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Relevant bibliographies by topics / Model-based Cluster

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Academic literature on the topic 'Model-based Cluster'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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Journal articles on the topic "Model-based Cluster"

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Younghwan Kim, Younghwan Kim, and Huy Kang Kim Younghwan Kim. "Cluster-based Deep One-Class Classification Model for Anomaly Detection." 網際網路技術學刊 22, no. 4 (July 2021): 903–11. http://dx.doi.org/10.53106/160792642021072204017.

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Banerjee, Saibal, and Azriel Rosenfeld. "Model-based cluster analysis." Pattern Recognition 26, no. 6 (June 1993): 963–74. http://dx.doi.org/10.1016/0031-3203(93)90061-z.

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Stahl, Daniel, and Hannah Sallis. "Model-based cluster analysis." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 4 (March 15, 2012): 341–58. http://dx.doi.org/10.1002/wics.1204.

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Endo, Yasunori, Ayako Heki, and Yukihiro Hamasuna. "Non Metric Model Based on Rough Set Representation." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (July 20, 2013): 540–51. http://dx.doi.org/10.20965/jaciii.2013.p0540.

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The non metricmodel is a kind of clustering method in which belongingness or the membership grade of each object in each cluster is calculated directly from dissimilarities between objects and in which cluster centers are not used. The clustering field has recently begun to focus on rough set representation instead of fuzzy set representation. Conventional clustering algorithms classify a set of objects into clusters with clear boundaries, that is, one object must belong to one cluster. Many objects in the real world, however, belong to more than one cluster because cluster boundaries overlap each other. Fuzzy set representation of clusters makes it possible for each object to belong to more than one cluster. The fuzzy degree of membership may, however, be too descriptive for interpreting clustering results. Rough set representation handles such cases. Clustering based on rough sets could provide a solution that is less restrictive than conventional clustering and more descriptive than fuzzy clustering. This paper covers two types of Rough-set-based Non Metric model (RNM). One algorithm is the Roughset-based Hard Non Metric model (RHNM) and the other is the Rough-set-based Fuzzy Non Metric model (RFNM). In both algorithms, clusters are represented by rough sets and each cluster consists of lower and upper approximation. The effectiveness of proposed algorithms is evaluated through numerical examples.

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Huang, He, and Hui Xiao. "Internet Industry Cluster Design Based on PDE Mathematical Model." Applied Mechanics and Materials 539 (July 2014): 959–63. http://dx.doi.org/10.4028/www.scientific.net/amm.539.959.

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The industrial cluster is formed by the common competitiveness elements of enterprise group. Under the cluster environment, common technology and common customer as well as distribution channel are composition of cluster development performance mode. On the basis of the parabolic PDE cluster development model, and combined with Internet industrial cluster analysis of virtual platform, the Internet structure industrial cluster analysis system is designed. In order to verify the validity and reliability of the model and system, this paper takes the cluster development of machining as an example to carry on the research for the system performance, which can get the virtual grid node and stress distribution of cluster processing center, finally we can obtain the industrial cluster investment and performance relationship table, to provide the theoretical guidance for the development of industrial clusters.

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Lim, Michael K., and So Young Sohn. "Cluster-based dynamic scoring model." Expert Systems with Applications 32, no. 2 (February 2007): 427–31. http://dx.doi.org/10.1016/j.eswa.2005.12.006.

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Fang, Yong Heng, and Jing Yi Yi. "Study on Evolution Mechanism of Industrial Cluster Based on Brusselator Model." Applied Mechanics and Materials 687-691 (November 2014): 4832–35. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4832.

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The article using Brusselator model analyses the evolution mechanism of industrial clusters. The study found, the formation of industrial clusters is an inner reinforcing cycle accumulation process, the competing interaction is an important condition for the evolution of industrial clusters, and cluster innovation driving the system to the state development more orderly, form the new dissipative structure, promote the evolution of industrial cluster.

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Lahoorpoor, Bahman, Hamed Faroqi, Abolghasem Sadeghi-Niaraki, and Soo-Mi Choi. "Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem." Sustainability 11, no. 11 (June 8, 2019): 3205. http://dx.doi.org/10.3390/su11113205.

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Bike sharing systems, as one of the complementary modes for public transit networks, are designed to help travelers in traversing the first/last mile of their trips. Different factors such as accessibility, availability, and fares influence these systems. The availability of bikes at certain times and locations is studied under rebalancing problem. The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems. First, the spatial and temporal patterns of bike sharing trips in the network are investigated. Second, a similarity measure based on the trips between stations is defined to discover groups of correlated stations, using a hierarchical agglomerative clustering method. Third, two levels for rebalancing are assumed as intra-clusters and inter-clusters with the aim of keeping the balance of the network at the beginning of days. The intra-cluster level keeps the balance of bike distribution inside each cluster, and the inter-cluster level connects different clusters in order to keep the balance between the clusters. Finally, rebalancing tours are optimized according to the positive or negative balance at both levels of the intra-clusters and inter-clusters using a single objective genetic algorithm. The rebalancing problem is modeled as an optimization problem, which aims to minimize the tour length. The proposed model is implemented in one week of bike sharing trip data set in Chicago, USA. Outcomes of the model are validated for two subsequent weekdays. Analyses show that the proposed model can reduce the length of the rebalancing tour by 30%.

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Xi, Yaoyi, Gang Chen, Bicheng Li, and Yongwang Tang. "Topic Evolution Analysis Based on Cluster Topic Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 1 (January 19, 2016): 66–75. http://dx.doi.org/10.20965/jaciii.2016.p0066.

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Topic evolution analysis helps to understand how the topics evolve or develop along the timeline. Aiming at the problem that existing researches did not mine the latent semantic information in depth and needed to pre-determine the number of clusters, this paper proposes cluster topic model based method to analyze topic evolution analysis. Firstly, a new topic model, namely cluster topic model, is built to complete document clustering while mining latent semantic information. Secondly, events are detected according to the cluster label of each document and evolution relationship between any two events is identified based on the aspect distributions of documents. Finally, by choosing the representative document of each event, topic evolution graph is constructed to display the development of the topic along the timeline. Experiments are presented to show the performance of our proposed technique. It is found that our proposed technique outperforms the comparable techniques in previous work.

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Teo, Boon K., and Hong Zhang. "Cluster of clusters (C2) model for electron counting of supracluster based on smaller cluster units." Inorganica Chimica Acta 144, no. 2 (April 1988): 173–76. http://dx.doi.org/10.1016/s0020-1693(00)86282-9.

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Dissertations / Theses on the topic "Model-based Cluster"

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Rapley, Veronica Elizabeth. "Model-based adaptive cluster sampling." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433939.

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Lin, Dong. "Model-based cluster analysis using Bayesian techniques." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Tantrum, Jeremy. "Model based and hybrid clustering of large datasets /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/8933.

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BARBERIS, STEFANO. "New developments in Cluster-Weighted Modeling." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241157.

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In questo lavoro vengono presentate due estensioni del modello Cluster Weighted (un modello mistura di regressioni che considera non solo la distribuzione condizionata ma anche la distribuzione marginale delle covariate). La prima estensione proposta presenta il modello GAM-CWM. Si tratta di un modello molto flessibile, in grado di descrivere relazioni complesse tra una variabile risposta e un insieme di covariate in ogni componente della mistura. In questo modello proposto la classe dei Generalized Additive Models è stata inserita all’interno del framework CWM. La seconda estensione, è relativa alla regressione beta che rappresenta l'approccio standard per modellare una variabile dipendente in [0,1]. In alcune situazioni, un problema che potrebbe sorgere è una conseguenza diretta della flessibilità della distribuzione beta, perché quando viene utilizzata come componente in un modello mistura potrebbe essere troppo flessibile a causa della grande varietà di forme (incluse le forme multimodali) che può assumere. In questo caso quindi può essere difficile capire e interpretare il significato di ciascun gruppo latente. Per questo motivo, abbiamo sviluppato un'estensione dei modelli mistura di beta focalizzandoci sul sottoinsieme della distribuzioni beta unimodali, con l'obiettivo di migliorare l'interpretazione di ciascun componente e quindi interpretare meglio il rispettivo cluster nella popolazione. Infine, un pacchetto R in fase di sviluppo che sarà pubblicato sul CRAN implementa le metodologie proposte. La stima di questi modelli viene eseguita tramite la massima verosimiglianza con l'algoritmo EM. Con dati simulati e reali esaminiamo le prestazioni, i limiti e i benefici confrontando i nuovi modelli proposti con modelli simili presentati e disponibili in letteratura.
In this work two extensions of Cluster Weighted Models (a mixture model with random covariate) are presented for model-based clustering applications. The first presents the Generalized Additive Cluster Weighted Model. This is a very flexible model, able to capture complex relations between a response variable and a set of covariates in each mixture component introducing the Generalized Additive Model into the CWM framework. The second, is related to the beta regression that represents the standard approach to model a dependent variable with the range in the unit interval [0,1]. In some situations, a problem that could arise is a direct consequence of flexibility of the beta distribution, because when it is considered as a mixture component it may be too flexible due to the great variety of shapes (including multi-modal shapes) that can assume so that it may be difficult to understand easily the real meaning of each component. For this reason, we developed an extension of the beta mixture models focusing on the subset of unimodal beta distribution, with the aim of improving the interpretation of each mixture component and then identifying better the respective cluster in the population. Finally, an R package under development that will published on the CRAN implements the proposed methodologies. The estimation of these models is performed via maximum likelihood with EM algorithm. With simulated and real data we investigate the performances, limits and benefits comparing this model with other models related to it.

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Annakula, Chandravyas. "Hierarchical and partitioning based hybridized blocking model." Kansas State University, 2017. http://hdl.handle.net/2097/35468.

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Master of Science
Department of Computing and Information Sciences
William H. Hsu
(Higgins, Savje, & Sekhon, 2016) Provides us with a sampling blocking algorithm that enables large and complex experiments to run in polynomial time without sacrificing the precision of estimates on a covariate dataset. The goal of this project is to run the different clustering algorithms on top of clusters formed from above mentioned blocking algorithm and analyze the performance and compatibility of the clustering algorithms. We first start with applying the blocking algorithm on a covariate dataset and once the clusters are formed, we then apply our clustering algorithm HAC (Hierarchical Agglomerative Clustering) or PAM (Partitioning Around Medoids) on the seeds of the clusters. This will help us to generate more similar clusters. We compare our performance and precision of our hybridized clustering techniques with the pure clustering techniques to identify a suitable hybridized blocking model.

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Mohamed, Esha [Verfasser], and Ralf [Akademischer Betreuer] Münnich. "Design-based and model-based estimation in adaptive cluster sampling / Esha Mohamed ; Betreuer: Ralf Münnich." Trier : Universität Trier, 2017. http://d-nb.info/1197807535/34.

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Malsiner-Walli, Gertraud, Sylvia Frühwirth-Schnatter, and Bettina Grün. "Model-based clustering based on sparse finite Gaussian mixtures." Springer, 2016. http://dx.doi.org/10.1007/s11222-014-9500-2.

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In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number of non-empty components visited during MCMC sampling. Specifying a shrinkage prior, namely the normal gamma prior, on the component means leads to improved parameter estimates as well as identification of cluster-relevant variables. After estimating the mixture model using MCMC methods based on data augmentation and Gibbs sampling, an identified model is obtained by relabeling the MCMC output in the point process representation of the draws. This is performed using K-centroids cluster analysis based on the Mahalanobis distance. We evaluate our proposed strategy in a simulation setup with artificial data and by applying it to benchmark data sets. (authors' abstract)

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Shaffer, Anne, Monica Whitehead, Molly Davis, Diana Morelen, and Cynthia Suveg. "A Model‐Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior." Digital Commons @ East Tennessee State University, 2018. https://doi.org/10.1111/famp.12326.

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In a diverse community sample of mothers (N = 108) and their preschool‐aged children (Mage = 3.50 years), this study conducted person‐oriented analyses of maternal emotion regulation (ER) based on a multimethod assessment incorporating physiological, observational, and self‐report indicators. A model‐based cluster analysis was applied to five indicators of maternal ER: maternal self‐report, observed negative affect in a parent–child interaction, baseline respiratory sinus arrhythmia (RSA), and RSA suppression across two laboratory tasks. Model‐based cluster analyses revealed four maternal ER profiles, including a group of mothers with average ER functioning, characterized by socioeconomic advantage and more positive parenting behavior. A dysregulated cluster demonstrated the greatest challenges with parenting and dyadic interactions. Two clusters of intermediate dysregulation were also identified. Implications for assessment and applications to parenting interventions are discussed.

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Whitehead, Monica R., Anne Shaffer, Molly Faye Davis, Diana M. Morelen, and Cynthia Suveg. "A Model-Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/745.

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Liu, Jinghui. "Approaches to improve the precision of similarity patterns and reproducibility for cluster analysis infinite mixture model based cluster analyses for gene expression data /." Cincinnati, Ohio : University of Cincinnati, 2008. http://rave.ohiolink.edu/etdc/view.cgi?acc_num=ucin1211903300.

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Books on the topic "Model-based Cluster"

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Rojas, Thomas D. National forest economic clusters: A new model for assessing national-forest-based natural resources products and services. Portland, OR: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2007.

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Rojas, Thomas D. National forest economic clusters: A new model for assessing national-forest-based natural resources products and services. Portland, OR: United States Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2007.

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Otsuka, K., and Tetsushi Sonobe. Cluster-Based Industrial Development: An East Asian Model. Palgrave Macmillan, 2006.

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Cluster-Based Industrial Development: An East Asian Model. Palgrave Macmillan, 2006.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2020.

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Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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Book chapters on the topic "Model-based Cluster"

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Nizamani, Sarwat, Nasrullah Memon, and Uffe Kock Wiil. "Cluster Based Text Classification Model." In Lecture Notes in Social Networks, 265–83. Vienna: Springer Vienna, 2011. http://dx.doi.org/10.1007/978-3-7091-0388-3_14.

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Huang, He. "Cluster Enterprises’ Internationalization Based on Cluster Risk Evaluation Model." In Lecture Notes in Electrical Engineering, 347–53. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4853-1_45.

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Sridevi, K. N., Surekha Pinnapati, and S. Prakasha. "Hierarchical Cluster-Based Model to Evaluate Accuracy Metrics Based on Cluster Efficiency." In Intelligent Sustainable Systems, 667–78. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6369-7_61.

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Bensmail, Halima, and Jacqueline J. Meulman. "MCMC Inference for Model-based Cluster analysis." In Studies in Classification, Data Analysis, and Knowledge Organization, 191–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72253-0_26.

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Kumar, Pardeep, Samit Barai, Babji Srinivasan, and Nihar R. Mohapatra. "Process Model Accuracy Enhancement Using Cluster Based Approach." In Physics of Semiconductor Devices, 33–36. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03002-9_9.

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Romoozi, Morteza, Hamideh Babaei, Mahmood Fathy, and Mojtaba Romoozi. "A Cluster-Based Mobility Model for Intelligent Nodes." In Computational Science and Its Applications – ICCSA 2009, 565–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02454-2_41.

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Li, Xiaotu, Jizhou Sun, Jiawan Zhang, Zhaohui Qi, and Gang Li. "A Modified Parallel Computation Model Based on Cluster." In Computational Science and Its Applications – ICCSA 2004, 252–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24709-8_27.

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Barnat, Jiří, Luboš Brim, and Ivana Černá. "Cluster-Based LTL Model Checking of Large Systems." In Formal Methods for Components and Objects, 259–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11804192_13.

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Hennig, Christian, and Pietro Coretto. "The Noise Component in Model-based Cluster Analysis." In Data Analysis, Machine Learning and Applications, 127–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78246-9_16.

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Pallis, George, Lefteris Angelis, and Athena Vakali. "Model-Based Cluster Analysis for Web Users Sessions." In Lecture Notes in Computer Science, 219–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11425274_23.

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Conference papers on the topic "Model-based Cluster"

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Sriprayoonsakul and Uthayopas. "An energy-based implicit co-scheduling model for Beowulf cluster." In Proceedings IEEE International Conference on Cluster Computing CLUSTR-03. IEEE, 2003. http://dx.doi.org/10.1109/clustr.2003.1253336.

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Akihiro Nomura, Hiroya Matsuba, and Yutaka Ishikawa. "Network performance model for TCP/IP based cluster computing." In 2007 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2007. http://dx.doi.org/10.1109/clustr.2007.4629232.

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Gupta, Nikunj, Rohit Ashiwal, Bine Brank, Sateesh K. Peddoju, and Dirk Pleiter. "Performance Evaluation of ParalleX Execution model on Arm-based Platforms." In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020. http://dx.doi.org/10.1109/cluster49012.2020.00080.

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Wang, Shen-Ge. "Cluster-based binary printer model." In IS&T/SPIE Electronic Imaging, edited by Reiner Eschbach, Gabriel G. Marcu, Shoji Tominaga, and Alessandro Rizzi. SPIE, 2009. http://dx.doi.org/10.1117/12.811626.

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Yuan, Liang, and Yunquan Zhang. "A Locality-based Performance Model for Load-and-Compute Style Computation." In 2012 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2012. http://dx.doi.org/10.1109/cluster.2012.25.

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Zhu, Niu, Lu, Shen, and Gao. "A cluster-based solution for high performance hmmpfam using EARTH execution model." In Proceedings IEEE International Conference on Cluster Computing CLUSTR-03. IEEE, 2003. http://dx.doi.org/10.1109/clustr.2003.1253296.

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Thonglek, Kundjanasith, Kohei Ichikawa, Keichi Takahashi, Hajimu Iida, and Chawanat Nakasan. "Improving Resource Utilization in Data Centers using an LSTM-based Prediction Model." In 2019 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2019. http://dx.doi.org/10.1109/cluster.2019.8891022.

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Rang, Wei, Donglin Yang, Dazhao Cheng, Kun Suo, and Wei Chen. "Data Life Aware Model Updating Strategy for Stream-based Online Deep Learning." In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020. http://dx.doi.org/10.1109/cluster49012.2020.00049.

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Gustedt, Jens, Emmanuel Jeannot, and Farouk Mansouri. "Optimizing Locality by Topology-Aware Placement for a Task Based Programming Model." In 2016 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2016. http://dx.doi.org/10.1109/cluster.2016.87.

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Lastovetsky, Alexey, and Vladimir Rychkov. "Building the communication performance model of heterogeneous clusters based on a switched network." In 2007 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2007. http://dx.doi.org/10.1109/clustr.2007.4629284.

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Reports on the topic "Model-based Cluster"

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Ків, Арнольд Юхимович, D. Fuks, Наталя Володимирівна Моісеєнко, and Володимир Миколайович Соловйов. Silicon-aluminum bonding in Al alloys. Transport and Telecommunication Institute, 2002. http://dx.doi.org/10.31812/0564/1033.

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Ab initio calculation was performed to investigate the nature of Si-Al bonding in Al based alloys. Total electronic energy Etot for different configurations of the model cluster Si2Al6 was calculated. When the model cluster consists of two perfect tetrahedrons there is a strong influence of the Si-Si distance on the Si-Al adiabatic potential. The equilibrium distance between Si and Al atoms increases with the length of Si-Si bond increasing. It was concluded that description of Si clusters in Al matrix demands an account of the angle depending part of Si-Al interaction.

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Fraley, Chris, Adrian Raftery, and Ron Wehrensy. Incremental Model-Based Clustering for Large Datasets With Small Clusters. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada459790.

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Rojas, Thomas D. National forest economic clusters: a new model for assessing national-forest-based natural resources products and services. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2007. http://dx.doi.org/10.2737/pnw-gtr-703.

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Nagahi, Morteza, Raed Jaradat, Mohammad Nagahisarchoghaei, Ghodsieh Ghanbari, Sujan Poudyal, and Simon Goerger. Effect of individual differences in predicting engineering students' performance : a case of education for sustainable development. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40700.

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The academic performance of engineering students continues to receive attention in the literature. Despite that, there is a lack of studies in the literature investigating the simultaneous relationship between students' systems thinking (ST) skills, Five-Factor Model (FFM) personality traits, proactive personality scale, academic, demographic, family background factors, and their potential impact on academic performance. Three established instruments, namely, ST skills instrument with seven dimensions, FFM traits with five dimensions, and proactive personality with one dimension, along with a demographic survey, have been administrated for data collection. A cross-sectional web-based study applying Qualtrics has been developed to gather data from engineering students. To demonstrate the prediction power of the ST skills, FFM traits, proactive personality, academic, demographics, and family background factors on the academic performance of engineering students, two unsupervised learning algorithms applied. The study results identify that these unsupervised algorithms succeeded to cluster engineering students' performance regarding primary skills and characteristics. In other words, the variables used in this study are able to predict the academic performance of engineering students. This study also has provided significant implications and contributions to engineering education and education sustainable development bodies of knowledge. First, the study presents a better perception of engineering students' academic performance. The aim is to assist educators, teachers, mentors, college authorities, and other involved parties to discover students' individual differences for a more efficient education and guidance environment. Second, by a closer examination at the level of systemic thinking and its connection with FFM traits, proactive personality, academic, and demographic characteristics, understanding engineering students' skillset would be assisted better in the domain of sustainable education.

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