Abstract: Artificial Intelligence (AI) represents a diverse field focused on creating machines that can perform tasks typically requiring human intelligence. At the core of AI lies machine learning (ML), a fundamental aspect enabling systems to learn and enhance their performance through experience rather than explicit programming. Amidst the array of tools in AI, machine learning stands out due to its capacity to identify patterns, make forecasts, and adapt to new information, ushering in a new era of intelligent systems. Artificial Neural Networks (ANN) fall under supervised learning and are inspired by the human brain's structure and functionality. ANNs comprise interconnected nodes organized into layers, including an input layer, one or more hidden layers, and an output layer. These networks are trained using labeled datasets to comprehend intricate patterns and relationships within the data. Optimization algorithms play a crucial role in refining machine learning models to achieve optimal performance. These algorithms aim to minimize or maximize an objective function representing the model's performance metric. Gradient descent, a foundational optimization technique, iteratively adjusts model parameters to minimize the error between predicted and actual outcomes. Other optimization tools, such as evolutionary algorithms, simulated annealing, and genetic algorithms, offer diverse approaches to finding optimal solutions within the expansive space of model parameters.

AI, ANN, RNN, Load Frequency Control, Optimization