Journal of Intelligent Systems最新文献_第5页

Wireless sensor node localization algorithm combined with PSO-DFP 结合PSO-DFP的无线传感器节点定位算法

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0323

Jingjing Sun, Peng Zhang, Xiaohong Kong

引用次数: 0

Smart robots’ virus defense using data mining technology 基于数据挖掘技术的智能机器人病毒防御

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0065

Jiao Ye, Hemant N. Patel, Sankaranamasivayam Meena, Renato R. Maaliw, Samuel-Soma M. Ajibade, Ismail Keshta

引用次数: 0

Motion vector steganography algorithm of sports training video integrating with artificial bee colony algorithm and human-centered AI for web applications 结合人工蜂群算法和以人为本的web应用AI的运动训练视频运动矢量隐写算法

IF 3

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0093

Jinmao Tong, Zhongwang Cao, Wenjiang J. Fu

{"title":"Motion vector steganography algorithm of sports training video integrating with artificial bee colony algorithm and human-centered AI for web applications","authors":"Jinmao Tong, Zhongwang Cao, Wenjiang J. Fu","doi":"10.1515/jisys-2022-0093","DOIUrl":"https://doi.org/10.1515/jisys-2022-0093","url":null,"abstract":"Abstract In multimedia correspondence, steganography schemes are commonly applied. To reduce storage capacity, multimedia files, including images, are always compressed. Most steganographic video schemes are, therefore, not compression tolerant. In the frame sequences, the video includes extra hidden space. Artificial intelligence (AI) creates a digital world of real-time information for athletes, sponsors, and broadcasters. AI is reshaping business, and although it has already produced a significant impact on other sectors, the sports industry is the newest and most receptive one. Human-centered AI for web applications has substantially influenced audience participation, strategic plan execution, and other aspects of the sports industry that have traditionally relied heavily on statistics. Thus, this study presents the motion vector steganography of sports training video integrating with the artificial bee colony algorithm (MVS-ABC). The motion vector stenography detects the hidden information from the motion vectors in the sports training video bitstreams. Artificial bee colony (ABC) algorithm optimizes the block assignment to inject a hidden message into a host video, in which the block assignment is considered a combinatorial optimization problem. The experimental analysis evaluates the data embedding performance using steganographic technology compared with existing embedding technologies, using the ABC algorithm compared with other genetic algorithms. The findings show that the proposed model can give the highest performance in terms of embedding capacity and the least error rate of video steganography compared with the existing models.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"347 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77697577","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

A lattice-transformer-graph deep learning model for Chinese named entity recognition 中文命名实体识别的格-变换-图深度学习模型

IF 3

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-2014

Min Lin, Yanyan Xu, Chenghao Cai, Dengfeng Ke, Kaile Su

{"title":"A lattice-transformer-graph deep learning model for Chinese named entity recognition","authors":"Min Lin, Yanyan Xu, Chenghao Cai, Dengfeng Ke, Kaile Su","doi":"10.1515/jisys-2022-2014","DOIUrl":"https://doi.org/10.1515/jisys-2022-2014","url":null,"abstract":"Abstract Named entity recognition (NER) is the localization and classification of entities with specific meanings in text data, usually used for applications such as relation extraction, question answering, etc. Chinese is a language with Chinese characters as the basic unit, but a Chinese named entity is normally a word containing several characters, so both the relationships between words and those between characters play an important role in Chinese NER. At present, a large number of studies have demonstrated that reasonable word information can effectively improve deep learning models for Chinese NER. Besides, graph convolution can help deep learning models perform better for sequence labeling. Therefore, in this article, we combine word information and graph convolution and propose our Lattice-Transformer-Graph (LTG) deep learning model for Chinese NER. The proposed model pays more attention to additional word information through position-attention, and therefore can learn relationships between characters by using lattice-transformer. Moreover, the adapted graph convolutional layer enables the model to learn both richer character relationships and word relationships and hence helps to recognize Chinese named entities better. Our experiments show that compared with 12 other state-of-the-art models, LTG achieves the best results on the public datasets of Microsoft Research Asia, Resume, and WeiboNER, with the F1 score of 95.89%, 96.81%, and 72.32%, respectively.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88834255","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: 1

Environmental landscape design and planning system based on computer vision and deep learning 基于计算机视觉和深度学习的环境景观设计与规划系统

IF 3

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0092

Xiubo Chen

{"title":"Environmental landscape design and planning system based on computer vision and deep learning","authors":"Xiubo Chen","doi":"10.1515/jisys-2022-0092","DOIUrl":"https://doi.org/10.1515/jisys-2022-0092","url":null,"abstract":"Abstract Environmental landscaping is known to build, plan, and manage landscapes that consider the ecology of a site and produce gardens that benefit both people and the rest of the ecosystem. Landscaping and the environment are combined in landscape design planning to provide holistic answers to complex issues. Seeding native species and eradicating alien species are just a few ways humans influence the region’s ecosystem. Landscape architecture is the design of landscapes, urban areas, or gardens and their modification. It comprises the construction of urban and rural landscapes via coordinating the creation and management of open spaces and economics, finding a job, and working within a confined project budget. There was a lot of discussion about global warming and water shortages. There is a lot of hope to be found even in the face of seemingly insurmountable obstacles. AI is becoming more significant in many urban landscape planning and design elements with the advent of web 4.0 and Human-Centred computing. It created a virtual reality-based landscape to create deep neural networks (DNNs) to make deep learning (DL) more user-friendly and efficient. Users may only manipulate physical items in this environment to manually construct neural networks. These setups are automatically converted into a model, and the real-time testing set is reported and aware of the DNN models that users are producing. This research presents a novel strategy for combining DL-DNN with landscape architecture, providing a long-term solution to the problem of environmental pollution. Carbon dioxide levels are constantly checked when green plants are in and around the house. Plants, on either hand, remove toxins from the air, making it easier to maintain a healthy environment. Human-centered Artificial Intelligence-based web 4.0 may be used to assess and evaluate the data model. The study findings can be sent back into the design process for further modification and optimization.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"65 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88959754","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: 1

Data analysis with performance and privacy enhanced classification 具有性能和隐私增强分类的数据分析

IF 3

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0215

R. Tajanpure, A. Muddana

{"title":"Data analysis with performance and privacy enhanced classification","authors":"R. Tajanpure, A. Muddana","doi":"10.1515/jisys-2022-0215","DOIUrl":"https://doi.org/10.1515/jisys-2022-0215","url":null,"abstract":"Abstract Privacy is the main concern in cyberspace because, every single click of a user on Internet is recognized and analyzed for different purposes like credit card purchase records, healthcare records, business, personalized shopping store experience to the user, deciding marketing strategy, and the list goes on. Here, the user’s personal information is considered a risk process. Though data mining applications focus on statistically useful patterns and not on the personal data of individuals, there is a threat of unrestricted access to individual records. Also, it is necessary to maintain the secrecy of data while retaining the accuracy of data classification and quality as well. For real-time applications, the data analytics carried out should be time efficient. Here, the proposed Convolution-based Privacy Preserving Algorithm (C-PPA) transforms the input into lower dimensions while preserving privacy which leads to better mining accuracy. The proposed algorithm is evaluated over different privacy-preserving metrics like accuracy, precision, recall, and F1-measure. Simulations carried out show that the average increment in the accuracy of C-PPA is 14.15 for Convolutional Neural Network (CNN) classifier when compared with results without C-PPA. Overlap-add C-PPA is proposed for parallel processing which is based on overlap-add convolution. It shows an average accuracy increment of 12.49 for CNN. The analytics show that the algorithm benefits regarding privacy preservation, data utility, and performance. Since the algorithm works on lowering the dimensions of data, the communication cost over the Internet is also reduced.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"102 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84761303","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

Accurate and real-time object detection in crowded indoor spaces based on the fusion of DBSCAN algorithm and improved YOLOv4-tiny network 基于DBSCAN算法与改进的YOLOv4-tiny网络融合的室内拥挤空间精确实时目标检测

IF 3

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0268

Jianing Shen, Yang Zhou

{"title":"Accurate and real-time object detection in crowded indoor spaces based on the fusion of DBSCAN algorithm and improved YOLOv4-tiny network","authors":"Jianing Shen, Yang Zhou","doi":"10.1515/jisys-2022-0268","DOIUrl":"https://doi.org/10.1515/jisys-2022-0268","url":null,"abstract":"Abstract Real-time object detection is an integral part of internet of things (IoT) application, which is an important research field of computer vision. Existing lightweight algorithms cannot handle target occlusions well in target detection tasks in indoor narrow scenes, resulting in a large number of missed detections and misclassifications. To this end, an accurate real-time multi-scale detection method that integrates density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm and the improved You Only Look Once (YOLO)-v4-tiny network is proposed. First, by improving the neck network of the YOLOv4-tiny model, the detailed information of the shallow network is utilized to boost the average precision of the model to identify dense small objects, and the Cross mini-Batch Normalization strategy is adopted to improve the accuracy of statistical information. Second, the DBSCAN clustering algorithm is fused with the modified network to achieve better clustering effects. Finally, Mosaic data enrichment technique is adopted during model training process to improve the capability of the model to recognize occluded targets. Experimental results show that compared to the original YOLOv4-tiny algorithm, the mAP values of the improved algorithm on the self-construct dataset are significantly improved, and the processing speed can well meet the requirements of real-time applications on embedded devices. The performance of the proposed model on public datasets PASCAL VOC07 and PASCAL VOC12 is also better than that of other advanced lightweight algorithms, and the detection ability for occluded objects is significantly improved, which meets the requirements of mobile terminals for real-time detection in crowded indoor environments.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"114 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85065332","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: 1

Intelligent auditing techniques for enterprise finance 企业财务智能审计技术

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0011

Chen Peng, Guixian Tian

{"title":"Intelligent auditing techniques for enterprise finance","authors":"Chen Peng, Guixian Tian","doi":"10.1515/jisys-2023-0011","DOIUrl":"https://doi.org/10.1515/jisys-2023-0011","url":null,"abstract":"Abstract With the need of social and economic development, the audit method is also continuously reformed and improved. Traditional audit methods have defects of comprehensively considering various risk factors, and cannot meet the needs of enterprise financial work. To improve the effectiveness of audit work and meet the financial needs of enterprises, a solution for intelligent auditing of enterprise finance is proposed, including intelligent analysis of accounting vouchers and of audit reports. Then, Bi-directional Long Short-Term Memory (BiLSTM) neural network is used to classify the audit problems under three text feature extraction methods. The test results show that the accuracy, recall rate, and F 1 value of the COWORDS-IOM algorithm in the aggregate clustering of accounting vouchers are 85.12, 83.28, and 84.85%, respectively, which are better than the self-organizing map algorithm before the improvement. The accuracy rate, recall rate, and F 1 value of Word2vec TF-IDF LDA-BiLSTM model for intelligent analysis of audit reports are 87.43, 87.88, and 87.66%, respectively. This shows that the proposed method has good performance in accounting voucher clustering and intelligent analysis of audit reports, which can provide guidance for the development of enterprise financial intelligence software to a certain extent.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"18 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":"135261073","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

Construction pit deformation measurement technology based on neural network algorithm 基于神经网络算法的施工坑变形测量技术

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0292

Yong Wu, Xiaoli Zhou

{"title":"Construction pit deformation measurement technology based on neural network algorithm","authors":"Yong Wu, Xiaoli Zhou","doi":"10.1515/jisys-2022-0292","DOIUrl":"https://doi.org/10.1515/jisys-2022-0292","url":null,"abstract":"Abstract The current technology of foundation pit deformation measurement is inefficient, and its accuracy is not ideal. Therefore, an intelligent prediction model of foundation pit deformation based on back propagation neural network (BPNN) is proposed to predict the foundation pit deformation intelligently, with high accuracy and efficiency, so as to improve the safety of the project. Firstly, to address the shortcomings of BPNNs, which rely on the initial parameter settings and tend to fall into local optimum and unstable performance, this study adopts the modified particle swarm optimization (MPSO) to optimise the parameters of BPNNs and constructs a pit deformation prediction model based on the MPSO–BP algorithm to achieve predictive measurements of pit deformation. After training and testing the data samples, the results show that the prediction accuracy of the MPSO–BP pit deformation prediction model is 99.76%, which is 2.25% higher than that of the particle swarm optimization–back propagation (PSO–BP) pit deformation prediction model and 3.01% higher than that of the BP pit deformation prediction model. The aforementioned results show that the MPSO–BP pit deformation prediction model proposed in this study can effectively predict the pit deformation variables of construction projects and provide data support for the protective measures of the staff, which is helpful for the cause of construction projects in China.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"49 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":"135361368","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

CMOR motion planning and accuracy control for heavy-duty robots 重型机器人CMOR运动规划与精度控制

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0050

Congju Zuo, Weihua Wang, Liang Xia, Feng Wang, Pucheng Zhou, Leiji Lu

{"title":"CMOR motion planning and accuracy control for heavy-duty robots","authors":"Congju Zuo, Weihua Wang, Liang Xia, Feng Wang, Pucheng Zhou, Leiji Lu","doi":"10.1515/jisys-2023-0050","DOIUrl":"https://doi.org/10.1515/jisys-2023-0050","url":null,"abstract":"Abstract Factors like rising work costs and the imminent transformation and upgrading of manufacturing industries are driving the rapid development of the industrial robotics market. In this study, by analyzing the structure of the transport arm and China Fusion Engineering Test Reactor and performing mathematical modeling, a feasible solution for the robot can be obtained using the dynamic ant colony optimization algorithm and grayscale values. However, for multiple degree of freedom robots, due to a large number of joints, the pure use of joint angle restrictions cannot avoid their own mutual interference. The design of the transport arm robot’s own collision algorithm is shown, which focuses on each linkage as a rod wrapped by a cylinder. The experiment shows that the relationship between the integrated center of mass and the whole machine center of mass can get the action area of the whole machine center of mass of the robot, according to which the relationship between the radius of the catch circle and time of the projection area of the whole machine center of mass of the robot in the horizontal plane can be obtained. The maximum outer circle radius <m:math xmlns:m=\"http://www.w3.org/1998/Math/MathML\"> <m:msub> <m:mrow> <m:mi>r</m:mi> </m:mrow> <m:mrow> <m:mtext>com </m:mtext> </m:mrow> </m:msub> <m:mo>=</m:mo> <m:mn>267.977</m:mn> <m:mspace width=\".25em\" /> <m:mtext>mm</m:mtext> </m:math> {r}_{text{com}}=267.977hspace{.25em}text{mm} , according to the stability criterion <m:math xmlns:m=\"http://www.w3.org/1998/Math/MathML\"> <m:msub> <m:mrow> <m:mi>r</m:mi> </m:mrow> <m:mrow> <m:mtext>ssa </m:mtext> </m:mrow> </m:msub> <m:mo>></m:mo> <m:msub> <m:mrow> <m:mi>r</m:mi> </m:mrow> <m:mrow> <m:mtext>con </m:mtext> </m:mrow> </m:msub> </m:math> {r}_{text{ssa}}gt {r}_{text{con}} , can be obtained, so the stability analysis of the gait switching process can be judged to be correct and effective.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"91 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":"135599800","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