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Periodic analysis of scenic spot passenger flow based on combination neural network prediction model 基于组合神经网络预测模型的景区客流周期性分析
IF 3 Q2 Computer Science Pub Date : 2024-01-01 DOI: 10.1515/jisys-2023-0158
Fang Yin
To prevent in a short time the rapid increase of tourists and corresponding traffic restriction measures’ lack in scenic areas, this study established a prediction model based on an improved convolutional neural network (CNN) and long- and short-term memory (LSTM) combined neural network. The study used this to predict the inflow and outflow of tourists in scenic areas. The model uses a residual unit, batch normalization, and principal component analysis to improve the CNN. The experimental results show that the model works best when batches’ quantity is 10, neurons’ quantity in the LSTM layer is 50, and the number of iterations is 50 on a workday; on non-working days, it is best to choose 10, 100, or 50. Using root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) as evaluation indicators, the inflow and outflow RMSEs of this study model are 82.51 and 89.80, MAEs are 26.92 and 30.91, NRMSEs are 3.99 and 3.94, and MAPEs are 1.55 and 1.53. Among the various models, this research model possesses the best prediction function. This provides a more accurate prediction method for the prediction of visitors’ flow rate in scenic spots. Meanwhile, the research model is also conducive to making corresponding flow-limiting measures to protect the ecology of the scenic area.
为了在短时间内防止景区游客的快速增长和相应交通限制措施的缺失,本研究建立了一个基于改进的卷积神经网络(CNN)和长短期记忆(LSTM)组合神经网络的预测模型。研究以此来预测景区游客的流入和流出情况。该模型使用残差单元、批量归一化和主成分分析来改进 CNN。实验结果表明,在工作日,当批次数量为 10、LSTM 层神经元数量为 50、迭代次数为 50 时,模型效果最佳;在非工作日,最好选择 10、100 或 50。以均方根误差(RMSE)、归一化均方根误差(NRMSE)、平均绝对百分比误差(MAPE)和平均绝对误差(MAE)为评价指标,本研究模型的流入和流出均方根误差为 82.51 和 89.80,MAE 为 26.92 和 30.91,NRMSE 为 3.99 和 3.94,MAPE 为 1.55 和 1.53。在各种模型中,本研究模型的预测功能最佳。这为景区游客流量预测提供了一种更为准确的预测方法。同时,该研究模型也有利于制定相应的限流措施,保护景区生态环境。
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引用次数: 0
Online English writing teaching method that enhances teacher–student interaction 加强师生互动的在线英语写作教学方法
IF 3 Q2 Computer Science Pub Date : 2024-01-01 DOI: 10.1515/jisys-2023-0235
Yaqiu Jiang
A significant component of the online learning platform is the online exercise assessment system, which has access to a wealth of past student exercise data that may be used for data mining research. However, the data from the present online exercise system is not efficiently used, making each exercise less relevant for students and decreasing their interest and interaction with the teacher as she explains the activities. In light of this, this research creates an exercise knowledge map based on the connections between workouts, knowledge points, and previous tournaments. The neural matrix was then improved using cross-feature sharing and feature augmentation units to deconstruct the workout recommendation model. The study also developed an interactive text sentiment analysis model based on the expansion of the self-associative word association network to assess how students interacted after the introduction of the personalized exercise advice teaching approach. The outcomes demonstrated that the suggested model’s mean diversity value at completion was 0.93, an increase of 0.14 and 0.23 over collaborative filtering algorithm and DeepFM (deep factor decompose modle), respectively, and that the proposed model’s final convergence value was 92.3%, an improvement of 2.3 and 4.1% over the latter two models. The extended model used in the study outperformed the support vector machine (SVM) and Random Forest models in terms of accuracy by 5.9 and 1.7%, respectively. In terms of F1 value indicator, the model proposed by the research has a value of 90.4%, which is 2.5 and 2.1% higher than the SVM model and Random Forest model; in terms of recall rate indicators, the model proposed by the research institute has a value of 94.3%, which is an increase of 6.2 and 9.8% compared to the latter two models. This suggests that the study’s methodology has some application potential and is advantageous in terms of customized recommendation and interactive sentiment recognition.
在线学习平台的一个重要组成部分是在线练习评估系统,该系统可以访问大量过去的学生练习数据,这些数据可用于数据挖掘研究。然而,目前在线练习系统中的数据并没有得到有效利用,使得每个练习与学生的相关性降低,也降低了学生的兴趣以及在教师讲解活动时与教师的互动。有鉴于此,本研究根据练习、知识点和以往比赛之间的联系创建了练习知识图谱。然后利用交叉特征共享和特征增强单元改进神经矩阵,解构锻炼推荐模型。该研究还开发了基于自关联词联想网络扩展的交互式文本情感分析模型,以评估学生在引入个性化锻炼建议教学方法后的互动情况。研究结果表明,建议模型完成时的平均多样性值为 0.93,比协同过滤算法和 DeepFM(深度因子分解模型)分别提高了 0.14 和 0.23;建议模型的最终收敛值为 92.3%,比后两种模型分别提高了 2.3 和 4.1%。研究中使用的扩展模型的准确率分别比支持向量机(SVM)和随机森林模型高出 5.9% 和 1.7%。在F1值指标方面,研究提出的模型值为90.4%,比SVM模型和随机森林模型分别高出2.5%和2.1%;在召回率指标方面,研究所提出的模型值为94.3%,比后两种模型分别提高了6.2%和9.8%。这表明该研究方法具有一定的应用潜力,在定制化推荐和交互式情感识别方面具有优势。
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引用次数: 0
Neural network big data fusion in remote sensing image processing technology 遥感图像处理技术中的神经网络大数据融合
IF 3 Q2 Computer Science Pub Date : 2024-01-01 DOI: 10.1515/jisys-2023-0147
Xiaobo Wu
Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks.
遥感(RS)图像处理在过去几年中取得了重大进展,但仍面临一些问题,如大规模 RS 图像数据处理困难、复杂背景识别困难、处理精度和效率低等。为了改善 RS 图像处理中存在的问题,本研究将 ConvNext-卷积神经网络(CNN)与大数据(BD)并行处理。此外,它还将现有的 RS 图像处理与高维数据分析等技术相结合。在此过程中,大数据并行处理和高维数据分析技术改善了大规模 RS 图像数据在预处理阶段处理难度大、效率低的问题。ConvNext-CNN 优化了 RS 图像处理中的特征提取和物体检测两大模块,改善了复杂背景识别的难题,提高了 RS 图像处理的精度。同时,通过实验分析了神经网络(NN)和北斗融合后的 RS 图像处理技术与传统 RS 图像处理技术在多方面的性能。本研究采用传统的 RS 图像处理技术以及与神经网络和北斗相结合的 RS 图像处理技术处理了 2328 个样本数据集。传统 RS 图像处理技术的图像处理准确率和召回率分别为 81%和 82%,F1 值约为 0.81(F1 值是准确率和召回率的调和平均值,是结合准确率和召回率来评价结果质量的指标,F1 值越高,表明检索系统的整体性能越好)。集成了 NN 和 BD 的 RS 图像处理技术的准确率和召回率分别为 97% 和 98%,其 F1 值约为 0.97。经过对这些实验过程和最终输出结果的分析,可以确定结合了 NN 和 BD 的 RS 图像处理技术可以改善大规模数据处理困难、复杂背景下识别困难、处理精度和效率低等问题。在本研究中,与 NN 和 BD 技术相结合的 RS 图像处理技术在 NN 和 BD 技术的帮助下具有更强的适应性,可以调整参数,可以应用于更多的任务中。
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引用次数: 0
Research on the construction and reform path of online and offline mixed English teaching model in the internet era 互联网时代线上线下混合式英语教学模式的构建与改革路径研究
IF 3 Q2 Computer Science Pub Date : 2024-01-01 DOI: 10.1515/jisys-2023-0230
Ying Lan
The Internet era resulted in the rise and advancement of MOOK, WeChat, and mobile networks, making it possible to expand English teaching methods. However, the English teaching industry has the problem of not valuing students’ personalized cognition, and the accuracy of teaching resource delivery is low. Therefore, the research uses the noise gate analysis method to design a cognitive diagnostic model for students and designs an English teaching resource recommendation model in view of a convolutional joint probability matrix (JPM) decomposition algorithm. The research results showed that the cognitive diagnostic model designed in the study had a higher accuracy. Compared to traditional algorithms, the overall recommendation effect of the English teaching resource recommendation model had an average improvement of 11.63% and compared to the JPM algorithm combined with cognitive diagnosis (CD), the overall recommendation effect value had an average improvement of 1.977%. When recommending complex teaching resources, the recommendation effect value had an average improvement of 11.54% compared to traditional algorithms, and the overall average improvement was 1.877% compared to the JPM algorithm combined with CD. In the experimental group, with the assistance of the research algorithm, students’ grades improved by an average of 2.38 points, which was significantly higher than the 0.89 points in the control group. The experiment showcases that the CD and recommendation model designed by the research has higher accuracy, can help improve the efficiency of teaching resource recommendation, reduces teaching costs, and has certain application value.
互联网时代催生了MOOK、微信、移动网络的兴起与发展,使英语教学方式的拓展成为可能。然而,英语教学行业存在不重视学生个性化认知、教学资源投放精准度低等问题。因此,本研究利用噪声门分析方法设计了学生认知诊断模型,并针对卷积联合概率矩阵(JPM)分解算法设计了英语教学资源推荐模型。研究结果表明,本研究设计的认知诊断模型具有较高的准确性。与传统算法相比,英语教学资源推荐模型的整体推荐效果平均提高了11.63%,与结合认知诊断(CD)的JPM算法相比,整体推荐效果值平均提高了1.977%。在推荐复杂教学资源时,与传统算法相比,推荐效果值平均提高了 11.54%,与结合 CD 的 JPM 算法相比,总体平均提高了 1.877%。在实验组中,在研究算法的帮助下,学生的成绩平均提高了 2.38 分,明显高于对照组的 0.89 分。实验表明,该研究设计的 CD 和推荐模型具有较高的准确性,有助于提高教学资源推荐的效率,降低教学成本,具有一定的应用价值。
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引用次数: 0
Improved rapidly exploring random tree using salp swarm algorithm 使用 salp 蜂群算法改进快速探索随机树
IF 3 Q2 Computer Science Pub Date : 2024-01-01 DOI: 10.1515/jisys-2023-0219
Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Ahmed T. Sadiq
Due to the limitations of the initial rapidly exploring random tree (RRT) algorithm, robotics faces challenges in path planning. This study proposes the integration of the metaheuristic salp swarm algorithm (SSA) to enhance the RRT algorithm, resulting in a new algorithm termed IRRT-SSA. The IRRT-SSA addresses issues inherent in the original RRT, enhancing efficiency and path-finding capabilities. A detailed explanation of IRRT-SSA is provided, emphasizing its distinctions from the core RRT. Comprehensive insights into parameterization and algorithmic processes contribute to a thorough understanding of its implementation. Comparative analysis demonstrates the superior performance of IRRT-SSA over the basic RRT, showing improvements of approximately 49, 54, and 54% in average path length, number of nodes, and number of iterations, respectively. This signifies the enhanced effectiveness of the proposed method. Theoretical and practical implications of IRRT-SSA are highlighted, particularly its influence on practical robotic applications, serving as an exemplar of tangible benefits.
由于初始快速探索随机树(RRT)算法的局限性,机器人在路径规划方面面临着挑战。本研究提出将元启发式萨尔普群算法(SSA)集成到 RRT 算法中,从而产生了一种称为 IRRT-SSA 的新算法。IRRT-SSA 解决了原始 RRT 中固有的问题,提高了效率和寻路能力。本文详细解释了 IRRT-SSA,强调了它与核心 RRT 的区别。对参数化和算法过程的全面了解有助于深入理解其实施。对比分析表明,IRRT-SSA 的性能优于基本 RRT,在平均路径长度、节点数和迭代次数方面分别提高了约 49%、54% 和 54%。这表明所提出的方法更加有效。报告强调了 IRRT-SSA 的理论和实践意义,特别是它对实际机器人应用的影响,并以实例说明了它的切实益处。
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引用次数: 0
Real-time semantic segmentation based on BiSeNetV2 for wild road 基于 BiSeNetV2 的野外道路实时语义分割
IF 3 Q2 Computer Science Pub Date : 2024-01-01 DOI: 10.1515/jisys-2023-0205
Honghuan Chen, Xiaoke Lan
State-of-the-art segmentation models have shown great performance in structured road segmentation. However, these models are not suitable for the wild roads, which are highly unstructured. To tackle the problem of real-time semantic segmentation of wild roads, we propose a Multi-Information Concatenate Network based on BiSeNetV2 and construct a segmentation dataset Dalle Molle institute for artificial intelligence feature segmentation (IDSIAFS) based on Dalle Molle institute for artificial intelligence. The proposed model removes structural redundancy and optimizes the semantic branch based on BiSeNetV2. Moreover, the Dual-Path Semantic Inference Layer (TPSIL) reduces computation by designing the channel dimension of the semantic branch feature map and aggregates feature maps of different depths. Finally, the segmentation results are achieved by fusing both shallow detail information and deep semantic information. Experiments on the IDSIAFS dataset demonstrate that our proposed model achieves an 89.5% Intersection over Union. The comparative experiments on Cityscapes and India driving dataset benchmarks show that proposed model achieves good inference accuracy and faster inference speed.
最先进的分割模型在结构化道路分割方面表现出色。然而,这些模型并不适合高度非结构化的野外道路。为了解决野外道路的实时语义分割问题,我们提出了基于 BiSeNetV2 的多信息串联网络,并构建了基于 Dalle Molle 人工智能研究所特征分割(IDSIAFS)的分割数据集。所提出的模型基于 BiSeNetV2 消除了结构冗余并优化了语义分支。此外,双路径语义推理层(TPSIL)通过设计语义分支特征图的通道维度和聚合不同深度的特征图来减少计算量。最后,通过融合浅层细节信息和深层语义信息实现分割结果。在 IDSIAFS 数据集上的实验表明,我们提出的模型实现了 89.5% 的交叉率(Intersection over Union)。在城市景观和印度驾驶数据集基准上进行的对比实验表明,所提出的模型具有良好的推理准确性和更快的推理速度。
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引用次数: 0
Validation of machine learning ridge regression models using Monte Carlo, bootstrap, and variations in cross-validation 验证机器学习岭回归模型使用蒙特卡罗,bootstrap和交叉验证的变化
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0224
Robbie T. Nakatsu
Abstract In recent years, there have been several calls by practitioners of machine learning to provide more guidelines on how to use its methods and techniques. For example, the current literature on resampling methods is confusing and sometimes contradictory; worse, there are sometimes no practical guidelines offered at all. To address this shortcoming, a simulation study was conducted that evaluated ridge regression models fitted on five real-world datasets. The study compared the performance of four resampling methods, namely, Monte Carlo resampling, bootstrap, k-fold cross-validation, and repeated k-fold cross-validation. The goal was to find the best-fitting λ (regularization) parameter that would minimize mean squared error, by using nine variations of these resampling methods. For each of the nine resampling variations, 1,000 runs were performed to see how often a good fit, average fit, and poor fit λ value would be chosen. The resampling method that chose good fit values the greatest number of times was deemed the best method. Based on the results of the investigation, three general recommendations are made: (1) repeated k-fold cross-validation is the best method to select as a general-purpose resampling method; (2) k = 10 folds is a good choice in k-fold cross-validation; (3) Monte Carlo and bootstrap are underperformers, so they are not recommended as general-purpose resampling methods. At the same time, no resampling method was found to be uniformly better than the others.
近年来,机器学习从业者多次呼吁提供更多关于如何使用机器学习方法和技术的指导方针。例如,目前关于重采样方法的文献是混乱的,有时甚至是矛盾的;更糟糕的是,有时根本没有提供实用的指导方针。为了解决这一缺点,进行了一项模拟研究,评估了在五个真实数据集上拟合的脊回归模型。研究比较了蒙特卡罗重采样、自举、k-fold交叉验证和重复k-fold交叉验证四种重采样方法的性能。目标是通过使用这些重采样方法的九种变体,找到将均方误差最小化的最佳拟合λ(正则化)参数。对于9个重新采样变量中的每一个,执行1,000次运行,以查看选择良好拟合、平均拟合和差拟合λ值的频率。选择拟合值次数最多的重采样方法为最佳方法。根据调查结果,提出了三个一般性建议:(1)重复k-fold交叉验证是通用重采样方法的最佳选择;(2)在k-fold交叉验证中,k = 10是较好的选择;(3)蒙特卡罗和bootstrap表现不佳,因此不推荐它们作为通用重采样方法。同时,没有一种重采样方法的均匀性优于其他方法。
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引用次数: 0
HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme HWCD:一种使用小波进行图像压缩、使用混淆进行加密和使用扩散方案进行解密的混合方法
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-9056
H. R. Latha, Alagarswamy Ramaprasath
Abstract Image data play important role in various real-time online and offline applications. Biomedical field has adopted the imaging system to detect, diagnose, and prevent several types of diseases and abnormalities. The biomedical imaging data contain huge information which requires huge storage space. Moreover, currently telemedicine and IoT based remote health monitoring systems are widely developed where data is transmitted from one place to another. Transmission of this type of huge data consumes more bandwidth. Along with this, during this transmission, the attackers can attack the communication channel and obtain the important and secret information. Hence, biomedical image compression and encryption are considered the solution to deal with these issues. Several techniques have been presented but achieving desired performance for combined module is a challenging task. Hence, in this work, a novel combined approach for image compression and encryption is developed. First, image compression scheme using wavelet transform is presented and later a cryptography scheme is presented using confusion and diffusion schemes. The outcome of the proposed approach is compared with various existing techniques. The experimental analysis shows that the proposed approach achieves better performance in terms of autocorrelation, histogram, information entropy, PSNR, MSE, and SSIM.
图像数据在各种实时在线和离线应用中发挥着重要作用。生物医学领域已经采用成像系统来检测、诊断和预防多种疾病和异常。生物医学成像数据信息量巨大,需要巨大的存储空间。此外,目前广泛开发了远程医疗和基于物联网的远程健康监测系统,其中数据从一个地方传输到另一个地方。这种类型的大数据传输消耗更多的带宽。同时,在这种传输过程中,攻击者可以攻击通信通道,获取重要的机密信息。因此,生物医学图像压缩和加密被认为是解决这些问题的解决方案。已经提出了几种技术,但要实现组合模块所需的性能是一项具有挑战性的任务。因此,在这项工作中,开发了一种新的图像压缩和加密组合方法。首先提出了一种基于小波变换的图像压缩方案,然后提出了一种基于混淆和扩散的加密方案。该方法的结果与现有的各种技术进行了比较。实验分析表明,该方法在自相关、直方图、信息熵、PSNR、MSE和SSIM方面都取得了较好的性能。
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引用次数: 1
Multi-sensor remote sensing image alignment based on fast algorithms 基于快速算法的多传感器遥感图像对准
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0289
Tao Shu
Abstract Remote sensing image technology to the ground has important guiding significance in disaster assessment and emergency rescue deployment. In order to realize the fast automatic registration of multi-sensor remote sensing images, the remote sensing image block registration idea is introduced, and the image reconstruction is processed by using the conjugate gradient descent (CGD) method. The scale-invariant feature transformation (SIFT) algorithm is improved and optimized by combining the function-fitting method. By this way, it can improve the registration accuracy and efficiency of multi-sensor remote sensing images. The results show that the average peak signal-to-noise ratio of the image processed by the CGD method is 25.428. The average root mean square value is 17.442. The average image processing time is 6.093 s. These indicators are better than the passive filter algorithm and the gradient descent method. The average accuracy of image registration of the improved SIFT registration method is 96.37%, and the average image registration time is 2.14 s. These indicators are significantly better than the traditional SIFT algorithm and speeded-up robust features algorithm. It is proved that the improved SIFT registration method can effectively improve the accuracy and operation efficiency of multi-sensor remote sensing image registration methods. The improved SIFT registration method effectively solves the problems of low accuracy and long time consumption of traditional multi-sensor remote sensing image fast registration methods. While maintaining high registration accuracy, it improves the image registration speed and provides technical support for a rapid disaster assessment after major disasters such as earthquakes and floods. And it has an important value for the development of the efficient post-disaster rescue deployment.
遥感影像技术对地面灾害评估和应急救援部署具有重要的指导意义。为了实现多传感器遥感图像的快速自动配准,引入了遥感图像分块配准思想,采用共轭梯度下降(CGD)方法对图像进行重构。结合函数拟合方法对尺度不变特征变换(SIFT)算法进行了改进和优化。这样可以提高多传感器遥感图像的配准精度和配准效率。结果表明,经CGD方法处理后的图像平均峰值信噪比为25.428。均方根平均值为17.442。平均图像处理时间为6.093 s。这些指标优于无源滤波算法和梯度下降法。改进SIFT配准方法的平均配准精度为96.37%,平均配准时间为2.14 s。这些指标明显优于传统的SIFT算法和加速鲁棒特征算法。实验证明,改进后的SIFT配准方法能有效提高多传感器遥感图像配准方法的精度和运算效率。改进的SIFT配准方法有效地解决了传统多传感器遥感图像快速配准方法精度低、耗时长的问题。在保持高配准精度的同时,提高了图像配准速度,为地震、洪水等重大灾害后的快速灾害评估提供技术支持。对开展高效的灾后救援部署具有重要价值。
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引用次数: 0
An intelligent algorithm for fast machine translation of long English sentences 一种用于英语长句子快速机器翻译的智能算法
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0257
Hengheng He
Abstract Translation of long sentences in English is a complex problem in machine translation. This work briefly introduced the basic framework of intelligent machine translation algorithm and improved the long short-term memory (LSTM)-based intelligent machine translation algorithm by introducing the long sentence segmentation module and reordering module. Simulation experiments were conducted using the public corpus and the local corpus containing self-collected linguistic data. The improved algorithm was compared with machine translation algorithms based on a recurrent neural network and LSTM. The results suggested that the LSTM-based machine translation algorithm added with the long sentence segmentation module and reordering module effectively segmented long sentences and translated long English sentences more accurately, and the translation was more grammatically correct.
摘要英语长句的翻译是机器翻译中的一个复杂问题。本文简要介绍了智能机器翻译算法的基本框架,并通过引入长句切分模块和重排模块对基于LSTM的智能机器翻译算法进行了改进。使用公共语料库和包含自收集语言数据的局部语料库进行仿真实验。将改进算法与基于递归神经网络和LSTM的机器翻译算法进行了比较。结果表明,加入长句切分模块和重排模块的基于lstm的机器翻译算法能有效切分长句,翻译英语长句的准确性更高,翻译的语法正确性更强。
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引用次数: 0
期刊
Journal of Intelligent Systems
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