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Cloud-based secure data storage and access control for internet of medical things using federated learning 使用联合学习实现基于云的医疗物联网安全数据存储和访问控制
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-05-19 DOI: 10.1108/ijpcc-02-2022-0041
Priyanka kumari Bhansali, Dilendra Hiran, Hemant Kothari, K. Gulati
PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has
目的本文的目的是计算是最近出现的一种云模型,它为客户提供了无限的设施,降低了客户存储和计算的速率,并提高了易用性,导致在云中存储数据的企业和个人数量激增。云服务被各种组织(教育、医疗和商业)用来存储其数据。例如,在医疗保健行业,患者医疗数据被外包给云服务器。客户可以通过云访问他们的医疗数据,而不是依赖医疗服务提供商。设计/方法论/方法本节解释了所提出的用于安全数据存储和访问控制的基于云的医疗保健系统,称为基于哈希的密文策略属性签名加密(hCP-ABES)。它为访问控制提供了更精细的粒度、安全性、身份验证和医疗数据的用户机密性。它通过散列、加密和签名增强了基于密文策略属性的加密(CP-ABE)。拟议的架构包括保护机制,以确保医疗保健和医疗信息可以通过云在卫生系统之间安全交换。图2描绘了拟议作品的建筑设计。查找对于医疗保健相关的应用程序,与托管在云服务器上的常见文档的安全联系变得越来越重要。然而,设计一种有效和安全的数据访问方法存在许多限制,包括云服务器性能、大量数据用户和各种安全要求。这项工作为经典的CP-ABE技术添加了散列和签名。它保护医疗保健数据的机密性,同时允许细粒度的访问控制。根据对安全需求的分析,这项工作使用联合学习来实现健康信息的隐私和完整性。独创性/价值物联网(IoT)技术和智能诊断植入物通过允许在任何时间、任何地点远程访问和筛查患者的健康问题,增强了医疗保健系统。医疗物联网设备监测患者的健康状况,并将这些信息组合到医疗记录中,然后将这些信息传输到云端,由医疗服务提供商查看以供决策。然而,在信息传输方面,电子健康记录的安全性和保密性成为一个主要问题。这项工作为智能医疗系统提供了有效的数据存储和访问控制,以保护机密性。CP-ABE确保了数据的机密性,还允许在更精细的级别上控制数据访问。此外,它还允许所有者在云层下建立动态的患者健康数据共享策略。hCP-ABES提出了医疗数据的细粒度数据访问、安全性、身份验证和用户隐私。本文通过散列、加密和签名对CP-ABE进行了增强。对所提出的方法进行了评估,结果表明,与其他使用联合学习的访问控制方案相比,所提出的hCP-ABES是可行的。
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引用次数: 3
A new federated genetic algorithm-based optimization technique for multi-criteria vehicle route planning using ArcGIS network analyst 一种新的基于联邦遗传算法的基于ArcGIS网络分析的多准则车辆路径规划优化技术
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-05-17 DOI: 10.1108/ijpcc-02-2022-0082
Da’ad Ahmad Albalawneh, M. A. Mohamed
PurposeUsing a real-time road network combined with historical traffic data for Al-Salt city, the paper aims to propose a new federated genetic algorithm (GA)-based optimization technique to solve the dynamic vehicle routing problem. Using a GA solver, the estimated routing time for 300 chromosomes (routes) was the shortest and most efficient over 30 generations.Design/methodology/approachIn transportation systems, the objective of route planning techniques has been revised from focusing on road directors to road users. As a result, the new transportation systems use advanced technologies to support drivers and provide them with the road information they need and the services they require to reduce traffic congestion and improve routing problems. In recent decades, numerous studies have been conducted on how to find an efficient and suitable route for vehicles, known as the vehicle routing problem (VRP). To identify the best route, VRP uses real-time information-acquired geographical information systems (GIS) tools.FindingsThis study aims to develop a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences.Originality/valueFurthermore, developing a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences. An adaptive genetic algorithm (GA) is used to determine the optimal time route, taking into account factors that affect vehicle arrival times and cause delays. In addition, ArcGIS' Network Analyst tool is used to determine the best route based on the user's preferences using a real-time map.
目的利用Al Salt city的实时道路网络和历史交通数据,提出一种新的基于联合遗传算法的优化技术来解决动态车辆路径问题。使用遗传算法求解器,300条染色体(路线)的估计路由时间是30代中最短、最有效的。设计/方法/方法在交通系统中,路线规划技术的目标已经从关注道路指挥者到道路使用者进行了修订。因此,新的交通系统使用先进的技术来支持驾驶员,并为他们提供所需的道路信息和服务,以减少交通拥堵并改善路线问题。近几十年来,人们对如何为车辆找到有效和合适的路线进行了大量研究,称为车辆路线问题(VRP)。为了确定最佳路线,VRP使用实时信息获取地理信息系统(GIS)工具。发现本研究旨在使用ArcGIS网络分析师开发一种路线规划工具,以提高成本和服务质量,并考虑几个因素,根据用户的偏好确定最佳路线。独创性/价值此外,使用ArcGIS网络分析师开发路线规划工具,以提高成本和服务质量,并考虑几个因素,根据用户的偏好确定最佳路线。考虑到影响车辆到达时间和造成延误的因素,使用自适应遗传算法(GA)来确定最佳时间路线。此外,ArcGIS的网络分析工具用于使用实时地图根据用户的偏好确定最佳路线。
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引用次数: 2
Trusted routing protocol for federated UAV ad hoc network 联邦无人机自组织网络的可信路由协议
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-04-26 DOI: 10.1108/ijpcc-01-2022-0011
Elham Kariri, Kusum Yadav
PurposeIn the final step, the trust model is applied to the on-demand federated multipath distance vector routing protocol (AOMDV) to introduce path trust as a foundation for routing selection in the route discovery phase, construct a trusted path, and implement a path warning mechanism to detect malicious nodes in the route maintenance phase, respectively.Design/methodology/approachA trust-based on-demand multipath distance vector routing protocol is being developed to address the problem of flying ad-hoc network being subjected to internal attacks and experiencing frequent connection interruptions. Following the construction of the node trust assessment model and the presentation of trust evaluation criteria, the data packet forwarding rate, trusted interaction degree and detection packet receipt rate are discussed. In the next step, the direct trust degree of the adaptive fuzzy trust aggregation network compute node is constructed. After then, rely on the indirect trust degree of neighbouring nodes to calculate the trust degree of the node in the network. Design a trust fluctuation penalty mechanism, as a second step, to defend against the switch attack in the trust model.FindingsWhen compared to the lightweight trust-enhanced routing protocol (TEAOMDV), it significantly improves the data packet delivery rate and throughput of the network significantly.Originality/valueAdditionally, it reduces the amount of routing overhead and the average end-to-end delay.
目的在最后一步,将信任模型应用于按需联合多径距离矢量路由协议(AOMDV),分别在路由发现阶段引入路径信任作为路由选择的基础,构建可信路径,并在路由维护阶段实现路径警告机制以检测恶意节点。设计/方法/方法正在开发一种基于信任的按需多径距离矢量路由协议,以解决飞行自组织网络受到内部攻击和频繁连接中断的问题。在建立节点信任评估模型和提出信任评估标准的基础上,讨论了数据包转发率、可信交互度和检测包接收率。下一步,构造自适应模糊信任聚合网络计算节点的直接信任度。然后,依靠相邻节点的间接信任度来计算节点在网络中的信任度。第二步,设计一个信任波动惩罚机制,以抵御信任模型中的切换攻击。发现与轻量级信任增强路由协议(TEAOMDV)相比,它显著提高了网络的数据包传输速率和吞吐量。独创性/价值此外,它还减少了路由开销和平均端到端延迟。
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引用次数: 0
Classification of disordered patient’s voice by using pervasive computational algorithms 应用普适计算算法对患者语音进行分类
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-01-25 DOI: 10.1108/ijpcc-07-2021-0158
A. Maddali, Habibullah Khan
PurposeCurrently, the design, technological features of voices, and their analysis of various applications are being simulated with the requirement to communicate at a greater distance or more discreetly. The purpose of this study is to explore how voices and their analyses are used in modern literature to generate a variety of solutions, of which only a few successful models exist.Design/methodologyThe mel-frequency cepstral coefficient (MFCC), average magnitude difference function, cepstrum analysis and other voice characteristics are effectively modeled and implemented using mathematical modeling with variable weights parametric for each algorithm, which can be used with or without noises. Improvising the design characteristics and their weights with different supervised algorithms that regulate the design model simulation.FindingsDifferent data models have been influenced by the parametric range and solution analysis in different space parameters, such as frequency or time model, with features such as without, with and after noise reduction. The frequency response of the current design can be analyzed through the Windowing techniques.Original valueA new model and its implementation scenario with pervasive computational algorithms’ (PCA) (such as the hybrid PCA with AdaBoost (HPCA), PCA with bag of features and improved PCA with bag of features) relating the different features such as MFCC, power spectrum, pitch, Window techniques, etc. are calculated using the HPCA. The features are accumulated on the matrix formulations and govern the design feature comparison and its feature classification for improved performance parameters, as mentioned in the results.
目的目前,语音的设计、技术特征及其对各种应用的分析都在模拟中,要求以更大的距离或更谨慎的方式进行通信。本研究的目的是探索声音及其分析如何在现代文学中被用来产生各种解决方案,其中只有少数成功的模式存在。设计/方法使用数学建模对中频倒谱系数(MFCC)、平均幅度差函数、倒谱分析和其他语音特征进行了有效建模和实现,每个算法的参数权重可变,可以在有噪声或无噪声的情况下使用。用不同的监督算法改进设计特征及其权重,这些算法规范设计模型模拟。发现不同的数据模型受到参数范围的影响,并在不同的空间参数(如频率或时间模型)中进行解分析,具有降噪、降噪和降噪后的特征。当前设计的频率响应可以通过窗口技术进行分析。原始值使用HPCA计算具有普适计算算法(PCA)的新模型及其实现场景(如具有AdaBoost的混合PCA(HPCA)、具有特征袋的PCA和具有特征袋改进的PCA),这些算法与MFCC、功率谱、间距、窗口技术等不同特征相关。这些特征在矩阵公式上累积,并控制设计特征比较及其特征分类,以改进性能参数,如结果中所述。
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引用次数: 0
Network traffic analysis through deep learning for detection of an army of bots in health IoT network 通过深度学习进行网络流量分析,以检测健康物联网网络中的机器人大军
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-01-21 DOI: 10.1108/ijpcc-10-2021-0259
G. K, Brahmananda S.H.
PurposeIoT has a wide range of applications in the health-care sector and has captured the interest of many academic and industrial communities. The health IoT devices suffer from botnet attacks as all the devices are connected to the internet. An army of compromised bots may form to launch a DDoS attack, steal confidential data of patients and disrupt the service, and hence detecting this army of bots is paramount. This study aims to detect botnet attacks in health IoT devices using the deep learning technique.Design/methodology/approachThis paper focuses on designing a method to protect health IoT devices from botnet attacks by constantly observing communication network traffic and classifying them as benign and malicious flow. The proposed algorithm analyzes the health IoT network traffic through implementing Bidirectional long-short term memory, a deep learning technique. The IoT-23 data set is considered for this research as it includes diverse botnet attack scenarios.FindingsThe performance of the proposed method is evaluated using attack prediction accuracy. It results in the highest accuracy of 84.8%, classifying benign and malicious traffic.Originality/valueThe proposed method constantly monitors the health IoT network to detect botnet attacks and classifies the traffic as benign or attack. The system is implemented using the BiLSTM algorithm and trained using the IoT-23 data set. The diversity of attack scenarios of the IoT-23 data set demonstrates the proposed algorithm's competence in detecting botnet types in a heterogeneous environment.
目标物联网在医疗保健领域有着广泛的应用,并引起了许多学术界和工业界的兴趣。由于所有设备都连接到互联网,健康物联网设备都会受到僵尸网络攻击。一支被入侵的机器人大军可能会发起DDoS攻击,窃取患者的机密数据并扰乱服务,因此检测这支机器人大军至关重要。本研究旨在使用深度学习技术检测健康物联网设备中的僵尸网络攻击。设计/方法论/方法本文专注于设计一种方法,通过不断观察通信网络流量并将其分类为良性和恶意流量,来保护健康物联网设备免受僵尸网络攻击。该算法通过实现深度学习技术双向长短期记忆来分析健康物联网网络流量。IoT-23数据集被考虑用于本研究,因为它包括不同的僵尸网络攻击场景。发现使用攻击预测精度来评估所提出方法的性能。它对良性和恶意流量进行了分类,准确率最高,为84.8%。独创性/价值所提出的方法不断监测健康物联网网络,以检测僵尸网络攻击,并将流量分类为良性或攻击。该系统使用BiLSTM算法实现,并使用IoT-23数据集进行训练。IoT-23数据集攻击场景的多样性表明了所提出的算法在异构环境中检测僵尸网络类型的能力。
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引用次数: 4
A lightweight and flexible mutual authentication and key agreement protocol for wearable sensing devices in WBAN WBAN中一种轻量级、灵活的可穿戴传感设备相互认证和密钥协商协议
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-01-14 DOI: 10.1108/ijpcc-06-2021-0144
Sandeep Kumar Reddy Thota, C. Mala, G. Krishnan
PurposeA wireless body area network (WBAN) is a collection of sensing devices attached to a person’s body that is typically used during health care to track their physical state. This paper aims to study the security challenges and various attacks that occurred while transferring a person’s sensitive medical diagnosis information in WBAN.Design/methodology/approachThis technology has significantly gained prominence in the medical field. These wearable sensors are transferring information to doctors, and there are numerous possibilities for an intruder to pose as a doctor and obtain information about the patient’s vital information. As a result, mutual authentication and session key negotiations are critical security challenges for wearable sensing devices in WBAN. This work proposes an improved mutual authentication and key agreement protocol for wearable sensing devices in WBAN. The existing related schemes require more computational and storage requirements, but the proposed method provides a flexible solution with less complexity.FindingsAs sensor devices are resource-constrained, proposed approach only makes use of cryptographic hash-functions and bit-wise XOR operations, hence it is lightweight and flexible. The protocol’s security is validated using the AVISPA tool, and it will withstand various security attacks. The proposed protocol’s simulation and performance analysis are compared to current relevant schemes and show that it produces efficient outcomes.Originality/valueThis technology has significantly gained prominence in the medical sector. These sensing devises transmit information to doctors, and there are possibilities for an intruder to pose as a doctor and obtain information about the patient’s vital information. Hence, this paper proposes a lightweight and flexible protocol for mutual authentication and key agreement for wearable sensing devices in WBAN only makes use of cryptographic hash-functions and bit-wise XOR operations. The proposed protocol is simulated using AVISPA tool and its performance is better compared to the existing methods. This paper proposes a novel improved mutual authentication and key-agreement protocol for wearable sensing devices in WBAN.
目的无线体域网络(WBAN)是附着在人体上的传感设备的集合,通常在医疗保健期间用于跟踪人体的身体状态。本文旨在研究无线宽带网络在传输个人敏感医疗诊断信息时所面临的安全挑战和各种攻击。设计/方法/方法这项技术在医学领域已获得显著的突出地位。这些可穿戴传感器将信息传递给医生,而入侵者冒充医生获取患者重要信息的可能性有很多。因此,相互认证和会话密钥协商是无线宽带网络中可穿戴传感设备面临的关键安全挑战。本文提出了一种改进的WBAN可穿戴传感设备相互认证和密钥协商协议。现有的相关方案对计算量和存储量的要求较高,而本文提出的方法提供了一种灵活且复杂度较低的解决方案。发现sas传感器设备资源受限,所提出的方法仅利用加密哈希函数和按位异或操作,因此轻量级且灵活。该协议的安全性通过AVISPA工具验证,能够抵御各种安全攻击。将该协议的仿真和性能分析与现有的相关方案进行了比较,结果表明该协议能够产生有效的结果。独创性/价值这项技术在医疗领域获得了显著的突出地位。这些传感装置将信息传送给医生,入侵者就有可能冒充医生获取病人的重要信息。因此,本文提出了一种轻量级、灵活的WBAN可穿戴传感设备相互认证和密钥协商协议,该协议仅利用加密哈希函数和按位异或操作。利用AVISPA工具对该协议进行了仿真,结果表明该协议的性能优于现有协议。提出了一种新的改进的WBAN可穿戴传感设备相互认证和密钥协商协议。
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引用次数: 0
Underwater target tracking in three-dimensional environment using intelligent sensor technique 基于智能传感器技术的三维水下目标跟踪
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-01-01 DOI: 10.1108/IJPCC-07-2021-0154
B. Jagan, S. Rao, M. Lakshmi
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引用次数: 0
A novel utilization-aware and power-delay-aware intelligent DMA controller for video streaming used in AI applications 一种用于人工智能应用中视频流的新型利用率感知和功率延迟感知智能DMA控制器
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-01-01 DOI: 10.1108/IJPCC-11-2021-0280
B. SantoshKumar, E. KrishnaKumar
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引用次数: 0
Intrinsic interference suppressed FBMC QAM for MU-MIMO systems in computing and communications 计算和通信中MU-MIMO系统固有干扰抑制FBMC QAM
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-01-01 DOI: 10.1108/IJPCC-09-2021-0217
K. ShobhaY., H. Rangaraju
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引用次数: 0
A novel approach for detection and classification of re-entrant crack using modified CNNetwork 一种利用改进的细胞神经网络检测和分类凹入裂纹的新方法
IF 2.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-12-21 DOI: 10.1108/ijpcc-08-2021-0200
Shadrack Fred Mahenge, Ala Alsanabani
PurposeIn the purpose of the section, the cracks that are in the construction domain may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse.Design/methodology/approachIn the modern era of digital image processing, it has captured the importance in all the domain of engineering and all the fields irrespective of the division of the engineering, hence, in this research study an attempt is made to deal with the wall cracks which are found or searched during the building inspection process, in the present context in association with the unique U-net architecture is used with convolutional neural network method.FindingsIn the construction domain, the cracks may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Hence, for the modeling of the proposed system, it is considered with the image database from the Mendeley portal for the analysis. With the experimental analysis, it is noted and observed that the proposed system was able to detect the wall cracks, search the flat surface by the result of no cracks found and it is successful in dealing with the two phases of operation, namely, classification and segmentation with the deep learning technique. In contrast to other conventional methodologies, the proposed methodology produces excellent performance results.Originality/valueThe originality of the paper is to find the portion of the cracks on the walls using deep learning architecture.
目的在本节中,施工区域内的裂缝可能很常见,通常通过可见范围内的人工检查来固定,但对于同一建筑中可能存在于人眼较远位置但可以用相机捕捉到的裂缝。如果裂缝尺寸很大,可以看到,但由于墙体施工中的缺陷,裂缝很少,需要真实的信息和确认才能成功完成墙体裂缝,因为墙体中的这些裂缝会导致结构倒塌。设计/方法/方法在数字图像处理的现代时代,无论工程的划分如何,它都在所有工程领域和所有领域中占据了重要地位,因此,在本研究中,试图处理在建筑检查过程中发现或搜索到的墙裂缝,在当前上下文中,结合独特的U-net架构与卷积神经网络方法一起使用。发现在建筑领域,裂缝可能很常见,通常通过可见范围内的人工检查来固定,但对于同一建筑中可能存在于人眼较远位置但可以用相机捕捉到的裂缝。如果裂缝尺寸很大,可以看到,但由于墙体施工中的缺陷,裂缝很少,需要真实的信息和确认才能成功完成墙体裂缝,因为墙体中的这些裂缝会导致结构倒塌。因此,对于所提出的系统的建模,考虑使用Mendeley门户网站的图像数据库进行分析。通过实验分析,可以注意到并观察到,所提出的系统能够检测墙壁裂缝,并根据未发现裂缝的结果搜索平面,并且能够成功地处理深度学习技术的两个操作阶段,即分类和分割。与其他传统方法相比,所提出的方法产生了优异的性能结果。独创性/价值本文的独创性是使用深度学习建筑来发现墙壁上的裂缝部分。
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引用次数: 2
期刊
International Journal of Pervasive Computing and Communications
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