With the emergence of the 5G network, the count of analysis papers associated with the 6G Internet of Things (IoT) has rapidly increased due to the rising attention of researchers in next-generation technology, 6G networks and IoT techniques. Owing to this, grasping the overall research topics and directions is a complex task. To mutually address the significant issues of 6G cellular IoT, i.e., information transmission, data aggregation and power supply, we proposed a variance-based integrating model for the 6G-IoT approach that considers energy, communication and computation (ECC). Initially, the base station (BS) charges huge IoT devices concurrently utilizing WPT in the downlink. After that, IoT devices gather the energy to perform the communication task and the computation task in the uplink in a similar spectrum. Also, the model integrates the optimization of transmit beams via the Improved Ant Colony Optimization (IACO) model to balance the system performance, power consumption and computational complexity. Further, this study exploited activated Remote Radio Units (RRUs) to improve the network performance and energy efficiency in the downlink model. The simulation outcomes evaluate the performance of the proposed work over the conventional models concerning error analysis. From the results, the MSE value in the IACO work is much lower, around 0.011, while the compared schemes achieved comparatively higher MSE values.

Using hybrid models, we present a novel method for detecting lung cancer in this study. Our method uses the random forest and convolutional neural network (CNN) techniques to incorporate machine learning and deep learning advantages. The proposed composite method combines structured clinical data with unprocessed imaging data for a more complete lung cancer diagnosis. The CNN component of our hybrid model excels at extracting features from images of lung cancer, while the random forest component excels at capturing complex relationships in structured data. For greater precision and consistency, the results of the two models may be averaged. The hybrid model outperforms the existing methods. The hybrid model acquired an accuracy rate of 98%. Future lung cancer detection will be rapid and accurate due to the hybrid model’s improved performance and decreased inference periods.