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Research on the application of search algorithm in computer communication network 搜索算法在计算机通信网络中的应用研究
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2021-0263
Hua Ai, Jianwei Chai, Jilei Zhang, S. Khanna, K. Ghafoor
Abstract This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation (CFA) problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large number of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks.
摘要本文通过降低网络运行成本和提高网络性能,缓解了以往文献报道的挑战。针对计算机通信网络中的链路容量与流量分配问题,提出了一种基于遗传算法的禁忌搜索方法。利用一种高效的现代超启发式搜索方法,讨论了计算机通信网络中链路的固定成本、延迟成本和可变成本对总运行成本的影响。本文分析了大量的计算机仿真结果,验证了禁忌搜索算法对CFA问题的有效性,并且与传统的拉格朗日松弛和次梯度优化算法相比,该算法的解的质量得到了显著提高。实验结果表明,随着可变成本加权系数的增大,可变成本占总成本的比例从10%增加到35%。增长相对缓慢,固定成本仍是主要组成部分。此外,由于可变成本的增加,禁忌搜索算法也会选择奢侈度较大的链路来降低可变成本,这使得固定成本略有增加,而网络延迟成本和平均延迟略有下降。与遗传算法相比,该方法在大规模或重载网络中具有更大的优势。
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引用次数: 0
Image denoising algorithm of social network based on multifeature fusion 基于多特征融合的社交网络图像去噪算法
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0019
Lanfei Zhao, Qidan Zhu
Abstract A social network image denoising algorithm based on multifeature fusion is proposed. Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient convolution neural structure of multifeature fusion is constructed for image denoising. The gray features of social network image are collected, and the gray values are denoising and cleaning. Based on the image features, multiple denoising is carried out to ensure the accuracy of social network image denoising algorithm and improve the accuracy of image processing. Experiments show that the average noise of the image processed by the algorithm designed in this study is reduced by 8.6905 dB, which is much larger than that of other methods, and the signal-to-noise ratio of the output image is high, which is maintained at about 30 dB, which has a high effect in the process of practical application.
摘要提出了一种基于多特征融合的社交网络图像去噪算法。基于多特征融合理论,将社会网络图像去噪过程视为神经网络的拟合过程,构造了一种简单高效的多特征融合卷积神经结构用于图像去噪。采集社交网络图像的灰度特征,对灰度值进行去噪和清洗。根据图像特征进行多重去噪,保证社交网络图像去噪算法的准确性,提高图像处理的精度。实验表明,本研究设计的算法处理后的图像平均噪声降低了8.6905 dB,大大大于其他方法,并且输出图像的信噪比较高,保持在30 dB左右,在实际应用过程中效果良好。
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引用次数: 1
Construction of an IoT customer operation analysis system based on big data analysis and human-centered artificial intelligence for web 4.0 构建面向web 4.0的基于大数据分析和以人为本的人工智能的物联网客户运营分析系统
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0067
Xinxin Liu, Baojing Liu, Chenye Han, Wei Li
Abstract Internet of thing (IoT) building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT edge applications security and approval. There are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation (BDA-CO) system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques.
物联网(IoT)建筑传感器可以捕获多种类型的建筑操作、性能和条件,并将其发送到中央仪表板进行数据分析,以支持决策。传统上,笔记本电脑和手机是主要的互联网连接设备。物联网跟踪通过网络上连接的设备、客户和应用收集和分析各种物联网数据,拉近设备与企业之间的距离。对物联网边缘应用程序的安全性和审批缺乏要求。目前还没有针对物联网事件的最佳操作实践。审计和日志记录需求不包括物联网元素。在本文中,基于大数据分析的客户运营(BDA-CO)系统分析了该操作。随着数据使用量的指数级增长,物联网设备的爆炸式发展反映了大数据增长与物联网的理想重叠。大数据分析不断发展的网络提出了一些关于数据性能、分布、分析和数据收集保护的琐碎问题。物联网几乎改变了建筑行业的所有特征。以人为本的人工智能被描述为由于人类输入而不断改进的系统,同时也在人类和机器人之间提供有效的体验。物联网是确保更高建筑性能的关键因素。这是很长一段时间以来的第一次技术进化,将真正的发明变成了一个严重依赖纸张和手工流程的行业。物联网在建筑中的好处现在明显比目前的手工流程要重得多。因此,越来越多的建筑公司探索并采用物联网战略来应对其生产力挑战,提高效率和利润。仿真分析表明,与现有技术相比,所提出的BDA-CO模型的信任得分提高了98.5%,准确率检测率提高了93.4%,概率率提高了97.6%,安全性提高了98.7%,假阴性率降低了21.3%,响应时间降低了10.5%,延迟率降低了19.9%,丢包率降低了15.4%。
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引用次数: 2
Data mining applications in university information management system development 数据挖掘在高校信息管理系统开发中的应用
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0006
Minshun Zhang, Jun-Chen Fan, A. Sharma, Ashima Kukkar
Abstract Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level.
摘要为了解决信息传输不可靠的问题,提高工作效率,现代管理被大力提倡。现代管理的基本目标是更有效地发挥学校培养人才和服务社会的作用。本文主要研究了数据挖掘技术在高校信息管理系统开发中的应用。DM为各种教育领域提供了强大的方法。由于大量的学生信息可用于设计与学生学习行为相关的有价值的模式,因此教育领域的研究不断扩大。教育数据挖掘可以被教育机构用来评估学生的表现,帮助机构认识到学生的成就。在DM中,分类是一种众所周知的技术,经常用于确定学生的成绩。本文介绍了信息管理在高校IMS开发中的过程和关联规则的应用研究。结果表明,该课程覆盖了整个领域,最小事务支持数为2,minconf = 70%。结果还表明,选择一门课程的学生也倾向于选择另一门课程。数据决策理论在高校信息管理中的应用,将极大地提高管理人员的数据分析能力,提高管理水平。
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引用次数: 14
A novel method to find the best path in SDN using firefly algorithm 一种利用萤火虫算法寻找SDN中最佳路径的新方法
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0063
Tameem Hameed Obaida, Hanan Abbas Salman
Abstract Over the previous three decades, the area of computer networks has progressed significantly, from traditional static networks to dynamically designed architecture. The primary purpose of software-defined networking (SDN) is to create an open, programmable network. Conventional network devices, such as routers and switches, may make routing decisions and forward packets; however, SDN divides these components into the Data plane and the Control plane by splitting distinct features away. As a result, switches can only forward packets and cannot make routing decisions; the controller makes routing decisions. OpenFlow is the communication interface between the switches and the controller. It is a protocol that allows the controller to identify the network packet’s path across the switches. This project uses the SDN environment to implement the firefly optimization algorithm to determine the shortest path between two nodes in a network. The firefly optimization algorithm was implemented using Ryu control. The results reveal that using the firefly optimization algorithm improves the selected short path between the source and destination.
在过去的三十年里,计算机网络领域从传统的静态网络发展到动态设计的体系结构,取得了长足的进步。软件定义网络(SDN)的主要目的是创建一个开放的、可编程的网络。传统的网络设备,如路由器和交换机,可以做出路由决定并转发数据包;然而,SDN通过分离不同的特性将这些组件划分为数据平面和控制平面。因此,交换机只能转发数据包,不能做出路由决策;控制器做出路由决策。OpenFlow是交换机和控制器之间的通信接口。它是一种协议,允许控制器识别网络数据包在交换机之间的路径。本项目使用SDN环境实现萤火虫优化算法,确定网络中两个节点之间的最短路径。萤火虫优化算法采用Ryu控制实现。结果表明,采用萤火虫优化算法可以提高源和目标之间选择的短路径。
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引用次数: 2
Edge detail enhancement algorithm for high-dynamic range images 高动态范围图像边缘细节增强算法
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0008
Lanfei Zhao, Qidan Zhu
Abstract Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements.
现有的图像增强方法存在数据传输速度慢、转换效果差等问题,导致图像识别率和识别效率较低。为了解决这些问题,提高图像特征的识别精度和识别效率,本研究提出了一种针对高动态范围图像的边缘细节增强算法。对原始图像进行傅里叶变换,通过频域高斯滤波和傅里叶反变换得到低频和高频图像。低频图像采用对比度有限的自适应直方图均衡化处理,高频图像采用非锐化掩模和灰度变换处理。对低频增强图像和高频增强图像进行加权融合,增强图像的边缘细节。最后,实验结果表明,所提出的高动态范围图像边缘细节增强算法在实际应用过程中保持了80%以上的图像识别率,识别时间在1200 min以内,增强了图像效果,提高了图像特征的识别精度和识别效率,完全满足了研究要求。
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引用次数: 0
College music teaching and ideological and political education integration mode based on deep learning 基于深度学习的高校音乐教学与思想政治教育一体化模式
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0031
Xiaoshu Wang, Su-hua Zhao, Jingwen Liu, Liyan Wang
Abstract In order to highlight the role of music teaching in the teaching of ideological and political courses, this study puts forward research on the integration of music teaching and ideological and political teaching. This study analyzes the promotion and necessity of college music teaching to ideological and political work, constructs a fusion model of college music teaching and ideological and political work, introduces deep learning methods, and weakens the influence of errors in the data of college music teaching and ideological and political work. This study also optimized the integration mode of college music teaching and ideological and political work and realized the model research of college music teaching and ideological and political work. The experimental results show that the resource output amplitude controlled by the deep learning method has the best stability, and there is no large amplitude fluctuation during the experiment. The output amplitude and control time of the fusion resource are guaranteed and the fusion path of music teaching and ideological and political education is clearer. The maximum control time of the fusion resource of this method is 23.55 ms.
摘要为了突出音乐教学在思想政治课教学中的作用,本研究提出了音乐教学与思想政治课教学整合的研究。分析了高校音乐教学对思想政治工作的促进作用和必要性,构建了高校音乐教学与思想政治工作的融合模式,引入深度学习方法,弱化了高校音乐教学与思想政治工作数据误差的影响。优化了高校音乐教学与思想政治工作的整合模式,实现了高校音乐教学与思想政治工作的模式研究。实验结果表明,深度学习方法控制的资源输出幅度具有最好的稳定性,实验过程中没有出现较大的幅度波动。融合资源的输出幅度和控制时间得到保证,音乐教学与思想政治教育的融合路径更加清晰。该方法对融合资源的最大控制时间为23.55 ms。
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引用次数: 4
Research on computer static software defect detection system based on big data technology 基于大数据技术的计算机静态软件缺陷检测系统研究
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2021-0260
Zhaoxia Li, Jianxing Zhu, K. Arumugam, J. Bhola, Rahul Neware
Abstract To study the static software defect detection system, based on the traditional static software defect detection system design, a new static software defect detection system design based on big data technology is proposed. The proposed method can optimize the distribution of test resources and improve the quality of software products by predicting the potential defect program modules and design the software and hardware of the static software defect detection system of big data technology. It is found that the traditional static software defect detection system design based on code source data takes a long time, averaging 65 h /day. However, the traditional static software defect detection system based on deep learning has a short detection time, averaging 35 h/day. In this article, the detection time of the static software defect detection system based on big data is shorter than that of the other two traditional system designs, with an average of 15 h/day. Because the system design adjusts the operating state of the system, it improves the accuracy of data operation. On the premise of data collection, the system inspection research is completed, which ensures the operational safety of software data, alleviates the contradiction between system and data to a high degree, improves the efficiency of system operation, reduces unnecessary operations, further shortens the time required for inspection, improves the system performance, and has higher research and operation value.
摘要以静态软件缺陷检测系统为研究对象,在传统静态软件缺陷检测系统设计的基础上,提出了一种基于大数据技术的静态软件缺陷检测系统设计。该方法通过预测潜在缺陷程序模块,设计基于大数据技术的静态软件缺陷检测系统的软硬件,优化测试资源分配,提高软件产品质量。研究发现,传统的基于代码源数据的静态软件缺陷检测系统设计耗时较长,平均为65小时/天。而传统的基于深度学习的静态软件缺陷检测系统检测时间较短,平均为35小时/天。在本文中,基于大数据的静态软件缺陷检测系统的检测时间比其他两种传统系统设计的检测时间短,平均为15小时/天。由于系统设计调整了系统的运行状态,提高了数据操作的准确性。在数据采集的前提下,完成系统巡检研究,保证了软件数据的运行安全,在很大程度上缓解了系统与数据之间的矛盾,提高了系统运行效率,减少了不必要的操作,进一步缩短了巡检所需的时间,提高了系统性能,具有较高的研究和运行价值。
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引用次数: 0
An extensive review of state-of-the-art transfer learning techniques used in medical imaging: Open issues and challenges 医学影像中使用的最先进的迁移学习技术的广泛回顾:开放的问题和挑战
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0198
Abdulrahman Abbas Mukhlif, Belal Al-Khateeb, M. Mohammed
Abstract Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires specialized expertise to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset (VGG16, ResNet, Inception v3) in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F1 score, receiver operator characteristic curve, and area under the curve (AUC) were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions.
深度学习技术使用了大量的卷积神经网络技术,在包括图像处理和解释在内的各个领域都取得了优异的成绩。然而,随着这些网络深度的增长,对训练这些网络所需的大量标记数据的需求也在增加。特别是,医疗领域缺乏图像,因为在医疗领域获得标记医学图像的过程困难,昂贵,并且需要专门的专业知识来为图像添加标签。此外,该过程可能容易出错且耗时。目前的研究表明,迁移学习是解决这一问题的可行方法。迁移学习允许我们将从以前的过程中获得的知识转移到改进和解决新问题。本研究旨在对最近解决这一问题的研究以及用于评估这些方法的最重要指标进行全面调查。此外,本研究确定了迁移学习技术中的问题,并强调了医学数据集的问题以及在未来研究中可以解决的潜在问题。根据我们的综述,许多研究人员使用Imagenet数据集(VGG16、ResNet、Inception v3)上的预训练模型进行皮肤癌、乳腺癌和糖尿病视网膜病变的分类任务。这些技术需要对这些模型进行进一步的研究,因为它们需要在自然的、非医学的图像上进行训练。此外,许多研究人员使用数据增强技术来扩展他们的数据集,避免过拟合。然而,并没有足够的研究表明数据增强或不增强对性能的影响。正确率、查全率、精密度、F1评分、接收者操作者特征曲线和曲线下面积(AUC)是这些研究中最广泛使用的测量指标。此外,我们发现了黑色素瘤和乳腺癌数据集中存在的问题,并提出了相应的解决方案。
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引用次数: 9
Writing assistant scoring system for English second language learners based on machine learning 基于机器学习的英语第二语言学习者写作辅助评分系统
IF 3 Q2 Computer Science Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0009
Jianlan Lyu
Abstract To reduce the workload of paper evaluation and improve the fairness and accuracy of the evaluation process, a writing assistant scoring system for English as a Foreign Language (EFL) learners is designed based on the principle of machine learning. According to the characteristics of the data processing process and the advantages and disadvantages of the Browser/Server (B/S) structure, the equipment structure design of the project online evaluation teaching auxiliary system is further optimized. The panda method is used to read the data, the clean method is used to realize the data preprocessing, the model test is carried out, the cross validation method is selected, the data is divided in advance, and the process of programming the problem scoring system is further optimized, the automatic scoring technology is constructed by English teaching recognition module, feature extraction module and scoring module, the table structure of programming problems is designed, the auxiliary evaluation program of English writing is designed, and the design of writing auxiliary scoring system is completed. The analysis of the experimental results shows that the accuracy of the system is close to 90%, and the total average difference is 0.56. The system can normally take out a variety of test papers. Considering the subjectivity of manual scoring and the impact of key code setting on scoring, the carefully set key code can effectively improve the scoring accuracy of the system. The scoring strategy of the automatic scoring system is effective and the scoring effect is good, and it can be used in practical application.
摘要:为了减少论文评卷的工作量,提高评卷过程的公平性和准确性,基于机器学习原理设计了一个面向英语学习者的写作辅助评分系统。根据数据处理过程的特点和浏览器/服务器(B/S)结构的优缺点,对项目在线评价教学辅助系统的设备结构设计进行了进一步优化。采用熊猫法读取数据,采用clean法实现数据预处理,进行模型检验,选择交叉验证法,对数据进行预先划分,并对问题评分系统的编程过程进行进一步优化,构建了英语教学识别模块、特征提取模块和评分模块的自动评分技术,设计了编程问题的表结构;设计了英语写作辅助评价方案,完成了写作辅助评分系统的设计。实验结果分析表明,该系统的准确率接近90%,总平均差值为0.56。该系统可以正常取出各种试卷。考虑到人工计分的主观性和键码设置对计分的影响,精心设置键码可以有效提高系统的计分准确率。自动评分系统的评分策略有效,评分效果好,可用于实际应用。
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引用次数: 1
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Journal of Intelligent Systems
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