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Abstract With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service. Deep learning (DL) has achieved unprecedented success in various fields such as computer vision, large language model processing, and speech recognition due to its powerful representation capabilities and computational convenience. It has also made significant progress in the communication field in meeting stringent demands and overcoming deficiencies in existing technologies. The main purpose of this article is to uncover the latest advancements in the field of DL‐based algorithm methods in the physical layer of wireless communication, introduce their potential applications in the next generation of communication mechanisms, and finally summarize the open research questions. 

Abstract Pruning is showing huge potential for compressing and accelerating deep neural networks by eliminating redundant parameters. Along with more terminal chips integrated with AI accelerators for internet of things (IoT) devices, structured pruning is gaining popularity with the edge computing research area. Different from filter pruning and group-wise pruning, stripe-wise pruning (SWP) conducts pruning at the level of stripes in each filter. By introducing filter skeleton (FS) to each stripe, the existing SWP method sets an absolute threshold for the values in FS and removes the stripes whose corresponding values in FS could not meet the threshold. Starting with investigation into the process of stripe wise convolution, we use the statistical properties of the weights located on each stripe to learn the importance between those stripes in a filter and remove stripes with low importance. Our pruned VGG-16 achieves the existing results by a fourfold reduction in parameter with only 0.4% decrease in accuracy. Results from comprehensive experiments on IoT devices are also presented. 

Machine Learning models have the ability to streamline the process by which Youtube video comments are filtered between legitimate comments (ham) and spam. In order to integrate machine learning models into regular usage on media-sharing platforms, recent approaches have aimed to develop models trained on Youtube comments, which have emerged as valuable tools for the classification and have enabled the identification of spam content and enhancing user experience. In this paper, eight machine learning approaches are applied to spam detection for YouTube comments. The eight machine learning models include Gaussian Naive Bayes, logistic regression, K-nearest neighbors (KNN) classifier, multi-layer perceptron (MLP), support vector machine (SVM) classifier, random forest classifier, decision tree classifier, and voting classifier. All eight models perform very well, specifically random forest approach can achieve almost perfect performance with average precision of 100% and AUC-ROC of 0.9841. The computational complexity of the eight machine learning approaches are compared.