The quality of service (QoS) in 5G/6G communication enormously depends upon the mobility and agility of the network architecture. An increase in the possible uses of 5G vehicular network simultaneously expands the scope of the network’s quality of service (QoS). To this end, a safety-critical real-time system has become one of the most demanding criteria for the vehicular network. Although different mathematical and computation methods have traditionally been used to optimize the allocation of resources, but the nonconvexity of optimization issues creates unique type of challenges. In recent years, machine learning (ML) has emerged as a valuable tool for dealing with computational complexity that involves large amounts of data in heterogeneous vehicular networks. By using optimization and cutting-edge machine learning techniques, this article gives readers an insight about how 5G vehicular network resources can be allocated to reinforce network communication. Furthermore, a new federated deep reinforcement learning- (FDRL-) based vehicle communication method is presented as a new insight. Finally, a UAV-aided vehicular communication system based on FDRL-based UAVs is proposed as a novel resource management technique to optimize 5G and 6G quality of services.

This study describes a modified approach for the detection of cardiac abnormalities and QRS complexes using machine learning and support vector machine (SVM) classifiers. The suggested technique overtakes prevailing approaches in terms of both sensitivity and specificity, with 0.45 percent detection error rate for cardiac irregularities. Moreover, the vector machine classifiers validated the proposed method's superiority by accurately categorising four ECG beat types: normal, LBBBs, RBBBs, and Paced beat. The technique had 96.67 percent accuracy in MLP-BP and 98.39 percent accuracy in support of vector machine classifiers. The results imply that the SVM classifier can play an important role in the analysis of cardiac abnormalities. Furthermore, the SVM classifier also categorises ECG beats using DWT characteristics collected from ECG signals.

In recent days the quickly changing manufacturing environment has pushed organizations to accomplish more consumer satisfaction by improving item quality, lessening production cost, and acknowledging maintainability. Anomaly recognition impacts the nature of items and it is typically directed through visual quality assessment. The visual quality review of an item can be performed either physically or naturally. This research proposes a novel technique in manufacturing industry-based fault detection and control management using machine learning technique. Here the input data has been collected as manufacturing fault historical data by IoT (internet of things) module. This data has been processed for noise removal, normalization, and smoothening. The processed data features have been extracted by using kernel principal vector component analysis. Gaussian quadratic Kernelized Generative Adversarial Network has been used to manage the control of extracted features. The experimental analysis has been done in terms of RMSE, MAP, AUC, F-1 score, recall, accuracy, and precision. Upon reviewing our procedures after our research and testing, we discovered that Auto-Encoder Neural Network is the best effective algorithm for identifying a production line failure. One-Class SVM gives the highest accurate findings in our machine-based investigations.

A Posidonia oceanica waste marine plant was used to produce a wet-laid nonwoven web for multifunction applications. To study the effect of some parameters related to the web characteristics (sheet weight, binder ratio, and pulp ratio) on the mechanical and physical properties of the web, we used a Box–Behnken design plan with three levels. The diagram of the superposed contours graphic method was used to find the optimum parameters of the process for the application of the Posidonia nonwoven fiber on an insulation field. With the measurement of the thermal conductivity properties using the box method, the results demonstrated that the nonwoven fiber from Posidonia oceanica marine waste had good insulation properties in comparison with other classical natural fibers (hemp, flax) used in the field of insulation with the big advantage of being a natural product.