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Journal articles on the topic 'Multi-model model'

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Author: Grafiati

Published: 6 September 2023

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1

Li, Jiyun, Chenxi Jia, and Chen Qian. "Progressive Breast Cancer Diagnosis Model Based on Multi-classifier and Multi-modal Fusion." International Journal of Machine Learning and Computing 11, no. 6 (November 2021): 387–92. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1066.

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Lee, Jae Moon, and Thien Nguyen Phu. "Multi-Stream Fused Model: A Novel Real-Time Botnet Detecting Model." Bonfring International Journal of Data Mining 7, no. 2 (March 31, 2017): 06–10. http://dx.doi.org/10.9756/bijdm.8331.

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3

Kim. "Development of Multisite Spatio-Temporal Downscaling Model for Rainfall Using GCM Multi Model Ensemble." Journal of the Korean Society of Civil Engineers 35, no. 2 (2015): 327. http://dx.doi.org/10.12652/ksce.2015.35.2.0327.

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4

Alashhab, Mohamed Sayed, and Ehab A. Mlybari. "Developing a multi-item, multi-product, and multi-period supply chain planning optimization model." Indian Journal of Science and Technology 14, no. 37 (October 5, 2021): 2850–59. http://dx.doi.org/10.17485/ijst/v14i37.867.

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Prasad, G. Shyam Chandra, and K. Adi Narayana Reddy. "Sentiment Analysis Using Multi-Channel CNN-LSTM Model." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 489–94. http://dx.doi.org/10.5373/jardcs/v11sp12/20193243.

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Vala, Mr Manish, Kajal Patel, and Harsh Lad. "Multi Model Biometrics Data Retrieval Through: Big-Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 1273–77. http://dx.doi.org/10.31142/ijtsrd15933.

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7

Stünkel, Patrick, Harald König, Adrian Rutle, and Yngve Lamo. "Multi-Model Evolution through Model Repair." Journal of Object Technology 20, no. 1 (2021): 1:1. http://dx.doi.org/10.5381/jot.2021.20.1.a2.

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8

Duvall, Marshall, James B. Riggs, and Peter Lee. "Multi-model decoupled Generic Model Control." Control Engineering Practice 9, no. 5 (May 2001): 471–81. http://dx.doi.org/10.1016/s0967-0661(01)00007-7.

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9

Stoica, Petre, Yngve Selén, and Jian Li. "Multi-model approach to model selection." Digital Signal Processing 14, no. 5 (September 2004): 399–412. http://dx.doi.org/10.1016/j.dsp.2004.03.002.

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10

Lu, Jiaheng, and Irena Holubová. "Multi-model Databases." ACM Computing Surveys 52, no. 3 (July 27, 2019): 1–38. http://dx.doi.org/10.1145/3323214.

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11

Bashiri, Mahdi, and Hossein Badri. "A Dynamic Model for Expansion Planning of Multi Echelon Multi Commodity Supply Chain." International Journal of Engineering and Technology 2, no. 1 (2010): 85–93. http://dx.doi.org/10.7763/ijet.2010.v2.105.

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12

Muruganantham, B., and K. Vivekanandan. "Multi Perceptional Optimality Matrix Based Web Service Reliability Model." Journal of Software 10, no. 9 (September 2015): 1045–55. http://dx.doi.org/10.17706//jsw.10.9.1045-1055.

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Muruganantham, B., and K. Vivekanandan. "Multi Perceptional Optimality Matrix Based Web Service Reliability Model." Journal of Software 10, no. 9 (2015): 1045–55. http://dx.doi.org/10.17706/jsw.10.9.1045-1055.

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M, Archana H., Tejaswini Busnur, and Dr Poornima B. "System Model for Processing on Multi-Format of Dataset." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 1908–13. http://dx.doi.org/10.31142/ijtsrd17161.

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15

Tagarev, Todor, Salvatore Marco Pappalardo, and Nikolai Stoianov. "A Logical Model for Multi-Sector Cyber Risk Management." Information & Security: An International Journal 47, no. 1 (2020): 13–26. http://dx.doi.org/10.11610/isij.4701.

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Abramowitz, G. "Model independence in multi-model ensemble prediction." Australian Meteorological and Oceanographic Journal 59, no. 1SP (2010): 3–6. http://dx.doi.org/10.22499/2.5901.002.

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Titman, Andrew C., and Linda D. Sharples. "Model diagnostics for multi-state models." Statistical Methods in Medical Research 19, no. 6 (August 4, 2009): 621–51. http://dx.doi.org/10.1177/0962280209105541.

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18

Chen, Shaopei, Jianjun Tan, Christophe Claramunt, and Cyril Ray. "Multi-scale and multi-modal GIS-T data model." Journal of Transport Geography 19, no. 1 (January 2011): 147–61. http://dx.doi.org/10.1016/j.jtrangeo.2009.09.006.

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19

Chen, Yen-Chiu, and Hui-Ching Hsieh. "A Robust Controllers’ Negotiation Model in Multi-Domain SDN Environments." SIJ Transactions on Computer Networks & Communication Engineering 02, no. 06 (October 8, 2014): 01–07. http://dx.doi.org/10.9756/sijcnce/v2i6/0207190201.

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Chen, Yen-Chiu, and Hui-Ching Hsieh. "A Robust Controllers’ Negotiation Model in Multi-Domain SDN Environments." SIJ Transactions on Computer Networks & Communication Engineering 06, no. 01 (February 13, 2018): 10–16. http://dx.doi.org/10.9756/sijcnce/v6i1/03010020201.

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21

Prakash, Devesh, Amuly Ratn, Sumit Kumar, and Purnendu Bose. "Calibration of Algal Growth Model Using Multi-objective Genetic Algorithm." International Journal of Environmental Science and Development 6, no. 12 (2015): 901–7. http://dx.doi.org/10.7763/ijesd.2015.v6.719.

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22

Hong Zhang, Hong Zhang, Kun Jiang Hong Zhang, Chuanqi Cheng Kun Jiang, Jie Cao Chuanqi Cheng, and Wenyue Zhang Jie Cao. "Multi-source Heterogeneous Data Fusion Model Based on FC-SAE." 網際網路技術學刊 23, no. 7 (December 2022): 1473–81. http://dx.doi.org/10.53106/160792642022122307003.

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<p>Multi-source heterogeneous data has different degrees of data correlation or data conflict. How to fuse this data and fully mine its inherent meanings to obtain more accurate decision information is a problem that needs to be solved urgently. This paper proposes a multi-source heterogeneous data fusion model based on fully connected layers and sparse autoencoders (short for FC-SAE) to solve the above problem. This model can effectively improve the time series forecasting performance compared with the traditional time series forecasting model. The MAE value is reduced by 4.4% and the RMSE value is reduced by 3.7%. In terms of fusion strategy, the method that uses the sparse autoencoder as the fusion strategy reduces the MAE value by 1.7% and the RMSE value by 2.3% compared with the method that uses the fully connected layer as the fusion strategy.</p> <p> </p>

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23

Hai-Ping Gan, Hai-Ping Gan, Lin Cao Hai-Ping Gan, Pei-Ran Song Lin Cao, Xiao-Peng Cao Pei-Ran Song, Bing-Nan Du Xiao-Peng Cao, Kang-Ning Du Bing-Nan Du, and Ya-Nan Guo Kang-Ning Du. "Multi-satellite Mission Planning Algorithm Based on Preemptive Priority Model." 電腦學刊 34, no. 1 (February 2023): 225–38. http://dx.doi.org/10.53106/199115992023023401017.

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<p>Aiming at the problems of high time-consuming and insufficient guarantee of high-priority task completion in the large-scale multi-satellite multi-observation mission planning application, a multi-satellite multi-mission planning model based on preemptive priority model is proposed. Combined with the whole-neighborhood greedy search strategy, an improved tabu search algorithm is designed, taking the combination of satellite observation windows as the decision variables. The designed algorithm realizes an efficient solver to the proposed mission planning model. The simulation results show that the average solving time of the algorithm is 144s under the problem scale of 100 satellites and 3000 tasks. Compared with the existing algorithm, the calculation time is shortened by at least 75% under the premise that the total number of planning tasks is comparable, which is more in line with the high time efficiency requirements for satellite mission planning in engineering applications.</p> <p> </p>

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24

Chang-You Zhang, Chang-You Zhang, Jing-Jing Wang Chang-You Zhang, Li-Xia Wan Jing-Jing Wang, and Ruo-Xue Yu Li-Xia Wan. "An Emotional Analysis Method Based on Multi Model Ensemble Learning." 電腦學刊 34, no. 1 (February 2023): 001–11. http://dx.doi.org/10.53106/199115992023023401001.

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<p>Traditional machine learning models generally use weak supervision model, which is difficult to adapt to the scene of multi classification for emotional text. Therefore, a multi model ensemble learning algorithm for emotional text classification is proposed. The algorithm takes the labeled emotional text data as the training sample, uses the improved TF-IDF algorithm to train the word vector space model, selects three weakly supervised machine learning algorithms, linear SVC, xgboost and logistic regression, to construct the base classifier, and uses the random forest algorithm to construct the meta classifier. It realizes the function of dividing emotional text into three categories: positive, neutral and negative. From the simulation and test results, the AUC values of the multi model ensemble learning algorithm model for each category are 0.93, 0.94 and 1.00, and the AP values are 0.87, 0.86 and 1.00, and the indicators of accuracy and recall are better than the single machine learning model, which realizes the high performance and high accuracy for emotional text classification.</p> <p> </p>

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25

Nadjakov, E. G., and I. N. Mikhailov. "Interacting multi-boson model." Journal of Physics G: Nuclear Physics 13, no. 10 (October 1987): 1221–29. http://dx.doi.org/10.1088/0305-4616/13/10/011.

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26

Gachet, Alexandre, and Patrick Brézillon. "A Multi-Level Model." Journal of Decision Systems 14, no. 1-2 (January 2005): 9–37. http://dx.doi.org/10.3166/jds.14.9-37.

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27

Carter, Timothy R. "Multi-model yield projections." Nature Climate Change 3, no. 9 (August 28, 2013): 784–86. http://dx.doi.org/10.1038/nclimate1995.

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28

Axley, J. "Multi‐zone IAQ model." Batiment International, Building Research and Practice 17, no. 4 (July 1989): 228–35. http://dx.doi.org/10.1080/01823328908726976.

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29

Choi, Y. S., and Roger Lui. "Multi-dimensional electrochemistry model." Archive for Rational Mechanics and Analysis 130, no. 4 (1995): 315–42. http://dx.doi.org/10.1007/bf00375143.

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30

BADR, A., Z. BINDER, and D. REY. "Weighted multi-model control." International Journal of Systems Science 23, no. 1 (January 1992): 145–49. http://dx.doi.org/10.1080/00207729208949196.

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31

Rapp, Vincent, Kevin Bailly, Thibaud Senechal, and Lionel Prevost. "Multi-Kernel Appearance Model." Image and Vision Computing 31, no. 8 (August 2013): 542–54. http://dx.doi.org/10.1016/j.imavis.2013.04.006.

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32

Di Palma, F., and L. Magni. "A Multi-Model Structure for Model Predictive Control." IFAC Proceedings Volumes 36, no. 18 (September 2003): 543–48. http://dx.doi.org/10.1016/s1474-6670(17)34725-0.

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33

Moiseenko, Evgenii, Michalis Kokologiannakis, and Viktor Vafeiadis. "Model checking for a multi-execution memory model." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (October 31, 2022): 758–85. http://dx.doi.org/10.1145/3563315.

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Multi-execution memory models, such as Promising and Weakestmo, are an advanced class of weak memory consistency models that justify certain outcomes of a concurrent program by considering multiple candidate executions collectively. While this key characteristic allows them to support effective compilation to hardware models and a wide range of compiler optimizations, it makes reasoning about them substantially more difficult. In particular, we observe that Promising and Weakestmo inhibit effective model checking because they allow some suprisingly weak behaviors that cannot be generated by examining one execution at a time. We therefore introduce Weakestmo2, a strengthening of Weakestmo by constraining its multi-execution nature, while preserving the important properties of Weakestmo: DRF theorems, compilation to hardware models, and correctness of local program transformations. Our strengthening rules out a class of surprisingly weak program behaviors, which we attempt to characterize with the help of two novel properties: load buffering race freedom and certification locality. In addition, we develop WMC, a model checker for Weakestmo2 with performance close to that of the best tools for per-execution models.

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34

Wagena, Moges B., Gopal Bhatt, Elyce Buell, Andrew R. Sommerlot, Daniel R. Fuka, and Zachary M. Easton. "Quantifying model uncertainty using Bayesian multi-model ensembles." Environmental Modelling & Software 117 (July 2019): 89–99. http://dx.doi.org/10.1016/j.envsoft.2019.03.013.

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35

Di Palma, F., and L. Magni. "A multi-model structure for model predictive control." Annual Reviews in Control 28, no. 1 (January 2004): 47–52. http://dx.doi.org/10.1016/j.arcontrol.2004.01.004.

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36

Ha, Il Do, Youngjo Lee, and Gilbert MacKenzie. "Model selection for multi-component frailty models." Statistics in Medicine 26, no. 26 (2007): 4790–807. http://dx.doi.org/10.1002/sim.2879.

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37

Katsanevakis, Stelios. "Modelling fish growth: Model selection, multi-model inference and model selection uncertainty." Fisheries Research 81, no. 2-3 (November 2006): 229–35. http://dx.doi.org/10.1016/j.fishres.2006.07.002.

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38

Lin, Wei, Yuefei Zhu, and Ruijie Cai. "Processing of Cryptographic Function Identification Based on Multi-feature Progressive Model." Journal of Advances in Computer Networks 3, no. 3 (2015): 180–85. http://dx.doi.org/10.7763/jacn.2015.v3.163.

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39

Lateef, Rana Abdulrahman, and Dr Ayad Rodhan Abbas. "A Proposed ConvXGBoost Model for Human Activity Recognition with Multi Optimizers." Webology 19, no. 1 (January 20, 2022): 1703–15. http://dx.doi.org/10.14704/web/v19i1/web19114.

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The wide use of smartphones and later smartwatches equipped with a set of sensors such as location, motion, and direction blaze the trail for researchers to better recognize human activity. However, researches on using inertial or motion sensors (i.e., accelerometer, gyroscope) for human activity recognition (HAR) has intensified and reside a great confrontation to be faced. Lately, many deep learning methods have been suggested to improve the human activity classification and discrimination performance to reach an optimal accuracy. Therefore, this paper applies a Convolutional eXtreme Gradient Boosting (ConvXGBoost), which combines Convolutional Neural Network (CNN) represented by AlexNet to learn the input features automatically, followed by XGBoost decision tree used to predict the class label and thereof recognize the performed activity. Human activities are collected from sensors as time series data. Therefore, we suggested using one-dimensional AlexNet (1D AlexNet) model instead of 2D. The AlexNet model is compiled with two optimizers Adam and Stochastic Gradient Descent (SGD) which are applied consecutively. The suggested architecture was trained and evaluated on the “WISDM Smartphone and Smartwatch Activity and Biometric Dataset” that consists of raw data for eighteen activities recorded from phone and watch. The experiments revealed that using multi optimizer with a convolutional neural network improved the accuracy of recognition by 5%. Moreover, a proposed ConvXGBoost model outperformed the performance of other models works with the dataset as mentioned above with an overall accuracy of 98-99% depends on the device used.

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40

Oliveira, SRM. "Multi-Model for Planning High Complexity Environment using Hybrid Intelligent Architecture." International Journal of Advances in Management and Economics 01, no. 04 (July 2, 2012): 60–73. http://dx.doi.org/10.31270/ijame/01/04/2012/09.

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41

Rasul, Azad, Amanj Ahmad Hamdamin Dewana, and Saadaldeen Muhammad Nuri Saed. "Multi-model tourist forecasting: case study of Kurdistan Region of Iraq." Tourism and Travelling 2, no. 1 (August 2, 2019): 24–34. http://dx.doi.org/10.21511/tt.2(1).2019.04.

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The tourism industry has been one of the leading service industries in the global economy in recent years and the number of international tourism in 2018 reached 1.4 billion. The goal of the research is to evaluate the performance of various methods for forecasting tourism data and predict the number of tourists during 2019 and 2022. Performance of 15 prediction models (i.e. Local linear structural, Naïve, Holt, Random walk, ARIMA) was compared. Based on error measurements matrix (i.e. RMSE, MAE, MAPE, MASE), the most accurate method was selected to forecast the total number of tourists from 2019 to 2022 to Kurdistan Region (KR), then forecasts were performed for each governorate in KR. The results show that among 15 examined models of tourist forecasting in KR, Local linear structural and ARIMA (7,3,0) model performed best. The number of tourists to KR and each governorate in KR is predicted to increase by most experimented models, especially those which demonstrated higher accuracy. Generally, the number of tourist to KR predicted by ARIMA (7,3,0) is a lot bigger than Local linear structure. Linear structural predicted the number increase to 3,137,618 and 3,462,348 in 2020 and 2022, respectively, while ARIMA (7,3,0) predicted the number of tourists to KR to increase rapidly to 3,748,416 and 8,681,398 in 2020 and 2022.

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42

Koizumi, Takayuki, Nobutaka Tsujiuchi, and Azusa Nakai. "50081 Development and Estimation of Multi-body Child Human Model(Biomechanics)." Proceedings of the Asian Conference on Multibody Dynamics 2010.5 (2010): _50081–1_—_50081–8_. http://dx.doi.org/10.1299/jsmeacmd.2010.5._50081-1_.

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43

Chen, Zhenquan, and William F. McTernan. "Multi-substrate, multi-option groundwater transport model." Journal of Contaminant Hydrology 11, no. 3-4 (November 1992): 215–44. http://dx.doi.org/10.1016/0169-7722(92)90018-a.

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44

Thangavel, Sakthi, and Sebastian Engell. "An efficient model-error model update strategy for multi-stage NMPC with model-error model." IFAC-PapersOnLine 53, no. 2 (2020): 7217–22. http://dx.doi.org/10.1016/j.ifacol.2020.12.553.

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45

Duan, Qingyun, Newsha K. Ajami, Xiaogang Gao, and Soroosh Sorooshian. "Multi-model ensemble hydrologic prediction using Bayesian model averaging." Advances in Water Resources 30, no. 5 (May 2007): 1371–86. http://dx.doi.org/10.1016/j.advwatres.2006.11.014.

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46

Munastiwi, Erni. "Manajemen Model Pembinaan Kelompok Guru PAUD Model ‘Multi-Workshop’." AL-ATHFAL : JURNAL PENDIDIKAN ANAK 4, no. 1 (December 28, 2018): 51–60. http://dx.doi.org/10.14421/al-athfal.2018.41-04.

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Tujuan penelitian adalah mengkaji manajemen program parenting/ pola asuh kreatif. Kreatif dalam mengasuh anak saat ini sangat diperlukan, karena zaman berubah sangat cepat. Kondisi inilah menjadi tantangan. Agar tujuan parenting/ pengasuhan tepat sasaran dan sesuai kondisi, maka perlu dikelola dengan baik. Oleh karena itu, manajemen program parenting kreatif perlu dikaji secara mendalam. Penelitian ini merupakan penelitian deskriptif dengan pendekatan kualitatif. Pemilihan subyek penelitian dilakukan dengan menggunakan teknik purposive. Subyek penelitian ini adalah kepala lembaga pendidikan anak usia dini, guru, orangtua, anak didik dan nara sumber program parenting. Teknik pengumpulan data menggunakan cara observasi, wawancara, dan dokumentasi. Analisis data melalui tahap reduksi data, displai data, dan penarikan kesimpulan. Uji keabsahan data penelitian menggunakan triangulasi sumber. Hasil penelitian mendeskripsikan implementasi manajemen program parenting kreatif pada tingkat satuan pendidikan anak usia dini

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47

Lukito, Yuan. "Multi Layer Perceptron Model for Indoor Positioning System Based on Wi-Fi." Jurnal Teknologi dan Sistem Komputer 5, no. 3 (July 31, 2017): 123–28. http://dx.doi.org/10.14710/jtsiskom.5.3.2017.123-128.

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Indoor positioning system issue is an open problem that still needs some improvements. This research explores the utilization of multilayer perceptron in determining someone’s position inside a building or a room, which generally known as Indoor Positioning System. The research was conducted in some steps: dataset normalization, multilayer perceptron implementation, training process of multilayer perceptron, evaluation, and analysis. The training process has been conducted many times to find the best parameters that produce the best accuracy rate. The experiment produces 79,16% as the highest accuracy rate. Compared to previous research, this result is comparably lower and needs some parameters tweaking or changing the neural networks architectures.

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48

久保田, 萌々, 真樹藤川, and 真樹史鈴木. "Proposal and Evaluation of Multi-model CAPTCHA with Typoglycemia." 産業応用工学会論文誌 11, no. 1 (2023): 54–64. http://dx.doi.org/10.12792/jjiiae.11.1.54.

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49

Huang, Jiangyin, and Jing Zhao. "Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV Model." MATEC Web of Conferences 139 (2017): 00030. http://dx.doi.org/10.1051/matecconf/201713900030.

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50

Guo, Zhengbing, and Meng Wang. "Multi-task continuous learning model." Journal of Physics: Conference Series 1873, no. 1 (April 1, 2021): 012093. http://dx.doi.org/10.1088/1742-6596/1873/1/012093.

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