Hybrid beamforming plays a critical role in evaluating wireless communication technology, particularly for millimeter-wave (mmWave) multiple-input multiple-out (MIMO) communication. Several hybrid beamforming systems are investigated for millimeter-wave multiple-input multiple-output (MIMO) communication. The deployment of huge grant-free transmission in the millimeter-wave (mmWave) band is required due to the growing demands for spectrum resources in upcoming enormous machine-type communication applications. Ultra-high data speed, reduced latency, and improved connection are all promised by the development of 5G mmWave networks. Yet, due to severe route loss and directional communication requirements, there are substantial obstacles to transmission reliability and energy efficiency. To address this limitation in this research we present an intelligent transmission control scheme tailored to 5G mmWave networks. Transport control protocol (TCP) performance over mmWave links can be enhanced for network protocols by utilizing the mmWave scalable (mmS)-TCP. To ensure that users have the stronger average power, we suggest a novel method called row compression two-stage learning-based accurate multi-path processing network with received signal strength indicator-based association strategy (RCTS-AMP-RSSI-AS) for an estimate of both the direct and indirect channels. To change user scenarios and maintain effective communication constantly, we utilize the innovative method known as multi-user scenario-based MATD3 (Mu-MATD3). To improve performance, we introduce the novel method of “digital and analog beam training with long-short term memory (DAH-BT-LSTM)”. Finally, as optimizing network performance requires bottleneck-aware congestion reduction, the low-latency congestion control schemes (LLCCS) are proposed. The overall proposed method improves the performance of 5G mmWave networks.

Online social networks have become a crucial medium for global communication, with many users accessing their accounts multiple times a day. This trend was particularly pronounced during the Covid-19 crisis, as quarantines and extended periods at home drove a significant increase in online activity. However, this surge in usage also brought about negative societal behaviors, such as heightened instances of offensive language, hate speech, and online bullying. Therefore, detecting these abuses electronically has become necessary, despite the slowness that accompanies manual detection, particularly with large data. This paper proposes a model for the automatic identification of offensive language in Arabic tweets using fine-tuned word embedding models. The proposed GWO-RWN model combines a Random Weight Network (RWN) as the classifier with a Grey Wolf Optimizer (GWO) to optimize the RWN configuration. Experiments were conducted on the ArCybC dataset of 4505 Arabic tweets, using an 60% training, 20% validation and 20% testing split. Word2Vec embeddings with CBOW and Skip-G architectures and GloVe embeddings were evaluated, and the proposed GWO-RWN was compared with GA-, PSO-, and ABC-optimized RWN models over 30 independent runs. The proposed approach achieved an accuracy of 90.5%, with precision, recall, and F1-score of 0.891, 0.888, and 0.889, respectively, using Skip-G embeddings. This represents a 4.8-percentage-point improvement over the non-optimized RWN, which achieved an accuracy of 85.7%.This exceeds previously reported results on the same dataset, including 88.2% accuracy with a GA-optimized SVM and 86.3% accuracy with an SVM using word embeddings.