首页|Reports from Lebanese American University Highlight Recent Research in Artificia l Intelligence (Advanced Fractional Mathematics, Fractional Calculus, Algorithms and Artificial Intelligence with Applications in Complex Chaotic Systems)
Reports from Lebanese American University Highlight Recent Research in Artificia l Intelligence (Advanced Fractional Mathematics, Fractional Calculus, Algorithms and Artificial Intelligence with Applications in Complex Chaotic Systems)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting from Lebanese American University by N ewsRx journalists, research stated, "Chaos, comprehended characteristically, is the mathematical property of a dynamical system which is a deterministic mathema tical model in which time can be either continuous or discrete as a variable. Th ese respective models are investigated as mathematical objects or can be employe d for describing a target system." The news editors obtained a quote from the research from Lebanese American Unive rsity: "As a long-term aperiodic and random-like behavior manifested by many non linear complex dynamic systems, chaos induces that the system itself is inherent ly unstable and disordered, which requires the revealing of representative and a ccessible paths towards affluence of complexity and experimental processes so th at novelty, diversity and robustness can be generated. Hence, complexity theory focuses on nondeterministic systems, whereas chaos theory rests on deterministi c systems. These entailments demonstrate that chaos and complexity theory provid e a synthesis of emerging wholes of individual components rather than the orient ation of analyzing systems in isolation. Therefore, mathematical modeling and sc ientific computing are among the chief tools to solve the challenges and problem s related to complex and chaotic systems through innovative ways ascribed to dat a science with a precisely tailored approach which can examine the data applied. The complexity definitions need to be weighed over different data offering a hi ghly extensive applicability spectrum with more practicality and convenience owi ng to the fact that the respective processes lie in the concrete mathematical fo undations, which all may as well indicate that the methods are required to be ex amined thoroughly regarding their mathematical foundation along with the related methods to be applied. Furthermore, making use of chaos theory can be considere d to be a way to better understand the internal machinations of neural networks, and the amalgamation of chaos theory as well as Artificial Intelligence (AI) ca n open up stimulating possibilities acting instrumental to tackle diverse challe nges, with AI algorithms providing improvements in the predictive capabilities v ia the introduction of adaptability, enabling chaos theory to respond to even sl ight changes in the input data, which results in a higher level of predictive ac curacy. Therefore, chaos-based algorithms are employed for the optimization of n eural network architectures and training processes. Fractional mathematics, with the application of fractional calculus techniques geared towards the problems' solutions, describes the existence characteristics of complex natural, applied s ciences, scientific, engineering related and medical systems more accurately to reflect the actual state properties co-evolving entities and patterns of the sys tems concerning nonlinear dynamic systems and modeling complexity evolution with fractional chaotic and complex systems. Complexity entails holistic understandi ng of various processes through multi-stage integrative models across spanning s cales for expounding complex systems while following actuality across evolutiona ry path. Moreover, Fractional Calculus (FC), related to the dynamics of complica ted real-world problems, ensures emerging processes adopting fractional dynamics rather than the ordinary integer-ordered ones, which means the related differen tial equations feature non integer valued derivatives. Given that slight perturb ation leads to a significantly divergent future concatenation of events, pinning down the state of different systems precisely can enable one to unveil uncertai nty to some extent. Predicting the future evolution of chaotic systems can scree n the direction towards distant horizons with extensive applications in order to understand the internal machinations of neural and chaotic complex systems."
Lebanese American UniversityAlgorithmsArtificial IntelligenceCalculusCyborgsEmerging TechnologiesEngineeringMachine LearningMathematics