2026-04-17T17:42:28.208Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:8712 2022-03-19T15:46:00Z 3

Support Vector Machine Performance Improvements for Cryptocurrency Value Forecasting by Enhanced Sine Cosine Algorithm, Chapter in AIS Algorithms for Intelligent Systems: ICCVR 2021: Computer Vision and Robotics, Springer 2022 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/8712 https://link.springer.com/chapter/10.1007/978-981-16-8225-4_40 M. Salb M. Zivkovic N. Bacanin A. Chhabra M. Suresh For crypto investors, anticipating market behaviour is critical. They make judgements that result in profit or loss based on the prediction. The prediction often involves the use of previous data to estimate the future behaviour of market prices. The machine learning methodology is used for prediction. In recent years, nature-inspired algorithms have been effectively employed in the optimization of several machine learning models. Swarm metaheuristics algorithms, a group of nature-inspired algorithms, have shown to be outstanding optimization algorithms in the field of machine learning and a variety of other practical applications. This work provides one such methodology, namely improve the support vector machine model by using an improved version of the method sine cosine to anticipate cryptocurrency values. The basic SCA was enhanced with a simple exploration mechanism and then evaluated by comparing it to other techniques run on identical sets of data. The findings of the conducted experiment show that the suggested strategy outperformed the other alternatives considered in the study. bookPart Springer, Singapore 527 536 10.1007/978-981-16-8225-4_40 AIS Algorithms for Intelligent Systems: ICCVR 2021: Computer Vision and Robotics