Inception Residual RNN-LSTM Hybrid Model for Predicting Pension Coverage Trends Among Private-Sector Workers in the USA

2025-02-10T10:01:35+08:00

Pensions are fundamental to financial security in retirement, especially in the U.S., where they play a critical role in ensuring stability for retirees and fostering broader economic benefits. However, predicting pension coverage trends poses significant challenges due to the complexity of labor markets, demographic shifts, and economic variabilities. Traditional statistical models, though foundational, often fail to handle the nonlinear patterns inherent in pension data. To address these limitations, we propose the Inception residual RNN-LSTM hybrid model, which combines the strengths of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks with residual connections. This model captures diverse temporal patterns while mitigating vanishing gradient issues, delivering superior performance in predicting pension coverage trends. Experimental results demonstrate that our model outperforms traditional machine learning models and standalone deep learning architectures like RNN and LSTM. Its robust performance across key metrics highlights its potential as a reliable tool for forecasting complex pension trends and aiding policymakers, employers, and financial institutions in effective retirement planning.

Real-Time Personalized Ad Recommendation Based on User Behavioral Analysis

2025-04-29T13:47:36+08:00

Real-time personalized ad recommendation systems are crucial for enhancing user engagement and satisfaction. To address the challenge of delivering highly relevant ads in a dynamic, large-scale environment, this paper proposes a novel approach that integrates real-time user behavior analysis with advanced time series modeling and stream processing techniques. Specifically, the system leverages Long Short-Term Memory (LSTM) networks to capture both short-term and long-term user preferences, ensuring accurate and personalized ad recommendations. By utilizing stream processing frameworks like Apache Kafka and Apache Flink, the system supports high-throughput data ingestion and low-latency processing, even under high user concurrency. Experimental results demonstrate that the proposed system outperforms traditional methods and state-of-the-art models in terms of recommendation accuracy, response time, and user satisfaction. This approach offers significant advantages in real-time ad delivery and provides a scalable, efficient solution for personalized advertising in large-scale applications.

Artificial Intelligence Advances

Inception Residual RNN-LSTM Hybrid Model for Predicting Pension Coverage Trends Among Private-Sector Workers in the USA

Real-Time Personalized Ad Recommendation Based on User Behavioral Analysis