Analyzing nonlinear dynamical systems and chaos using machine learning in physical and biological systems
Gendlal VaidyaDepartment of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, 441110, IndiaSyed Mohd Uzair IqbalSymbiosis Law School Pune (SLS-P), Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR), Symbiosis International (Deemed University), Pune, Maharashtra, 411014, IndiaMehraj Israr KhanKapil GuptaDepartment of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur, Maharashtra, 441108, IndiaAnorgul AshirovaDepartment of General Professional Sciences, Mamun University, Khiva, 220900, UzbekistanShweta GoyalDepartment of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, 248002, India
2026en
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ABI
Annotatsiya
Chaos and nonlinear dynamical systems are important for understanding complicated things that happen in biology and physics. The complexity and sensitivity to starting conditions of these systems make it hard for traditional analysis methods to work with them. In this study used Machine Learning and Deep learning Based method, to successfully look at and predict chaotic and nonlinear behaviours. After data is gathered from real-world and computer-simulated biological and physical systems, it is preprocessed and features are extracted in a way that works best for time-series and spatial datasets.
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