Application Of Network Technology Research Articles

The Role and Considerations of Educational Applications in Achieving Educational Resource Equilibrium

After the pandemic, the application and integration of network technology into various educational technologies for teaching purposes are gradually maturing and developing, opening new chapters in the field of education. The rapid development of remote education, online learning, and online training propelled by educational applications has ushered in a new era. The emergence of such educational models also plays a role in promoting the balance of educational resources. Although some regions cannot achieve complete relief due to the lack of access to networks and electronic devices, it marks a positive beginning and suggests new possibilities. Through practical case studies, this article thoroughly analyzes the impact of educational application technologies on the balance of educational resources. It explores the practical challenges that need to be overcome for future development.

Improving Data Aggregation Performance in Wireless Sensor Networks using Software-Defined Networks

In this paper, we present a novel methodology to improve the performance of collection operations in wireless sensor networks by the application of software-defined networking technology on (SD WISE) platform. The conditions for selecting the grouped nodes in the controller were determined by adjusting the weights of the (Dijekstra) algorithm. The grouped nodes that include the node were determined based on the paths chosen by the algorithm. The SDN-WISE platform supports reading the payload of the packet and not just the header, in addition to the possibility of dealing with a packet depending on another packet, and the flexibility to modify the routing tables to achieve the appropriate rules for the proposed aggregation algorithm. The results show a significant reduction in the energy consumed after applying the novel suggested algorithm.

Analysis of the Application of Computer Network Technology in Electronic Information Engineering

Analysis of the Application of Computer Network Technology in Electronic Information Engineering

VeriBypasser: An automatic image verification code recognition system based on CNN

The rapid development of network technology has brought great convenience to people’s daily life in recent years. The application of network technology in finance, economy, and education is flourishing, dramatically improving people’s quality of life and promoting society’s development. However, the development of network technology brought some new technical problems. Authenticating and controlling access to shared resources has become a significant problem, which limits the development of smart cities. Nowadays, most websites use accounts and passwords for authentication. In order to prevent violent scanning, image-based human–machine inspection solutions are widely used on major platforms. Nonetheless, some human–computer verification schemes can be bypassed or attacked by artificial intelligence. This paper proposes an improved generation method of image verification code and an automatic recognition system based on Cellular Neural Network (CNN), which can achieve an accuracy of 85.20% for a mix of the visual reasoning Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) containing 4-bit case-insensitive letters and digits, the slider CAPTCHA containing one slide, and inference puzzle CAPTCHA containing a 4 × 2 grid. A brief evaluation of system threat assignment with such verification approaches based on four machine learning methods is also proposed.

Application of Deep Neural Network and Human-Computer Interaction Technology Based on Multimodal Perception in Art Design

Abstract The essence of art design is the process of emotional interaction with humans. In this paper, emotions are classified using multimodal fusion features, and abstract fusion features are obtained by sampling the multimodal matching tensor using average pooling. The matching fusion matrix in the tensor operator is used to convert from two-modal to multi-modal matching. Virtual interaction model in art design controls the design objectives, and the optimized virtual world is constructed by using virtual reality technology, so as to build an immersive art design model. Finally, a study was conducted to examine the impact of the use of emotion perception and interaction technology in art product design with students from the School of Design and Art, University of G. The results show that the liking degree of three-color matching in the cognitive experiment test is 0.307, which is higher than the liking degree of two-color matching of 0.223, indicating that overall three-color matching samples are more popular in art design. This study provides effective evaluation and guidance for designing art products that are emotional and innovative.

SCD: A Detection System for DDoS Attacks based on SAE-CNN Networks

The pervasive application of network technology has given rise to a numerous of network attacks, including Distributed Denial of Service (DDoS) attacks. DDoS attacks can lead to the collapse of network resources, making the target server unable to support legitimate users, which is a critical issue in cyberspace security. In complex real-world network environments, differentiating DDoS attack traffic from normal traffic is a challenging task, making it significant to effectively distinguish between attack types in order to resist DDoS attacks. However, traditional DDoS attack detection methods have certain limitations in terms of data preprocessing and detection efficiency. In this paper, we propose a lightweight framework based on deep learning called SAE-CNN-Detection (SCD), which combines stacked autoencoder network (SAE) and convolutional neural network (CNN) for DDoS attacks detection. The CIC-DDoS2019 dataset is used to simulate network traffic that has suffered from DDoS attacks, and this system employs adaptive preprocessing techniques for the dataset. The results demonstrate that multi-classification experiment achieves an accuracy of 97.2% for DDoS attack types, while the binary classification experiment achieves an accuracy of 99.1%.

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Nontechnical losses of electrical energy (NTLEE) have been a persistent issue in both the Russian and global electric power industries since the end of the 20th century. Every year, these losses result in tens of billions of dollars in damages. Promptly identifying unscrupulous consumers can prevent the onset of NTLEE sources, substantially reduce the amount of NTLEE and economic damages to network grids, and generally improve the economic climate. The contemporary advancements in machine learning and artificial intelligence facilitate the identification of NTLEE sources through anomaly detection in energy consumption data. This article aims to analyze the current efficacy of computational methods in locating, detecting, and identifying nontechnical losses and their origins, highlighting the application of neural network technologies. Our research indicates that nearly half of the recent studies on identifying NTLEE sources (41%) employ neural networks. The most utilized tools are convolutional networks and autoencoders, the latter being recognized for their high-speed performance. This paper discusses the main metrics and criteria for assessing the effectiveness of NTLEE identification utilized in training and testing phases. Additionally, it explores the sources of initial data, their composition, and their impact on the outcomes of various algorithms.

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight.

There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance.

English Listening Teaching Mode under Artificial Intelligence Speech Synthesis Technology

To discuss the application of artificial intelligence (AI) speech synthesis technology and wireless network technology in English listening teaching, firstly, the present situation of English listening teaching is analyzed. Secondly, a questionnaire is designed to investigate the feasibility of AI speech synthesis technology in English listening teaching. Finally, the control group (a traditional mode of English listening teaching) and experimental group (AI speech synthesis technology) are set up, compared, and analyzed. The survey shows that for natural fluency, speech synthesis has the lowest score. The textbook audio score is the highest, with an average score of 4.23. According to the mean opinion score (MOS), the AI speech synthesis technology belongs to high-quality speech coding. Therefore, the English audio generated by combining AI speech synthesis technology under wireless network technology also has high quality. Meanwhile, 113 people in the survey consider that the clear articulation of the synthesized audio is full marks, which is clearer than the English audio matching the textbook, indicating that students think the English audio of the supporting textbook is poor in the clear articulation, which affects students’ listening. Moreover, the English listening scores of the experimental group are notably better than those of the control group. It is hoped that the traditional teaching mode can be better integrated with wireless networks and AI technology, thereby improving the clarity of English listening, providing mobile learning, and advancing students' English performance and learning efficiency.

Development and application of neural network technology in solving geodynamic problems

The present study is aimed at searching the most effective, easy-to-use method for seismic zoning, in order to consider the ongoing environmental change in real time. The study demonstrates the relevance and necessity of tackling this problem, provides characteristics and peculiarities of current microzonation methods, and discusses the problem of seismic microzoning. The paper highlights traditional and neural network approaches as two directions in solving the problem, and determines their advantages and disadvantages. In addition, the paper provides significant arguments for applying the neural network approach and defines the perspectives for the solution. An intermediate problem of geophysics is suggested to be solved using the neural network approach. The current study results involved searching networks able to provide sufficient accuracy for obtaining pictures of geological sections and conducting analysis of predicted results for a number of neural networks. Multilayer perceptron is considered to give the most reliable results. The future work is supposed to develop an algorithm for building a map of seismic microzonation by means of neural network technology.

Application of wireless sensor network technology based on artificial intelligence in security monitoring system

Abstract The safety monitoring system has been used to monitor and manage engineering safety operation. The application scope of the safety monitoring system is very wide. It has a wide range of applications in the fields of pipeline safety monitoring, electrical safety monitoring and household safety monitoring. This article studied the application process of the household safety monitoring system. Many home safety accidents are caused by inadequate monitoring of safety problems. Therefore, it is very important to establish a household safety monitoring system. Traditional home safety monitoring systems only rely on cameras for safety monitoring, and the traditional home safety monitoring system uses too few sensors. With the continuous development of wireless sensor network (WSN) technology, it is possible to build a sensor node network, but provides real-time information for home security monitoring to the greatest extent. This article compared the home safety monitoring system based on the WSN technology of artificial intelligence (AI) with the traditional home safety monitoring system. The experimental results showed that in the large-scale home environment, the average monitoring accuracy of the traditional home security monitoring system and the home security monitoring system based on the WSN technology of AI was 77.76 and 89.36%, respectively. In the small-scale home environment, the average monitoring accuracy of the traditional home safety monitoring system and the home safety monitoring system based on the WSN technology of AI were 87.63 and 94.43%, respectively. Monitoring accuracy refers to the accuracy of the household safety monitoring system in detecting safety issues. Therefore, the application of the WSN technology based on artificial intelligence to the home safety monitoring system can effectively improve the accuracy of home safety monitoring.

Retracted: Application of Computer and Network Technology in Physical Fitness Work

Retracted: Application of Computer and Network Technology in Physical Fitness Work

Application of wireless network technology in the oral English teaching under the background of 5G mobile communication

Application of wireless network technology in the oral English teaching under the background of 5G mobile communication

Application of Artificial Intelligence Technology and Network Technology in Multimedia Courseware Making Course Education

With the continuous advancement of curriculum reform, how to improve the classroom efficiency and optimize the learning classroom model has become a topic of increasing concern for scholars and teachers. With the development and popularization of computer network, distance education has been widely used. It is in this case that the resource library came into being, which provides rich resources for Liu Xiye to make multimedia courseware. The learning of courses plays a fundamental role in education, which is reflected in modern distance education, that is, how to make courseware better. At the same time, online courses are also getting more and more attention from people. Online educators have higher and higher requirements for the design of each part of online courses. How to design feedback in the learning of online courses is also a must for every designer. Based on artificial intelligence education courseware, this paper introduces some audio and video technologies used in making multimedia CAI (computer-assisted instruction) courseware.

Retracted: Application of Neural Network and Virtual Reality Technology in Digital Video Effects

Retracted: Application of Neural Network and Virtual Reality Technology in Digital Video Effects

Retracted: The Application of 5G Network Technology in the Innovative Development of Physical Education

Retracted: The Application of 5G Network Technology in the Innovative Development of Physical Education

Retracted: Application of Computer Network Technology in Network Music Education System

Retracted: Application of Computer Network Technology in Network Music Education System

Retracted: Application of 5G Network Technology in Basketball Teaching Innovation and Reform

Retracted: Application of 5G Network Technology in Basketball Teaching Innovation and Reform

Digital forensics challenges and readiness for 6G Internet of Things (IoT) networks

AbstractThe development of sixth‐generation (6G) wireless communication technology is expected to provide super high‐speed data transmission, and advanced network performance than the current fifth‐generation (5G) and be fully functional by the 2030s. This development will have a significant impact and add improvements to digital extended reality (XR), autonomous systems, vehicular ad hoc networks (VANETs), artificial intelligence (AI), underwater communications, blockchain technology, pervasive biomedical informatics and smart cities built on the digital infrastructure backbone of the Internet of Things (IoT). The ubiquitous nature of this large‐scale 6G‐enabled IoT that offers faster connectivity capabilities and integrates both terrestrial and non‐terrestrial networks will not only create new data security and privacy issues but also provide a treasure trove of digital evidence useful for digital forensic examiners investigating security incidents and cybercrime. However, for digital forensic examiners, evidence collection, preservation and analysis will become a priority in the successful deployment of 6G IoT networks. In this study, we define key applications of 6G network technology to the Internet of Things and its existing architectures. The survey introduces potential digital forensic challenges and related issues affecting digital forensic investigations specific to 6G IoT networks. Finally, we highlight and discuss forensic readiness and future research directions for identified challenges within the 6G IoT network environments.This article is categorized under: Digital and Multimedia Science > IoT Forensics

Research on Technology-Mediated Task-based Teaching Approach in Oral English Teaching based on the Chinese Context

Task-Based Language Teaching (TBLT) offers great pedagogical value in oral English teaching. However, various challenges have been encountered in China, a common phenomenon of silence in speaking classes is still serious. Thus, implementing TBLT may not effectively foster language processing and conversational interaction in China. However, with the application and development of network technology, many scholars have conducted research on this, proving that combining Task-Based Language Teaching (TBLT) with technology can significantly help learners' oral input and output, and provide rich ways to enhance learners' speaking performance. Therefore, this study aims to synthesis TBLT approach and technology to explore technology-mediated task-based language teaching (TMTBLT) application to strengthen oral English teaching in China.