International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Innovative Analysis of Emblem Ink Mold Pattern Based on Style Transfer Algorithm and 5G Network
Get PDFAiming at the problems of poor pattern innovation analysis and difficult pattern innovation, a pattern innovation method based on Huimo ink mold is proposed. In the process of making the pattern analysis of the emblem ink mold, the complex design process of innovative analysis and design, as well as the massive analysis data, need to be analyzed with the help of intelligent algorithms. At the same time, wireless network technology has less application in Huimo ink molds, which affects the development of traditional Huimo skills. Therefore, this paper proposes an innovative method of emblem ink mold pattern based on a style transfer algorithm, which innovatively designs different styles of emblem ink mold patterns and extracts the framework elements of innovative patterns. Firstly, the style transfer algorithm is used to collect the relevant data for the creation of the ink mold pattern, and the data of different styles are summarized through the 5G network and multimedia network, and the ink mold pattern is carried out according to the style transfer characteristics of division, innovation of innovation. Then, according to the style transfer algorithm, the style transmission is carried out in combination with the multimedia network to promote the feature extraction of the ink mold pattern. The research results show that with the support of multimedia network technology, the style transfer algorithm can improve the innovation level of emblem ink engraving patterns and promote the development of the emblem ink creation process
Automated Speed and Lane Change decision-making Model using Support Vector Machine
Get PDFOne of the major obstacles that the auto industry must overcome is the rise of autonomous vehicles. The study of lane-changing is an important part of this problem. Previous studies on autonomous vehicle lane changes have predominantly focused on lane change path planning and path monitoring, with limited attention given to the autonomous vehicle's lane change decision-making process. This paper introduces a novel Lane Change Decision-Making Model for autonomous vehicles using the Support Vector Machine (SVM) method. The suggested model employs real-time sensor data to assess whether or not a lane change is possible, taking into account the proximity of other vehicles (cars, buses, motorbikes), and adjusting speed as necessary to ensure a seamless transition. Researching the various facets of lane changes in autonomous vehicles allows for decision-making that is grounded in utility, safety, and tolerance. The implementation of a support vector machine (SVM) technique with Bayesian parameter optimization is used to deal with the non-linearity and complexity of the process of autonomous lane change decision-making. Finally, we compare the suggested SVM model against a rule-based lane change model using the test data. The SVM-based strategy is shown to improve lane change decision-making in a comprehensive simulation exercise, which in turn improves the safety and efficiency of autonomous driving systems. The experiment also use a real vehicle to gauge the efficacy of the underlying decision-making model
Optimization Analysis of Two-Dimensional Animation Special Effects Design by Style Transfer Algorithm
Get PDFThe application of 5G communication technology and ultra- wideband technology in animation design has gradually improved the level of animation special effects design, and made the style transfer algorithm a research hotspot. The original two-dimensional animation special effects design cannot solve the problem of special effects optimization, and the special effects after optimization are poor. Therefore, this paper proposes a style transfer algorithm based on 5G communication to optimize and analyze the design of two-dimensional animation special effects. Firstly, ultra-wideband communication technology and animation technology are used to obtain the design parameters of animation special effects, and the design scheme is transformed through style transfer , and judge the special effects scheme according to the animation characteristics, and discard irrelevant 3D information. Then, according to the ultra- wide communication technology, the change rate and display effect of the special effect are analyzed, and compared with the actual reception effect, and adjusted Parameters and indicators for 2D animation special effects design. The special effect design results show that under the conditions of 5G network and ultra-wide communication, the style transfer algorithm can improve the realization effect of animation special effects. The lifting rate is greater than the actual design requirements, which can meet the needs of special effects design
Design and Simulation of Microstrip Patch Antenna Design for Advanced wireless communication using CST Software
No full textA single-layer dual-band small-sized rectangular patch antenna with transmission line feed is designed to implement wireless local area networks (WLAN). The required antenna consists of a rectangular patch with a dielectric material with a dielectric constant of 2.4. The use of cavity model with transmission line feed has the advantages of low profile, high gain and wide bandwidth of the antenna. The research work presents a small-sized wide-gain patch antenna with good gain. A rectangular slot is integrated on the ground metal plane. The study of integrated antennas for 5G wireless communications, operating in K-band and Ka-band frequencies, is discussed our paper. From the simulation, the return loss, gain, radiation efficiency and S-factor show that the return loss is -35.184 dB and -39.527 dB using the slot length (L2 = 12 mm)
Intelligent System For Brain Disease Diagnosis Using Rotation Invariant Features And Fuzzy Neural Network
Get PDFThe characteristic features of the magnetic resonant image (MRI) for Alzheimer’s patient’s brain image and normal image can be distinguished in terms of dimensional features with the help of wavelet decomposition. From the literature review, it is observed that when datasets used are a combination of the MR images having a very mild cognitive impairment and mild cognitive impairment, the performance of the classifier reduces. Because the features of this kind of MR image are difficult to distinguish from normal brain images. To solve this problem, the lossless feature extraction method along with the feature reduction method having a selection approach is suggested as a solution here. In this paper, the 12 directional, rotation invariant two-dimensional discrete-time continuous wavelet transform (R-DTCWT) and a genetic algorithm (GA) are used for feature selection and feature vector size reduction. The fuzzy neural network (FNN) which is suitable for pattern recognition is used here. The FNN with and without feature reduction is evaluated for identification of combinational dataset, shows satisfactory performance over an artificial neural network (ANN), probabilistic neural network (PNN) classifiers. This method is compared with other state of algorithm to prove the enhanced performanc
A Blockchain Framework for Preserving Music Intellectual Property Rights
Get PDFThe continuous strengthening of intellectual property protection has made the analysis of music intellectual property framework a research hotspot, and also promoted the integration of blockchain and intellectual property. The original IP protection framework could not solve the problem of music IP protection, and IP protection was ineffective. Therefore, this paper proposes a framework based on blockchain technology to analyze the protection of music intellectual property rights. Firstly, the blockchain server is used to store the music intellectual property rights, and the continuity judgment is made according to the characteristics of the intellectual property data to form the block data of the time series. Then, according to the intellectual property results stored in each server, the blockchain framework with different protection levels is compared with the intellectual property protection requirements. After simulation test and analysis, the framework based on blockchain technology can improve the security of music intellectual property data, reduce the tampering rate of property rights data, remove the centralization of intellectual property rights, and simplify the intellectual property protection process
An Improved and Optimized Gated Recurrent Unit and Long Short-Term Memory Model for Fake News Detection
Get PDFThis study presents a novel approach for detecting counterfeit news, employing an advanced hybrid model that integrates Enhanced Gated Recurrent Unit and Long Short-Term Memory networks, termed as IGRU-LSTM. Initially, the database is assembled from the Information Security and Object Technology (ISOT) database and Wikipedia databases. From the database, the real and fake news is detected by considering news reviews. The dataset may contain unwanted information line URLs and symbols, which should be corrected to achieve efficient fake news detection. So, the pre-processing technique should be considered such as special symbol removal, URLS removal, upper to lower case conversion and replace contractions. After that, the pre-processed data is sent to the embedding procedure for word embedding. Finally, the IGRU-LSTM classifier is utilized for classifying real and fake news detection. In the combined GRU-LSTM framework, we incorporate the Enhanced Wild Horse Optimisation (EWHO) algorithm to optimize the selection of optimal weighting parameters. We utilize MATLAB for implementing this method. To evaluate the effectiveness of our approach, we analyze key performance metrics like precision, recall, and accuracy, and compare them with established methods including CNN-PSO, CNN-FO, and standard CNN
Analysis of Regional Characteristics of Jinnan Folk Paper-cutting by Dynamic Programming Algorithm
Get PDFThe role of regional characteristics analysis in the development of folk paper-cutting in southern Jinnan makes folk paper-cutting change regionally, and also makes Jinnan folk paper-cutting a hot spot. However, in the process of analyzing the folk paper-cutting area, there are problems such as poor analysis effect and a small amount of analysis data. The main reason is that the wireless network technology in the southern Jinnan region is backward, which restricts the development of folk paper cutting. Therefore, this paper proposes a folk paper-cutting feature analysis method based on a dynamic programming method to plan the characteristics of folklore paper-cutting in different regions. Firstly, collaborative wireless communication technology is used to collect folk paper-cutting data, and the data of different regions are summarized by dynamic programming method, and the regional division of papercutting is carried out according to the characteristics of folklore, leaving common characteristics. Then, according to dynamic call wireless communication technology, the transmission of regional characteristics is carried out to promote the integration of the characteristics of folk paper cutting. The results of the regional characteristic analysis show that with the support of collaborative wireless communication, the dynamic programming method can improve the level of folk paper-cutting in southern Jinnan, and promote the development of paper-cutting culture by using collaborative wireless network communication, can meet the requirements of Jinnan cultural construction
Restrictive Voting Technique for Faces Spoofing Attack
Get PDFFace anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications
Hybrid Intrusion Detection Model for Enhancing the Security and Reducing the Computational Cost
Get PDFArtificial Intelligence (AI) is becoming essential technology in Cybersecurity. It represents a revolution in detecting and analyzing intrusions based on predictive models and classification methods. Various recent studies discussed the applications of artificial intelligence in Intrusion Detection Systems to improve the accuracy of the classifiers in detecting cyber-attacks but ignored the computational cost of running the algorithm which is considered a crucial factor of the model evaluation. The aim of this paper is to solve this security issue by using dimensionality reduction techniques and machine learning algorithms. To raise their effectiveness and thus enhance network security, a hybrid classifier with high accuracy and low computational cost is proposed. It combines Decision Tree (DT) and Linear Regression (LR) techniques with AdaBoost technique to build a powerful model for detecting cyber-attacks. The hybrid model included 5 stages, (i) selecting and analyzing the dataset, (ii) pre-processing it, (iii) reducing the dimensions using the Principal Component Analysis (PCA), (iv) classifying stage and (v) evaluating the model using the dataset UNSW-NB15. The model has been compared with several state-of-the-art algorithms. The results have shown that the proposed hybrid model achieved a high accuracy (99%) and the runtime was significantly reduced by half using PCA principle