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  • Hindawi Limited  (8)
  • Mathematics  (8)
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  • Hindawi Limited  (8)
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  • Mathematics  (8)
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  • 1
    Online Resource
    Online Resource
    Deep Learning Models and Social Governance Guided by Fair Policies
    Hindawi Limited ; 2022
    In:  Scientific Programming Vol. 2022 ( 2022-3-18), p. 1-11
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2022 ( 2022-3-18), p. 1-11
    Abstract: With the rapid advancement of information technology, artificial intelligence and machine learning have become the central technology tools for information sharing. To speed up the efficiency of information resource transmission of national government departments and improve the informatization level of government social management and public service systems, the persona system is designed using an artificial neural network, and a social service and management resource pool system is developed. The behavior data randomly generated by users in daily life is collected and cleaned, and training samples are extracted for training an artificial neural network. Next, the demographic attribute tags and interest tags are modelled, and the social service and management resource pool system is built and tested. Results show that for the population attribute label construction, the index value using the app name is mapped to 0 or 1, and the sample sampling ratio is set to 1.0. The proposed model achieved the overall accuracies of 85.2%, 74.5%, and 99.0% for the prediction of constructed age, academic qualifications, and interest label, respectively. The constructed system greatly deepens the visualization of the characteristics of social governance elements. The system can enhance the level of resource sharing by government departments and provide the foundation for spatial decision-making in smart social governance.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    URL: Article
    DOI: 10.1155/2022/8376325
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2070004-0
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    Online Resource
  • 2
    Online Resource
    Online Resource
    A Rapid Temporal Bone Localization Method Based on Machine Visual Detection Markers
    Hindawi Limited ; 2022
    In:  Scientific Programming Vol. 2022 ( 2022-9-23), p. 1-11
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2022 ( 2022-9-23), p. 1-11
    Abstract: The number of hearing-impaired people is increasing year by year; robotic cochlear drilling surgery is one of the safest methods to treat deafness. Looking at the issue of low efficiency of temporal bone posture positioning in cochlear implantation robotic drilling, a novel auxiliary ring marker temporal bone positioning method was proposed to improve temporal bone posture positioning efficiency, optimize the operation time, and reduce auxiliary injuries caused by the surgery. First, the temporal bone visual positioning assistant ring was designed based on the requirements for cochlear robotic drilling surgery. The target detection was conducted on the auxiliary ring and image processing and feature point extraction methods were designed. Then, the three-dimensional coordinates of the measured feature points were obtained by binocular vision, and the auxiliary ring and temporal bone postures were estimated. Finally, the auxiliary ring and temporal bone localization methods were validated. The experiment results indicated that the temporal bone was located quickly and effectively in a total time of about 33 ms, which was faster and more accurate than traditional visual localization methods and could satisfy real-time temporal bone localization during surgery. This study can reduce the time of temporal bone visual positioning in cochlear implant drilling operations, greatly improving the robot’s capabilities to extract visual information during the operation, which has a better auxiliary role for future research and applications of the cochlear implant drilling operation.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    URL: Article
    DOI: 10.1155/2022/7202627
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2070004-0
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Sensors Anomaly Detection of Industrial Internet of Things Based on Isolated Forest Algorithm and Data Compression
    Hindawi Limited ; 2021
    In:  Scientific Programming Vol. 2021 ( 2021-1-30), p. 1-9
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2021 ( 2021-1-30), p. 1-9
    Abstract: Aiming at solving network delay caused by large chunks of data in industrial Internet of Things, a data compression algorithm based on edge computing is creatively put forward in this paper. The data collected by sensors need to be handled in advance and are then processed by different single packet quantity K and error threshold e for multiple groups of comparative experiments, which greatly reduces the amount of data transmission under the premise of ensuring the instantaneity and effectiveness of data. On the basis of compression processing, an outlier detection algorithm based on isolated forest is proposed, which can accurately identify the anomaly caused by gradual change and sudden change and control and adjust the action of equipment, in order to meet the control requirement. As is shown by experimental simulation, the isolated forest algorithm based on partition outperforms box graph and K-means clustering algorithm based on distance in anomaly detection, which verifies the feasibility and advantages of the former in data compression and detection accuracy.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    URL: Article
    DOI: 10.1155/2021/6699313
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2070004-0
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Multitarget Detection of Transmission Lines Based on DANet and YOLOv4
    Hindawi Limited ; 2021
    In:  Scientific Programming Vol. 2021 ( 2021-12-17), p. 1-12
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2021 ( 2021-12-17), p. 1-12
    Abstract: In order to accurately identify targets such as insulators, shock hammers, bird nests, and spacers on high-voltage transmission lines, this paper proposes a multitarget detection model for transmission lines based on DANet and YOLOv4. First, the DANet and YOLOv4 are fused to solve the difficulty in understanding the scene and the discrimination of pixels caused by the complex and diverse scenes of UAV’ (unmanned aerial vehicle) aerial images (lighting, viewing angle, scale, occlusion, and so on) so as to improve the significance of the detection target. Gaussian function and KL (Kullback–Leibler) divergence are used to improve the nonmaximum suppression in YOLOv4 so as to improve the recognition rate of occluded targets; the focal loss function and the balanced cross entropy function are used to improve the loss function of YOLOv4 in order to reduce the impact of not only the imbalance between the background and the detection target but also the imbalance among the samples, which is aimed at improving the accuracy of the detection. Then, a data set is made for the experiment by using the UAV inspection image provided by a power grid company in Eastern Inner Mongolia. Finally, the algorithm proposed in this paper is compared with other target detection algorithms. Experimental results show that the average detection accuracy of the proposed algorithm can reach 94.7%, and the detection time of each image is 0.05 seconds. The method has good accuracy, real-time, and robustness.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    URL: Article
    DOI: 10.1155/2021/6235452
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2070004-0
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    Online Resource
  • 5
    Online Resource
    Online Resource
    A Two-Tier Energy-Aware Resource Management for Virtualized Cloud Computing System
    Hindawi Limited ; 2016
    In:  Scientific Programming Vol. 2016 ( 2016), p. 1-15
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2016 ( 2016), p. 1-15
    Abstract: The economic costs caused by electric power take the most significant part in total cost of data center; thus energy conservation is an important issue in cloud computing system. One well-known technique to reduce the energy consumption is the consolidation of Virtual Machines (VMs). However, it may lose some performance points on energy saving and the Quality of Service (QoS) for dynamic workloads. Fortunately, Dynamic Frequency and Voltage Scaling (DVFS) is an efficient technique to save energy in dynamic environment. In this paper, combined with the DVFS technology, we propose a cooperative two-tier energy-aware management method including local DVFS control and global VM deployment. The DVFS controller adjusts the frequencies of homogenous processors in each server at run-time based on the practical energy prediction. On the other hand, Global Scheduler assigns VMs onto the designate servers based on the cooperation with the local DVFS controller. The final evaluation results demonstrate the effectiveness of our two-tier method in energy saving.
    Type of Medium: Online Resource
    ISSN: 1058-9244 , 1875-919X
    URL: Article
    DOI: 10.1155/2016/4386362
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2016
    detail.hit.zdb_id: 2070004-0
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    Online Resource
  • 6
    Online Resource
    Online Resource
    Optimization Models and Algorithms for Services and Operations Management
    Hindawi Limited ; 2017
    In:  Scientific Programming Vol. 2017 ( 2017), p. 1-2
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2017 ( 2017), p. 1-2
    Type of Medium: Online Resource
    ISSN: 1058-9244 , 1875-919X
    URL: Article
    DOI: 10.1155/2017/4216785
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2017
    detail.hit.zdb_id: 2070004-0
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    Online Resource
  • 7
    Online Resource
    Online Resource
    Application of Data Mining in WITMED: Identification of Prognostic Genes in Oral Cancer
    Hindawi Limited ; 2021
    In:  Scientific Programming Vol. 2021 ( 2021-12-18), p. 1-12
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2021 ( 2021-12-18), p. 1-12
    Abstract: In recent years, the booming development of big data, cloud computing, Internet of Things, and other technologies provides conditions for the popularization and application of smart city. The combination of big data and medical information produces the emerging field of WITMED (Wise Information Technology of Med). WITMED is essential for the prospering growth of smart cities, which assumed a high quality of medical service is the most challenging goal for the city government. In this paper, the main attention is paid to the method of targeted gene therapy, which provides a new method for the treatment of oral cancer in inhibiting the growth, differentiation, invasion, and metastasis of oral cancer cells; therefore, the physical and psychological adverse effects of surgery and chemotherapy on patients are reduced and the survival and prognosis of patients are improved. Targeted gene therapy methods need to select the appropriate gene; that is, data mining methods are used to analyze a large number of complex genetic data from smart cities to obtain appropriate genetic markers, which makes the effect of targeted gene therapy better, and also provide some reference for the research of oral cancer gene direction and provide some basis for clinical treatment.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    URL: Article
    DOI: 10.1155/2021/4907687
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2070004-0
    Location Call Number Limitation Availability
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    Online Resource
  • 8
    Online Resource
    Online Resource
    A Deep Convolutional Neural Network Based Risk Identification Method for E-Commerce Supply Chain Finance
    Hindawi Limited ; 2022
    In:  Scientific Programming Vol. 2022 ( 2022-4-14), p. 1-10
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2022 ( 2022-4-14), p. 1-10
    Abstract: With the popularity of the Internet, the rise of e-commerce platforms has led to the rapid development of supply chain (SC) financial services in China, and the competitiveness of commercial banks and core enterprises in the supply chain is now gradually increasing, rapidly expanding into an important area of competition between the two. As an emerging force rebounding from the economic downturn, e-commerce platform transactions, with their unique characteristics of informatization, diversification, and convenience, have provided a broad space for Internet SC finance. The article mainly analyzes the risk identification method of e-commerce SC finance, analyzes its risk from the financing process, gives corresponding data support for the matters or processes that may cause financing risk based on DCNN model, and takes Jingdong SC finance as an example and analyzes its main financing methods and risk identification process; based on different experimental comparisons, a multigroup experimental study shows that the accuracy of supply chain finance risk identification using deep convolutional neural network models can reach 95.36%, which demonstrates the effectiveness of the proposed method by providing better performance compared to traditional BP and SVM networks.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    URL: Article
    DOI: 10.1155/2022/6298248
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2070004-0
    Location Call Number Limitation Availability
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