Last-Mile Optimization Using Neural Networks

Abstract

In the era of extensive data acquisition from manufacturing and transportation processes, the utilization of machine learning and deep learning techniques has emerged as a potent force for informed decision-making and optimized deliveries in contemporary urban landscapes. This study presents a novel approach grounded in deep learning, where product data are systematically gathered to construct a multilayer perceptron neural network model. This model proves instrumental in efficiently classifying product flows within the urban milieu. To validate its efficacy, machine learning classifiers are deployed, and their performance is juxtaposed with the neural network model. Addressing the critical question of the paper’s significance, our experimental evaluation unequivocally demonstrates the superior classification accuracy of the proposed multilayer perceptron model when compared to traditional machine learning models operating on the same product dataset. This advancement is not merely a theoretical achievement but translates into tangible improvements in last-mile delivery processes, marked by significant cost reduction and the mitigation of delays. The transformative potential of our approach is further underscored by the strategic application of a deep learning algorithm for optimization and illustrative purposes. This holistic methodology not only positions our work as a noteworthy contribution to the realm of product classification but also establishes a concrete pathway for enhancing the sustainability and efficiency of urban logistics. This paper, thus, goes beyond the conventional application of machine learning models, offering a paradigm shift in the intersection of deep learning, urban logistics, and sustainable development.

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

“…The Learning Focal Point (LFP) algorithm stands out for its capability to capture nuanced details, facilitating a sophisticated interpretation of emotions. By honing in on these essential facial features, the algorithm contributes to a comprehensive understanding of the subtle nuances in the student's expressions, enriching the subsequent stages of emotion analysis and enhancing the system's ability to discern and categorize a diverse range of emotional states with precision [26], [27], [28].…”