The rapid growth of ultra-dense mobile edge computing (UDEC) in 5G IoT networks has intensified energy inefficiencies and latency bottlenecks exacerbated by dynamic channel conditions and imperfect CSI in real-world deployments. This paper introduces POTMEC, a power optimization framework that combines a channel-aware adaptive power allocator using real-time SNR measurements, a MATLAB-trained RL model for joint offloading decisions and a decaying step-size algorithm guaranteeing convergence. Computational offloading is a productive technique to overcome mobile battery life issues by processing a few parts of the mobile application on the cloud. It investigated how multi-access edge computing can reduce latency and energy usage. The experiments demonstrate that the proposed model reduces transmission energy consumption by 27.5% compared to baseline methods while maintaining the latency below 15 ms in ultra-dense scenarios. The simulation results confirm a 92% accuracy in near-optimal offloading decisions under dynamic channel conditions. This work advances sustainable edge computing by enabling energy-efficient IoT deployments in 5G ultra-dense networks without compromising QoS.

The resilience of urban transportation system is an important basis for building a resilient city. The existing assessment methods are faced with the challenges of complex influencing factors, difficult quantification of resilience index and low accuracy of traditional single model. In order to solve these problems, this study proposes a dynamic resilience evaluation method of urban transportation system based on deep learning model. This method constructs a resilience assessment model of urban transportation system, quantifies and integrates dynamic influencing factors. Convolutional neural network (CNN) was used to extract the dynamic characteristics of the toughness index, combined with Bidirectional Gated Recurrent Unit (BiGRU) to explore the time correlation, and the attention mechanism was used to enhance the important features. Taking Zhengzhou metropolitan area as an example, the empirical analysis found that the overall resilience of the urban transportation system in this region was at a medium to high level and showed a slow rising trend (resilience index increased from 0.319 in 2013 to 0.347 in 2022, with an average annual growth rate of 8.611%). However, there are significant differences in resilience levels among cities within the region (Zhengzhou has the highest annual mean resilience index (0.802) and Pingdingshan has the lowest (0.105). Compared with the traditional model, the model shows better performance in terms of mean absolute percentage error (MAPE = 0.06275%), root mean square error (RMSE = 0.018698%) and coefficient of determination (R2= 0.9912), which verifies the validity and reliability of the model.

Worldwide, cervical cancer is a major public health concern, and early detection is essential for successful management and treatment. Ensemble machine learning (ML) has emerged as a promising approach for improving the accuracy and reliability of cervical cancer diagnosis systems. This study evaluated the performance of various ensemble ML models for cervical cancer diagnosis using a large dataset of cervical cell images. The performance of different ensemble models was compared, including random forest (RF), gradient boosting (GB), and stacking, with conventional ML models, such as logistic regression and support vector machine (SVM). The bagging ensemble model developed reported training of 0.991667, 0.827586, 1.0, and 0.90566 for accuracy, precision, recall, and F-measure (F1-score), respectively. Furthermore, the interpretability of ensemble models was investigated using feature importance and partial dependence plots. The interpretability analysis revealed that ensemble models can provide valuable insights into the key features and factors that contribute to cervical cancer diagnosis. In conclusion, the findings suggest that ensemble ML is a promising approach for developing accurate and reliable cervical cancer diagnosis systems and improving the interpretability of these models.