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Power system stability is crucial for ensuring the reliable operation of electrical grids. Instabilities can lead to blackouts, equipment damage, and economic losses. Traditional control methods may struggle to handle the complexity and non-linearity of power systems. This study proposes a novel approach that integrates neuro-fuzzy control with genetic algorithms to enhance power system stability. Neuro-fuzzy systems excel at handling complex and non-linear systems, while genetic algorithms offer efficient optimization capabilities. The neurofuzzy control and genetic algorithms provides a robust framework for optimizing power system stability. This approach aims to mitigate the challenges posed by system complexities and uncertainties. Through simulations and case studies, the effectiveness of the proposed method is demonstrated. The integrated approach shows improved stability performance compared to conventional methods. Additionally, the flexibility of the system allows for adaptation to varying operating conditions and disturbances. [ABSTRACT FROM AUTHOR]

In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-FuzzyGenetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time. [ABSTRACT FROM AUTHOR]

Efficient traffic management in wireless networks is crucial for optimizing resource utilization and enhancing overall network performance. This paper introduces a novel approach to dynamic routing algorithms utilizing evolutionary algorithms for effective wireless traffic management. The proposed system leverages the adaptability and optimization capabilities of evolutionary algorithms to dynamically adjust routing paths based on real-time network conditions. Our algorithm employs a genetic programming framework to evolve and refine routing strategies, considering factors such as network congestion, link quality, and traffic load. This dynamic approach enables the network to autonomously adapt to changing conditions, ensuring optimal route selection for data transmission. The evolutionary nature of the algorithm allows it to continually learn and improve, making it well-suited for the dynamic and unpredictable nature of wireless environments. The effectiveness of the proposed algorithm is evaluated through extensive simulations, demonstrating significant improvements in terms of throughput, latency, and overall network efficiency compared to traditional static routing approaches. The system ability to handle diverse traffic patterns and adapt to varying network scenarios positions it as a robust solution for nextgeneration wireless networks. [ABSTRACT FROM AUTHOR]