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Edge computing is an important cornerstone for the construction of 5G networks, but with the development of Internet technology, the computer nodes are extremely vulnerable in attacks, especially clone attacks, causing casualties. The principle of clonal node attack is that the attacker captures the legitimate nodes in the network and obtains all their legitimate information, copies several nodes with the same ID and key information, and puts these clonal nodes in different locations in the network to attack the edge computing devices, resulting in network paralysis. How to quickly and efficiently identify clone nodes and isolate them becomes the key to prevent clone node attacks and improve the security of edge computing. In order to improve the degree of protection of edge computing and identify clonal nodes more quickly and accurately, based on edge computing of machine learning, this paper uses case analysis method, the literature analysis method, and other methods to collect data from the database, and uses parallel algorithm to build a model of clonal node recognition. The results show that the edge computing based on machine learning can greatly improve the efficiency of clonal node recognition, the recognition speed is more than 30% faster than the traditional edge computing, and the recognition accuracy reaches 0.852, which is about 50% higher than the traditional recognition. The results show that the edge computing clonal node method based on machine learning can improve the detection success rate of clonal nodes and reduce the energy consumption and transmission overhead of nodes, which is of great significance to the detection of clonal nodes.
Xiang Xiao; Ming Zhao. Edge computing clone node recognition system based on machine learning. Neural Computing and Applications 2021, 1 -12.
AMA StyleXiang Xiao, Ming Zhao. Edge computing clone node recognition system based on machine learning. Neural Computing and Applications. 2021; ():1-12.
Chicago/Turabian StyleXiang Xiao; Ming Zhao. 2021. "Edge computing clone node recognition system based on machine learning." Neural Computing and Applications , no. : 1-12.
As with the maturation and diversification of social services, social data outsourcing has become pervasive. In this context, online social networks outsource their real-time social data to a social data provider, who responses to query requests issued from data consumers. However, dishonest or malicious social data providers might tamper with collected social data by adding fake data and deleting/modifying raw data, and further return inauthentic query results to data consumers. In this article, we make the first attempt to study the problem of authenticity verification for dynamic social data. To this end, we first propose a novel authenticity verification framework, and then, propose a self-balancing hash tree scheme to tackle challenges brought on by the vast scale and dynamics of social data. To maximize the search efficiency for the self-balancing hash tree, we introduce a balance factor to define an unbalanced node as its balance factor is not chosen from a predetermined set. Furthermore, we propose three algorithms to balance unbalanced nodes in the self-balancing hash tree. Rigorous theoretical analyses prove that our framework can determinately detect any fake query results returned by data consumers. Experimental results built on a real Twitter dataset show that our scheme is efficient and effective.
Xin Yao; Xiaoping Yan; Ming Zhao. Authenticity Verification for Dynamic Social Data Outsourcing. IEEE Systems Journal 2021, PP, 1 -11.
AMA StyleXin Yao, Xiaoping Yan, Ming Zhao. Authenticity Verification for Dynamic Social Data Outsourcing. IEEE Systems Journal. 2021; PP (99):1-11.
Chicago/Turabian StyleXin Yao; Xiaoping Yan; Ming Zhao. 2021. "Authenticity Verification for Dynamic Social Data Outsourcing." IEEE Systems Journal PP, no. 99: 1-11.
With the rapid development of Internet of Things technology, wireless sensor networks have been widely used in many places. This study mainly discusses the routing optimization strategy of the IoT perceptive layer based on the improved cat swarm algorithm. This study simulates a perceptive network with 100 nodes deployed randomly. As SDWSN for Internet of Things applications, in order to simulate the data transmission requirements of IoT communication and ensure the fairness of experimental comparison, this study uses the pseudo-random mechanism to generate the source address and destination address of data packets. A special SDN controller node is added to the network. The SDN controller node broadcasts information to each sensing node, and the common sensing node sends node information to the SDN controller. The SDN controller can survive the global time graph of the entire network according to the information of the common node. In order to avoid the problem of high energy consumption of cluster heads caused by long-distance data transmission, the cat algorithm protocol adopts multi-hop communication between cluster heads and BS and uses network overhead index to quantify link overhead as the basis for cluster heads to select the next hop node. When the inter-cluster multi-hop route is successfully established, the wireless sensor node begins to collect data and send it to BS node. Six monitoring nodes, two coordinators and one workstation were selected as the test objects. The data volume sent by each node was 2000, and the accuracy rate of test transmission information at different rates and transmission distances was determined. The group network coverage rate of cat swarm algorithm is always above 95%, and the average energy loss of nodes is the highest and less than 36%. The results show that the aggregate of energy consumption of cluster heads and the variance of energy consumption are the lowest in the improved cat cluster algorithm, which ensures the reliable transmission of node data.
Xiang Xiao; Ming Zhao. Routing optimization strategy of IoT awareness layer based on improved cat swarm algorithm. Neural Computing and Applications 2021, 1 -12.
AMA StyleXiang Xiao, Ming Zhao. Routing optimization strategy of IoT awareness layer based on improved cat swarm algorithm. Neural Computing and Applications. 2021; ():1-12.
Chicago/Turabian StyleXiang Xiao; Ming Zhao. 2021. "Routing optimization strategy of IoT awareness layer based on improved cat swarm algorithm." Neural Computing and Applications , no. : 1-12.
In cardiology, ultrasound is often used to diagnose heart disease associated with myocardial infarction. This study aims to develop robust segmentation techniques for segmenting the left ventricle (LV) in ultrasound images to check myocardium movement during heartbeat. The proposed technique utilizes machine learning (ML) techniques such as the active contour (AC) and convolutional neural networks (CNNs) for segmentation. Medical experts determine the consistency between the proposed ML approach, which is a state-of-the-art deep learning method, and the manual segmentation approach. These methods are compared in terms of performance indicators such as the ventricular area (VA), ventricular maximum diameter (VMXD), ventricular minimum diameter (VMID), and ventricular long axis angle (AVLA) measurements. Furthermore, the Dice similarity coefficient, Jaccard index, and Hausdorff distance are measured to estimate the agreement of the LV segmented results between the automatic and visual approaches. The obtained results indicate that the proposed techniques for LV segmentation are useful and practical. There is no significant difference between the use of AC and CNN in image segmentation; however, the AC method could obtain comparable accuracy as the CNN method using less training data and less run-time.
Xiliang Zhu; Yang Wei; Yu Lu; Ming Zhao; Ke Yang; Shiqian Wu; Hui Zhang; Kelvin K.L. Wong. Comparative analysis of active contour and convolutional neural network in rapid left-ventricle volume quantification using echocardiographic imaging. Computer Methods and Programs in Biomedicine 2020, 199, 105914 .
AMA StyleXiliang Zhu, Yang Wei, Yu Lu, Ming Zhao, Ke Yang, Shiqian Wu, Hui Zhang, Kelvin K.L. Wong. Comparative analysis of active contour and convolutional neural network in rapid left-ventricle volume quantification using echocardiographic imaging. Computer Methods and Programs in Biomedicine. 2020; 199 ():105914.
Chicago/Turabian StyleXiliang Zhu; Yang Wei; Yu Lu; Ming Zhao; Ke Yang; Shiqian Wu; Hui Zhang; Kelvin K.L. Wong. 2020. "Comparative analysis of active contour and convolutional neural network in rapid left-ventricle volume quantification using echocardiographic imaging." Computer Methods and Programs in Biomedicine 199, no. : 105914.
We propose a robust technique for segmenting magnetic resonance images of post-atrial septal occlusion intervention in the cardiac chamber. A variant of the U-Net architecture is used to perform atrial segmentation via a deep convolutional neural network, and we compare performance with the Kass snake model. It can be used to determine the surgical success of atrial septal occlusion (ASO) pre- and post- the implantation of the septal occluder, which is based on the volume restoration of the right atria (RA) and left atria (LA). The method was evaluated on a test dataset containing 550 two-dimensional image slices, outperforming conventional active contouring regarding the Dice similarity coefficient, Jaccard index, and Hausdorff distance, and achieving segmentation in the presence of ghost artifacts that occlude the atrium outline. This problem has been unsolvable using traditional machine learning algorithm pertaining to active contouring via the Kass snake algorithm. Moreover, the proposed technique is closer to manual segmentation than the snakes active contour model in mean of atrial area (M-AA), mean of atrial maximum diameter (M-AMXD), mean atrial minimum diameter (M-AMID), and mean angle of the atrial long axis (M-AALA). After segmentation, we compute the volume ratio of right to left atria, obtaining a smaller ratio that indicates better restoration. Hence, the proposed technique allows to evaluate the surgical success of atrial septal occlusion and may support diagnosis regarding the accurate evaluation of atrial septal defects before and after occlusion procedures.
Ming Zhao; Yang Wei; Yu Lu; Kelvin K.L. Wong. A novel U-Net approach to segment the cardiac chamber in magnetic resonance images with ghost artifacts. Computer Methods and Programs in Biomedicine 2020, 196, 105623 .
AMA StyleMing Zhao, Yang Wei, Yu Lu, Kelvin K.L. Wong. A novel U-Net approach to segment the cardiac chamber in magnetic resonance images with ghost artifacts. Computer Methods and Programs in Biomedicine. 2020; 196 ():105623.
Chicago/Turabian StyleMing Zhao; Yang Wei; Yu Lu; Kelvin K.L. Wong. 2020. "A novel U-Net approach to segment the cardiac chamber in magnetic resonance images with ghost artifacts." Computer Methods and Programs in Biomedicine 196, no. : 105623.
Due to their mobile character, ground vehicles and unmanned aerial vehicles (UAVs) are currently being considered as sensing devices that can collect data in the Internet of Things (IoT). Building and enhancing trust and security environments in data collection processes are fundamental and essential requirements. Here, we proposed a novel scheme named “Trust Data Collections via Vehicles joint with UAVs in the Smart Internet of Things” (T‐SIoTs scheme), which targets to establish a trust‐based environment for data collections by utilizing both trust vehicles and UAVs. First, to optimize security aspect, data center (DC) selected trust‐based vehicles as mobile data collectors via analyzing and digging historical datasets. To promise coverage regions of data collections, several static stations are established, which can be utilized as static data collectors. Second, UAVs are arranged by the DC to collect data stored by both trust‐based vehicles and static data collectors. In the T‐SIoTs scheme, trajectories of UAVs are designed according to shortest‐distance‐first routing scheme. Comprehensive theoretical analyses and experiments have been provided to evaluate and support the T‐SIoTs scheme. Compared with the previous studies, the T‐SIoTs scheme can improve the security ratio by 46.133% to 54.60% approximately. And with the routing scheme, the energy consumptions of UAVs can be reduced by 46.93% approximately.
Ting Li; Wei Liu; Tian Wang; Zhao Ming; Xiong Li; Ming Ma. Trust data collections via vehicles joint with unmanned aerial vehicles in the smart Internet of Things. Transactions on Emerging Telecommunications Technologies 2020, 1 .
AMA StyleTing Li, Wei Liu, Tian Wang, Zhao Ming, Xiong Li, Ming Ma. Trust data collections via vehicles joint with unmanned aerial vehicles in the smart Internet of Things. Transactions on Emerging Telecommunications Technologies. 2020; ():1.
Chicago/Turabian StyleTing Li; Wei Liu; Tian Wang; Zhao Ming; Xiong Li; Ming Ma. 2020. "Trust data collections via vehicles joint with unmanned aerial vehicles in the smart Internet of Things." Transactions on Emerging Telecommunications Technologies , no. : 1.
With the development of society and the exhaustion of fossil energy, we need to identify new alternative energy sources. Nuclear energy is an ideal choice, but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors. Therefore, we studied the radiation performance of the fusion material RAFM steel. We used the GDM algorithm to upgrade the annealing stabilization process of simulated annealing algorithm. The yield stress performance of RAFM steel was successfully predicted by the hybrid model, which combined simulated annealing with the support vector machine for the first time. The prediction process was as follows: first, we used the improved annealing algorithm to optimize the SVR model after training on a training dataset. Next, we established the yield stress prediction model of RAFM steel. By testing the model and conducting sensitivity analysis on the model, we can conclude that, compared with other similar models such as the ANN, linear regression, generalized regression neural network, and random forest, the predictive attribute variables cover all of the variables in the training set. Moreover, the generalization performance of the model on the test set is superior to that of other similar prediction models. Thus, this paper introduces a new method for the study of RAFM steel.
Sifan Long; Ming Zhao. Theoretical study of GDM-SA-SVR algorithm on RAFM steel. Artificial Intelligence Review 2020, 53, 4601 -4623.
AMA StyleSifan Long, Ming Zhao. Theoretical study of GDM-SA-SVR algorithm on RAFM steel. Artificial Intelligence Review. 2020; 53 (6):4601-4623.
Chicago/Turabian StyleSifan Long; Ming Zhao. 2020. "Theoretical study of GDM-SA-SVR algorithm on RAFM steel." Artificial Intelligence Review 53, no. 6: 4601-4623.
Mobile edge computing (MEC) is envisioned as a promising platform for supporting emerging computation‐intensive applications on capacity and resource constrained mobile devices (MDs). In this platform, the task with high computing resource demand can be offloaded to edge nodes for computing. Moreover, the computing result can be cached to edge nodes. When other MDs request the task that has been cached, the edge nodes can directly return the result to MD. However, the storage capacity of edge nodes is limited, the effective task prediction and caching scheme is one of the key issues for MEC. In this article, a matrix completion technology based content popularity prediction joint cache placement (MCTCPP‐CP) scheme is proposed to tackle this issue for MEC. On the one hand, the MCTCPP‐CP scheme is the first scheme using matrix completion (MC) technology to content popularity prediction. It proved by experiments that the accuracy of using MC technology to estimate caching content is improved compared with the previous methods. On the other hand, a cache placement decision approach based on the benefit of unit storage is proposed. Extensive numerical studies demonstrate the superior performance of our MCTCPP‐CP scheme. The key performance indicators such as task duration, hit rate, estimated error are better than previous schemes by about: 0.13% to 14.01%, 17.28% to 37.65%, and 8.17%.
Jiawei Tan; Wei Liu; Tian Wang; Ming Zhao; Anfeng Liu; Shaobo Zhang. A high‐accurate content popularity prediction computational modeling for mobile edge computing using matrix completion technology. Transactions on Emerging Telecommunications Technologies 2020, 32, 1 .
AMA StyleJiawei Tan, Wei Liu, Tian Wang, Ming Zhao, Anfeng Liu, Shaobo Zhang. A high‐accurate content popularity prediction computational modeling for mobile edge computing using matrix completion technology. Transactions on Emerging Telecommunications Technologies. 2020; 32 (6):1.
Chicago/Turabian StyleJiawei Tan; Wei Liu; Tian Wang; Ming Zhao; Anfeng Liu; Shaobo Zhang. 2020. "A high‐accurate content popularity prediction computational modeling for mobile edge computing using matrix completion technology." Transactions on Emerging Telecommunications Technologies 32, no. 6: 1.
Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the capacity of the MEC-BS, the connection between the vehicle and the MEC-BS may be intermittent. If we can expect the availability of MEC-BS through cognitive computing, we can significantly improve the performance in a mobile environment. Based on this idea, we propose a offloading optimization algorithm based on availability prediction. We examine the admission control decision of MEC-BS and the mobility problem, in which we improve the accuracy of availability prediction based on Empirical Mode Decomposition(EMD) and LSTM in deep learning. Firstly, we calculate the availability of MEC, completion time, and energy consumption together to minimize the overall cost. Then, we use a game method to obtain the optimal offloading decision. Finally, the experimental results show that the algorithm can save energy and shorten the completion time more effectively than other existing algorithms in the mobile environment.
Chaoxiong Cui; Ming Zhao; Kelvin Wong. An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges. Sensors 2019, 19, 4467 .
AMA StyleChaoxiong Cui, Ming Zhao, Kelvin Wong. An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges. Sensors. 2019; 19 (20):4467.
Chicago/Turabian StyleChaoxiong Cui; Ming Zhao; Kelvin Wong. 2019. "An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges." Sensors 19, no. 20: 4467.
For most regression tasks, we often use an ensemble learning technology of Bagging algorithm. However, the traditional Bagging algorithm is susceptible to extreme values. This leads to high bias and high variance in the prediction process. Therefore, this paper proposes an improved Bagging algorithm based on the best decision Committee model and the idea of selecting the base learner, and we have presented the idea of using the decision-making committee to filter learner, train the decision-making committee by the base learner to classify the error on the test set. Using the optimal interval separation factor’s mathematical model which is derived by the Lagrange multiplier method to classify the evaluation levels. The decision committee is trained according to the assigned evaluation level, and the learner is selected and assembled according to the decision result of the decision committee members. Meanwhile, our theoretical analysis shows that there are two different cases, which we can use maximum likelihood estimation and stochastic process theory to build mathematical models for analysis. The analysis results based on reduced activated ferritic/martensitic (RAFM) steel data sets show that the proposed algorithm can be applied to data sets with high dimension, high redundancy, high contradictory samples, sparse data sets, and then, we gives the strict theoretical framework to guarantees the further development and promotion. This gives algorithm model.
Sifan Long; Ming Zhao; Jieqiong Song. A novel PCA-DC-Bagging algorithm on yield stress prediction of RAFM steel. Computing 2019, 102, 19 -42.
AMA StyleSifan Long, Ming Zhao, Jieqiong Song. A novel PCA-DC-Bagging algorithm on yield stress prediction of RAFM steel. Computing. 2019; 102 (1):19-42.
Chicago/Turabian StyleSifan Long; Ming Zhao; Jieqiong Song. 2019. "A novel PCA-DC-Bagging algorithm on yield stress prediction of RAFM steel." Computing 102, no. 1: 19-42.
As the explosive development of social network sites, social data has successfully captured either individuals’ or entities’ attention due to having tremendous commercial value. The initial step to my various insights from social data is how to obtain authentic social data. Social data outsourcing as a new paradigm has been pervasive, in which a social data provider collects integrity social data from different social network sites and resells to data consumers on demand. However, some dishonest activities, like adding fake data, deleting/modifying raw data, drive us to consider verifiable topic-based rank search problem on social data outsourcing scenario. To guarantee the authenticity of social data, we propose two schemes. In our basic scheme, social network sites generate unforgeable auxiliary information and outsource to the social data provider as well as social data. Data consumers can probabilistically verify the correctness and completeness of the query results with the help of verification objects derived from auxiliary information. To reduce the number of the most related topic labels, we propose an enhanced scheme, in which the social network sites first cluster similar topics together, and then generate auxiliary information. Rigorous security and performance analyses prove that our proposed schemes are safe and effective. In addition, our experimental results built on a real Twitter dataset which demonstrates the efficacy and efficiency of our schemes.
Xin Yao; Yizhu Zou; Zhigang Chen; Ming Zhao; Qin Liu. Topic-based rank search with verifiable social data outsourcing. Journal of Parallel and Distributed Computing 2019, 134, 1 -12.
AMA StyleXin Yao, Yizhu Zou, Zhigang Chen, Ming Zhao, Qin Liu. Topic-based rank search with verifiable social data outsourcing. Journal of Parallel and Distributed Computing. 2019; 134 ():1-12.
Chicago/Turabian StyleXin Yao; Yizhu Zou; Zhigang Chen; Ming Zhao; Qin Liu. 2019. "Topic-based rank search with verifiable social data outsourcing." Journal of Parallel and Distributed Computing 134, no. : 1-12.
In the body sensor networks (BSNs), the data redundancy and transmission delay are two problems for improving network performance. In the previous scheme, multi-sensor fusion is used to reduce the energy consumption of the sensor network, but it causes larger delay. To reduce the amount of redundant data and transmission delay, an adjusting forwarder nodes and duty cycle using packet aggregation routing (AFNDCAR) scheme is proposed for BSNs. The main contributions of the AFNDCAR scheme are as follows: (a) nodes select forwarder nodes based on the higher energy consumption, longer queue length of packets and waiting time of packets aggregation to reduce the transmission delay. (b) In the dense network, the number of forwarder nodes and the probability for generating data packets are adjusted to minimize the transmission delay and maximum the packet transmission success ratio. (c) In the sparse network, the duty cycle of nodes can be adjusted using the energy left of nodes. The theoretical analysis show that the amount of redundancy data, the transmission delay and energy efficiency of nodes improved significantly.
Xiao Liu; Ming Zhao; Anfeng Liu; Kelvin Kian Loong Wong. Adjusting forwarder nodes and duty cycle using packet aggregation routing for body sensor networks. Information Fusion 2019, 53, 183 -195.
AMA StyleXiao Liu, Ming Zhao, Anfeng Liu, Kelvin Kian Loong Wong. Adjusting forwarder nodes and duty cycle using packet aggregation routing for body sensor networks. Information Fusion. 2019; 53 ():183-195.
Chicago/Turabian StyleXiao Liu; Ming Zhao; Anfeng Liu; Kelvin Kian Loong Wong. 2019. "Adjusting forwarder nodes and duty cycle using packet aggregation routing for body sensor networks." Information Fusion 53, no. : 183-195.
Mobile Edge Computing (MEC) is an innovative technique, which can provide cloud-computing near mobile devices on the edge of networks. Based on the MEC architecture, this paper proposes an ARIMA-BP-based Selective Offloading (ABSO) strategy, which minimizes the energy consumption of mobile devices while meeting the delay requirements. In ABSO, we exploit an ARIMA-BP model for estimating computation capacity of the edge cloud, and then design a Selective Offloading Algorithm for obtaining offloading strategy. Simulation results reveal that the ABSO can apparently decrease the energy consumption of mobile devices in comparison with other offloading methods.
Ming Zhao; Ke Zhou. Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks. Algorithms 2019, 12, 48 .
AMA StyleMing Zhao, Ke Zhou. Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks. Algorithms. 2019; 12 (2):48.
Chicago/Turabian StyleMing Zhao; Ke Zhou. 2019. "Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks." Algorithms 12, no. 2: 48.
Due to the dynamic change of the opportunistic network topology and the lack of stable information transmission paths between nodes, the traditional topology-based routing algorithm cannot achieve the desired routing performance. To address of this problem, this paper proposes a routing algorithm based on trajectory prediction (RATP). The routing protocol based on trajectory prediction can efficiently and quickly adapt to the network link quality instability and the dynamic changes of network topology. RATP algorithm constructs a node mobility model by analyzing the historical mobility characteristics of the nodes. According to the node prediction information, the metric value of the candidate node is calculated, and the node with the smaller metric value is selected as the data forwarding node, which can effectively reduce the packet loss rate and avoids excessive consumption. Simulation results show that compared with other algorithms, the proposed algorithm has higher data delivery ratio, and end-to-end data delay and routing overhead are significantly reduced.
Peijun Zou; Ming Zhao; Jia Wu; Leilei Wang. Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks. Information 2019, 10, 49 .
AMA StylePeijun Zou, Ming Zhao, Jia Wu, Leilei Wang. Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks. Information. 2019; 10 (2):49.
Chicago/Turabian StylePeijun Zou; Ming Zhao; Jia Wu; Leilei Wang. 2019. "Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks." Information 10, no. 2: 49.
Images crowdsourcing of mobile devices can be applied to many real-life application scenarios. However, this type of scenario application often faces issues such as the limitation of bandwidth, insufficient storage space, and the processing capability of CPU. These lead to only a few photos that can be crowdsourced. Therefore, it is a great challenge to use a limited number of resources to select photos and make it possible to cover the target area maximally. In this paper, the geographic and geometric information of the photo called data-unit is used to cover the target area as much as possible. Compared with traditional content-based image delivery methods, the network delay and computational costs can be greatly reduced. In the case of resource constraints, this paper uses the utility of photos to measure the coverage of the target area, and improves a photo utility calculation method based on data-unit. In the meantime, this paper proposes the minimum selection problem of images under the coverage requirements, and designs a selection algorithm based on greedy strategies. Compared with other traditional random selection algorithms, the results prove the effectiveness and superiority of the minimum selection algorithm.
Jieqiong Song; Ming Zhao; Sifan Long. The Minimum Selection of Crowdsourcing Images under the Resource Budget. Symmetry 2018, 10, 256 .
AMA StyleJieqiong Song, Ming Zhao, Sifan Long. The Minimum Selection of Crowdsourcing Images under the Resource Budget. Symmetry. 2018; 10 (7):256.
Chicago/Turabian StyleJieqiong Song; Ming Zhao; Sifan Long. 2018. "The Minimum Selection of Crowdsourcing Images under the Resource Budget." Symmetry 10, no. 7: 256.
In many developing or underdeveloped countries, limited medical resources and large populations may affect the survival of mankind. The research for the medical information system and recommendation of effective treatment methods may improve diagnosis and drug therapy for patients in developing or underdeveloped countries. In this study, we built a system model for the drug therapy, relevance parameter analysis, and data decision making in non-small cell lung cancer. Based on the probability analysis and status decision, the optimized therapeutic schedule can be calculated and selected, and then effective drug therapy methods can be determined to improve relevance parameters. Statistical analysis of clinical data proves that the model of the probability analysis and decision making can provide fast and accurate clinical data.
Jia Wu; Yanlin Tan; Zhigang Chen; Ming Zhao. Data Decision and Drug Therapy Based on Non-Small Cell Lung Cancer in a Big Data Medical System in Developing Countries. Symmetry 2018, 10, 152 .
AMA StyleJia Wu, Yanlin Tan, Zhigang Chen, Ming Zhao. Data Decision and Drug Therapy Based on Non-Small Cell Lung Cancer in a Big Data Medical System in Developing Countries. Symmetry. 2018; 10 (5):152.
Chicago/Turabian StyleJia Wu; Yanlin Tan; Zhigang Chen; Ming Zhao. 2018. "Data Decision and Drug Therapy Based on Non-Small Cell Lung Cancer in a Big Data Medical System in Developing Countries." Symmetry 10, no. 5: 152.
The vehicular communication networks, which can employ mobile, intelligent sensing devices with participatory sensing to gather data, could be an efficient and economical way to build various applications based on big data. However, high quality data gathering for vehicular communication networks which is urgently needed faces a lot of challenges. So, in this paper, a fine-grained data collection framework is proposed to cope with these new challenges. Different from classical data gathering which concentrates on how to collect enough data to satisfy the requirements of applications, a Quality Utilization Aware Data Gathering (QUADG) scheme is proposed for vehicular communication networks to collect the most appropriate data and to best satisfy the multidimensional requirements (mainly including data gathering quantity, quality, and cost) of application. In QUADG scheme, the data sensing is fine-grained in which the data gathering time and data gathering area are divided into very fine granularity. A metric named “Quality Utilization” (QU) is to quantify the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms are proposed. The first algorithm is to ensure that the application which has obtained the specified quantity of sensing data can minimize the cost and maximize data quality by maximizing QU. The second algorithm is to ensure that the application which has obtained two requests of application (the quantity and quality of data collection, or the quantity and cost of data collection) could maximize the QU. The third algorithm is to ensure that the application which aims to satisfy the requirements of quantity, quality, and cost of collected data simultaneously could maximize the QU. Finally, we compare our proposed scheme with the existing schemes via extensive simulations which well justify the effectiveness of our scheme.
Yingying Ren; Anfeng Liu; Ming Zhao; Changqin Huang; Tian Wang. Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks. Wireless Communications and Mobile Computing 2018, 2018, 1 -25.
AMA StyleYingying Ren, Anfeng Liu, Ming Zhao, Changqin Huang, Tian Wang. Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks. Wireless Communications and Mobile Computing. 2018; 2018 ():1-25.
Chicago/Turabian StyleYingying Ren; Anfeng Liu; Ming Zhao; Changqin Huang; Tian Wang. 2018. "Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks." Wireless Communications and Mobile Computing 2018, no. : 1-25.
In real network environment, nodes may acquire the communication destination during data transmission and find a suitable neighbor node to perform effective data classification transmission. This is similar to finding certain transmission targets during data transmission with mobile devices. However, the node cache space in networks is limited, and waiting for the destination node can also cause end-to-end delay. To improve the transmission environment, this study established Data Transmission Probability and Cache Management method. According to selection of high meeting probability node, cache space is reconstructed by node. It is good for nodes to improve delivery ratio and reduce delay. Through experiments and the comparison of opportunistic network algorithms, this method improves the cache utilization rate of nodes, reduces data transmission delay, and improves the overall network efficiency.
Jia Wu; Zhigang Chen; Ming Zhao. Information Transmission Probability and Cache Management Method in Opportunistic Networks. Wireless Communications and Mobile Computing 2018, 2018, 1 -9.
AMA StyleJia Wu, Zhigang Chen, Ming Zhao. Information Transmission Probability and Cache Management Method in Opportunistic Networks. Wireless Communications and Mobile Computing. 2018; 2018 ():1-9.
Chicago/Turabian StyleJia Wu; Zhigang Chen; Ming Zhao. 2018. "Information Transmission Probability and Cache Management Method in Opportunistic Networks." Wireless Communications and Mobile Computing 2018, no. : 1-9.
Quickly and efficiently transmitting data to sink via intelligent routing is an important issue in wireless sensor networks. In previous scenarios, there has existed the phenomenon of “energy hole,”which results in difficulties in synchronous optimization of energy and delay. Thus, a smart High-Speed Backbone Path (HSBP) construction approach is proposed in this paper. In the HSBP approach, several High-speed Backbone Paths (HBPs) are established at different locations of the network, and the duty cycles of nodes on the HBPs are increased to 1; therefore, the data are forwarded by HBPs without the existence of sleeping delay, which greatly reduces transmission latency. Furthermore, the HBPs are built in regions with adequate residual energy, and they are switched periodically; thus, more nodes can be utilized to equalize the energy consumption. A comprehensive performance analysis demonstrates that the HSBP approach has obvious advantages in improving network performance compared with previous studies; it reduces transmission delay by 48.10% and improves energy utilization by 38.21% while guaranteeing the same network lifetime.
Anfeng Liu; Mingfeng Huang; Ming Zhao; Tian Wang. A Smart High-Speed Backbone Path Construction Approach for Energy and Delay Optimization in WSNs. IEEE Access 2018, 6, 13836 -13854.
AMA StyleAnfeng Liu, Mingfeng Huang, Ming Zhao, Tian Wang. A Smart High-Speed Backbone Path Construction Approach for Energy and Delay Optimization in WSNs. IEEE Access. 2018; 6 ():13836-13854.
Chicago/Turabian StyleAnfeng Liu; Mingfeng Huang; Ming Zhao; Tian Wang. 2018. "A Smart High-Speed Backbone Path Construction Approach for Energy and Delay Optimization in WSNs." IEEE Access 6, no. : 13836-13854.
Social networks like Facebook and SINA have been rapidly growing and accumulating a sheer volume of data such as social links between the users, user claims, and their comments. The work is motivated by the proliferation of social networks and large amounts of information that is voluntarily broadcast on them, which generates an interest in finding ways to predict individual sentiment that applies in public sentiment warning, advertisement, and recommendation. However, the traditional user sentiment prediction model has shortcoming of high complexity, which renders inefficiencies of individual sentiment prediction in social networks. To tackle this challenge, in this study, we develop an individual sentiment prediction method LDA-TIM based on the individual interest preferences and social influence. Then, based on the objective function we trained a logistic regression classifier to predict individual sentiment polarity. Finally, extensive experiments are conducted to evaluate the performance of our approach by using two large-scale real-world data collected from SINA. The experimental results on the two large-scale-real-word data set both reveal that each of the components are critical to obtaining satisfactory performance on our data. Experiments show the F1-Measure value of the individual sentiment approach can reach 70.99%.
Wenxin Kuang; Ming Zhao. LDA-TIM: An Approach for Individual Sentiment Prediction in Social Networks. Communications in Computer and Information Science 2018, 247 -261.
AMA StyleWenxin Kuang, Ming Zhao. LDA-TIM: An Approach for Individual Sentiment Prediction in Social Networks. Communications in Computer and Information Science. 2018; ():247-261.
Chicago/Turabian StyleWenxin Kuang; Ming Zhao. 2018. "LDA-TIM: An Approach for Individual Sentiment Prediction in Social Networks." Communications in Computer and Information Science , no. : 247-261.