Machine Learning-Based Intelligent Wireless Communication System for Solving Real-World Security Issues

Abstract

The intelligent wireless system focuses on integrating with the advanced technologies like machine learning and related approaches in order to enhance the performance, productivity, and output. The implementation of machine learning approaches is mainly applied in order to enhance the efficient communication system, enable creation of variable node locations, support collection of data and information, analyze the pattern, and forecast so as to provide better services to the end users. The efficiency of using these technologies tend to lower the cost and support in deploying the resources effectively. The wireless network system tends to enhance the bandwidth, and the application of novel machine learning approaches supports detection of unrelated data and information and enables analysis of latency at each part of the communication channel. The study involves critically analyzing the key determinants of machine learning approaches in supporting enhanced intelligent network communication in the industries. The researchers are aimed at gathering both primary data and secondary data for the study. The respondents are chosen in the industry so that they can provide better inputs and insights related to the area of research. The key determinants considered for the study are machine learning-influenced management of hotspots, identification of critical congestion points, spectrum availability, and management. The analysis is made using SPSS data analysis package based on which it is noted that all the factors make major influences towards the intelligent communication, and hence machine learning supports critically in enhancing the user experience effectively.

Multi-agent Reinforcement Learning Approach to Enhance Proactive and Resilience System based IoT

“…To begin, the network should be taught to look for patterns in the input data that are not immediately apparent, such as the weights of the connections between nodes in the network. A similar dataset might be used to make predictions about its behavior [11]. The proposed strategy aids agents in anticipating the next move of the opposite party before making an offer.…”

“…It is possible to acquire cooperative behavior without full communication between the controllers using data-driven control schemes based on multi-agent reinforcement learning (RL) with the correct training process and reward function. Reinforcement learning-based multi-agent generation control is recommended in [4] [11] for better control coordination in this case because the ideal actions for all agents are divided into distinct phases. Control performance is constrained by the discretization of actions in traditional Reinforcement learning-based To ensure that limitations are maintained when more than one line is down, an offline training of the multi-agent framework is carried out to determine where and how large shunts are needed.…”

Retracted: Machine Learning-Based Intelligent Wireless Communication System for Solving Real-World Security Issues

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”