Computers, materials & continua最新文献_第2页

Honeypot Game Theory against DoS Attack in UAV Cyber 针对无人机网络DoS攻击的蜜罐博弈理论

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.037257

Shangting Miao, Yang Li, Quan Pan

{"title":"Honeypot Game Theory against DoS Attack in UAV Cyber","authors":"Shangting Miao, Yang Li, Quan Pan","doi":"10.32604/cmc.2023.037257","DOIUrl":"https://doi.org/10.32604/cmc.2023.037257","url":null,"abstract":"A space called Unmanned Aerial Vehicle (UAV) cyber is a new environment where UAV, Ground Control Station (GCS) and business processes are integrated. Denial of service (DoS) attack is a standard network attack method, especially suitable for attacking the UAV cyber. It is a robust security risk for UAV cyber and has recently become an active research area. Game theory is typically used to simulate the existing offensive and defensive mechanisms for DoS attacks in a traditional network. In addition, the honeypot, an effective security vulnerability defense mechanism, has not been widely adopted or modeled for defense against DoS attack UAV cyber. With this motivation, the current research paper presents a honeypot game theory model that considers GCS and DoS attacks, which is used to study the interaction between attack and defense to optimize defense strategies. The GCS and honeypot act as defenses against DoS attacks in this model, and both players select their appropriate methods and build their benefit function models. On this basis, a hierarchical honeypot and G2A network delay reward strategy are introduced so that the defender and the attacker can adjust their respective strategies dynamically. Finally, by adjusting the degree of camouflage of the honeypot for UAV network services, the overall revenue of the defender can be effectively improved. The proposed method proves the existence of a mixed strategy Nash equilibrium and compares it with the existing research on no delay rewards and no honeypot defense scheme. In addition, this method realizes that the UAV cyber still guarantees a network delay of about ten milliseconds in the presence of a DoS attack. The results demonstrate that our methodology is superior to that of previous studies.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CF-BFT: A Dual-Mode Byzantine Fault-Tolerant Protocol Based on Node Authentication 基于节点认证的双模拜占庭容错协议CF-BFT

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.040600

Zhiruo Zhang, Feng Wang, Yang Liu, Yang Lu, Xinlei Liu

{"title":"CF-BFT: A Dual-Mode Byzantine Fault-Tolerant Protocol Based on Node Authentication","authors":"Zhiruo Zhang, Feng Wang, Yang Liu, Yang Lu, Xinlei Liu","doi":"10.32604/cmc.2023.040600","DOIUrl":"https://doi.org/10.32604/cmc.2023.040600","url":null,"abstract":"The consensus protocol is one of the core technologies in blockchain, which plays a crucial role in ensuring the block generation rate, consistency, and safety of the blockchain system. Blockchain systems mainly adopt the Byzantine Fault Tolerance (BFT) protocol, which often suffers from slow consensus speed and high communication consumption to prevent Byzantine nodes from disrupting the consensus. In this paper, this paper proposes a new dual-mode consensus protocol based on node identity authentication. It divides the consensus process into two subprotocols: Check_BFT and Fast_BFT. In Check_BFT, the replicas authenticate the primary’s identity by monitoring its behaviors. First, assume that the system is in a pessimistic environment, Check_BFT protocol detects whether the current environment is safe and whether the primary is an honest node; Enter the fast consensus stage after confirming the environmental safety, and implement Fast_BFT protocol. It is assumed that there are nodes in total. If more than nodes identify that the primary is honest, it will enter the Fast_BFT process. In Fast_BFT, the primary is allowed to handle transactions alone, and the replicas can only receive the messages sent by the primary. The experimental results show that the CF-BFT protocol significantly reduces the communication overhead and improves the throughput and scalability of the consensus protocol. Compared with the SAZyzz protocol, the throughput is increased by 3 times in the best case and 60% in the worst case.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation 基于临床知识的混合Swin变压器用于脑肿瘤分割

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.042069

Xiaoliang Lei, Xiaosheng Yu, Hao Wu, Chengdong Wu, Jingsi Zhang

{"title":"Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation","authors":"Xiaoliang Lei, Xiaosheng Yu, Hao Wu, Chengdong Wu, Jingsi Zhang","doi":"10.32604/cmc.2023.042069","DOIUrl":"https://doi.org/10.32604/cmc.2023.042069","url":null,"abstract":"Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging (MRI) imaging is crucial in the pre-surgical planning of brain tumor malignancy. MRI images’ heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging. Furthermore, recent studies have yet to fully employ MRI sequences’ considerable and supplementary information, which offers critical a priori knowledge. This paper proposes a clinical knowledge-based hybrid Swin Transformer multimodal brain tumor segmentation algorithm based on how experts identify malignancies from MRI images. During the encoder phase, a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a Convolutional Neural Network (CNN)-based backbone to represent local features have been constructed. Instead of directly connecting all the MRI sequences, the proposed method re-organizes them and splits them into two groups based on MRI principles and characteristics: T1 and T1ce, T2 and Flair. These aggregated images are received by the dual-stem Swin Transformer-based encoder branch, and the multimodal sequence-interacted cross-attention module (MScAM) captures the interactive information between two sets of linked modalities in each stage. In the CNN-based encoder branch, a triple down-sampling module (TDsM) has been proposed to balance the performance while downsampling. In the final stage of the encoder, the feature maps acquired from two branches are concatenated as input to the decoder, which is constrained by MScAM outputs. The proposed method has been evaluated on datasets from the MICCAI BraTS2021 Challenge. The results of the experiments demonstrate that the method algorithm can precisely segment brain tumors, especially the portions within tumors.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Layer Deep Sparse Representation for Biological Slice Image Inpainting 生物切片图像的多层深度稀疏表示

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.041416

Haitao Hu, Hongmei Ma, Shuli Mei

{"title":"Multi-Layer Deep Sparse Representation for Biological Slice Image Inpainting","authors":"Haitao Hu, Hongmei Ma, Shuli Mei","doi":"10.32604/cmc.2023.041416","DOIUrl":"https://doi.org/10.32604/cmc.2023.041416","url":null,"abstract":"Biological slices are an effective tool for studying the physiological structure and evolution mechanism of biological systems. However, due to the complexity of preparation technology and the presence of many uncontrollable factors during the preparation processing, leads to problems such as difficulty in preparing slice images and breakage of slice images. Therefore, we proposed a biological slice image small-scale corruption inpainting algorithm with interpretability based on multi-layer deep sparse representation, achieving the high-fidelity reconstruction of slice images. We further discussed the relationship between deep convolutional neural networks and sparse representation, ensuring the high-fidelity characteristic of the algorithm first. A novel deep wavelet dictionary is proposed that can better obtain image prior and possess learnable feature. And multi-layer deep sparse representation is used to implement dictionary learning, acquiring better signal expression. Compared with methods such as NLABH, Shearlet, Partial Differential Equation (PDE), K-Singular Value Decomposition (K-SVD), Convolutional Sparse Coding, and Deep Image Prior, the proposed algorithm has better subjective reconstruction and objective evaluation with small-scale image data, which realized high-fidelity inpainting, under the condition of small-scale image data. And the -level time complexity makes the proposed algorithm practical. The proposed algorithm can be effectively extended to other cross-sectional image inpainting problems, such as magnetic resonance images, and computed tomography images.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model 用ElecBERT语言模型改进Twitter基于选举对话的情感分析

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.041520

Asif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Maqbool Khan

{"title":"Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model","authors":"Asif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Maqbool Khan","doi":"10.32604/cmc.2023.041520","DOIUrl":"https://doi.org/10.32604/cmc.2023.041520","url":null,"abstract":"Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics. In recent years, the rise of social media platforms (SMPs) has provided a rich source of data for analyzing public opinions, particularly in the context of election-related conversations. Nevertheless, sentiment analysis of election-related tweets presents unique challenges due to the complex language used, including figurative expressions, sarcasm, and the spread of misinformation. To address these challenges, this paper proposes Election-focused Bidirectional Encoder Representations from Transformers (ElecBERT), a new model for sentiment analysis in the context of election-related tweets. Election-related tweets pose unique challenges for sentiment analysis due to their complex language, sarcasm, and misinformation. ElecBERT is based on the Bidirectional Encoder Representations from Transformers (BERT) language model and is fine-tuned on two datasets: Election-Related Sentiment-Annotated Tweets (ElecSent)-Multi-Languages, containing 5.31 million labeled tweets in multiple languages, and ElecSent-English, containing 4.75 million labeled tweets in English. The model outperforms other machine learning models such as Support Vector Machines (SVM), Naïve Bayes (NB), and eXtreme Gradient Boosting (XGBoost), with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages, and an accuracy of 0.9930 and F1-score of 0.9899 on ElecSent-English. The performance of different models was compared using the 2020 United States (US) Presidential Election as a case study. The ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet, with the ElecBERT-English model achieving a Mean Absolute Error (MAE) of 6.13. This paper presents a valuable contribution to sentiment analysis in the context of election-related tweets, with potential applications in political analysis, social media management, and policymaking.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification 基于CNN特征提取与选择的EPO优化棉花叶病分类

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.035860

Mehwish Zafar, Javeria Amin, Muhammad Sharif, Muhammad Almas Anjum, Seifedine Kadry, Jungeun Kim

{"title":"CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification","authors":"Mehwish Zafar, Javeria Amin, Muhammad Sharif, Muhammad Almas Anjum, Seifedine Kadry, Jungeun Kim","doi":"10.32604/cmc.2023.035860","DOIUrl":"https://doi.org/10.32604/cmc.2023.035860","url":null,"abstract":"Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized features fusion-based model is proposed, in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features, each model extracts the feature vector of length N 1000. After that, the extracted features are serially concatenated having a feature vector length N 2000. The most prominent features are selected using Emperor Penguin Optimizer (EPO) method. The method is evaluated on two publically available datasets, such as Kaggle cotton disease dataset-I, and Kaggle cotton-leaf-infection-II. The EPO method returns the feature vector of length 1 755, and 1 824 using dataset-I, and dataset-II, respectively. The classification is performed using 5, 7, and 10 folds cross-validation. The Quadratic Discriminant Analysis (QDA) classifier provides an accuracy of 98.9% on 5 fold, 98.96% on 7 fold, and 99.07% on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor (KNN) provides 99.16% on 5 fold, 98.99% on 7 fold, and 99.27% on 10 fold using Kaggle cotton-leaf-infection dataset-II.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph-Based Feature Learning for Cross-Project Software Defect Prediction 跨项目软件缺陷预测的基于图的特征学习

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.043680

Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Redhwan Algabri, Sungon Lee

引用次数: 0

Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors 基于深度学习和智能可穿戴物联网传感器的新型冠状病毒智能监测框架

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.038757

Fadhil Mukhlif, Norafida Ithnin, Roobaea Alroobaea, Sultan Algarni, Wael Y. Alghamdi, Ibrahim Hashem

{"title":"Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors","authors":"Fadhil Mukhlif, Norafida Ithnin, Roobaea Alroobaea, Sultan Algarni, Wael Y. Alghamdi, Ibrahim Hashem","doi":"10.32604/cmc.2023.038757","DOIUrl":"https://doi.org/10.32604/cmc.2023.038757","url":null,"abstract":"The World Health Organization (WHO) refers to the 2019 new coronavirus epidemic as COVID-19, and it has caused an unprecedented global crisis for several nations. Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks, which were previously only experienced by Chinese residents. Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems. Every time the pandemic surprises them by providing new values for various parameters, all the connected research groups strive to understand the behavior of the pandemic to determine when it will stop. The prediction models in this research were created using deep neural networks and Decision Trees (DT). DT employs the support vector machine method, which predicts the transition from an initial dataset to actual figures using a function trained on a model. Extended short-term memory networks (LSTMs) are a special sort of recurrent neural network (RNN) that can pick up on long-term dependencies. As an added bonus, it is helpful when the neural network can both recall current events and recall past events, resulting in an accurate prediction for COVID-19. We provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring framework. We developed a data analysis methodology, including data preparation and dataset splitting. We examine two popular algorithms, LSTM and Decision tree on the official datasets. Moreover, we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the pandemic. Key issues and challenges are discussed for future improvement. It is expected that the results these methods provide for the Health Scenario would be reliable and credible.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data 基于深度学习的树木病害识别与分类研究

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.037958

Uzair Aslam Bhatti, Sibghat Ullah Bazai, Shumaila Hussain, Shariqa Fakhar, Chin Soon Ku, Shah Marjan, Por Lip Yee, Liu Jing

{"title":"Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data","authors":"Uzair Aslam Bhatti, Sibghat Ullah Bazai, Shumaila Hussain, Shariqa Fakhar, Chin Soon Ku, Shah Marjan, Por Lip Yee, Liu Jing","doi":"10.32604/cmc.2023.037958","DOIUrl":"https://doi.org/10.32604/cmc.2023.037958","url":null,"abstract":"Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases. The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes. Through rigorous training and evaluation, the proposed system achieved an impressive accuracy rate of 99%. This mobile application serves as a convenient and valuable advisory tool, providing early detection and guidance in real agricultural environments. The significance of this research lies in its potential to revolutionize plant disease detection and management practices. By automating the identification process through deep learning algorithms, the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise. The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solving Arithmetic Word Problems of Entailing Deep Implicit Relations by Qualia Syntax-Semantic Model 用Qualia语法语义模型求解深层隐含关系的算术词问题

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.041508

Hao Meng, Xinguo Yu, Bin He, Litian Huang, Liang Xue, Zongyou Qiu

{"title":"Solving Arithmetic Word Problems of Entailing Deep Implicit Relations by Qualia Syntax-Semantic Model","authors":"Hao Meng, Xinguo Yu, Bin He, Litian Huang, Liang Xue, Zongyou Qiu","doi":"10.32604/cmc.2023.041508","DOIUrl":"https://doi.org/10.32604/cmc.2023.041508","url":null,"abstract":"Solving arithmetic word problems that entail deep implicit relations is still a challenging problem. However, significant progress has been made in solving Arithmetic Word Problems (AWP) over the past six decades. This paper proposes to discover deep implicit relations by qualia inference to solve Arithmetic Word Problems entailing Deep Implicit Relations (DIR-AWP), such as entailing commonsense or subject-domain knowledge involved in the problem-solving process. This paper proposes to take three steps to solve DIR-AWPs, in which the first three steps are used to conduct the qualia inference process. The first step uses the prepared set of qualia-quantity models to identify qualia scenes from the explicit relations extracted by the Syntax-Semantic (S<sup>2</sup>) method from the given problem. The second step adds missing entities and deep implicit relations in order using the identified qualia scenes and the qualia-quantity models, respectively. The third step distills the relations for solving the given problem by pruning the spare branches of the qualia dependency graph of all the acquired relations. The research contributes to the field by presenting a comprehensive approach combining explicit and implicit knowledge to enhance reasoning abilities. The experimental results on Math23K demonstrate hat the proposed algorithm is superior to the baseline algorithms in solving AWPs requiring deep implicit relations.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0