Artículos de revistas: "Multisources data"

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Cui, Chen, Shuang Wu, Zhenyong Wang, Qing Guo y Wei Xiang. "A Polar Codes-Based Distributed UEP Scheme for the Internet of Things". Wireless Communications and Mobile Computing 2021 (16 de diciembre de 2021): 1–10. http://dx.doi.org/10.1155/2021/5875797.

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The Internet of Things (IoT), which is expected to support a massive number of devices, is a promising communication scenario. Usually, the data of different devices has different reliability requirements. Channel codes with the unequal error protection (UEP) property are rather appealing for such applications. Due to the power-constrained characteristic of the IoT services, most of the data has short packets; therefore, channel codes are of short lengths. Consequently, how to transmit such nonuniform data from multisources efficiently and reliably becomes an issue be solved urgently. To address this issue, in this paper, a distributed coding scheme based on polar codes which can provide UEP property is proposed. The distributed polar codes are realized by the groundbreaking combination method of noisy coded bits. With the proposed coding scheme, the various data from multisources can be recovered with a single common decoder. Various reliability can be achieved; thus, UEP is provided. Finally, the simulation results show that the proposed coding scheme is viable.

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Yan, Puchen, Qisheng Han, Yangming Feng y Shaozhong Kang. "Estimating LAI for Cotton Using Multisource UAV Data and a Modified Universal Model". Remote Sensing 14, n.º 17 (30 de agosto de 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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Hamze, Ouazene, Chebbo y Maatouk. "Multisources of Energy Contracting Strategy with an Ecofriendly Factor and Demand Uncertainties". Energies 12, n.º 20 (16 de octubre de 2019): 3928. http://dx.doi.org/10.3390/en12203928.

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This study presents a mathematical formulation to optimize contracting capacity strategiesof multisources of energy for institutional or industrial consumers considering demand uncertainties.The objective consists of minimizing the total costs composed of the different types of energy contractcapacity costs, penalty price, and an ecofriendly factor. The penalty price is charged on the demand ofenergy exceeding the total contract capacities. The ecofriendly factor encourages the use of renewableenergy and reduces the traditional energy used in the optimal mix of energy sources. The proposedmodel is tested based on demand of energy inspired from real data. These numerical experiments areanalyzed to illustrate the impact of encouraging the use of renewable energy sources by introducingthe ecofriendly factor and the influence of penalty price and uncertainty in the demand of energy.The results show that in the presence of low penalty price or low uncertainty a large amount ofecofriendly support is needed for using more renewable energy sources in the optimal contractcapacity combination.

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Xi, Jianjun y Wenben Li. "2.5D Inversion Algorithm of Frequency-Domain Airborne Electromagnetics with Topography". Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/1468514.

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We presented a 2.5D inversion algorithm with topography for frequency-domain airborne electromagnetic data. The forward modeling is based on edge finite element method and uses the irregular hexahedron to adapt the topography. The electric and magnetic fields are split into primary (background) and secondary (scattered) field to eliminate the source singularity. For the multisources of frequency-domain airborne electromagnetic method, we use the large-scale sparse matrix parallel shared memory direct solver PARDISO to solve the linear system of equations efficiently. The inversion algorithm is based on Gauss-Newton method, which has the efficient convergence rate. The Jacobian matrix is calculated by “adjoint forward modelling” efficiently. The synthetic inversion examples indicated that our proposed method is correct and effective. Furthermore, ignoring the topography effect can lead to incorrect results and interpretations.

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Korenromp, Eline L., Keith Sabin, John Stover, Tim Brown, Leigh F. Johnson, Rowan Martin-Hughes, Debra ten Brink et al. "New HIV Infections Among Key Populations and Their Partners in 2010 and 2022, by World Region: A Multisources Estimation". JAIDS Journal of Acquired Immune Deficiency Syndromes 95, n.º 1S (1 de enero de 2024): e34-e45. http://dx.doi.org/10.1097/qai.0000000000003340.

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Background: Previously, The Joint United Nations Programme on HIV/AIDS estimated proportions of adult new HIV infections among key populations (KPs) in the last calendar year, globally and in 8 regions. We refined and updated these, for 2010 and 2022, using country-level trend models informed by national data. Methods: Infections among 15–49 year olds were estimated for sex workers (SWs), male clients of female SW, men who have sex with men (MSM), people who inject drugs (PWID), transgender women (TGW), and non-KP sex partners of these groups. Transmission models used were Goals (71 countries), AIDS Epidemic Model (13 Asian countries), Optima (9 European and Central Asian countries), and Thembisa (South Africa). Statistical Estimation and Projection Package fits were used for 15 countries. For 40 countries, new infections in 1 or more KPs were approximated from first-time diagnoses by the mode of transmission. Infection proportions among nonclient partners came from Goals, Optima, AIDS Epidemic Model, and Thembisa. For remaining countries and groups not represented in models, median proportions by KP were extrapolated from countries modeled within the same region. Results: Across 172 countries, estimated proportions of new adult infections in 2010 and 2022 were both 7.7% for SW, 11% and 20% for MSM, 0.72% and 1.1% for TGW, 6.8% and 8.0% for PWID, 12% and 10% for clients, and 5.3% and 8.2% for nonclient partners. In sub-Saharan Africa, proportions of new HIV infections decreased among SW, clients, and non-KP partners but increased for PWID; elsewhere these groups' 2010-to-2022 differences were opposite. For MSM and TGW, the proportions increased across all regions. Conclusions: KPs continue to have disproportionately high HIV incidence.

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Tolosa, Mr Mihiretu Wakwoya. "Action Research on Exploring the Effectiveness of Continuous Assessment on English Common Course in a Case of Plant Science Year I Students Aksum University Shire Campus". IJOHMN (International Journal online of Humanities) 5, n.º 4 (5 de agosto de 2019): 1–14. http://dx.doi.org/10.24113/ijohmn.v5i4.112.

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This action research was aimed mainly to investigate the effectiveness of continuous assessment in English common course in case of students of plant science first year program at shire campus. The study involved 55 (M =15, F= 40) students and 1 male English common course instructor as participant of the study. It also employed three data gathering tools: questionnaire, interview and document analysis. Data obtained from these multisources were analyzed quantitatively and qualitatively in which case percentage and verbal description were used respectively. Hence, the finding indicates that there were no bolded theoretical and practical implementation gaps of CA among instructors and students. However, many impressing factors were found, which impede the implementation of CA. Among these, large number of students in a section, instructors’ workload, students’ attitudes toward CA, lack of specific criteria for checking subjective form of students’ assignment and project work were some. Generally, the study attempts to forward action to be taken to tackle the problem, such as lessen teachers’ workload, minimizing number of students in one section accordance with MEO policy, proposing clear-cut criteria for checking and giving feedback for subjective case assignments. Moreover, instructors need to motivate students to work or involve in CA as well as committed themselves to implement effectively that contributed to prove quality of education. Key words: Continuous assessment, effectiveness, exploring.

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Radwan, Ahmed E. "Integrated reservoir, geology, and production data for reservoir damage analysis: A case study of the Miocene sandstone reservoir, Gulf of Suez, Egypt". Interpretation 9, n.º 4 (4 de agosto de 2021): SH27—SH37. http://dx.doi.org/10.1190/int-2021-0039.1.

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Reservoir damage is considered one of the major challenges in the oil and gas industry. Many studies have been conducted to understand formation damage mechanisms in borehole wells, but few studies have been conducted to analyze the data to detect the source, causes, and mitigations for each well where damage has occurred. I have investigated and quantified the reasons and mitigation of reservoir damage problems in the middle Miocene reservoir within the El Morgan oil field at the southern central Gulf of Suez, Egypt. I used integrated production, reservoir, and geologic data sets and their history during different operations to assess the reservoir damage in El Morgan-XX well. The collected data include the reservoir rock type, fluid, production, core analysis, rock mineralogy, geology, water chemistry, drilling fluids, perforations depth intervals, workover operations, and stimulation history. The integration of different sets of data gave a robust analysis of reservoir damage causes and helps to suggest suitable remediation. Based on these results, I conclude the following: (1) Workover fluid has been confirmed as the primary damage source, (2) the reservoir damage mechanisms could be generated by multisources including solids and filtrate invasions, fluid/rock interaction (deflocculating of kaolinite clay), water blockage, salinity chock, and the high sulfate content of the invaded fluid, and (3) multidata integration leads to appropriate reservoir damage analysis and effective design of the stimulation treatment. Furthermore, minimizing fluid invasion into the reservoir section by managing the overbalance during drilling and workover operations could be very helpful. Fluid types and solids should be considered when designing the stimulation treatment and compatibility tests should be performed. Long periods of completion fluid in boreholes are not recommended, particularly if the completion fluid pressure and reservoir pressure are out of balance, as well as the presence of sensitive formation minerals.

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Li, Chaofeng. "Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks". Wireless Communications and Mobile Computing 2022 (22 de agosto de 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 y Xintao Li. "Multisource Target Data Fusion Tracking Method for Heterogeneous Network Based on Data Mining". Wireless Communications and Mobile Computing 2022 (10 de junio de 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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Dai Song, 戴嵩, 孙喜明 Sun Ximing, 张精明 Zhang Jingming, 朱永山 Zhu Yongshan, 王斌 Wang Bin y 宋冬梅 Song Dongmei. "基于多尺度卷积神经网络的多源数据融合岩性分类方法". Laser & Optoelectronics Progress 61, n.º 14 (2024): 1437005. http://dx.doi.org/10.3788/lop232491.

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Goldring, Ellen B., Madeline Mavrogordato y Katherine Taylor Haynes. "Multisource Principal Evaluation Data". Educational Administration Quarterly 51, n.º 4 (4 de noviembre de 2014): 572–99. http://dx.doi.org/10.1177/0013161x14556152.

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Liu, Xiaolun. "Local Government Governance Path Optimization Based on Multisource Big Data". Mathematical Problems in Engineering 2022 (21 de junio de 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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Ma, Junwei, Pengfei Chen y Lijuan Wang. "A Comparison of Different Data Fusion Strategies’ Effects on Maize Leaf Area Index Prediction Using Multisource Data from Unmanned Aerial Vehicles (UAVs)". Drones 7, n.º 10 (26 de septiembre de 2023): 605. http://dx.doi.org/10.3390/drones7100605.

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The leaf area index (LAI) is an important indicator for crop growth monitoring. This study aims to analyze the effects of different data fusion strategies on the performance of LAI prediction models, using multisource images from unmanned aerial vehicles (UAVs). For this purpose, maize field experiments were conducted to obtain plants with different growth status. LAI and corresponding multispectral (MS) and RGB images were collected at different maize growth stages. Based on these data, different model design scenarios, including single-source image scenarios, pixel-level multisource data fusion scenarios, and feature-level multisource data fusion scenarios, were created. Then, stepwise multiple linear regression (SMLR) was used to design LAI prediction models. The performance of models were compared and the results showed that (i) combining spectral and texture features to predict LAI performs better than using only spectral or texture information; (ii) compared with using single-source images, using a multisource data fusion strategy can improve the performance of the model to predict LAI; and (iii) among the different multisource data fusion strategies, the feature-level data fusion strategy performed better than the pixel-level fusion strategy in the LAI prediction models. Thus, a feature-level data fusion strategy is recommended for the creation of maize LAI prediction models using multisource UAV images.

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NOVOSAD, Mariia-Ruslana. "MULTISOURCE INTELLIGENT PARKING ASSISTANT". Herald of Khmelnytskyi National University. Technical sciences 313, n.º 5 (27 de octubre de 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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Zhang, Haibo. "Music Emotion Representation Learning Based on Multisource Data Fusion and Its Application". Mobile Information Systems 2022 (27 de septiembre de 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 y Jing Ma. "HeteMSD: A Big Data Analytics Framework for Targeted Cyber-Attacks Detection Using Heterogeneous Multisource Data". Security and Communication Networks 2019 (2 de mayo de 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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Wang, Zichi. "Multisource Data Hiding in Digital Images". Symmetry 14, n.º 5 (27 de abril de 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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Choi, Yunseok y Tariq Alkhalifah. "Multisource waveform inversion of marine streamer data using normalized wavefield". GEOPHYSICS 78, n.º 5 (1 de septiembre de 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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Datcu, M., F. Melgani, A. Piardi y S. B. Serpico. "Multisource data classification with dependence trees". IEEE Transactions on Geoscience and Remote Sensing 40, n.º 3 (marzo de 2002): 609–17. http://dx.doi.org/10.1109/tgrs.2002.1000321.

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Amarsaikhan, D. y T. Douglas*. "Data fusion and multisource image classification". International Journal of Remote Sensing 25, n.º 17 (septiembre de 2004): 3529–39. http://dx.doi.org/10.1080/0143116031000115111.

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Jin, Xuebo, Yingbo Wang, Chen Liu, Tianyi Liu y Tingli Su. "Multisource Data Analysis for Stock Prediction". International Journal of u- and e- Service, Science and Technology 10, n.º 7 (31 de julio de 2017): 111–24. http://dx.doi.org/10.14257/ijunesst.2017.10.7.02.

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Burge, Harriet A., Mary L. Jelks y Jean A. Chapman. "Quality control of multisource aeroallergen data". Grana 25, n.º 3 (diciembre de 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, n.º 5 (junio de 2004): 493–99. http://dx.doi.org/10.1016/j.cageo.2004.02.002.

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Cui, Zhiyong, Kristian Henrickson, Salvatore Antonio Biancardo, Ziyuan Pu y Yinhai Wang. "Establishing Multisource Data-Integration Framework for Transportation Data Analytics". Journal of Transportation Engineering, Part A: Systems 146, n.º 5 (mayo de 2020): 04020024. http://dx.doi.org/10.1061/jtepbs.0000331.

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Gong, Zhi Hua, Peng Wei Duan, Yong Guang Li y Rui Yue. "Multi-Structural Non-Linear Data Fusion Method". Applied Mechanics and Materials 530-531 (febrero de 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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Gao, Tong, Hao Chen y 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, n.º 11 (26 de mayo de 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 y Chao Sun. "Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data". Sustainability 13, n.º 23 (25 de noviembre de 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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Brown, Allison, Devon Currie, Megan Mercia, Marcy Mintz, Karen Fruetel y Aliya Kassam. "Does the Implementation of Competency-Based Medical Education Impact the Quality of Narrative Feedback? A Retrospective Analysis of Assessment Data in a Canadian Internal Medicine Residency Program". Canadian Journal of General Internal Medicine 17, n.º 4 (24 de noviembre de 2022): 67–85. http://dx.doi.org/10.22374/cjgim.v17i4.640.

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Background: As training programs implement competency-based models of training oriented around entrustable professional activities (EPAs), the role of traditional assessment tools remains unclear. While rating scales remain emphasized, few empirical studies have explored the utility of narrative comments between methods and models of training. Objective: Compare the quality of narrative comments between in-training evaluation reports (ITERs) and workplace-based assessments (WBAs) of EPAs before and after the formal implementation of a competencybased model of training. Methods: Retrospective analysis of assessment data from 77 residents in the core Internal Medicine (IM) residency program at the University of Calgary between 2015 and 2020, including data collected during a 2-year pilot of WBAs before the official launch of Competency by Design on July 1, 2019. The quality of narrative comments from 2,928 EPAs and 3,608 ITERs was analyzed using the standardized Completed Clinical Evaluation Report Rating (CCERR). Results: CCERR scores were higher on EPAs than ITERs [F (26,213) = 210, MSE = 4,541, p < 0.001, η2 = 0.064]. CCERR scores for EPAs decreased slightly upon formal implementation of Competence by Design but remained higher than the CCERR scores for ITERs completed at that period of time. Conclusions: The quality of narrative comments may be higher on EPAs than traditional ITER evaluations. While programmatic assessment requires the use of multiple tools and methods, programs must consider whether such methods lead to complementarity or redundancy. Résumé Contexte: Alors que les programmes de formation mettent en œuvre des modèles de formation axés sur les compétences et orientés en fonction des activités professionnelles confiables (APC), le rôle des outils d’évaluation traditionnels reste flou. Si les échelles de notation restent privilégiées, peu d’études empiriques explorent l’utilité des commentaires narratifs entre les méthodes et les modèles de formation. Objectif: Comparer la qualité des commentaires narratifs entre les fiches d’évaluation en cours de formation (FECF) et les évaluations des APC sur le lieu de travail avant et après la mise en œuvre officielle d’un modèle de formation axé sur les compétences. Méthodologie: Analyse rétrospective des données d’évaluation de 77 résidents du programme de résidence en médecine interne tronc commun de l’Université de Calgary entre 2015 et 2020, comprenant les données recueillies au cours d’un projet pilote de deux ans d’évaluations en milieu de travail avant le lancement officiel de l’initiative La compétence par conception, le 1er juillet 2019. La qualité des commentaires narratifs de 2,928 APC et de 3,608 FECF a été analysée à l’aide du Completed Clinical Evaluation Report Rating (CCERR) normalisé. Résultats: Les scores du CCERR sont plus élevés pour les APC que pour les FECF [F (26,213) = 210, rétroac-tion multisources = 4,541, p < 0.001, η2 = 0.064]. Les scores du CCERR pour les APC diminuent légèrement au moment de la mise en œuvre officielle de l’initiative La compétence par conception, mais demeurent plus élevés que ceux pour les FECF effectuées à cette période. Conclusions: La qualité des commentaires narratifs serait meilleure pour les APC que pour les FECF traditionnelles. Bien que l’évaluation des programmes nécessite l’utilisation de multiples outils et méthodes, les programmes doivent se demander si l’utilisation de telles méthodes se veut complémentaire ou redondante.

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Qin, Zhengna, Haojie Liao, Ling Chen y Lei Zhang. "Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion". Security and Communication Networks 2021 (16 de diciembre de 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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Huang, Yahui y Daozhong Lei. "Multisource data acquisition based on single-chip microcomputer and sensor technology". Open Computer Science 12, n.º 1 (1 de enero de 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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Guo, Xuan, Haizhong Qian, Fang Wu y Junnan Liu. "A Method for Constructing Geographical Knowledge Graph from Multisource Data". Sustainability 13, n.º 19 (24 de septiembre de 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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Xiao, Xiao. "Characteristic Analysis of Multisource Heterogeneous Data in Digital City Planning Based on Internet of Things". Mobile Information Systems 2021 (12 de noviembre de 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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Jiang, Mengmeng, Qiong Wu y Xuetao Li. "Multisource Heterogeneous Data Fusion Analysis of Regional Digital Construction Based on Machine Learning". Journal of Sensors 2022 (10 de enero de 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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Lv, Zhihan. "Editorial: Multisource Data, Multimedia Content, Multimodal Interaction". Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 11, n.º 2 (7 de junio de 2018): 89–90. http://dx.doi.org/10.2174/235209651102180607133515.

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Ravat, Franck y Jiefu Song. "A Unified Approach to Multisource Data Analyses". Fundamenta Informaticae 162, n.º 4 (4 de agosto de 2018): 311–59. http://dx.doi.org/10.3233/fi-2018-1727.

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Yammarino, Francis J. "Modern Data Analytic Techniques for Multisource Feedback". Organizational Research Methods 6, n.º 1 (enero de 2003): 6–14. http://dx.doi.org/10.1177/1094428102239423.

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Xiao, Fuyuan, Junhao Wen, Witold Pedrycz y Masayoshi Aritsugi. "Complex Evidence Theory for Multisource Data Fusion". Chinese Journal of Information Fusion 1, n.º 2 (30 de septiembre de 2024): 134–59. http://dx.doi.org/10.62762/cjif.2024.999646.

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Data fusion is a prevalent technique for assembling imperfect raw data coming from multiple sources to capture reliable and accurate information. Dempster–Shafer evidence theory is one of useful methodologies in the fusion of uncertain multisource information. The existing literature lacks a thorough and comprehensive review of the recent advances of Dempster– Shafer evidence theory for data fusion. Therefore, the state of the art has to be surveyed to gain insight into how Dempster–Shafer evidence theory is beneficial for data fusion and how it evolved over time. In this paper, we first provide a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory and its extensions, collectively referred to as classical evidence theory, from three aspects of uncertainty modeling, fusion, and decision making. Next, we study and explore complex evidence theory for data fusion in both closed world and open world contexts that benefits from the frame of complex plane modelling. We then present classical and complex evidence theory framework-based multisource data fusion algorithms, which are applied to pattern classification to compare and demonstrate their applicabilities. The research results indicate that the complex evidence theory framework can enhance the capabilities of uncertainty modeling and reasoning by generating constructive interference through the fusion of appropriate complex basic belief assignment functions modeled by complex numbers. Through analysis and comparison, we finally propose several challenges and identify open future research directions in evidence theorybased data fusion.

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Sepulveda, Martin-J. "Multisource Data for Total Worker Health Insights". Journal of Occupational and Environmental Medicine 56, n.º 7 (julio de 2014): 699. http://dx.doi.org/10.1097/jom.0000000000000227.

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

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Chen, Xiangyu, Kaisa Zhang, Gang Chuai, Weidong Gao, Zhiwei Si, Yijian Hou y 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, n.º 4 (14 de febrero de 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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Kaplan, Adam y Eric F. Lock. "Prediction With Dimension Reduction of Multiple Molecular Data Sources for Patient Survival". Cancer Informatics 16 (1 de enero de 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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Xiao, Dianliang, Tiantao Zhang, Xudong Zhou, Guangshun Zheng y Haoran Song. "Safety Monitoring of Expressway Construction Based on Multisource Data Fusion". Journal of Advanced Transportation 2020 (1 de septiembre de 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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Xu, Xiuyan. "An Intelligent Classification Method of Multisource Enterprise Financial Data Based on SAS Model". Computational Intelligence and Neuroscience 2022 (24 de marzo de 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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Li, Shan, Bin Feng, Wei Zhang, Yubin Feng y Zhidu Huang. "Distribution Network Disaster Early Warning and Production Decision Support System Based on Multisource Data". Mathematical Problems in Engineering 2023 (26 de mayo de 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 y Hangxing Yang. "Urban Functional Zone Recognition Integrating Multisource Geographic Data". Remote Sensing 13, n.º 23 (23 de noviembre de 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 y Ru-bin Zheng. "Grade Prediction in Blended Learning Using Multisource Data". Scientific Programming 2021 (11 de septiembre de 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 y Philip Swain. "Probabilistic and Evidential Approaches for Multisource Data Analysis". IEEE Transactions on Geoscience and Remote Sensing GE-25, n.º 3 (mayo de 1987): 283–93. http://dx.doi.org/10.1109/tgrs.1987.289800.

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Briem, G. J., J. A. Benediktsson y J. R. Sveinsson. "Multiple classifiers applied to multisource remote sensing data". IEEE Transactions on Geoscience and Remote Sensing 40, n.º 10 (enero de 2002): 2291–99. http://dx.doi.org/10.1109/tgrs.2002.802476.

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Fromont, Elisa, Rene Quiniou y Marie Odile Cordier. "Learning rules from multisource data for cardiac monitoring". International Journal of Biomedical Engineering and Technology 3, n.º 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 y Samuel Benya. "Modeling Polycentric Urbanization Using Multisource Big Geospatial Data". Remote Sensing 11, n.º 3 (4 de febrero de 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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