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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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Decision Making: P-value Method01:09

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Lightweight Deep Learning Model for Marketing Strategy Optimization and Characteristic Analysis.

Yang Su1, Chonghong Wang1, Xuejiao Sun1

  • 1Yantai Institute of Technology, School of Economics and Management, Yantai 264005, China.

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This study introduces a deep learning model for e-commerce marketing, analyzing user behavior to predict preferences and optimize sales strategies. The model demonstrates superior accuracy and effectiveness in enhancing marketing outcomes.

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Area of Science:

  • Artificial Intelligence
  • Marketing Science
  • Consumer Psychology

Background:

  • Traditional market models are declining due to technological advancements.
  • Evolving consumer cognition necessitates adaptive marketing strategies for businesses in the internet age.

Purpose of the Study:

  • To analyze the relationship between sales development and psychological factors.
  • To construct a deep learning-based fusion model for predicting consumer preferences and enabling accurate e-commerce marketing.

Main Methods:

  • Utilized deep learning neural network structures for preference prediction.
  • Developed user portraits by analyzing purchasing behavior and credit literacy.
  • Implemented a hash algorithm for targeted product recommendations.

Main Results:

  • The proposed model achieved 79.56% accuracy, 77.43% recall, and an F1 score of 0.785 in multi-modal recognition analysis.
  • User attribute features were extracted, leading to the construction of individual user portrait models with 13 topics and weight ratios.
  • Optimized marketing strategies based on user portraits yielded remarkable effects, with marketing success rates ranging from 69% to 82%.

Conclusions:

  • The developed deep learning model is superior to existing methods, offering significant utility for e-commerce.
  • The model provides an intelligent and efficient dynamic strategy service for enterprises, enhancing marketing effectiveness and profitability.