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Journal articles on the topic 'Multi-model'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

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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2

GAYRATJANOVNA, AVAZOVA GULNAZA. "MODEL IN MULTI-CLOUD TELECOMMUNICATIONS NETWORKS." International Journal of Advance Scientific Research 4, no. 3 (March 1, 2024): 55–58. http://dx.doi.org/10.37547/ijasr-04-03-12.

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This articleaddresses the critical challenge of developing an effective data protection model tailored for multi-cloud telecommunications networks, a pressing need in the era of ubiquitous cloud computing and escalating cybersecurity threats. As telecommunications infrastructure increasingly relies on multi-cloud environments to deliver services, traditional security models fall short, necessitating innovative approaches to protect sensitive data across dispersed cloud platforms.

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3

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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4

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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5

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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6

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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7

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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8

Ting Yu, Ting Yu, Lihua Zhang Ting Yu, and Hongbing Liu Lihua Zhang. "Service Recommendation Method based on Multi Model Fusion." 電腦學刊 34, no. 6 (December 2023): 063–74. http://dx.doi.org/10.53106/199115992023123406005.

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<p>In recent years, the rapid development of service-oriented computing technology has increased the burden of choice for software developers when developing service-based applications. Existing Web service recommendation systems often face two challenges. First, developers are required to input keywords for service search, but due to their lack of knowledge in the relevant field, the keywords entered by the developers are usually freestyle, causing an inability to accurately locate services. Second, it is exceedingly difficult to extract services that meet the requirements due to the 99.8% sparseness of the application service interaction records. To address the above challenges, a framework for service recommendation through multi-model fusion (SRM) is proposed in this paper. Firstly, we employ graph neural network algorithms to deeply mine historical records, extract the features of applications and services, and calculate their preferences. Secondly, we use the BERT model to analyze text information and use the attention mechanism and fully connected neural networks to deeply mine the matching degree between candidate services and development requirements. The two models mentioned above are further merged to obtain the final service recommendation list. Extensive experiments on datasets demonstrate that SRM can significantly enhance the effectiveness of recommendations in service recommendation scenarios.</p> <p> </p>

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9

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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10

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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12

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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13

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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14

Andrews Dodzi Kobla, Dzikunu. "Multi-Job Scheduling and Optimization Model for Cloud Computing." International Journal of Science and Research (IJSR) 12, no. 3 (March 5, 2023): 713–20. http://dx.doi.org/10.21275/sr23313071201.

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15

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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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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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29

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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30

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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31

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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32

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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33

Hailong Yang, Hailong Yang, Tian Xia Hailong Yang, Zeyu Xia Tian Xia, and Dayong Zhai Zeyu Xia. "Multi-Interference and Multi-Model Dynamic Scheduling of the Small Satellite Based on Dual Population Genetic Algorithm." 網際網路技術學刊 24, no. 6 (November 2023): 1199–209. http://dx.doi.org/10.53106/160792642023112406003.

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<p>Small satellites have the outstanding advantages of flexible reconfiguration and strong system robustness through large-scale network operation, which has attracted attention at domestic and overseas in recent years. However, how to solve the scheduling problem in large-scale satellite constellation/cluster production is always the key to increasing the volume production of satellites. In this paper, the existing production line framework and the critical technologies of intelligent manufacturing are analyzed, and the intelligent production line flow is proposed. Based on the establishment of the job shop scheduling (JSP) model, the Interference of multi-model scheduling is classified, and by improving the dynamic scheduling strategy of the dual population genetic algorithm, we solve the multi-model scheduling problem. The simulation results show that the scheduling scheme can minimize the influence of interference events on the schedule, which proves the superiority and effectiveness of the scheduling strategy.</p> <p> </p>

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34

Wang, S. B., S. C. Wang, Z. G. Liu, S. Zhang, and Y. Guo. "Multi-agent cooperative multi-model adaptive guidance law." Aeronautical Journal 125, no. 1288 (March 4, 2021): 1103–29. http://dx.doi.org/10.1017/aer.2021.7.

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ABSTRACTA multi-agent engagement scenario is considered in which a high-value aircraft launches two defenders to intercept two homing missiles aimed at the aircraft. Under the assumption that all aircrafts have first-order linear dynamic characteristics, a combined multiple-mode adaptive estimation (MMAE) and a two-way cooperative optimal guidance law are proposed for the target–defenders team. Considering the full cooperation of the target and both the two defenders, the two-way cooperative strategies provide the analytical expressions for their optimal control input, enabling the target–defenders team to intercept the missiles with minimal control effort. To successfully intercept the missiles, MMAE is used to identify the guidance laws adopted by the missiles and estimate their states. The simulation results show that the target cooperating with the defenders to perform lure manoeuvres for the missiles can improve the guidance performance of the defenders as well as reduce the control effort of the defenders for intercepting the missiles.

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35

Mallozzi, Lina, and Roberta Messalli. "Multi-Leader Multi-Follower Model with Aggregative Uncertainty." Games 8, no. 3 (June 22, 2017): 25. http://dx.doi.org/10.3390/g8030025.

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36

Jahanbani G, Ashkan, and Tom A . Jelmert. "A Multi-Layer Multi-Region Well Test Model." International Journal of Engineering Trends and Technology 19, no. 3 (January 25, 2015): 154–58. http://dx.doi.org/10.14445/22315381/ijett-v19p227.

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37

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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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

Sona S., Sharviya. "Demonstration of the Multi-Server Queuing Model Using Big-M Method." International Journal of Science and Research (IJSR) 12, no. 12 (December 5, 2023): 1778–81. http://dx.doi.org/10.21275/mr231224215119.

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44

Dominic Suta, Damas. "Impact of Workplace Environment on Employees' Performance: A Multi - Mediation Model." International Journal of Science and Research (IJSR) 13, no. 4 (April 5, 2024): 1667–73. http://dx.doi.org/10.21275/sr24408155744.

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45

Tian, Shu Rong, Xiao Shu Sun, and Xi Jing Sun. "Multi-Sensor Interactive Multi-Model PHD Filter for Maneuvering Multi-Target Tracking." Applied Mechanics and Materials 336-338 (July 2013): 200–203. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.200.

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In maneuvering multiple targets tracking problem, Probability Hypothesis Density(PHD) filter can be used to estimate the multi-target state and the number at each time step, but single model method may not provide accurate estimates. In this paper, an interactive multiple model PHD filter is proposed, and then multiple sensor interactive multiple model PHD filter is proposed to improve the tracking of multiple maneuvering targets. PHD particle filter implementation is used to perform the proposed method consisting of multiple maneuvering targets.

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46

Montesinos-López, Osval A., Abelardo Montesinos-López, José Crossa, Fernando H. Toledo, Oscar Pérez-Hernández, Kent M. Eskridge, and Jessica Rutkoski. "A Genomic Bayesian Multi-trait and Multi-environment Model." G3: Genes|Genomes|Genetics 6, no. 9 (June 24, 2016): 2725–44. http://dx.doi.org/10.1534/g3.116.032359.

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47

Liu, Yanzhe, Bingxiang Liu, Jiajia Yu, and Zhijian Yu. "Multi-Angle Movie Reviews Analysis Based on Multi Model." Journal of Physics: Conference Series 1757, no. 1 (January 1, 2021): 012128. http://dx.doi.org/10.1088/1742-6596/1757/1/012128.

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48

Cao, Ping, Jie Gao, and Zuping Zhang. "Multi-View Based Multi-Model Learning for MCI Diagnosis." Brain Sciences 10, no. 3 (March 20, 2020): 181. http://dx.doi.org/10.3390/brainsci10030181.

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Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).

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49

Hung, Y. F., and K. L. Chien. "A Multi-Class Multi-Level Capacitated Lot Sizing Model." Journal of the Operational Research Society 51, no. 11 (November 2000): 1309. http://dx.doi.org/10.2307/254215.

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50

XIE, Wenjie, De XU, Yingjun TANG, and Geng CUI. "Multi-Scale Multi-Level Generative Model in Scene Classification." IEICE Transactions on Information and Systems E94-D, no. 1 (2011): 167–70. http://dx.doi.org/10.1587/transinf.e94.d.167.

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