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Furniture design based on image color extraction algorithm 基于图像色彩提取算法的家具设计
Pub Date : 2024-07-16 DOI: 10.1016/j.sasc.2024.200123
Binglu Chen , Guanyu Chen , Qianqian Hu

With the increasing demand for personalized and customized home products, how to realize the innovative design of furniture and improve the design efficiency has become a research hotspot for related professionals. Aiming at these problems, the study extracts the main color of furniture images by optimizing the K-mean clustering algorithm, uses the simulated annealing algorithm to color-match the furniture, and reconstructs the image by edge detection to design a furniture design method based on image color extraction. The results revealed that in the foreground part, the correct rate of color match based on the design method was 95.7%, and in the background part, the correct rate of color match based on the design method was 94.81 %, which proved its effectiveness. The average feature point extraction time and the average feature point matching time of the design-based algorithm were 5.45 ms and 9.83 ms, respectively, which proved its high computational efficiency. In furniture color edge detection and overall color match, the image obtained based on the design method was significantly clearer, and the overall coherence, saturation and brightness were closer to the input image. In addition to raising the standard of furniture design, the study's design methodology increases design efficiency and offers solid technical support for the area.

随着人们对个性化、定制化家居产品的需求日益增长,如何实现家具的创新设计、提高设计效率已成为相关专业人员的研究热点。针对这些问题,本研究通过优化K均值聚类算法提取家具图像的主色调,利用模拟退火算法对家具进行配色,并通过边缘检测重建图像,设计了一种基于图像颜色提取的家具设计方法。结果表明,在前景部分,基于该设计方法的颜色匹配正确率为 95.7%,在背景部分,基于该设计方法的颜色匹配正确率为 94.81%,证明了其有效性。基于设计的算法的平均特征点提取时间和平均特征点匹配时间分别为 5.45 ms 和 9.83 ms,证明了其较高的计算效率。在家具颜色边缘检测和整体颜色匹配方面,基于设计方法得到的图像明显更清晰,整体连贯性、饱和度和亮度更接近输入图像。该研究的设计方法不仅提高了家具设计的水平,还提高了设计效率,为该领域提供了坚实的技术支持。
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引用次数: 0
Optimizing capacitor size and placement in radial distribution networks for maximum efficiency 优化径向配电网络中电容器的大小和位置,实现最高效率
Pub Date : 2024-07-05 DOI: 10.1016/j.sasc.2024.200111
R. Arunjothi, K.P. Meena

As distribution systems continue to expand, they face challenges such as increased system losses and inadequate voltage regulation. To address these issues, shunt capacitors are being deployed in distribution networks. These capacitors offer reactive power compensation, enhance power factor, improve voltage profiles, promote system stability, and significantly reduce losses. However, determining the appropriate capacitor sizes and their optimal placements requires careful consideration of both technical and economic factors. The nonlinear nature of optimal capacitor placement and sizing, leveraging optimization techniques becomes crucial in identifying the best locations and values for capacitors. This paper demonstrates the effective utilization of Particle Swarm Optimization (PSO) and Real Coded Genetic Algorithm (RCGA) optimization techniques for capacitor placement and selection. The optimization techniques are applied to a 33-bus IEEE standard radial distribution system, to reduce the real power loss and to improve the voltage profile considering both constant and variable loads. Both PSO and RCGA algorithms identify suitable locations for the placement of capacitors for reactive power compensation within the distribution system. By optimizing the objective function associated with capacitor placement costs and maximizing annual cost savings, the PSO and RCGA techniques yield promising results. After implementing the optimal capacitor placements at the identified candidate nodes, a significant reduction in losses within the radial distribution system is observed. Moreover, the cost savings achieved through optimal placement and sizing are substantial.

随着配电系统的不断扩大,它们面临着系统损耗增加和电压调节不足等挑战。为了解决这些问题,并联电容器正在配电网络中得到部署。这些电容器可提供无功补偿、提高功率因数、改善电压曲线、促进系统稳定并显著降低损耗。然而,确定合适的电容器大小及其最佳位置需要仔细考虑技术和经济因素。最佳电容器布置和大小的非线性特性,使得利用优化技术来确定电容器的最佳位置和数值变得至关重要。本文展示了粒子群优化(PSO)和真实编码遗传算法(RCGA)优化技术在电容器布置和选择中的有效应用。这些优化技术被应用于 33 总线 IEEE 标准径向配电系统,以减少实际功率损耗,并在考虑恒定和可变负载的情况下改善电压曲线。PSO 和 RCGA 算法都能确定在配电系统中放置电容器进行无功补偿的合适位置。通过优化与电容器安置成本相关的目标函数,最大限度地节约年度成本,PSO 和 RCGA 技术取得了可喜的成果。在确定的候选节点实施最佳电容器布置后,可观察到径向配电系统内的损耗显著减少。此外,通过优化电容器的布置和大小,还节省了大量成本。
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引用次数: 0
Quantum maximum power point tracking (QMPPT) for optimal solar energy extraction 量子最大功率点跟踪 (QMPPT) 实现最佳太阳能提取
Pub Date : 2024-07-03 DOI: 10.1016/j.sasc.2024.200118
Habib Feraoun , Mehdi Fazilat , Reda Dermouche , Said Bentouba , Mohamed Tadjine , Nadjet Zioui

Solar energy is key to achieving a more environmentally responsible future. One way to exploit it is to use semiconductor technology through solar panels to generate clean, sustainable, and controllable energy. However, the use of such solutions must be optimised by methods such as maximum power point tracking (MPPT) to extract the maximum available solar energy. Although MPPT algorithms have been widely used and improved, the use of newer approaches, such as quantum computing, appears to hold the promise of achieving new performance levels, particularly for real-time MPPT implementation. The goal of this work is to develop and test a quantum algorithm for the photovoltaic (PV) energy MPPT problem using quantum particle swarm optimisation. The performance of the classic and quantum MPPT algorithms was evaluated under three main operating conditions: normal, high-temperature, and partial shading conditions. This represents a variety of environmental scenarios that can affect the efficiency of solar power generation. According to the study's results, the classical algorithm recorded 0.15% more power than the quantum algorithm in normal operating conditions, and the quantum algorithm generated 3.33% more power in higher temperature tests and 0.89% more power in the partial shading test. Moreover, the quantum algorithm recorded lower duty cycles for the three tests. While the classical algorithm may have a slight edge in power output under normal operation conditions, the quantum algorithm indicates superior performance in challenging conditions and consistently reveals more promising overall efficiency.

太阳能是实现对环境更加负责的未来的关键。利用太阳能的一种方法是通过太阳能电池板使用半导体技术来产生清洁、可持续和可控的能源。然而,必须通过最大功率点跟踪(MPPT)等方法优化此类解决方案的使用,以提取最大可用太阳能。尽管 MPPT 算法已得到广泛应用和改进,但量子计算等新方法的使用似乎有望实现新的性能水平,特别是在实时 MPPT 实施方面。这项工作的目标是利用量子粒子群优化技术,针对光伏(PV)能量 MPPT 问题开发和测试量子算法。经典和量子 MPPT 算法的性能在三种主要工作条件下进行了评估:正常、高温和部分遮光条件。这代表了可能影响太阳能发电效率的各种环境情况。研究结果表明,在正常工作条件下,经典算法比量子算法多发电 0.15%;在高温测试中,量子算法多发电 3.33%;在部分遮光测试中,量子算法多发电 0.89%。此外,量子算法在三个测试中的占空比都较低。虽然经典算法在正常运行条件下的功率输出可能略胜一筹,但量子算法在具有挑战性的条件下表现出卓越的性能,并始终显示出更有前景的整体效率。
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引用次数: 0
Development of a multi-level feature fusion model for basketball player trajectory tracking 开发用于篮球运动员轨迹跟踪的多级特征融合模型
Pub Date : 2024-07-03 DOI: 10.1016/j.sasc.2024.200119
Tao Wang

To solve the problems of low matching degree, long tracking time, and low accuracy of multi-target tracking in the process of athlete motion trajectory tracking using deep learning technology, a new athlete motion trajectory tracking model was proposed in this study. The study first optimized the current object detection algorithm in basketball, utilized a hybrid attention mechanism to extract object features, and improved the non-maximum suppression strategy. Then, a hybrid branch network was introduced to improve the residual network and a new athlete identity recognition model was proposed. Finally, a new trajectory tracking model was designed by combining the object detection model and the athlete identity recognition model. The research results indicated that in the object detection experiment, the detection time of the proposed object detection algorithm was always below 0.4 s, and its average accuracy reached up to 0.63. In trajectory tracking testing, the final built tracking model had a multi-target tracking accuracy of up to 0.98, and its tracking overlap rate was as low as 0.02. This study has the following two contributions. Firstly, a new model of athlete trajectory tracking is proposed, which improves the accuracy and efficiency of multi-target tracking by optimizing object detection algorithm and introducing hybrid branch network. Second, the model has excellent performance in both object detection and track tracking, which can not only provide a new solution for athletes' motion trajectory tracking, but also significantly improve the effect of motion tracking.

为了解决利用深度学习技术进行运动员运动轨迹跟踪过程中匹配度低、跟踪时间长、多目标跟踪精度低等问题,本研究提出了一种新的运动员运动轨迹跟踪模型。研究首先优化了当前篮球运动中的物体检测算法,利用混合注意力机制提取物体特征,并改进了非最大抑制策略。然后,引入混合分支网络来改进残差网络,并提出了新的运动员身份识别模型。最后,结合物体检测模型和运动员身份识别模型,设计了一种新的轨迹跟踪模型。研究结果表明,在物体检测实验中,所提出的物体检测算法的检测时间始终低于 0.4 s,平均准确率高达 0.63。在轨迹跟踪测试中,最终建立的跟踪模型的多目标跟踪精度高达 0.98,跟踪重叠率低至 0.02。本研究有以下两个贡献。首先,提出了一种新的运动员轨迹跟踪模型,通过优化物体检测算法和引入混合分支网络,提高了多目标跟踪的精度和效率。其次,该模型在物体检测和轨迹跟踪方面都有优异的表现,不仅能为运动员运动轨迹跟踪提供新的解决方案,还能显著提高运动跟踪的效果。
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引用次数: 0
A hybrid machine learning model for classifying gene mutations in cancer using LSTM, BiLSTM, CNN, GRU, and GloVe 使用 LSTM、BiLSTM、CNN、GRU 和 GloVe 对癌症基因突变进行分类的混合机器学习模型
Pub Date : 2024-06-25 DOI: 10.1016/j.sasc.2024.200110
Sanad Aburass , Osama Dorgham , Jamil Al Shaqsi

In our study, we introduce a novel hybrid ensemble model that synergistically combines LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations in cancer. This model was rigorously tested using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset, demonstrating exceptional performance across all evaluation metrics. Notably, our approach achieved a training accuracy of 80.6 %, precision of 81.6 %, recall of 80.6 %, and an F1 score of 83.1 %, alongside a significantly reduced Mean Squared Error (MSE) of 2.596. These results surpass those of advanced transformer models and their ensembles, showcasing our model's superior capability in handling the complexities of gene mutation classification. The accuracy and efficiency of gene mutation classification are paramount in the era of precision medicine, where tailored treatment plans based on individual genetic profiles can dramatically improve patient outcomes and save lives. Our model's remarkable performance highlights its potential in enhancing the precision of cancer diagnoses and treatments, thereby contributing significantly to the advancement of personalized healthcare.

在我们的研究中,我们介绍了一种新型混合集合模型,该模型将 LSTM、BiLSTM、CNN、GRU 和 GloVe 嵌入协同结合,用于癌症基因突变分类。该模型在 Kaggle 的 "个性化医疗"(Personalized Medicine:重新定义癌症治疗》数据集进行了严格测试,在所有评估指标中均表现出优异的性能。值得注意的是,我们的方法达到了 80.6% 的训练准确率、81.6% 的精确率、80.6% 的召回率和 83.1% 的 F1 分数,同时显著降低了 2.596 的平均平方误差 (MSE)。这些结果超过了先进的转换器模型及其集合,展示了我们的模型在处理复杂的基因突变分类方面的卓越能力。在精准医疗时代,基因突变分类的准确性和效率至关重要,基于个体基因图谱的定制化治疗方案可以显著改善患者的预后并挽救生命。我们模型的出色表现彰显了它在提高癌症诊断和治疗的精确度方面的潜力,从而为推动个性化医疗做出了巨大贡献。
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引用次数: 0
The application of deep learning-based technique detection model in table tennis teaching and learning 基于深度学习的技术检测模型在乒乓球教学中的应用
Pub Date : 2024-06-21 DOI: 10.1016/j.sasc.2024.200116
Shunshui He

With the development of computer technology, the teaching methods of table tennis have ushered in a new technological revolution. To solve the problem of traditional teaching methods overly focusing on athlete limbs and athlete force movements, this study uses an improved deep learning algorithm technology detection model to analyze the trajectory of table tennis and provide targeted tactical training for athletes. The results showed that the success rate and accuracy score of the model were 95 % and 96 %, respectively, with a calculation time of only 21.75 ms, indicating high analytical accuracy and computational efficiency. Meanwhile, the winning rate of the training strategy under this method can reach over 65 %, effectively improving the winning rate of athletes. This proves that the proposed technology detection model has good algorithm performance and data analysis ability, and can provide data support for table tennis training and teaching work.

随着计算机技术的发展,乒乓球教学方法迎来了新的技术革命。为解决传统教学方法过分关注运动员肢体和运动员发力动作的问题,本研究采用改进的深度学习算法技术检测模型,对乒乓球运动轨迹进行分析,为运动员提供有针对性的战术训练。结果表明,该模型的成功率和准确率得分分别为95%和96%,计算时间仅为21.75 ms,显示出较高的分析精度和计算效率。同时,该方法下的训练策略胜率可达 65 % 以上,有效提高了运动员的胜率。这证明所提出的技术检测模型具有良好的算法性能和数据分析能力,可以为乒乓球训练和教学工作提供数据支持。
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引用次数: 0
Innovative design of wood texture images for indoor furniture based on variable space 基于可变空间的室内家具木质纹理图像创新设计
Pub Date : 2024-06-19 DOI: 10.1016/j.sasc.2024.200114
Chuan Xue, Ling Jin

In the design of furniture wood texture images, image restoration is a key issue. This study proposes a Bregmanized operator splitting optimization algorithm based on variable space. This study combines variable spatial morphology to process texture images and effectively extract image features using different operators, thereby achieving image restoration. The results of comparing the proposed algorithm with other image processing algorithms showed that the research algorithm achieved a peak signal-to-noise ratio of 29.86 and a structural similarity index of 0.87 in image denoising, respectively, and had a good denoising effect. In terms of image deblurring, the research algorithm had the lowest root mean square error values on the France and Boat datasets, with values of 8.98 and 8.82, respectively, indicating that the image processed by the algorithm had a high similarity with the real image. In terms of image resolution reconstruction, the peak signal-to-noise ratio and root mean square error values of the research algorithm reached 29.74 and 12.67, respectively, indicating that the reconstructed image had the best fit with the original image and the smallest error. In summary, the proposed algorithm has shown good performance in image processing and can be effectively applied in fields such as image denoising, deblurring, and resolution reconstruction. It provides effective methods and technical support for innovative design of wood texture images in indoor furniture.

在家具木材纹理图像设计中,图像修复是一个关键问题。本研究提出了一种基于可变空间的 Bregman 化算子分割优化算法。该研究结合可变空间形态学处理纹理图像,利用不同算子有效提取图像特征,从而实现图像修复。将所提算法与其他图像处理算法进行对比的结果表明,该研究算法在图像去噪方面的峰值信噪比分别达到了 29.86,结构相似度指数达到了 0.87,具有良好的去噪效果。在图像去模糊方面,研究算法在法国和船数据集上的均方根误差值最低,分别为 8.98 和 8.82,表明算法处理后的图像与真实图像具有较高的相似度。在图像分辨率重建方面,研究算法的信噪比峰值和均方根误差值分别达到 29.74 和 12.67,表明重建后的图像与原始图像的拟合度最好,误差最小。综上所述,所提出的算法在图像处理中表现出了良好的性能,可以有效地应用于图像去噪、去毛刺和分辨率重建等领域。它为室内家具木材纹理图像的创新设计提供了有效的方法和技术支持。
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引用次数: 0
A pose generation model for animated characters based on DCNN and PFNN 基于 DCNN 和 PFNN 的动画人物姿势生成模型
Pub Date : 2024-06-19 DOI: 10.1016/j.sasc.2024.200115
Boli Wang

In the current field of animation and gaming, the action collection cost for 3D animated character generation is high, and the accuracy of action recognition is poor. Therefore, to reduce the cost of generating 3D animated characters and improve the similarity between animated characters and real humans, a 3D action recognition and animated character generation model based on ResNet and phase function neural network is proposed. The experiment outcomes denote that the raised model begins to converge at 50 iterations, with a minimum loss value of 0.13. The convergence speed and loss value are better than other models. In human pose classification, the raised algorithm has the highest accuracy of 99.46 % and an average accuracy of 99.13 %. The highest classification precision and average precision are 97.79 % and 97.33 %, respectively. In terms of human pose orientation classification, the average accuracy and precision of the raised algorithm are 98.09 % and 97.41 %, respectively, which are also higher than other models. In addition, the mean per joint position error of the proposed algorithm is the highest at 80.1 mm and the lowest at 79.3 mm, respectively. The average recognition time for each image is only 46.8 ms, which is lower than other algorithms. In addition, the average update times of the algorithm and the Unreal Engine are 39.28 ms and 27.52 ms, respectively, and both run at different frame rates. The above results indicate that the proposed 3D human pose recognition and animated character generation model based on ResNet and phase function neural network can not only improve the accuracy of pose recognition, but also improve recognition speed, effectively reducing the cost of 3D animated character generation. The animation character generation method includes data collection and the application after data collection, which shows the various roles that deep learning technology can play in the field of computer graphics animation, and also provides excellent solutions for other computer graphics problems.

在当前的动画和游戏领域,生成三维动画角色的动作采集成本较高,动作识别的准确性较差。因此,为了降低生成三维动画角色的成本,提高动画角色与真人的相似度,提出了一种基于 ResNet 和相位函数神经网络的三维动作识别和动画角色生成模型。实验结果表明,提出的模型在 50 次迭代时开始收敛,最小损失值为 0.13。收敛速度和损失值均优于其他模型。在人体姿态分类中,凸起算法的最高准确率为 99.46%,平均准确率为 99.13%。最高分类精度和平均精度分别为 97.79 % 和 97.33 %。在人体姿态方位分类方面,凸起算法的平均准确率和精确度分别为 98.09 % 和 97.41 %,也高于其他模型。此外,提出的算法的平均每个关节位置误差最大,分别为 80.1 毫米和 79.3 毫米。每幅图像的平均识别时间仅为 46.8 毫秒,低于其他算法。此外,该算法和虚幻引擎的平均更新时间分别为 39.28 毫秒和 27.52 毫秒,并且两者都以不同的帧速率运行。以上结果表明,所提出的基于 ResNet 和相位函数神经网络的三维人体姿态识别和动画角色生成模型不仅能提高姿态识别的准确率,还能提高识别速度,有效降低三维动画角色生成的成本。该动画角色生成方法包括数据采集和数据采集后的应用,体现了深度学习技术在计算机图形动画领域可以发挥的多种作用,也为其他计算机图形问题提供了很好的解决方案。
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引用次数: 0
Telugu language hate speech detection using deep learning transformer models: Corpus generation and evaluation 使用深度学习转换器模型检测泰卢固语仇恨言论:语料库生成与评估
Pub Date : 2024-06-19 DOI: 10.1016/j.sasc.2024.200112
Namit Khanduja , Nishant Kumar , Arun Chauhan

In today's digital era, social media has become a new tool for communication and sharing information, with the availability of high-speed internet it tends to reach the masses much faster. Lack of regulations and ethics have made advancement in the proliferation of abusive language and hate speech has become a growing concern on social media platforms in the form of posts, replies, and comments towards individuals, groups, religions, and communities. However, the process of classification of hate speech manually on online platforms is cumbersome and impractical due to the excessive amount of data being generated. Therefore, it is crucial to automatically filter online content to identify and eliminate hate speech from social media. Widely spoken resource-rich languages like English have driven the research and achieved the desired result due to the accessibility of large corpora, annotated datasets, and tools. Resource-constrained languages are not able to achieve the benefits of advancement due to a lack of data corpus and annotated datasets. India has diverse languages that change with demographics and languages that have limited data availability and semantic differences. Telugu is one of the low-resource Dravidian languages spoken in the southern part of India.

In this paper, we present a monolingual Telugu corpus consisting of tweets posted on Twitter annotated with hate and non-hate labels and experiments to provide a comparison of state-of-the-art fine-tuned deep learning models (mBERT, DistilBERT, IndicBERT, NLLB, Muril, RNN+LSTM, XLM-RoBERTa, and Indic-Bart). Through transfer learning and hyperparameter tuning, the models are compared for their effectiveness in classifying hate speech in Telugu text. The fine-tuned mBERT model outperformed all other fine-tuned models achieving an accuracy of 98.2. The authors also propose a deployment model for social media accounts.

在当今的数字时代,社交媒体已成为沟通和分享信息的新工具,随着高速互联网的普及,它往往能更快地接触到大众。由于缺乏监管和道德规范,辱骂性语言泛滥成灾,仇恨言论在社交媒体平台上以针对个人、团体、宗教和社区的帖子、回复和评论的形式日益受到关注。然而,由于产生的数据量过大,在网络平台上手动对仇恨言论进行分类的过程既繁琐又不切实际。因此,自动过滤在线内容以识别和消除社交媒体中的仇恨言论至关重要。英语等广泛使用的资源丰富的语言由于拥有大量语料库、注释数据集和工具,推动了相关研究并取得了预期成果。资源有限的语言由于缺乏数据语料库和注释数据集,无法获得进步带来的好处。印度的语言多种多样,会随着人口结构的变化而变化,而且语言的数据可用性有限,语义也存在差异。在本文中,我们介绍了一个单语泰卢固语语料库,该语料库由 Twitter 上发布的带有仇恨和非仇恨标签的推文组成,并通过实验对最先进的微调深度学习模型(mBERT、DistilBERT、IndicBERT、NLLB、Muril、RNN+LSTM、XLM-RoBERTa 和 Indic-Bart)进行了比较。通过迁移学习和超参数调整,比较了这些模型在泰卢固文仇恨言论分类中的有效性。经过微调的 mBERT 模型的准确率达到了 98.2,超过了所有其他经过微调的模型。作者还提出了社交媒体账户的部署模型。
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引用次数: 0
The design of advertising text keyword recommendation for internet search engines 互联网搜索引擎的广告文本关键词推荐设计
Pub Date : 2024-06-12 DOI: 10.1016/j.sasc.2024.200109
Fang Wang, Liuying Yu

As the growth of internet technology, human life is full of various advertisements. It is possible for individuals to obtain the advertising information they require, whether in an online or offline context. A research proposal is presented with the objective of enhancing the precision of online advertising recommendations. The proposal is based on the design of internet search engine advertising text keyword recommendation models, which integrate entity naming recognition models to facilitate tasks such as text classification and feature extraction. A recommendation algorithm based on content similarity is used to achieve keyword recommendation. Under the similarity calculation method of continuous bag-of-words model, when K is 100, the model weighted precision of the feature extraction method based on graph sorting and inverse text frequency index is 0.88, the weighted recall is 0.76, and the weighted F1-score is 0.82. In offline simulation testing, 85 % of the keyword recommendation model's recommendation time is less than 1 s, 99 % of the recommendation time is less than 2 s, and the recommendation cost can be significantly reduced by 75 %. In practical applications, the recommendation efficiency of this method can reach 96.3 %, and the recommendation precision can reach 95.8 %. The recommended satisfaction rate can reach 99.5 %. The results demonstrate that this method can provide highly accurate keyword recommendations and reduce the cost of advertising placement. Furthermore, it has been recognized and praised by users.

随着互联网技术的发展,人类生活中充斥着各种各样的广告。无论是在线还是离线环境下,个人都有可能获得所需的广告信息。本文提出的研究建议旨在提高在线广告推荐的精确度。该建议基于互联网搜索引擎广告文本关键词推荐模型的设计,该模型整合了实体命名识别模型,以促进文本分类和特征提取等任务。采用基于内容相似性的推荐算法实现关键词推荐。在连续词袋模型的相似度计算方法下,当 K 为 100 时,基于图排序和反文本频率指数的特征提取方法的模型加权精度为 0.88,加权召回率为 0.76,加权 F1-score 为 0.82。在离线模拟测试中,关键词推荐模型 85% 的推荐时间小于 1 秒,99% 的推荐时间小于 2 秒,推荐成本可显著降低 75%。在实际应用中,该方法的推荐效率可达 96.3%,推荐精度可达 95.8%。推荐满意率可达 99.5%。结果表明,该方法可以提供高精确度的关键词推荐,降低广告投放成本。此外,它还得到了用户的认可和好评。
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引用次数: 0
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Systems and Soft Computing
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