A Unified Complexity Theory Analyzing Medical and Chemical Algorithms

TThis study focus on exploring dynamic ways to unified , quantify a computational complexity theory which has traditionally been measured using asymptotic symbols such as Big- O, Big-Ɵ and Big-Ω .which only provides theoretical information behind the execution of an algorithm, neglecting a real-time situation where an algorithm can shows their actual behavior and also not able to reduce time and space complexity of an algorithm To solve this research gap, we introduce a unified complexity theory to measure actual complexity resource constraint by introducing a Universal Complexity Function. It is a mathematically formulated by comprising common factor that really impact on the execution of an algorithm such as time function, sequence of problem function and complexity difference functions. To utilize and provide our stance with respect to proposed model, we have implemented Python profiling devices like timeit and cProfile which gives us observational runtime information. This proposed model helps us to identify actual pattern which really impact on the health of an algorithm and help us to reduce them. We have also proposed indispensably optimization work ∫〖(f(t)-f(D)) 〗 to distinguish wasteful aspect and foresee execution design in both classical and AI-based calculations. Our proposed model is approved over 20 calculations; containing NP-hard issues, profound learning models, and quantum reenactments. It has come about as a solid relationship between the proposed measurements and commonsense calculation proficiency, uncovering impediments in conventional approaches and advertising a generalizable, measurable and versatile, and versatile system for cutting edge computational framework.