Abstract
To tackle the challenge of predicting food delivery times, the study introduces a novel approach crucial for streamlining food delivery service operations. The proposed model combines CNN (Convolution Neural Network)s and (LSTM) Long Short-Term Memory networks, leveraging the unique strengths of each design. The CNN component analyzes delivery data trends on a daily and weekly basis, identifying geographical patterns such as rush hours and seasonal variations. Meanwhile, the LSTM component focuses on recognizing spatiotemporal relationships and retaining data over time to effectively model sequential data. Compared to standard procedure, the model's simultaneous incorporation of spatial and temporal information leads to more precise predictions. This enhanced accuracy holds proven strategy for food delivery services, enabling them to more accurately estimate delivery times, reduce delays, and ultimately improve overall customer satisfaction. Keywords: (LSTM) Long short term memory, Time prediction, CNN (Convolution Neural Networks).
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