Modern communication networks rely on multiple protocols to manage data transmission efficiently. However, traditional protocol optimization techniques often struggle to adapt dynamically to changing network conditions, resulting in increased latency, packet loss, and reduced throughput. This challenge motivated the development of the Neural Network Based Protocol Optimizer (NN-PO). This paper presents the design and implementation of an intelligent optimization system that utilizes Artificial Neural Networks (ANNs) to analyze network traffic patterns and automatically optimize communication protocol parameters in real time. The NN-PO system collects network performance metrics such as bandwidth utilization, latency, packet delivery rate, congestion levels, and transmission errors. These parameters are processed through a trained neural network model that predicts optimal protocol configurations for varying network conditions. The system continuously learns from historical and real-time network data, enabling adaptive decision-making and improved communication efficiency.
Neural Networks, Protocol Optimization, Network Performance, Artificial Intelligence, Machine Learning, Communication Networks, Throughput Optimization, Latency Reduction, Network Traffic Analysis, Adaptive Systems.
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