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Journal articles on the topic 'Hybrid model'

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Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

Turgut, Mehmet, and Alp Ustundag. "A Hybrid Risk Evaluation Model for Automotive Production." International Journal of Machine Learning and Computing 4, no. 5 (2014): 458–62. http://dx.doi.org/10.7763/ijmlc.2014.v4.454.

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Douzi, Samira, Feda A. AlShahwan, Mouad Lemoudden, and Bouabid El Ouahidi. "Hybrid Email Spam Detection Model Using Artificial Intelligence." International Journal of Machine Learning and Computing 10, no. 2 (February 2020): 316–22. http://dx.doi.org/10.18178/ijmlc.2020.10.2.937.

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3

STONIČ, DAVID. "PREDSTAVITEV MODELA ZA PREPOZNAVANJE HIBRIDNIH GROŽENJ." 20 YEARS OF CONTEMPORARY MILITARY CHALLENGES/20 LET SODOBNIH VOJAŠKIH IZZIVOV, VOLUME 2018, ISSUE 20/4 (October 15, 2018): 105–18. http://dx.doi.org/10.33179/bsv.99.svi.11.cmc.20.4.5.

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Povzetek V spremenjenem varnostnem okolju moramo razviti ustrezne koncepte in orodja za pravočasno prepoznavanje in opredelitev hibridnih groženj, da bi se lahko uspešno odzvali. Oblikovali smo model, ki grožnje poveže z akterjem. Ovrednoteni obveščevalni podatki so vstopne informacije v izdelan model za ocenjevanje hibridne ogroženosti nacionalne varnosti. Izhodna informacija je identificirana hibridna grožnja. Ob izpolnitvi pogoja hibridne ogroženosti lahko nacionalnovarnostni sistem začne izvajati ukrepe, ki bi zmanjšali zmožnosti delovanja hibridnega akterja. Zavedanje o obstoju hibridnih groženj in njihovo identificiranje bo spodbudilo odločevalce v nacionalnovarnostnem sistemu, da bodo zagotovili ustrezno hibridno odpornost države, saj je primarna odgovornost za spoprijemanje s hibridnimi grožnjami predvsem odgovornost države. Nujen je celostni vladni pristop v povezavi z Evropsko unijo in Severnoatlantskim zavezništvom. Ključne besede: hibridne grožnje, indikatorji konvencionalnega ogrožanja, indikatorji nekonvencionalnega ogrožanja. Abstract In the changed security environment, we have to be able to develop appropriate concepts and tools for timely identification and definition of hybrid threats. We have developed a model, which relates threats with actors. The evaluated intelligence then serves as input information for the national security hybrid threat evaluation model. The output information is an identified hybrid threat. When a hybrid threat is identified, the national security system can take adequate measures to hinder the operational ability of hybrid actors. The awareness of hybrid threats and their identification will encourage decision makers in the national security system to ensure adequate hybrid resilience of the country, since countering hybrid threats is a state’s primary responsibility, which requires a comprehensive governmental approach in cooperation with the European Union and the North Atlantic Alliance. Key words: Hybrid threats, conventional threat indicators, unconventional threat indicators

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4

Zhang, Wei, and Youxian Sun. "Hybrid Automaton Model and Control of Hybird Systems." Developments in Chemical Engineering and Mineral Processing 9, no. 1-2 (May 15, 2008): 143–50. http://dx.doi.org/10.1002/apj.5500090217.

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5

Wang, Jing, and Nianlong Luo. "A New Hybrid Popular Model for Personalized Tag Recommendation." Journal of Computers 11, no. 2 (March 2016): 116–23. http://dx.doi.org/10.17706/jcp.11.2.116-123.

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6

Baker, Jim. "A Hybrid Model for a Hybrid Science." Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology 1, no. 1 (November 20, 2008): 1. http://dx.doi.org/10.1002/wnan.32.

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7

Bojović, Žana, and Jelena Stojkanović. "Flipped classroom: A model of hybrid teaching." Zbornik radova Pedagoskog fakulteta Uzice, no. 24 (2022): 11–28. http://dx.doi.org/10.5937/zrpfu2224011b.

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In the field of education, special attention has been paid recently to improving the quality of the teaching process as well as readiness to adapt to many challenges of modern society, especially in extraordinary circumstances such as the COVID-19 pandemic. One of the alternative ways of teaching is the model of the Flipped Classroom, whose basic settings come from the Theory of Self-Determination. The essence of the Flipped classroom is in creating a stimulating learning environment in accordance with one's own abilities and interests, outside the classic classrooms. The didactic-methodical implications of the application of this teaching model are reflected in the possibility of individualizing the learning process (students choose the type of teaching material offered, determine the time, place and pace of learning), in developing students' cognitive abilities for independent research, critical analysis and use of different sources (printed and electronic) for additional explanations of curriculum content. The received feedback increases the motivation to learn. The work of students is harmonized with their possibilities, interests and prior knowledge. Disadvantages of this model are: insufficient training of teachers to create and work on e-learning platforms, which requires additional training in computer literacy for both teachers and students, preparation of materials takes a lot of time, greater cognitive load for students, adapting the curriculum to this model, and students spending a lot time in front of a computer.

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8

Andreichenko, D. K., K. P. Andreichenko, and I. A. Batraeva. "Hybrid Automation Extended Model." Izvestiya of Saratov University. New Series. Series: Mathematics. Mechanics. Informatics 19, no. 1 (2019): 94–104. http://dx.doi.org/10.18500/1816-9791-2019-19-1-94-104.

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9

Haire, Mark S., and David P. Knobles. "Geoacoustic inversion hybrid model." Journal of the Acoustical Society of America 109, no. 5 (May 2001): 2394. http://dx.doi.org/10.1121/1.4744447.

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10

Dobrev, I., F. Massouh, and M. Rapin. "Actuator surface hybrid model." Journal of Physics: Conference Series 75 (July 1, 2007): 012019. http://dx.doi.org/10.1088/1742-6596/75/1/012019.

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11

Kurbatova, P., N. Eymard, and V. Volpert. "Hybrid Model of Erythropoiesis." Acta Biotheoretica 61, no. 3 (August 1, 2013): 305–15. http://dx.doi.org/10.1007/s10441-013-9188-2.

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12

Yamaguchi, Kazuhiro, and Kensuke Okada. "Hybrid cognitive diagnostic model." Behaviormetrika 47, no. 2 (May 29, 2020): 497–518. http://dx.doi.org/10.1007/s41237-020-00111-x.

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13

Schnack, E. "A hybrid BEM model." International Journal for Numerical Methods in Engineering 24, no. 5 (May 1987): 1015–25. http://dx.doi.org/10.1002/nme.1620240512.

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14

Pullagura, Lokaiah. "Analysis of Train Delay Prediction System based on Hybrid Model." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 2900–2903. http://dx.doi.org/10.5373/jardcs/v12sp7/20202433.

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15

T., Fahad Iqbal. "User Interest Prediction Model for Hybrid Tag Recommender Automation Systems." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 4175–85. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020131.

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16

Subramanian, Suresh, and Y. Angeline Christobel. "A Hybrid Machine Learning Model to Predict Heart Disease Accurately." Indian Journal of Science and Technology 15, no. 12 (March 27, 2022): 527–34. http://dx.doi.org/10.17485/ijst/v15i12.104.

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17

Dash, Ritesh, Chinmaya Behera, Pratik Ranjan Behera, Manas Ranjan Sarangi, and Kunjan Kumar Mohapatra. "A Review on Hybrid Energy System and A Model Simulation." International Journal of Scientific Research 3, no. 5 (June 1, 2012): 150–53. http://dx.doi.org/10.15373/22778179/may2014/46.

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18

Chuentawat, Ronnachai, Nittaya Kerdprasop, and Kittisak Kerdprasop. "The Forecast of PM10 Pollutant by Using a Hybrid Model." International Journal of Future Computer and Communication 6, no. 3 (September 2017): 128–32. http://dx.doi.org/10.18178/ijfcc.2017.6.3.504.

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19

Sen, Amit Prakash, Nirmal Kumar Rout, Tuhinansu Pradhan, and Amrit Mukherjee. "Hybrid Deep CNN Model for the Detection of COVID-19." Indian Journal Of Science And Technology 15, no. 41 (November 5, 2022): 2121–28. http://dx.doi.org/10.17485/ijst/v15i41.1421.

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20

Hasan, M. Jamil, M. Umma Kulsum, Umakanta Sarker, M. Quamrul Islam Matin, Nazmul Hoque Shahin, M. Shahjahan Kabir, Sezai Ercisli, and Romina Alina Marc. "Assessment of GGE, AMMI, Regression, and Its Deviation Model to Identify Stable Rice Hybrids in Bangladesh." Plants 11, no. 18 (September 7, 2022): 2336. http://dx.doi.org/10.3390/plants11182336.

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Twenty-six rice hybrids were evaluated at three locations. Data were normally distributed after running the Shapiro–Wilk test. Plant height and effective tillers/hills showed leptokurtic distribution, indicating these traits were controlled by fewer genes, whereas the rest of the attributes had platykurtic distribution, indicating these traits were controlled by many genes. Most of the traits were significant for variety, locations, and variety × locations. For yield stability, the data were analyzed using additive main effect and multiplicative interaction (AMMI), genotype and genotype–environment interaction (GGE), and Eberhart and Russell’s model. Among 26 hybrids, BRRI99A × BRRI38R and BRRI hybrid dhan5 exhibited high yields at three locations. BRRI99A × BRRI45R, BRRI99A × BRRI31R, IR79156A × BRRI38R, and BRRI hybrid dhan3 were selected for mega-environments: Gazipur and Ishwardi. Among the tested locations, Gazipur (E2) and Ishwardi (E3) were identified as mega-environments for the hybrid combinations, including BRRI99A × BRRI36R, BRRI99A × BRRI49R, IR79156A × BRRI31R, IR79156A × BRRI38R, BRRI hybrid dhan5, BRRI99A × BRRI38R, BRRI99A × BRRI45R, and BRRI99A × BRRI31R based on their average action and fixity. Gazipur and Ishwardi were the best environments because their discriminative and representative ability was remarkable. The hybrid assessment, as well as area selection for hybrid rice breeding in Bangladesh, were revealed in this study. The hybrid BRRI99A × BRRI38R, BRRI99A × BRRI36R, and IR79156A × Rline7 belonged to medium-to-long slender grain types. Nowadays, the citizens of Bangladesh prefer fine-grain rice. Therefore, these fine-grain hybrids can be cultivated as preferable commercial varieties at three locations, such as Barisal, Gazipur, and Ishwardi in Bangladesh. The stable hybrids identified in the current study can be recommended for cultivation throughout the whole country without compromising the loss of grain yield of rice.

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21

Clewley, Robert. "Hybrid Models and Biological Model Reduction with PyDSTool." PLoS Computational Biology 8, no. 8 (August 9, 2012): e1002628. http://dx.doi.org/10.1371/journal.pcbi.1002628.

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22

Drašković, Bojan, Veselinka Zečević, Zdravko Hojka, Milomir Filipović, Jelena Srdić, Natalija Kravić, and Vojka Babić. "Application of AMMI model in zoning of FAO 400-500 maize hybrids." Selekcija i semenarstvo 27, no. 1 (2021): 41–49. http://dx.doi.org/10.5937/selsem2101041d.

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Identification of high yielding and stable genotypes is one of the main goals in all breeding programmes. Estimation of hybrids is often aggravated due to the presence of genotype x environment (GE) interaction. One of the ways to eliminate negative effect of this interaction is the application of reliable statistical models such as AMMI model, which singles out high yielding and stable genotypes that have positive reaction to the improvement of production environments. This research aimed to establish specific maize hybrids interactions in different environments in two years by AMMI analysis. Twelve KWS maize hybrids belonging to FAO 400-500, were examined in two years over eight locations in Vojvodina. The highest yield in both years had the hybrid KWS2 (12.764 kg ha-1). Based on the AMMI1 model, hybrid KWS9 showed the highest stability and adaptability at all locations. According to AMMI2, hybrids KWS1, KWS3 and KWS12, had the highest stability and adaptability, while hybrids KWS2, KWS6, KWS8, KWS9 and KWS10 showed a satisfactory level of stability and it is necessary to pay attention to which locations they have positive interactions in order to be recommended in such regions. Nevertheless, based on the AMMI2, locations Temerin, Kikinda and Zrenjanin, showed similar interaction response, which points out that the number of trial locations could be reduced. Obtained results would contribute to the more precise decision in hybrids recommendation for the certain region, but also in defining further aims in maize breeding.

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23

ŞENEL, MİNE, BİLGİN ŞENEL, and SEVDA ÜRKER GÖKÇE. "STRATEGY DEVELOPMENT WITH A’WOT HYBRID MODEL." INTERNATIONAL REFEREED JOURNAL OF RESEARCH ON ECONOMICS MANAGEMENT, no. 11 (March 30, 2017): 1–24. http://dx.doi.org/10.17373/uheyad.2017.1.0001.

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24

Grauslyte, L., D. Gaffey, and S. Shepherd. "HYBRID MODEL FOR TRAINING HYBRID EXTRACORPOREAL MEMBRANE OXYGENATION SPECIALISTS." Journal of Cardiothoracic and Vascular Anesthesia 36 (December 2022): S45. http://dx.doi.org/10.1053/j.jvca.2022.09.072.

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25

Wu, Chong. "A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology." International Journal of Machine Learning and Computing 11, no. 3 (May 2021): 182–87. http://dx.doi.org/10.18178/ijmlc.2021.11.3.1033.

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26

Rashid, Mohd Fadzil Faisae Ab. "An Improved Mathematical Model to Predict Surface Roughness Using Hybrid Method." International Journal of Materials, Mechanics and Manufacturing 3, no. 1 (2015): 36–39. http://dx.doi.org/10.7763/ijmmm.2015.v3.162.

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27

AbdElHamid, Amr, Peng Zong, and Bassant Abdelhamid. "Advanced UAV Hybrid Simulator Model Based-on Dynamic Real Weather Data." International Journal of Modeling and Optimization 5, no. 4 (2015): 246–56. http://dx.doi.org/10.7763/ijmo.2015.v5.470.

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28

SITTIARTTAKORN, V., and S. BOONTO. "Hybrid Engine Model Using a Stirling Engine and a DC Motor." Journal of the Japan Society of Applied Electromagnetics and Mechanics 23, no. 3 (2015): 563–66. http://dx.doi.org/10.14243/jsaem.23.563.

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29

Lee, Seonggu, and Jitae Shin. "Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter." International Journal of Information and Electronics Engineering 9, no. 1 (March 2019): 34–38. http://dx.doi.org/10.18178/ijiee.2019.9.1.701.

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30

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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31

Pons, R., A. Subias, and L. Travé-Massuyès. "Hybrid Causal Model Based Diagnosis." IFAC Proceedings Volumes 45, no. 20 (January 2012): 678–83. http://dx.doi.org/10.3182/20120829-3-mx-2028.00188.

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32

Del Vecchio, Carmen, Osvaldo Barbarisi, and Alessandra Parisio. "Hybrid Model for Crane Scheduling." IFAC Proceedings Volumes 41, no. 2 (2008): 15837–42. http://dx.doi.org/10.3182/20080706-5-kr-1001.02677.

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33

Globa, Anastasia. "Hybrid Model for Tutorial Engagement." Voprosy obrazovaniya / Educational Studies Moscow, no. 3 (2022): 7–35. http://dx.doi.org/10.17323/1814-9545-2022-3-7-35.

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Technology has increasingly become a key component of teaching and learning, particularly with the rapid shift to delivery through online platforms as a result of the ongoing COVID-19 pandemic. In 2020–2022, students who studied online often struggled to achieve key learning outcomes and receive lower marks than their on-campus peers, demonstrating reduced engagement in an online environment. A shared concern identified within the School of Architecture, Design, and Planning at the University of Sydney, was the ability to deliver a sense of physical experience to students who are studying online. This paper details our proposed solution to improve engagement in online tutorials through a "Hybrid Model" that: leverages rapidly advancing virtual reality technology; integrates the fundamental principles of gamification and incorporates experiential learning into the learning process. These three key areas are further supported by a recommended tutorial structure or time proportion model. The manuscript presents the developed theoretical hybrid model informed by existing literature and studies; and illustrates this theory by examples of practical implementation and initial proof-of-concept studies. The manuscript further discusses future research that will focus on in-depth user studies and rigorous evaluation of the approach.

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34

Wu, Xunxun, Chang-Dong Wang, and Pengfei Jiao. "Hybrid-order Stochastic Block Model." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4470–77. http://dx.doi.org/10.1609/aaai.v35i5.16574.

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Community detection is a research hotspot in machine learning and data mining. However, most of the existing community detection methods only rely on the lower-order connectivity patterns, while ignoring the higher-order connectivity patterns, and unable to capture the building blocks of the complex network. In recent years, some community detection methods based on higher-order structures have been developed, but they mainly focus on the motif network composed of higher-order structures, which violate the original lower-order topological structure and are affected by the fragmentation issue, resulting in the deviation of community detection results. Therefore, there is still a lack of community detection methods that can effectively utilize higher-order connectivity patterns and lower-order connectivity patterns. To overcome the above limitations, this paper proposes the Hybrid-order Stochastic Block Model (HSBM) from the perspective of the generative model. Based on the classical stochastic block model, the generation of lower-order structure and higher-order structure of the network is modeled uniformly, and the original topological properties of the network are maintained while using higher-order connectivity patterns. At the same time, a heuristic algorithm for community detection is proposed to optimize the objective function. Extensive experiments on six real-world datasets show that the proposed method outperforms the existing approaches.

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Udoewa, Victor. "Hybrid Expeditionary Service Learning Model." International Journal for Service Learning in Engineering, Humanitarian Engineering and Social Entrepreneurship 17, no. 1 (May 9, 2022): 20–36. http://dx.doi.org/10.24908/ijsle.v17i1.14810.

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Service learning is an educational model that incorporates instruction, learning, service, and reflection. Many critics have highlighted weaknesses of service learning including its inability to address structural issues, build authentic relationships with communities, assess learning, and meet community goals. Expeditionary learning is a newer learning methodology that includes a focus on social justice, provides more time for building relationships and understanding community goals, and utilizes a portfolio-based assessment approach. Because Expeditionary Learning can address some of the weaknesses of service learning, we present Hybrid Expeditionary Service Learning--a new service-learning model that employs an expeditionary learning design and embeds both the classes and service of the service-learning program within learning expeditions. The paper highlights the rationale, benefits, and mechanics of how the expeditionary learning model reinforces service learning, and also how the service-learning model can reinforce expeditionary learning.

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36

Lysetsyi, Yu M. "SYSTEM MODEL OF HYBRID WARFARE." "Scientific Notes of Taurida V.I. Vernadsky University", series "Public Administration", no. 1 (2022): 111–16. http://dx.doi.org/10.32838/tnu-2663-6468/2022.1/19.

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37

Vikas, Om, and Pooja Arora. "Ranking Strategy Using Hybrid Model." International Journal of Computer Applications 5, no. 10 (August 10, 2010): 10–15. http://dx.doi.org/10.5120/950-1327.

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Srivastava, Praveen Ranjan, and Prajwal Eachempati. "A Hybrid Portfolio Selection Model." International Journal of Intelligent Information Technologies 16, no. 3 (July 2020): 100–116. http://dx.doi.org/10.4018/ijiit.2020070105.

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It is generally observed that investors approach asset managers and financial analysts to recommend a customized portfolio based on certain personalized preferences. The article discusses a methodology to build a hybrid personalized multi-criteria model in the Indian stock market context suiting investor preferences. The analytical hierarchy process (AHP) was used to compute the criteria weights and data envelopment analysis (DEA) was adopted to screen the best portfolios which were subsequently ranked by a fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) and evaluation based on distance from average solution (EDA). The rankings of portfolios were validated for robustness with the actual rankings awarded by Credit Rating Information Services of India Limited (CRISIL) to demonstrate the efficacy of the hybrid model and it was found that Fuzzy TOPSIS and EDA rankings were consistent with the CRISIL rankings proposed by expert investors.

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Jeon, Youtaek, and HyungJun Cho. "Model based hybrid decision tree." Journal of the Korean Data And Information Science Society 30, no. 3 (May 31, 2019): 515–24. http://dx.doi.org/10.7465/jkdi.2019.30.3.515.

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40

Anderson, John R., and Jonathan Betz. "A hybrid model of categorization." Psychonomic Bulletin & Review 8, no. 4 (December 2001): 629–47. http://dx.doi.org/10.3758/bf03196200.

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Müller, Bjørn, and Matt Lamont. "Hybrid Velocity Model Prestack Imaging." ASEG Extended Abstracts 2004, no. 1 (December 2004): 1–4. http://dx.doi.org/10.1071/aseg2004ab102.

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42

Hein, Rafał, and Cezary Orlikowski. "Hybrid Reduced Model of Rotor." Archive of Mechanical Engineering 60, no. 3 (September 1, 2013): 319–33. http://dx.doi.org/10.2478/meceng-2013-0021.

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Abstract In the paper, the authors describe the method of reduction of a model of rotor system. The proposed approach makes it possible to obtain a low order model including e.g. non-proportional damping or the gyroscopic effect. This method is illustrated using an example of a rotor system. First, a model of the system is built without gyroscopic and damping effects by using the rigid finite element method. Next, this model is reduced. Finally, two identical, low order, reduced models in two perpendicular planes are coupled together by means of gyroscopic and damping interaction to form one model of the system. Thus a hybrid model is obtained. The advantage of the presented method is that the number of gyroscopic and damping interactions does not affect the model range

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Bock, Marj, Mari Caballero, and Kelly O'Neal-Hixson. "The TCSEFP Hybrid eMentoring Model." Educational Renaissance 9, no. 1 (October 19, 2020): 23–30. http://dx.doi.org/10.33499/edren.v9i1.143.

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This article describes a hybrid eMentoring model used to support fellows during an 11-month fellowship program, the Teachers College Special Education Fellowship Program (TCSEFP). The TCSEFP is a residency in teaching (RIT) induction program that leads to the completion of a high incidence special education endorsement and a master’s degree in special education. During the fellowship, each participant receives extensive, explicit mentoring from onsite and off-site mentors. This hybrid eMentoring model, developed as a part of the TCSEFP, is facilitated in part via distance technologies.

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A.A. BARTOSH. "The Model of Hybrid Warfare." Military Thought 28, no. 002 (June 30, 2019): 1–19. http://dx.doi.org/10.21557/mth.54208773.

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45

Alkhodre, Ahmad B. "Hybrid fuzzy social mobility model." Karbala International Journal of Modern Science 2, no. 1 (March 2016): 29–40. http://dx.doi.org/10.1016/j.kijoms.2015.12.004.

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Qureshi, Pir Abdul Rasool, and Nasrullah Memon. "Hybrid model of content extraction." Journal of Computer and System Sciences 78, no. 4 (July 2012): 1248–57. http://dx.doi.org/10.1016/j.jcss.2011.10.012.

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47

Rusinowski, Henryk, and Wojciech Stanek. "Hybrid model of steam boiler." Energy 35, no. 2 (February 2010): 1107–13. http://dx.doi.org/10.1016/j.energy.2009.06.004.

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Zhang, Jie, Souma Chowdhury, and Achille Messac. "An adaptive hybrid surrogate model." Structural and Multidisciplinary Optimization 46, no. 2 (January 31, 2012): 223–38. http://dx.doi.org/10.1007/s00158-012-0764-x.

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49

Kim, Taesic, Wei Qiao, and Liyan Qu. "An Enhanced Hybrid Battery Model." IEEE Transactions on Energy Conversion 34, no. 4 (December 2019): 1848–58. http://dx.doi.org/10.1109/tec.2019.2935700.

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50

Wang, Shouhong. "Nonlinear regression: a hybrid model." Computers & Operations Research 26, no. 8 (July 1999): 799–817. http://dx.doi.org/10.1016/s0305-0548(98)00088-4.

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