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Journal articles on the topic 'MULTISOURCE DATA'

Author: Grafiati
Published: 11 September 2023

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1

Li, Chaofeng. "Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks." Wireless Communications and Mobile Computing 2022 (August 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/1642925.

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In order to solve the problem that the target is easily lost in the process of multisource target data fusion tracking, a multisource target data fusion tracking method based on data mining is proposed. Multisource target data fusion tracking belongs to location level fusion. Firstly, a hybrid heterogeneous network fusion model is established, and then, data features are extracted, and a fusion source big data acquisition algorithm is designed based on compressed sensing to complete data preprocessing to reduce the amount of data acquisition. Based on data mining association multisource fusion target, get the relationship between each measurement and target, and build multisource target data fusion tracking model to ensure the stable state of fusion results. It shows that the proposed method can save the tracking time and improve the tracking accuracy compared with the methods based on NNDA and PDA, which is more conducive to the real-time tracking of multisource targets.
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Guo, Hongyan, and Xintao Li. "Multisource Target Data Fusion Tracking Method for Heterogeneous Network Based on Data Mining." Wireless Communications and Mobile Computing 2022 (June 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/9291319.

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This research is on heterogeneous network fusion method of multisource target data based on data mining. Firstly, it is a distributed storage structure model for building heterogeneous network multisource target data. Then, using the phase space reconstruction method, a grid distribution structure model for data fusion tracking is constructed, and realize visual scheduling and automatic monitoring of multisource target data. Finally, according to the feature extraction results, analyze the statistical characteristics of multisource target data in heterogeneous networks, combined with the fuzzy tomographic analysis method, multilevel fusion, and adaptive mining of multisource target data, extract the associated feature quantities in it, and realize the fusion tracking of data. The simulation results show that, in relatively simple heterogeneous networks, the feature mining error of the proposed method is nearly 2.11% lower than the two traditional methods. In relatively complex heterogeneous networks, the feature mining error of the proposed method is nearly 6.48% lower than the two traditional methods. It can be seen that this method has better adaptability for fusion tracking of heterogeneous network multisource target data, the anti-interference ability is strong, and the tracking accuracy in the data fusion tracking process is also improved.
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Goldring, Ellen B., Madeline Mavrogordato, and Katherine Taylor Haynes. "Multisource Principal Evaluation Data." Educational Administration Quarterly 51, no. 4 (November 4, 2014): 572–99. http://dx.doi.org/10.1177/0013161x14556152.

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4

Liu, Xiaolun. "Local Government Governance Path Optimization Based on Multisource Big Data." Mathematical Problems in Engineering 2022 (June 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/1941558.

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With the development of Internet technology, multisource big data can collect and analyze information so as to provide people with a good vision. In the process of governance, local governments will have problems of incomplete information. With the development of big data, multisource and big data will have advanced nature. Therefore, based on multisource big data, this paper analyzes the multisource big data algorithm in detail and establishes a local government governance model based on multisource big data. Then, the proposed model is applied to the local government governance process of Beijing, Shanghai, Chongqing, and Tianjin, and the local governance situation of each city is compared and analyzed so as to provide some reference for the optimization of the local government governance path. The experimental results show that the local governance model based on multisource big data can optimize the local government governance path and point out the direction for the local government governance path.
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Zhang, Haibo. "Music Emotion Representation Learning Based on Multisource Data Fusion and Its Application." Mobile Information Systems 2022 (September 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/3983201.

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Based on the multisource data fusion and fusion of music emotion expression learning and its application, this paper further analyzes the actual influence of music emotion and Internet multisource data in emotion learning. This paper uses multisource data to improve the professionalism and concentration of music emotion and uses modern Internet technology to help users quickly integrate into music emotion learning. At the same time, the multisource data structure can develop the music learning structure to a high-quality level. It is precisely because of the emphasis of the multisource data model architecture that the learning mechanism of musical emotion representation can be continuously updated and improved, which is mutually promoted jointly with Internet technology.
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Ju, Ankang, Yuanbo Guo, Ziwei Ye, Tao Li, and Jing Ma. "HeteMSD: A Big Data Analytics Framework for Targeted Cyber-Attacks Detection Using Heterogeneous Multisource Data." Security and Communication Networks 2019 (May 2, 2019): 1–9. http://dx.doi.org/10.1155/2019/5483918.

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In the current enterprise network environment, multistep targeted cyber-attacks with concealment and advanced characteristics have become the main threat. Multisource security data are the prerequisite of targeted cyber-attacks detection. However, these data have characters of heterogeneity and semantic diversity, and existing attack detection methods do not take comprehensive data sources into account. Identifying and predicting attack intention from heterogeneous noisy data can be meaningful work. In this paper, we first review different data fusion mechanisms of correlating heterogeneous multisource data. On this basis, we propose a big data analytics framework for targeted cyber-attacks detection and give the basic idea of correlation analysis. Our approach will offer the ability to correlate multisource heterogeneous security data and analyze attack intention effectively.
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NOVOSAD, Mariia-Ruslana. "MULTISOURCE INTELLIGENT PARKING ASSISTANT." Herald of Khmelnytskyi National University. Technical sciences 313, no. 5 (October 27, 2022): 56–60. http://dx.doi.org/10.31891/2307-5732-2022-313-5-56-60.

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Problem searching for a parking space is time-consuming and highly relevant both in Ukraine and abroad. Time spent searching for a parking space leads to excessive traffic, more traffic jams, air pollution and increased fuel consumption. These factors also affect the daily stress levels of drivers. Due to this, the process of finding a parking space should be fast and convenient. At the same time, there has been significant development of real estate in Lviv over the past few years. Accordingly, the need for organizing the process of parking cars of residents of residential areas is growing. This paper presents the results of the development of an application for a quick and convenient search for a parking space. A review of similar software applications was conducted. Proposed solutions use various technologies to solve the problem of searching for a free parking space including IoT, sensors, machine learning for image recognition. Even though they solve the problem of searching for a free parking space, most of them can be expensive to implement, maintain, they don’t provide the ability to work with different data sources. An activity diagram of system is presented and it shows two main flows of the system: displaying the current state of parking spaces and displaying parking space by the number of the car entering the territory of the complex. System consists of three modules. The first module is responsible for working with different data sources, storing the status of parking spaces, processing requests. Image processing module is responsible for determining the occupied and free parking spaces from the image. The third module is responsible for the correct display of parking spaces and their statuses. It is also demonstrated how the application works with different data sources and how exceptions are handled. The system works correctly and has a сlear interface. The parking assistant is a great helper and significantly reduces the time required to find a free parking space.
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8

Wang, Zichi. "Multisource Data Hiding in Digital Images." Symmetry 14, no. 5 (April 27, 2022): 890. http://dx.doi.org/10.3390/sym14050890.

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In this paper, we propose a new data-hiding framework: multisource data hiding, in which multiple senders (multiple sources) are able to transmit different secret data to a receiver via the same cover image symmetrically. We propose two multisource data-hiding schemes, i.e., separable and anonymous, according to different applications. In the separable scheme, the receiver can extract the secret data transmitted by all senders using the symmetrical data-hiding key. A sender is unable to know the content of the secret data that is not transmitted by them (non-source sender). In the anonymous scheme, it is unnecessary to extract all secret data on the receiver side. The content extracted by the receiver is a co-determined result of the secret data transmitted by all senders. Details of the secret data are unknown to the receiver and the non-source senders. In addition, the two proposed schemes achieve multisource data hiding without decreasing the undetectability of data hiding.
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Datcu, M., F. Melgani, A. Piardi, and S. B. Serpico. "Multisource data classification with dependence trees." IEEE Transactions on Geoscience and Remote Sensing 40, no. 3 (March 2002): 609–17. http://dx.doi.org/10.1109/tgrs.2002.1000321.

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10

Amarsaikhan, D., and T. Douglas*. "Data fusion and multisource image classification." International Journal of Remote Sensing 25, no. 17 (September 2004): 3529–39. http://dx.doi.org/10.1080/0143116031000115111.

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Jin, Xuebo, Yingbo Wang, Chen Liu, Tianyi Liu, and Tingli Su. "Multisource Data Analysis for Stock Prediction." International Journal of u- and e- Service, Science and Technology 10, no. 7 (July 31, 2017): 111–24. http://dx.doi.org/10.14257/ijunesst.2017.10.7.02.

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Burge, Harriet A., Mary L. Jelks, and Jean A. Chapman. "Quality control of multisource aeroallergen data." Grana 25, no. 3 (December 1986): 247–50. http://dx.doi.org/10.1080/00173138609427726.

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Ogaja, Clement. "Multisource data analysis for geoscience applications." Computers & Geosciences 30, no. 5 (June 2004): 493–99. http://dx.doi.org/10.1016/j.cageo.2004.02.002.

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14

Choi, Yunseok, and Tariq Alkhalifah. "Multisource waveform inversion of marine streamer data using normalized wavefield." GEOPHYSICS 78, no. 5 (September 1, 2013): R197—R206. http://dx.doi.org/10.1190/geo2012-0491.1.

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Multisource full-waveform inversion based on the [Formula: see text]- and [Formula: see text]-norm objective functions cannot be applied to marine streamer data because it does not take into account the unmatched acquisition geometries between the observed and modeled data. To apply multisource full-waveform inversion to marine streamer data, we construct the [Formula: see text]- and [Formula: see text]-norm objective functions using the normalized wavefield. The new residual seismograms obtained from the [Formula: see text]- and [Formula: see text]-norms using the normalized wavefield mitigate the problem of unmatched acquisition geometries, which enables multisource full-waveform inversion to work with marine streamer data. In the new approaches using the normalized wavefield, we used the back-propagation algorithm based on the adjoint-state technique to efficiently calculate the gradients of the objective functions. Numerical examples showed that multisource full-waveform inversion using the normalized wavefield yields much better convergence for marine streamer data than conventional approaches.
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15

Gao, Tong, Hao Chen, and Junhong Lu. "Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data." Remote Sensing 14, no. 11 (May 26, 2022): 2553. http://dx.doi.org/10.3390/rs14112553.

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To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to improve classification; and the other is to extract shared information across sources for domain adaptation. However, typical feature extraction methods require the input represented as vectors or homogeneous tensors and fail to process multisource data represented as heterogeneous tensors. Therefore, the coupled heterogeneous Tucker decomposition (C-HTD) containing two sub-methods, namely coupled factor matrix-based HTD (CFM-HTD) and coupled core tensor-based HTD (CCT-HTD), is proposed to establish a unified feature extraction framework for multisource fusion and domain adaptation. To handle multisource heterogeneous tensors, multiple Tucker models were constructed to extract features of different sources separately. To cope with the supervised and semi-supervised cases, the class-indicator factor matrix was built to enhance the separability of features using known labels and learned labels. To mine the complementarity of paired multisource samples, coupling constraint was imposed on multiple factor matrices to form CFM-HTD to extract multisource information jointly. To extract domain-adapted features, coupling constraint was imposed on multiple core tensors to form CCT-HTD to encourage data from different sources to have the same class centroid. In addition, to reduce the impact of interference samples on domain adaptation, an adaptive sample-weighting matrix was designed to autonomously remove outliers. Using multiresolution multiangle optical and MSTAR datasets, experimental results show that the C-HTD outperforms typical multisource fusion and domain adaptation methods.
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Chen, Hui, Zhaoming Chu, and Chao Sun. "Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data." Sustainability 13, no. 23 (November 25, 2021): 13057. http://dx.doi.org/10.3390/su132313057.

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Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10−3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems.
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Cui, Zhiyong, Kristian Henrickson, Salvatore Antonio Biancardo, Ziyuan Pu, and Yinhai Wang. "Establishing Multisource Data-Integration Framework for Transportation Data Analytics." Journal of Transportation Engineering, Part A: Systems 146, no. 5 (May 2020): 04020024. http://dx.doi.org/10.1061/jtepbs.0000331.

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Gong, Zhi Hua, Peng Wei Duan, Yong Guang Li, and Rui Yue. "Multi-Structural Non-Linear Data Fusion Method." Applied Mechanics and Materials 530-531 (February 2014): 554–60. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.554.

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In systematic measurement and control mode composed of optics, radar and telemetry, trajectory measurement is in need of high precision. So, based on exterior trajectory parameter expressed by Hermite function, this paper proposes a multi-structure data fusion method with multisource heterogeneous measuring elements, which is called function restraint EMBET method. Based on the fusion simulation calculation and analysis of the same data of multisource heterogeneous measuring elements both using general EMBET method and function restraint EMBET method, it is proved that function restraint EMBET method is sensitive to errors, besides, it have the advantage of strong practicability and high precision.
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Qin, Zhengna, Haojie Liao, Ling Chen, and Lei Zhang. "Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion." Security and Communication Networks 2021 (December 16, 2021): 1–11. http://dx.doi.org/10.1155/2021/7915670.

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With the development of the Internet, big data collection, analysis, and processing are flourishing. The study aims to explore the performance management of power enterprises based on multisource information fusion and big data. First, the application of big data to enterprise management is analyzed. Second, the multisource information fusion method is introduced, and the multisource information fusion model is implemented. Finally, the fuzzy language algorithm is used to evaluate the performance management of power enterprises. The results show that the proposed multisource information fusion algorithm has high efficiency in evaluating enterprise performance management. The evaluation result is closer to the actual value than other algorithms, and the maximum acceleration ratio can reach 7, indicating that the algorithm is suitable for processing big data. The performance evaluation shows that enterprises pay most attention to the quality of their products; the weight reached 0.414; and the index weight difference is large. This study promotes the reform of the performance management mode and improves the management efficiency of enterprises through the proposed enterprise performance management strategy. It provides a great reference for the application of big data and information fusion technology.
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Lv, Zhihan. "Editorial: Multisource Data, Multimedia Content, Multimodal Interaction." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 11, no. 2 (June 7, 2018): 89–90. http://dx.doi.org/10.2174/235209651102180607133515.

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Ravat, Franck, and Jiefu Song. "A Unified Approach to Multisource Data Analyses." Fundamenta Informaticae 162, no. 4 (August 4, 2018): 311–59. http://dx.doi.org/10.3233/fi-2018-1727.

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22

Yammarino, Francis J. "Modern Data Analytic Techniques for Multisource Feedback." Organizational Research Methods 6, no. 1 (January 2003): 6–14. http://dx.doi.org/10.1177/1094428102239423.

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Sepulveda, Martin-J. "Multisource Data for Total Worker Health Insights." Journal of Occupational and Environmental Medicine 56, no. 7 (July 2014): 699. http://dx.doi.org/10.1097/jom.0000000000000227.

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Li, Hengyun, Mingming Hu, and Gang Li. "Forecasting tourism demand with multisource big data." Annals of Tourism Research 83 (July 2020): 102912. http://dx.doi.org/10.1016/j.annals.2020.102912.

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Huang, Yahui, and Daozhong Lei. "Multisource data acquisition based on single-chip microcomputer and sensor technology." Open Computer Science 12, no. 1 (January 1, 2022): 416–26. http://dx.doi.org/10.1515/comp-2022-0261.

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Abstract Today, data and information are flooded every day. Data are a reliable basis for scientific research. Their function is not only to clearly show real problems in various fields, but also to guide people to find the key factors that cause problems. The emergence of big data responds to this era of information explosion, and it is precisely by virtue of the accumulation of quantity that it presents the rules more clearly. No matter political, economic, cultural, and other fields are closely related to data. The application of microcontroller and sensor technology can help explore new branches of multisource data. However, the collection and analysis of multisource data only stays in the aspects of computer and communication technology. In view of the earlier problems, this article carried out scientific data collection and analysis of multisource data based on single-chip microcomputer and sensor technology. The research results showed that based on two algorithms, random early detection and weighted fair queuing, the analysis algorithm according to the Genetic Algorithm had a higher successful conversion rate. The power consumption of a node with better antenna performance was 9–10% lower than that of a node with poor antenna performance, which provided a basis for multisource data collection and analysis.
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Xiao, Xiao. "Characteristic Analysis of Multisource Heterogeneous Data in Digital City Planning Based on Internet of Things." Mobile Information Systems 2021 (November 12, 2021): 1–7. http://dx.doi.org/10.1155/2021/5731246.

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The purpose of this article is to use the Internet of Things related technology to analyze the characteristics of multisource and easy-to-purchase data for the different types of planning data and different levels of cognitive needs of participants in the entire urban planning process. This paper uses the ontology idea to reconstruct the relationship between multisource and heterogeneous planning data including Internet of Things data, planning documents, and planning drawings, to design the data semantic relationship of the ontology model elements, define the relationship between the data types, and implement the ontology-based method. The semantic expression algorithm in the planning field facilitates the exchange of various planning participants’ understanding of the planning scheme, at the same time, according to the classification of multisource heterogeneous data features, logical reasoning of ontology relationships, filtering redundant information, and multisource heterogeneous planning data visualization. Finally, the information of the same nature collected by the sensor nodes of the Internet of Things is batched, and the calculated fusion information is closer to the true value through a series of weighting formulas. Experiments prove that the feature analysis method proposed in this paper can maintain a loss of 0.02% and achieve an accuracy rate of 79.1% when the overall characteristics of digital city planning are reduced by 67%, which effectively proves the multisource heterogeneous data feature analysis for digital city planning importance.
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Guo, Xuan, Haizhong Qian, Fang Wu, and Junnan Liu. "A Method for Constructing Geographical Knowledge Graph from Multisource Data." Sustainability 13, no. 19 (September 24, 2021): 10602. http://dx.doi.org/10.3390/su131910602.

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Global problems all occur at a particular location on or near the Earth’s surface. Sitting at the junction of artificial intelligence (AI) and big data, knowledge graphs (KGs) organize, interlink, and create semantic knowledge, thus attracting much attention worldwide. Although the existing KGs are constructed from internet encyclopedias and contain abundant knowledge, they lack exact coordinates and geographical relationships. In light of this, a geographical knowledge graph (GeoKG) construction method based on multisource data is proposed, consisting of a modeling schema layer and a filling data layer. This method has two advantages: (1) the knowledge can be extracted from geographic datasets; (2) the knowledge on multisource data can be represented and integrated. Firstly, the schema layer is designed to represent geographical knowledge. Then, the methods of extraction and integration from multisource data are designed to fill the data layer, and a storage method is developed to associate semantics with geospatial knowledge. Finally, the GeoKG is verified through linkage rate, semantic relationship rate, and application cases. The experiments indicate that the method could automatically extract and integrate knowledge from multisource data. Additionally, our GeoKG has a higher success rate of linking web pages with geographic datasets, and its exact coordinates have increased to 100%. This paper could bridge the distance between a Geographic Information System and a KG, thus facilitating more geospatial applications.
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Jiang, Mengmeng, Qiong Wu, and Xuetao Li. "Multisource Heterogeneous Data Fusion Analysis of Regional Digital Construction Based on Machine Learning." Journal of Sensors 2022 (January 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/8205929.

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In modern urban construction, digitalization has become a trend, but the single source of information of traditional algorithms can not meet people’s needs, so the data fusion technology needs to draw estimation and judgment from multisource data to increase the confidence of data, improve reliability, and reduce uncertainty. In order to understand the influencing factors of regional digitalization, this paper conducts multisource heterogeneous data fusion analysis based on regional digitalization of machine learning, using decision tree and artificial neural network algorithm, compares the management efficiency and satisfaction of school population under different algorithms, and understands the data fusion and construction under different algorithms. According to the results, decision-making tree and artificial neural network algorithms were more efficient than traditional methods in building regional digitization, and their magnitude was about 60% higher. More importantly, the machine learning-based methods in multisource heterogeneous data fusion have been better than traditional calculation methods both in computational efficiency and misleading rate with respect to false alarms and missed alarms. This shows that machine learning methods can play an important role in the analysis of multisource heterogeneous data fusion in regional digital construction.
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Guo, Jianwei, and Yongbo Lv. "Research on Optimization Model of Multisource Traffic Information Collection Combination Based on Genetic Algorithm." Computational Intelligence and Neuroscience 2022 (January 27, 2022): 1–20. http://dx.doi.org/10.1155/2022/3793996.

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In order to reduce the excessive use of multisource traffic information collection system, a multisource traffic information collection combination optimization mode is proposed based on genetic algorithm in this paper. This model is mainly used to analyze the traffic management data in the city. According to the collected data information, the characteristics of the traffic equipment can be effectively analyzed. Basing on the market demand and supply relationship, the multisource traffic information collection combination optimization model is used to complete the reorganization and optimization of the traffic information in this paper, to acquire the main convolution feature variables of the model. The data information combination processing is performed according to the acquired feature variables, and the genetic algorithm is used to adjust the multisource traffic information. During the process of information fusion data analysis, the multisource traffic information clustering and fuzzy constraint control can be performed effectively to realize the optimization of the team’s traffic information collection combination. Finally, the simulation results show that the method proposed in this paper is more accurate in realizing the optimization process of multisource traffic information collection and combination and has a better degree of information fusion.
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Yan, Puchen, Qisheng Han, Yangming Feng, and Shaozhong Kang. "Estimating LAI for Cotton Using Multisource UAV Data and a Modified Universal Model." Remote Sensing 14, no. 17 (August 30, 2022): 4272. http://dx.doi.org/10.3390/rs14174272.

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Leaf area index(LAI) is an important indicator of crop growth and water status. With the continuous development of precision agriculture, estimating LAI using an unmanned aerial vehicle (UAV) remote sensing has received extensive attention due to its low cost, high throughput and accuracy. In this study, multispectral and light detection and ranging (LiDAR) sensors carried by a UAV were used to obtain multisource data of a cotton field. The method to accurately relate ground measured data with UAV data was built using empirical statistical regression models and machine learning algorithm models (RFR, SVR and ANN). In addition to the traditional spectral parameters, it is also feasible to estimate LAI using UAVs with LiDAR to obtain structural parameters. Machine learning models, especially the RFR model (R2 = 0.950, RMSE = 0.332), can estimate cotton LAI more accurately than empirical statistical regression models. Different plots and years of cotton datasets were used to test the model robustness and generality; although the accuracy of the machine learning model decreased overall, the estimation accuracy based on structural and multisources was still acceptable. However, selecting appropriate input parameters for different canopy opening and closing statuses can alleviate the degradation of accuracy, where input parameters select multisource parameters before canopy closure while structural parameters are selected after canopy closure. Finally, we propose a gap fraction model based on a LAImax threshold at various periods of cotton growth that can estimate cotton LAI with high accuracy, particularly when the calculation grid is 20 cm (R2 = 0.952, NRMSE = 12.6%). This method does not require much data modeling and has strong universality. It can be widely used in cotton LAI prediction in a variety of environments.
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Chen, Xiangyu, Kaisa Zhang, Gang Chuai, Weidong Gao, Zhiwei Si, Yijian Hou, and Xuewen Liu. "Urban Area Characterization and Structure Analysis: A Combined Data-Driven Approach by Remote Sensing Information and Spatial–Temporal Wireless Data." Remote Sensing 15, no. 4 (February 14, 2023): 1041. http://dx.doi.org/10.3390/rs15041041.

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Analysis of urban area function is crucial for urban development. Urban area function features can help to conduct better urban planning and transportation planning. With development of urbanization, urban area function becomes complex. In order to accurately extract function features, researchers have proposed multisource data mining methods that combine urban remote sensing and other data. Therefore, the research of efficient multisource data analysis tools has become a new hot topic. In this paper, a novel urban data analysis method combining spatiotemporal wireless network data and remote sensing data was proposed. First, a Voronoi-diagram-based method was used to divide the urban remote sensing images into zones. Second, we combined period and trend components of wireless network traffic data to mine urban function structure. Third, for multisource supported urban simulation, we designed a novel spatiotemporal city computing method combining graph attention network (GAT) and gated recurrent unit (GRU) to analyze spatiotemporal urban data. The final results prove that our method performs better than other commonly used methods. In addition, we calculated the commuting index of each zone by wireless network data. Combined with the urban simulation conducted in this paper, the dynamic changes of urban area features can be sensed in advance for a better sustainable urban development.
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Chen, Yongquan, Ying Jiang, and Haiyi Liu. "Analysis Method of App Software User Experience Based on Multisource Information Fusion." International Journal on Semantic Web and Information Systems 19, no. 1 (June 27, 2023): 1–22. http://dx.doi.org/10.4018/ijswis.325216.

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With the rapid development and popularization of intelligent terminals, app software has also developed rapidly. The research and practical value of mining user experience (UX) of app software form interaction information are becoming increasingly prominent. The interactive information of app software is multisource homogeneous and heterogeneous. In order to obtain more accurate and more comprehensive app software UX results, the fused multisource information should be analyzed. In this paper, the app software UX analysis method based on multisource information fusion is proposed. First, feature engineering is carried out to extract the features. Then, the feature combination tree is constructed after feature correlation mining. Finally, the multisource app software interactive data are fused, and the result is further analyzed to obtain the information of app software UX. The experiments clearly show that the method can effectively fuse multisource app software interaction data and help to comprehensively mine the app software UX embodied in the data.
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Xu, Xiuyan. "An Intelligent Classification Method of Multisource Enterprise Financial Data Based on SAS Model." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/8255091.

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An enterprise is often faced with a large amount of financial information and data information. It is inefficient to rely solely on manual work, and the accuracy is difficult to guarantee. For the multisource data of corporate finance, it is more difficult for financial personnel to accurately analyze the connections between the data. For the multisource financial data of enterprise, this is also a time-consuming and laborious task for financial personnel. At the same time, it is difficult to find the correlation between multiple sources of data and then formulate financial data that guides the development of the enterprise. With the advancement of intelligent algorithms, an intelligent classification algorithm similar to the SAS model has emerged, which can realize the intelligent classification of enterprise financial multisource data and accurately predict the future development trend, which is extremely beneficial to the development and performance of the enterprise. This article mainly combines the financial intelligence classification model SAS with clustering and decision tree methods to classify the financial multisource information and uses the neural network method to carry out the future development trend of corporate finance. The research results show that the maximum error of enterprise financial classification after using the intelligent classification method is only 3.71% and that the forecast error of the future development trend of enterprise finance is only 1.77%. This is an acceptable error range, and this intelligent classification method is also greatly improving the efficiency of corporate financial management.
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Kaplan, Adam, and Eric F. Lock. "Prediction With Dimension Reduction of Multiple Molecular Data Sources for Patient Survival." Cancer Informatics 16 (January 1, 2017): 117693511771851. http://dx.doi.org/10.1177/1176935117718517.

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Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal component analysis (PCA). However, the application of PCA is not straightforward for multisource data, wherein multiple sources of ‘omics data measure different but related biological components. In this article, we use recent advances in the dimension reduction of multisource data for predictive modeling. In particular, we apply exploratory results from Joint and Individual Variation Explained (JIVE), an extension of PCA for multisource data, for prediction of differing response types. We conduct illustrative simulations to illustrate the practical advantages and interpretability of our approach. As an application example, we consider predicting survival for patients with glioblastoma multiforme from 3 data sources measuring messenger RNA expression, microRNA expression, and DNA methylation. We also introduce a method to estimate JIVE scores for new samples that were not used in the initial dimension reduction and study its theoretical properties; this method is implemented in the R package R.JIVE on CRAN, in the function jive.predict.
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Li, Shan, Bin Feng, Wei Zhang, Yubin Feng, and Zhidu Huang. "Distribution Network Disaster Early Warning and Production Decision Support System Based on Multisource Data." Mathematical Problems in Engineering 2023 (May 26, 2023): 1–10. http://dx.doi.org/10.1155/2023/8929066.

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Aiming at the problems of long warning time and low warning accuracy in the traditional distribution network disaster early warning and production decision support systems, a distribution network disaster early warning and production decision support system based on multisource data is designed. The forecast information is collected through the data collector, the wind load and lightning trip rate of the line are calculated, all of the information is integrated together for multisource data fusion processing, and the distribution network disaster early warning model is constructed in accordance with the system hardware, which is designed with a data collector, gateway, man-machine interface, fault analysis module, disaster early warning module, and expert decision support module. According to the system hardware and software design, the design of a distribution network disaster early warning and production decision support system based on multisource data is realized. The simulation results show that the system has high accuracy and a short warning time.
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Chen, Siya, Hongyan Zhang, and Hangxing Yang. "Urban Functional Zone Recognition Integrating Multisource Geographic Data." Remote Sensing 13, no. 23 (November 23, 2021): 4732. http://dx.doi.org/10.3390/rs13234732.

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As the basic spatial unit of urban planning and management, it is necessary to understand the real development trend of urban functional zones in time and carry out reasonable planning adjustment. Because of the complexity of urban functional zones, the automatic recognition of urban functional zones has become a significant scientific problem in urban research. Urban functional zones contain natural and socioeconomic characteristics, but the existing identification methods fail to comprehensively consider these features. This paper proposes a framework that integrates multisource geographic data to recognize urban functional zone. We used high-resolution remote sensing imagery, point-of-interest (POI) data and high-spatial-resolution nighttime light imagery to extract both natural and socioeconomic features for urban functional zone accurate interpretation. Various features provide more accurate and comprehensive description for complex urban functional zone, so as to improve the recognition accuracy of urban functional zone. At present, there are few studies on urban functional zone recognition based on the combination of high-resolution remote sensing image, POI and high-resolution nighttime light imagery. The application potential of the combination of these three geographical data sources in urban function zone recognition needs to be explored. The experimental results show that the accuracy of urban functional zone recognition was obviously improved by the three data sources combination, the overall accuracy reached 80.30% and a comprehensive evaluation index reached 68.26%. This illustrate that the combination of the three data sources is beneficial to the urban functional zone recognition.
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Chen, Ling-qing, Mei-ting Wu, Li-fang Pan, and Ru-bin Zheng. "Grade Prediction in Blended Learning Using Multisource Data." Scientific Programming 2021 (September 11, 2021): 1–15. http://dx.doi.org/10.1155/2021/4513610.

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Today, blended learning is widely carried out in many colleges. Different online learning platforms have accumulated a large number of fine granularity records of students’ learning behavior, which provides us with an excellent opportunity to analyze students’ learning behavior. In this paper, based on the behavior log data in four consecutive years of blended learning in a college’s programming course, we propose a novel multiclassification frame to predict students’ learning outcomes. First, the data obtained from diverse platforms, i.e., MOOC, Cnblogs, Programming Teaching Assistant (PTA) system, and Rain Classroom, are integrated and preprocessed. Second, a novel error-correcting output codes (ECOC) multiclassification framework, based on genetic algorithm (GA) and ternary bitwise calculator, is designed to effectively predict the grade levels of students by optimizing the code-matrix, feature subset, and binary classifiers of ECOC. Experimental results show that the proposed algorithm in this paper significantly outperforms other alternatives in predicting students’ grades. In addition, the performance of the algorithm can be further improved by adding the grades of prerequisite courses.
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Lee, Tong, John Richards, and Philip Swain. "Probabilistic and Evidential Approaches for Multisource Data Analysis." IEEE Transactions on Geoscience and Remote Sensing GE-25, no. 3 (May 1987): 283–93. http://dx.doi.org/10.1109/tgrs.1987.289800.

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Briem, G. J., J. A. Benediktsson, and J. R. Sveinsson. "Multiple classifiers applied to multisource remote sensing data." IEEE Transactions on Geoscience and Remote Sensing 40, no. 10 (January 2002): 2291–99. http://dx.doi.org/10.1109/tgrs.2002.802476.

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Fromont, Elisa, Rene Quiniou, and Marie Odile Cordier. "Learning rules from multisource data for cardiac monitoring." International Journal of Biomedical Engineering and Technology 3, no. 1/2 (2010): 133. http://dx.doi.org/10.1504/ijbet.2010.029655.

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Xie, Zhiwei, Xinyue Ye, Zihao Zheng, Dong Li, Lishuang Sun, Ruren Li, and Samuel Benya. "Modeling Polycentric Urbanization Using Multisource Big Geospatial Data." Remote Sensing 11, no. 3 (February 4, 2019): 310. http://dx.doi.org/10.3390/rs11030310.

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Understanding the dynamics of polycentric urbanization is important for urban studies and management. This paper proposes an analytical model that uses multisource big geospatial data to characterize such dynamics to facilitate policy making. There are four main steps: 1) main centers and subcenters are identified using spatial cluster analysis and geographically weighted regression (GWR) based on Visible Infrared Imaging Radiometer Suite (VIIRS)/NPP and social media check-in data; 2) the built-up areas are extracted by using Defense Meteorological Satellite Program – Operational Linescan System (DMSP/OLS) gradient images; 3) the economic corridors that connect the main center and subcenters are constructed using road network data from Open Street Map (OSM) with the least-cost distance method; and 4) the major urban development direction is identified by analyzing the changes in built-up areas within the economic corridors. The model is applied to three major cities in northeastern, central, and northwestern China (Shenyang, Wuhan, and Xi'an) from 1992 to 2012.
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Payne, K., K. Samples, J. Epstein, A. Ostrander, J. W. Lee, J. P. Schmidt, S. Mathes, et al. "Multisource Data Integration for Georgia Land-Cover Mapping." Southeastern Geographer 43, no. 1 (2003): 1–27. http://dx.doi.org/10.1353/sgo.2003.0016.

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Salanova Grau, Josep Maria, Evangelos Mitsakis, Panagiotis Tzenos, Iraklis Stamos, Luigi Selmi, and Georgia Aifadopoulou. "Multisource Data Framework for Road Traffic State Estimation." Journal of Advanced Transportation 2018 (June 19, 2018): 1–9. http://dx.doi.org/10.1155/2018/9078547.

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This paper presents a framework for data collection, filtering, and fusion, together with a set of operational tools to validate, analyze, utilize, and highlight the added value of probe data. Data is collected by both conventional (loops, radars, and cameras) and innovative (Floating Car Data, detectors of Bluetooth devices) technologies and refers to travel times and traffic flows on road networks. The city of Thessaloniki, Greece, serves as a case study for the implementation of the proposed framework. The methodology includes the estimation of traffic flow based on measured travel time along predefined routes and short-term forecasting of traffic volumes and their spatial expansion in the road network. The proposed processes and the framework itself have the potential of being implemented in urban road networks.
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Weijian Wan and D. Fraser. "Multisource data fusion with multiple self-organizing maps." IEEE Transactions on Geoscience and Remote Sensing 37, no. 3 (May 1999): 1344–49. http://dx.doi.org/10.1109/36.763298.

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Wu, Jianping, Wenjie Li, Hongbo Du, Yu Wan, Shengfa Yang, and Yi Xiao. "Estimating river bathymetry from multisource remote sensing data." Journal of Hydrology 620 (May 2023): 129567. http://dx.doi.org/10.1016/j.jhydrol.2023.129567.

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Xiao, Dianliang, Tiantao Zhang, Xudong Zhou, Guangshun Zheng, and Haoran Song. "Safety Monitoring of Expressway Construction Based on Multisource Data Fusion." Journal of Advanced Transportation 2020 (September 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/8856360.

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China’s terrain is complex, both plain, microhill (heavy-hill) and mountainous terrain; the hidden dangers of highway construction are prominent. Construction site management, production safety management, and construction personnel management are difficult, and it is necessary to borrow advanced technology to establish information, and it is necessary to borrow advanced technology to establish information system to realize the visualization of safety monitoring. In the construction of highways, mountainous terrain is often complicated due to complex terrain, high mountains, and deep valleys. Excavation of the mountain mass is required to form high and steep slopes. For successful projects, safety monitoring is particularly important. Multisource data fusion is one of the computer application technologies. It is an information processing technology that is automatically analyzed and synthesized under certain criteria to complete the required decision-making and evaluation tasks. This paper analyzes high-speed data in the context of multisource data fusion. Study on highway slope construction safety monitoring. BP neural network fusion technology of multisource data fusion technology is used. A high-speed breccia-bearing silty clay slope is taken as the research object. The feedback information about the deployed monitoring system is fully used in the slope design and construction. The construction design parameters are reversed to predict the stability of the slope and ensure the safety of construction and operation of similar slopes of the entire expressway. The research in this paper finds that the maximum deviation between the slope displacement value and the measured value obtained by the slope monitoring based on multisource data fusion in this paper is 7.53%, which is less than 10%, which verifies the feasibility of the method in this paper. The research methods and ideas of this paper can also provide a reference for similar engineering research.
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Wang, Gang, Gang Chen, Huimin Zhao, Feng Zhang, Shanlin Yang, and Tian Lu. "Leveraging Multisource Heterogeneous Data for Financial Risk Prediction: A Novel Hybrid-Strategy-Based Self-Adaptive Method." MIS Quarterly 45, no. 4 (December 1, 2021): 1949–98. http://dx.doi.org/10.25300/misq/2021/16118.

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Emerging phenomena of ubiquitous multisource data offer promising avenues for making breakthroughs in financial risk prediction. While most existing methods for financial risk prediction are based on a single information source, which may not adequately capture various complex factors that jointly influence financial risks, we propose a hybrid-strategy-based self-adaptive method to effectively leverage heterogeneous soft information drawn from a variety of sources. The method uses a proposed new feature- sparsity learning method to adaptively integrate multisource heterogeneous soft features with hard features and a proposed improved evidential reasoning rule to adaptively aggregate base classifier predictions, thereby alleviating both the declarative bias and the procedural bias of the learning process. Evaluation in two cases at the individual level (concerning borrowers at a P2P lending platform) and the company level (concerning listed companies in the Chinese stock market) showed that, compared with relying solely on hard features, effectively incorporating multisource heterogeneous soft features using our proposed method enabled earlier prediction of financial risks with desirable performance.
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Wang, Xueliang, Nan Yang, Enjun Liu, Wencheng Gu, Jinglin Zhang, Shuo Zhao, Guijiang Sun, and Jian Wang. "Tree Species Classification Based on Self-Supervised Learning with Multisource Remote Sensing Images." Applied Sciences 13, no. 3 (February 2, 2023): 1928. http://dx.doi.org/10.3390/app13031928.

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In order to solve the problem of manual labeling in semi-supervised tree species classification, this paper proposes a pixel-level self-supervised learning model named M-SSL (multisource self-supervised learning), which takes the advantage of the information of plenty multisource remote sensing images and self-supervised learning methods. Based on hyperspectral images (HSI) and multispectral images (MSI), the features were extracted by combining generative learning methods with contrastive learning methods. Two kinds of multisource encoders named MAAE (multisource AAE encoder) and MVAE (multisource VAE encoder) were proposed, respectively, which set up pretext tasks to extract multisource features as data augmentation. Then the features were discriminated by the depth-wise cross attention module (DCAM) to enhance effective ones. At last, joint self-supervised methods output the tress species classification map to find the trade-off between providing negative samples and reducing the amount of computation. The M-SSL model can learn more representative features in downstream tasks. By employing the feature cross-fusion process, the low-dimensional information of the data is simultaneously learned in a unified network. Through the validation of three tree species datasets, the classification accuracy reached 78%. The proposed method can obtain high-quality features and is more suitable for label-less tree species classification.
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Probyn, Linda, Catherine Lang, George Tomlinson, and Glen Bandiera. "Multisource Feedback and Self-Assessment of the Communicator, Collaborator, and Professional CanMEDS Roles for Diagnostic Radiology Residents." Canadian Association of Radiologists Journal 65, no. 4 (November 2014): 379–84. http://dx.doi.org/10.1016/j.carj.2014.04.003.

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Purpose To develop a tool for the external and self-evaluation of residents in the Communicator, Collaborator, and Professional CanMEDS roles. Methods An academic teaching institution affiliated with 4 major urban hospitals conducted a survey that involved 46 residents and 216 hospital staff members. Residents selected at least 13 external evaluators from different categories (including physicians, nurses or technologists, peers or fellows, and support staff members) from their last 6 months of rotations. The external evaluators and residents answered 4 questions that pertained to each of the 3 CanMEDS roles being assessed. The survey results were analysed for feasibility, variance within and between rater groups, and the relationships between multisource and self-evaluation scores, and between multisource feedback and in-training evaluation report scores. Results The multisource feedback survey had an overall response rate of 73% with 683 evaluations sent out to 216 unique evaluators. The ratings from different groups of evaluators were only weakly correlated. Residents were most likely to receive their best rating from a collaborating physician and their worst rating from a site secretary or a program assistant. Generally, self-assessment scores were significantly lower than multisource feedback scores. Although there was a strong correlation within the multisource feedback data and within the in-training evaluation report data, there was a weak correlation among the data sets. Conclusions Multisource feedback provides useful feedback and scores that relate to critical CanMEDS roles that are not necessarily reflected in a resident's in-training evaluation report. The self-assessment feature of multisource feedback permits a resident to compare the accuracy of his or her assessments to improve their life-long learning skills.
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Li, Xu, Chunlei Xue, and Xiaobo Gu. "Athlete Gait Feature Recognition Method Based on Multisource Sensing Information." Security and Communication Networks 2022 (February 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/2857465.

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Aiming at the problem of low accuracy of two-dimensional gait recognition at present, a gait feature recognition method based on multisource sensing information is proposed. The multisource sensing information is combined to collect the athlete’s gait characteristics, collect the single frame gait image sequence of the human lower limbs during the movement, and extract the human body’s three-dimensional feature data during human walking by using the body structure and multisource sensing information, so as to realize the separation of the athlete’s gait image background. Finally, it is confirmed by experiments that the recognition rate of athlete gait feature recognition method based on multisource sensing information is significantly improved.
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