining from Health Data: Application of Feature Selection Approaches.
001461175 506__ $$aAccess limited to authorized users.
001461175 520__ $$aThis book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
001461175 588__ $$aDescription based on online resource; title from digital title page (viewed on April 19, 2023).
001461175 650_0 $$aFinance$$xData processing.
001461175 650_0 $$aMachine learning.
001461175 650_0 $$aDeep learning (Machine learning)
001461175 655_0 $$aElectronic books.
001461175 7001_ $$aAbedin, Mohammad Zoynul,$$eeditor.
001461175 7001_ $$aHajek, Petr,$$eeditor.
001461175 77608 $$iPrint version: $$z303118551X$$z9783031185519$$w(OCoLC)1344424195
001461175 77608 $$iPrint version:$$tNovel financial applications of machine learning and deep learning$$z9783031185519$$w(OCoLC)1355045443
001461175 830_0 $$aInternational series in operations research & management science ;$$v336.$$x2214-7934
001461175 852__ $$bebk
001461175 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-18552-6$$zOnline Access$$91397441.1
001461175 909CO $$ooai:library.usi.edu:1461175$$pGLOBAL_SET
001461175 980__ $$aBIB
001461175 980__ $$aEBOOK
001461175 982__ $$aEbook
001461175 983__ $$aOnline
001461175 994__ $$a92$$bISE