Affective design using machine learning: a survey and its prospect of conjoining big data

References

• Ahn, H. 2010. “Modeling and Analysis of Affective Influences on Human Experience, Prediction, Decision Making, and Behavior.” PhD Thesis., MIT.
   Google Scholar
• Akay, D., and M. Kurt. 2009. “A Neuro-Fuzzy Based Approach to Affective Design.” The International Journal of Advanced Manufacturing Technology 40 (5): 425–437. doi:10.1007/s00170-007-1367-3.
   Web of Science ®Google Scholar
• Aktar, D. E., A. S. Anagun, and G. Koksal. 2009. “Determination of Optimal Product Styles by Ordinal Logistic Regression versus Conjoint Analysis for Kitchen Faucets.” International Journal of Industrial Ergonomics 39 (5): 866–875. doi:10.1016/j.ergon.2009.06.007.
   Web of Science ®Google Scholar
• Bahn, S., C. Lee, C. S. Nam, and M. H. Yun. 2009. “Incorporating Affective Customer Needs for Luxuriousness into Product Design Attributes.” Human Factors and Ergonomics in Manufacturing 19 (2): 105–125. doi:10.1002/hfm.20140.
   Google Scholar
• Balters, S., and M. Steinert. 2017. “Capturing Emotion Reactivity through Physiology Measurement as a Foundation for Affective Engineering in Engineering Design Science and Engineering Practices.” Journal of Intelligent Manufacturing 28 (7): 1587–1607. doi:10.1007/s10845-015-1145-2.
   Web of Science ®Google Scholar
• Barnes, C., and S. P. Lillford. 2007. “Affective Design Decision-Making – Issues and Opportunities.” International Journal of CoCreation in Design and the Arts 3 (1): 135–146.
   Google Scholar
• Barnes, C., and S. P. Lillford. 2009. “Decision Support for the Design of Affective Products.” Journal of Engineering Design 20 (5): 477–492. doi:10.1080/09544820902875041.
   Web of Science ®Google Scholar
• Barone, S., A. Lombardo, and P. Tarantino. 2009. “Analysis of User Needs for the Redesign of a Postural Seat System.” Statistics for Innovation 3–25.
   Google Scholar
• Baxter, M. 1995. Product Design – Practical Methods for Systematic Development of New Products, Chapman & Hall.
   Google Scholar
• Bello-Orgaz, G., and J. J. Jason. 2016. “Social Big Data: Recent Achievements and New Challenges.” Information Fusion 28: 45–59. doi:10.1016/j.inffus.2015.08.005.
   PubMed Web of Science ®Google Scholar
• Bello-Orgaz, G., J. J. Jung, and D. Camacho. 2016. “Social Big Data: Recent Achievements and New Challenges.” Information Fusion 28: 45–59. doi:10.1016/j.inffus.2015.08.005.
   PubMed Web of Science ®Google Scholar
• Bhandari, U., T. Neben, K. Chang, and W. Y. Chua. 2017. “Effects of Interface Design Factors on Affective Responses and Quality Evaluations in Mobile Applications.” Computers in Human Behavior 72: 525–534. doi:10.1016/j.chb.2017.02.044.
   Web of Science ®Google Scholar
• Blei, D. M., A. Y. Ng, and M. I. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3 (4–5): 993–1022.
   Web of Science ®Google Scholar
• Breur, T. 2011. “Data Analysis across Various Media: Data Fusion, Direct Marketing, Clickstream Data and Social Media.” Journal of Direct, Data and Digital Marketing Practice 13 (2): 95–105. doi:10.1057/dddmp.2011.32.
   Google Scholar
• Card, S. K., J. D. Mackinlay, and B. Shneiderman. 1999. Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann.
   Google Scholar
• Castanedo, F. 2013. “A Review of Data Fusion Techniques.” The Scientific World Journal . doi:10.1155/2013/704504
   Google Scholar
• Chan, K. Y., C. K. Kwong, and T. S. Dillon. 2012. Computational Intelligence Techniques for New Product Design, Vol. 403, Studies in Computational Intelligence. German: Springer.
   Google Scholar
• Chan, K. Y., C. K. Kwong, T. S. Dillon, and K. Y. Fung. 2011. “An Intelligent Fuzzy Regression Approach for Affective Product Design that Captures Nonlinearity and Fuzziness.” Journal of Engineering Design 22 (8): 523–542. doi:10.1080/09544820903550924.
   Web of Science ®Google Scholar
• Chan, K. Y., H. K. Lam, K. F. C. Yiu, and T. S. Dillon. 2017. “A Flexible Fuzzy Regression for Addressing Uncertainty on Aesthetic Quality Assessments.” IEEE Transactions on Systems, Man and Cybernetics: Systems 47: 12.
   Web of Science ®Google Scholar
• Chan, K. Y., K. K. F. Yuen, V. Palade, and C. K. Kwong. 2014. “Computational Intelligence Techniques for New Product Development.” Neurocomputing 142: 1–3. doi:10.1016/j.neucom.2014.05.001.
   Web of Science ®Google Scholar
• Chan, K. Y., K. K. F. Yuen, V. Palade, and Y. Yue:. 2016. “Artificial Intelligence Techniques in Product Engineering.” Engineering Applications of Artificial Intelligence 47: 1–2. doi:10.1016/j.engappai.2015.11.003.
   Web of Science ®Google Scholar
• Chan, K. Y., and U. Engelke. 2017. “Varying Spread Fuzzy Regression for Affective Quality Estimation.” IEEE Transactions on Fuzzy Systems 27 (3): 594–613. doi:10.1109/TFUZZ.2016.2566812.
   Google Scholar
• Chang, C. C. 2008. “Factors Influencing Visual Comfort Appreciation of the Product Form of Digital Cameras.” International Journal of Industrial Ergonomics 38 (11–12): 1007–1016. doi:10.1016/j.ergon.2008.04.002.
   Web of Science ®Google Scholar
• Chen, C. H., L. Khoo, and W. Yan. 2003. “Evaluation of Multicultural Factors from Elicited Customer Requirements for New Product Development.” Research in Engineering Design 14 (3): 119–130. doi:10.1007/s00163-003-0032-6.
   Web of Science ®Google Scholar
• Chen, C. H., L. P. Khoo, and W. Yan. 2006. “An Investigation into Affective Design Using Sorting Technique and Kohonen Self-Organising Map.” Advances in Engineering Software 37 (5): 334–349. doi:10.1016/j.advengsoft.2005.07.001.
   Web of Science ®Google Scholar
• Chen, M., S. Mao, and Y. Liu. 2014. “Big Data: A Survey.” Mobile Networks and Applications 19 (2): 171–209. doi:10.1007/s11036-013-0489-0.
   Web of Science ®Google Scholar
• Condie, T., P. Mineiro, N. Polyzotis, and M. Weimer. 2013. “Machine Learning on Big Data.” Sigmod Conference, New York.
   Google Scholar
• Creusen, M. E. H., and J. P. L. Schoormans. 2005. “The Different Roles of Product Appearance in Consumer Choice.” Journal of Product Innovation Management 22 (1): 63–81. doi:10.1111/j.0737-6782.2005.00103.x.
   Web of Science ®Google Scholar
• Cuzzocrea, A., E. Mumolo, and P. Corona. 2015. “Cloud-Based Machine Learning Tools for Enhanced Big Data Applications.” ACM International Symposium on Cluster, Cloud and Grid Computing, Shenzhen, Guangdong, China, 908–914.
   Google Scholar
• Dai, W., and W. Ji. 2014. “A MapReduce Implementation of C4.5 Decision Tree Algorithm.” International Journal of Database Theory Applications 7 (1): 49–60. doi:10.14257/ijdta.
   Google Scholar
• De Byl, P. 2015. “A Conceptual Affective Design Framework for the Use of Emotions in Computer Game Design.” Cyberpsychology: Journal of Psychosocial Research on Cyberspace 9(3): article 4.
   Google Scholar
• Deng, J. L. 1982. “Control Problems of Grey Systems.” Systems & Control Letters 1 (5): 288–294. doi:10.1016/S0167-6911(82)80025-X.
   Web of Science ®Google Scholar
• Denil, M., A. Demiraj, N. Kalchbrenner, P. Blunsom, and N. Freitas. 2014. “Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network.” ArXiv 1–10.
   Google Scholar
• Devlin, J., R. Zbib, Z. Huang, T. Lamar, R. Schwartz, and J. Makhoul. 2014. “Fast and Robust Neural Network Joint Models for Statistical Machine Translation.” The 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, Maryland, USA.
   Google Scholar
• Diego-Mas, J. A., and J. Alcaide-Marzal. 2016. “Single Users’ Affective Responses Models for Product Form Design.” International Journal of Industrial Ergonomics 53: 102–114. doi:10.1016/j.ergon.2015.11.005.
   Web of Science ®Google Scholar
• Elden, L. 2007. Matrix Methods in Data Mining and Pattern Recognition. Society for Industrial and Applied Mathematics, (Siam).
   Google Scholar
• Fan, K. K., C. H. Chiu, and C. C. Yang. 2014. “Green Technology Automotive Shape Design Based on Neural Networks and Support Vector Regression.” Engineering Computations 31 (8): 1732–1745. doi:10.1108/EC-11-2012-0294.
   Web of Science ®Google Scholar
• Fettermann, D. C., and M. E. S. Echeveste. 2014. “New Product Development for Mass Customization: A Systematic Review.” Production & Manufacturing Research 2 (1): 266–290. doi:10.1080/21693277.2014.910715.
   Google Scholar
• Fung, K. Y., C. K. Kwong, K. W. M. Siu, and K. M. Yu. 2012. “A Multi-Objective Genetic Algorithm Approach to Rule Mining for Affective Product Design.” Expert Systems with Applications 39 (8): 7411–7419. doi:10.1016/j.eswa.2012.01.065.
   Web of Science ®Google Scholar
• Fung, K. Y., C. K. Kwong, K. Y. Chan, and H. Jiang. 2014. “A Guided Search Genetic Algorithm Using Mined Rules for Optimal Affective Product Design.” Engineering Optimization 46 (8): 1094–1108. doi:10.1080/0305215X.2013.823196.
   Web of Science ®Google Scholar
• Graner, M., and M. Missler-Behr. 2012. “The Use of Methods in New Product Development – A Review of Empirical Literature.” International Journal of Product Development 16 (2): 158–184. doi:10.1504/IJPD.2012.049063.
   Google Scholar
• Gruber, T. R. 1993. “A Translation Approach to Portable Ontology Specifications.” Knowledge Acquisition 5 (2): 199–220. doi:10.1006/knac.1993.1008.
   Web of Science ®Google Scholar
• Guo, F., W. L. Liu, F. T. Liu, H. Wang, and T. B. Wang. 2014. “Emotional Design Method of Product Presented in Multi-Dimensional Variables Based on Kansei Engineering.” Journal of Engineering Design 25 (4–6): 194–212. doi:10.1080/09544828.2014.944488.
   Web of Science ®Google Scholar
• Halavais, A. 2015. “Bigger Sociological Imaginations: Framing Big Social Data Theory and Methods.” Information, Communication & Society 15 (5): 583–594. doi:10.1080/1369118X.2015.1008543.
   Google Scholar
• Han, S. H., M. H. Yun, K. J. Kim, and J. Kwahk. 2000. “Evaluation of Product Usability: Development and Validation of Usability Dimensions and Design Elements Based on Empirical Models.” International Journal of Industrial Ergonomics 26 (4): 477–488. doi:10.1016/S0169-8141(00)00019-6.
   Web of Science ®Google Scholar
• He, Q., T. Shang, F. Zhuang, and Z. Shi. 2013. “Parallel Extreme Learning Machine for Regression Based on MapReduce.” Neurocomputing 102: 52–58. doi:10.1016/j.neucom.2012.01.040.
   Web of Science ®Google Scholar
• High, R. 2012. The Era of Cognitive Systems: An inside Look at Ibm Watson and How It Works.. IBM Corporation, Redbooks.
   Google Scholar
• Hofmann, T. 1999. “Probabilistic Latent Semantic Indexing.” 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA.
   Google Scholar
• Hong, S. W., S. H. Han, and K. J. Kim. 2008. “Optimal Balancing of Multiple Affective Satisfaction Dimensions: A Case Study on Mobile Phones.” International Journal of Industrial Ergonomics 38 (3–4): 1777–1799. doi:10.1016/j.ergon.2007.09.002.
   Web of Science ®Google Scholar
• Hsiao, S. W., and H. C. Huang. 2002. “A Neural Network Based Approach for Product Form Design.” Design Studies 23 (1): 67–84. doi:10.1016/S0142-694X(01)00015-1.
   Google Scholar
• Hsiao, S. W., and H. C. Tsai. 2005. “Applying a Hybrid Approach Based on Fuzzy Neural Network and Genetic Algorithm to Product from Design.” International Journal of Industrial Ergonomics 35 (5): 411–428. doi:10.1016/j.ergon.2004.10.007.
   Web of Science ®Google Scholar
• Hsiao, S. W., and M. C. Liu. 2002. “A Morphing Method for Shape Generation and Image Prediction in Product Design.” Design Studies 23 (6): 533–556. doi:10.1016/S0142-694X(01)00028-X.
   Google Scholar
• Jang, J. S. R. 1993. “ANFIS: Adaptive-Network-Based Fuzzy Inference Systems.” IEEE Transactions on Systems, Man and Cybernetics 23 (3): 665–685. doi:10.1109/21.256541.
   Web of Science ®Google Scholar
• Jang, J. S. R., and C. T. Sun. 1993. “Functional Equivalence between Radial Basis Function Networks and Fuzzy Inference Systems.” IEEE Transactions on Neural Networks 4 (1): 156–159. doi:10.1109/72.182710.
   PubMed Web of Science ®Google Scholar
• Jiang, H., C. K. Kwong, K. W. Siu, and Y. Liu. 2015b. “Rough Set and PSO-based ANFIS Approaches to Modeling Customer Satisfaction for Affective Product Design.” Advanced Engineering Informatics 29 (3): 727–738. doi:10.1016/j.aei.2015.07.005.
   Web of Science ®Google Scholar
• Jiang, H., C. K. Kwong, W. Y. Park, and K. M. Yu. 2018. “A Multi-Objective PSO Approach of Mining Association Rules for Affective Design Based on Online Customer Reviews.” Journal of Engineering Design. doi:10.1080/09544828.2018.1475629.
   Web of Science ®Google Scholar
• Jiang, H., C. K. Kwong, Y. Liu, and W. H. Ip. 2015a. “A Methodology of Integrating Affective Design with Defining Engineering Specifications for Product Design.” International Journal of Production Research 53 (8): 2472–2488. doi:10.1080/00207543.2014.975372.
   Web of Science ®Google Scholar
• Jiao, J., Y. Zhang, and M. Helander. 2006. “A Kansei Mining System for Affective Design.” Expert Systems with Applications 30 (4): 658–673. doi:10.1016/j.eswa.2005.07.020.
   Web of Science ®Google Scholar
• Jiao, J., Y. Zhang, and Y. Wang. 2007. “A Heuristic Genetic Algorithm for Product Portfolio Planning.” Computers & Operations Research 34 (6): 1777–1799. doi:10.1016/j.cor.2005.05.033.
   Web of Science ®Google Scholar
• Jiao, J. Q., Q. Xu, J. Du, Y. Zhang, M. Helander, and H. M. Khalid. 2008. “Analytical Affective Design with Ambient Intelligence for Mass Customization and Personalization.” International Journal of Flexible Manufacturing Systems 19 (4): 570–595. doi:10.1007/s10696-008-9032-1.
   Google Scholar
• Jin, J., Y. Liu, P. Ji, and H. Liu. 2016. “Understanding Big Consumer Opinion Data for Market-Driven Product Design.” International Journal of Production Research 54 (10): 3019–3041. doi:10.1080/00207543.2016.1154208.
   Web of Science ®Google Scholar
• Kaisler, S., F. Armour, J. A. Espinosa, and W. Money. 2013. “Big Data: Issues and Challenges Moving Forward.” Hawaii International Conference on System Sciences, Washington, DC, USA.
   Google Scholar
• Kaklauskas, A., A. Kuzminske, E. K. Zavadskas, A. Daniunas, G. Kaklauskas, M. Seniut, J. Raistenskis, A. Safonov, R. Kliukas, A. Juozapaitis, A. Radzeviciene, and R. Cerkauskiene. 2015. “Affective Tutoring System for Built Environment Management.” Computers & Education 82: 202–216. doi:10.1016/j.compedu.2014.11.016.
   Web of Science ®Google Scholar
• Katarya, R., and O. P. Verma. 2016. “Recent Developments in Affective Recommender Systems.” Physica A 461: 182–190. doi:10.1016/j.physa.2016.05.046.
   Web of Science ®Google Scholar
• Kim, S. H., and Y. J. Lee. 2016. “The User Experience of Smart-Phone Information Hierarchy and Screen Transition Patterns.” International Journal of Multimedia and Ubiquitous Engineering 11 (4): 293–302. doi:10.14257/ijmue.2016.11.4.29.
   Google Scholar
• Kongprasert, N., D. Brissaud, C. Bouchard, A. Aoussat, and S. Butdee. 2008. “How to Design and Process Brand Identity through an Integrated Innovative Approach.” Proceedings, IEEE International Conference on Industrial Engineering and Engineering Management, Singapore.
   Google Scholar
• Kuang, J., and P. Jiang. 2008. “Product Platform Design for a Product Family Based on Kansei Engineering.” Journal of Engineering Design 20 (6): 589–607. doi:10.1080/09544820802132410.
   Web of Science ®Google Scholar
• Kurosu, M., and K. Kashimura. 1995. “Apparent Usability Vs. Inherent Usability: Experimental Analysis on the Determinants of the Apparent Usability.” Proceedings of the Conference Companion on Human Factors in Computing Systems, Denver, CO, USA.
   Google Scholar
• Kusiak, A., and F. A. Salustri. 2007. “Computational Intelligence in Product Design Engineering: Review and Trends.” IEEE Transactions on Systems, Man and Cybernetics – Part C: Applications and Reviews 37 (5): 766–778. doi:10.1109/TSMCC.2007.900669.
   Web of Science ®Google Scholar
• Kwong, C. K., H. Jiang, and X. G. Luo. 2016. “AI-based Methodology of Integrating Affective Design, Engineering, and Marketing for Defining Design Specifications of New Products.” Engineering Applications of Artificial Intelligence 47: 49-60.
   Web of Science ®Google Scholar
• Kwong, C. K., K. Y. Fung, H. Jiang, K. Y. Chan, and K. W. M. Siu. 2013. “A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design.” The Scientific World Journal. doi:10.1155/2013/636948.
   Google Scholar
• Kwong, C. K., T. C. Wong, and K. Y. Chan. 2009. “A Methodology of Generating Customer Satisfaction Models for New Product Development Using a Neuro-Fuzzy Approach.” Expert Systems with Applications 36 (8): 11262–11270. doi:10.1016/j.eswa.2009.02.094.
   Web of Science ®Google Scholar
• Kwong, C. K., Y. Chen, K. Y. Chan, and X. Luo. 2010. “A Generalised Fuzzy Least-Squares Regression Approach to Modelling Relationships in QFD.” Journal of Engineering Design 21 (5): 601–613. doi:10.1080/09544820802563234.
   Web of Science ®Google Scholar
• Lai, H. H., Y. C. Lin, and C. H. Yeh. 2005. “Form Design of Product Image Using Grey Relational Analysis and Neural Network Models.” Computers & Operations Research 32 (10): 2689–2711. doi:10.1016/j.cor.2004.03.021.
   Web of Science ®Google Scholar
• Lai, H. H., Y. C. Lin, C. H. Yeh, and C. H. Wei. 2006. “User-Oriented Design for the Optimal Combination on Product Design.” International Journal of Production Economics 100 (2): 253–267. doi:10.1016/j.ijpe.2004.11.005.
   Web of Science ®Google Scholar
• Lai, H. H., Y. M. Chang, and H. C. Chang. 2005. “A Robust Design Approach for Enhancing the Feeling Quality of a Product: A Car Profile Case Study.” International Journal of Industrial Ergonomics 35 (5): 445–460. doi:10.1016/j.ergon.2004.10.008.
   Web of Science ®Google Scholar
• Lamping, J., R. Rao, and P. Pirolli. 1995. “A Focus+ Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies.” SIGCHI Conference on Human Factors in Computing Systems, Denver, CO, USA.
   Google Scholar
• Lanzotti, A., and P. Tarantino. 2008. “Kansei Engineering Approach for Total Quality Design and Continuous Innovation.” The TQM Journal 20 (4): 324–337. doi:10.1108/17542730810881311.
   Google Scholar
• Leskovec, J., A. Rajaraman, and J. D. Ullman. 2014. Mining of Massive Datasets. Cambridge University Press.
   Google Scholar
• Lin, Y. C., and C. C. Wei. 2016. “A Hybrid Consumer-Oriented Model for Product Affective Design: An Aspect of Visual Ergonomics.” Human Factors and Ergonomics in Manufacturing 27 (1): 17–29. doi:10.1002/hfm.20403.
   Web of Science ®Google Scholar
• Lin, Y. C., C. H. Yeh, and C. H. Hung. 2008. “A Neural Network Approach to the Optimal Combination of Product Color Design.” Proceedings, Fourth International Conference on Networked Computing and Advanced Information Management, Washington, DC, USA.
   Google Scholar
• Lin, Y. C., H. H. Lai, and C. H. Yeh. 2007. “Consumer-Oriented Product Form Design Based on Fuzzy Logic: A Case Study of Mobile Phones.” International Journal of Industrial Ergonomics 37 (6): 531–543. doi:10.1016/j.ergon.2007.03.003.
   Web of Science ®Google Scholar
• Ling, S. H., P. P. San, K. Y. Chan, F. H. F. Leung, and Y. Liu. 2014. “An Intelligent Swarm Based-Wavelet Neural Network for Affective Mobile Phone Design.” Neurocomputing 142: 30–38. doi:10.1016/j.neucom.2014.01.054.
   Web of Science ®Google Scholar
• López-Sánchez, D., A. G. Arrieta, and J. M. Corchado. 2018. “Neural Networks and Transfer Learning Applied to Multimedia Web Mining.” 14th International Conference on Distributed Computing and Artificial Intelligence, Porto, Portugal, 124-131.
   Google Scholar
• Lu, W., and J. F. Petiot. 2014. “Affective Design of Products Using an Audio-Based Protocol: Application to Eyeglass Frame.” International Journal of Industrial Ergonomics 44: 383–394. doi:10.1016/j.ergon.2014.01.004.
   Web of Science ®Google Scholar
• Luong, N. C., D. T. Hoang, P. Wang, D. Niyato, D. I. Kim, and Z. Han. 2016. “Data Collection and Wireless Communication in Internet of Things (Iot) Using Economic Analysis and Pricing Models: A Survey.” IEEE Communications Surveys and Tutorials 18 (4): 2546–2590. doi:10.1109/COMST.2016.2582841.
   Web of Science ®Google Scholar
• Ma, K. L., A. Stompel, J. Bielak, O. Ghattas, and E. J. Kim. 2003. “Visualizing Very Large-Scale Earthquake Simulations.” ACM/IEEE Conference in Supercomputing, Phoenix, AZ, USA.
   Google Scholar
• McDonagh, D., A. Bruseberg, and C. Haslam. 2002. “Visual Product Evaluation: Exploring Users’ Emotional Relationships with Products.” Applied Ergonomics 33: 231–240.
   PubMed Web of Science ®Google Scholar
• Mikolov, T., I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. 2013. “Distributed Representations of Words and Phrases and Their Compositionality.” In Advances in Neural Information Processing Systems, 3111–3119. MIT Press.
   Google Scholar
• Mitchell, T. M. 1997. Machine Learning. McGraw-Hill Science/Engineering/Math.
   Google Scholar
• Nagamachi, M. 1995. “Kansei Engineering: A New Ergonomic Consumer-Oriented Technology for Product Development.” International Journal of Industrial Ergonomics 15 (1): 3–11. doi:10.1016/0169-8141(94)00052-5.
   Web of Science ®Google Scholar
• Nagamachi, M. 2010. Kansei/Affective Engineering. Florida, US: CRC Press.
   Google Scholar
• Nishino, T., M. Nagamachi, and H. Tanaka. 2006. “Variable Precision Bayesian Rough Set Model and Its Application to Kansei Engineering.” Lecture Notes in Computer Science 4100: 190–206.
   Google Scholar
• Noble, C. H., and M. Kumar. 2008. “Using Product Design Strategically to Create Deeper Consumer Connections.” Business Horizons 51 (5): 441–450. doi:10.1016/j.bushor.2008.03.006.
   Web of Science ®Google Scholar
• Norman, D. 2004. Emotional Design: Why We Love or Hate Everyday Things. New York: Basic Books.
   Google Scholar
• Okamoto, R. H., T. Nishino, and M. Nagamachi. 2007. “Comparison between Statistical and Lower/Upper Approximations Rough Sets Models for Beer Can Design and Prototype Evaluation.” 10th International Conference on Quality Management and Organizational Development, Helsingborg, Sweden.
   Google Scholar
• Olvera-López, J. A., J. A. Carrasco-Ochoa, J. F. Martínez-Trinidad, and J. Kittler. 2010. “A Review of Instance Selection Methods.” Artificial Intelligence Review 34 (2): 133–143. doi:10.1007/s10462-010-9165-y.
   Web of Science ®Google Scholar
• Oztekin, A., A. Iseri, S. Zaim, and A. Nikov. 2013. “A Taguchi-Based Kansei Engineering Study of Mobile Phones at Product Design Stage.” Production Planning & Control 24 (6): 465–474. doi:10.1080/09537287.2011.633575.
   Web of Science ®Google Scholar
• Park, J., and S. H. Han. 2004. “A Fuzzy Rule-Based Approach to Modeling Affective User Satisfaction Towards Office Chair Design.” International Journal of Industrial Ergonomics 4 (1): 31–47. doi:10.1016/j.ergon.2004.01.006.
   Google Scholar
• Rawlings, J. O. 1988. Applied Regression Analysis: A Research Tool. Monterey: Wadsworth and Brooks.
   Google Scholar
• Rich, E., K. Knight, and S. B. Nair. 2009. Artificial Intelligence. McGraw Hill.
   Google Scholar
• Saridakis, K. M., and A. J. Dentsoras. 2008a. “Soft Computing in Engineering Design – A Review.” Advanced Engineering Informatics 22 (2): 202–221. doi:10.1016/j.aei.2007.10.001.
   Web of Science ®Google Scholar
• Saridakis, K. M., and A. J. Dentsoras. 2008b. “Soft Computing in Engineering Design – A Review.” Advanced Engineering Informatics 22 (2): 202–221. doi:10.1016/j.aei.2007.10.001.
   Web of Science ®Google Scholar
• Sekkeli, G., G. Koksal, I. Batmaz, and O. T. Bayrak. 2010. “Classification Models Based on Tanaka’s Fuzzy Linear Regression Approach: The Case of Customer Satisfaction Modeling.” Journal of Intelligent & Fuzzy Systems 21 (5): 341–351.
   Web of Science ®Google Scholar
• Shen, H. C., and K. C. Wang. 2016. “Affective Product Form Design Using Fuzzy Kansei Engineering and Creativity.” Journal of Ambient Intelligence and Humanized Computing 7 (6): 875–888. doi:10.1007/s12652-016-0402-3.
   Web of Science ®Google Scholar
• Shi, F., S. Sun, and J. Xu. 2012. “Employing Rough Sets and Association Rule Mining in KANSEI Knowledge Extraction.” Information Sciences 196: 118–128. doi:10.1016/j.ins.2012.02.006.
   Web of Science ®Google Scholar
• Shi, L., S. Olafsson, and Q. Chen. 2001. “An Optimization Framework for Product Design.” Management Science 47 (12): 1681–1692. doi:10.1287/mnsc.47.12.1681.10243.
   Web of Science ®Google Scholar
• Shieh, M. D., and C. C. Yang. 2008. “Classification Model for Product Form Design Using Fuzzy Support Vector Machines.” Computer & Industrial Engineering 55 (1): 150–164. doi:10.1016/j.cie.2007.12.007.
   Web of Science ®Google Scholar
• Shieh, M. D., and F. C. Hsu. 2013. “A Consensus Prediction Model of Affective Responses in Kansei Engineering System.” International Journal of Digital Content Technology and Its Applications 7 (64): 555–562. doi:10.4156/jdcta.vol7.issue1.64.
   Google Scholar
• Shieh, M. D., Y. Li, and C. C. Yang. 2018. “Comparison of Multi-Objective Evolutionary Algorithms in Hybrid Kansei Engineering System for Product Form Design.” Advanced Engineering Informatics 36: 31–42. doi:10.1016/j.aei.2018.02.002.
   Web of Science ®Google Scholar
• Shieh, M. D., Y. E. Yeh, and C. L. Huang. 2016. “Eliciting Design Knowledge from Affective Responses Using Rough Sets and Kansei Engineering System.” Journal of Ambient Intelligence and Humanized Computing 7: 1. doi:10.1007/s12652-015-0307-6.
   PubMed Web of Science ®Google Scholar
• Shimizu, Y., and Y. Jindo. 1995. “A Fuzzy Logic Analysis Method for Evaluation Human Sensitivities.” International Journal of Industrial Ergonomics 15: 39–47. doi:10.1016/0169-8141(95)91249-A.
   Web of Science ®Google Scholar
• Simpson, T. W. 2004. “Product Platform Design and Customization: Status and Promise.” Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18 (1): 3–20. doi:10.1017/S0890060404040028.
   Web of Science ®Google Scholar
• Singh, J. 2014. “Real Time BIG Data Analytic: Security Concern and Challenges with Machine Learning Algorithm.” Conference on IT in Business, Industry and Government, Indore, India.
   Google Scholar
• Smith, G. C., and S. Smith. 2012. “Latent Semantic Engineering – A New Conceptual Usercentered Design Approach.” Advanced Engineering Informative 26: 456–473. doi:10.1016/j.aei.2012.02.012.
   Web of Science ®Google Scholar
• Song, I., and G. Guido. 2006. “Affective Web Service Design.” Pacific Rim International Conference on Artificial Intelligence, Guilin, China.
   Google Scholar
• Song, I., and V. John. 2013. “Affective Core-Banking Services for Microfinance.” In Studies in Computational Intelligence, Original edition, Springer. 91–102. Springer.
   Google Scholar
• StorageServers. 2016. “How Much Data is Created Daily?”. https://storageservers.wordpress.com/2016/02/06/how-much-data-is-created-daily/.
   Google Scholar
• Sun, J., D. K. Kalenchuk, D. Xue, and P. Gu. 2000. “Design Candidate Identification Using Neural Network-Based Fuzzy Reasoning.” Robotics and Computer-Integrated Manufacturing 16 (5): 383–396. doi:10.1016/S0736-5845(00)00017-X.
   Web of Science ®Google Scholar
• Syutono, S. B., S. H. Abdul-Rashid, H. Aoyama, and Z. Taha. 2016. “Fuzzy-Based Taguchi Method for Multi-Response Optimization of Product Form Design in Kansei Engineering: A Case Study on Car Form Design.” Journal of Advanced Mechanical Design, Systems, and Manufacturing 10(9): Paper No.16-00363.
   Web of Science ®Google Scholar
• Sze, V., Y. H. Chen, T. J. Yang, and J. S. Emer. 2017. Efficient Processing of Deep Neural Networks: A Tutorial and Survey, In the Proceedings of the IEEE, Vol. 105, No. 12, pp.2295-2329, December.
   Google Scholar
• Takahashi, M. 1999. From Idea to Product. Hong Kong: Hong Kong Productivity Council.
   Google Scholar
• Tanaka, H., S. Uejima, and K. Asai. 1982. “Linear Regression Analysis with Fuzzy Model.” IEEE Transactions on Systems, Man and Cybernetics 12: 903–907. doi:10.1109/TSMC.1982.4308925.
   Web of Science ®Google Scholar
• Tanoue, C., K. Ishizaka, and M. Nagamachi. 1997. “Kansei Engineering: A Study on Perception of Vehicle Interior Image.” International Journal of Industrial Ergonomics 19 (2): 115–128. doi:10.1016/S0169-8141(96)00008-X.
   Web of Science ®Google Scholar
• Tsai, H. C., S. W. Hsiao, and F. K. Hung. 2006. “An Image Evaluation Approach for Parameter-Based Product Form and Color Design.” Computer-Aided Design 38 (2): 157–171. doi:10.1016/j.cad.2005.10.001.
   Web of Science ®Google Scholar
• Wang, C. 2018. “Combining Rough Set Theory with Fuzzy Cognitive Pairwise Rating to Construct a Product Platform for Tablet Design and Recommendation.” Journal of Engineering Design. doi:10.1080/09544828.2018.1448055.
   Web of Science ®Google Scholar
• Wang, C. H. 2015. “Integrating Kansei Engineering with Conjoint Analysis to Fulfil Market Segmentation and Product Customisation for Digital Cameras.” International Journal of Production Research 53 (8): 2427–2438. doi:10.1080/00207543.2014.974840.
   Web of Science ®Google Scholar
• Wang, C. H., and H. T. Chin. 2016. “Integrating Affective Features with Engineering Features to Seek the Optimal Product Varieties with respect to the Niche Segments.” Advanced Engineering Informative.
   Web of Science ®Google Scholar
• Wang, K. C. 2011. “A Hybrid Kansei Engineering Design Expert System Based on Grey System Theory and Support Vector Regression.” Expert Systems with Applications 38: 8738–8750.
   Web of Science ®Google Scholar
• Wang, K. C., and F. R. Ju. 2013. “An Innovative Design Methodology KKBDCA for Affective Product Development.” Mathematical Problems in Engineering. Article ID 629708.
   Web of Science ®Google Scholar
• Wang, L., L. Ke, P. Liu, R. Ranjan, and L. Chen. 2014. “IK-SVD: Dictionary Learning for Spatial Big Data via Incremental Atom Update.” Computing in Science and Engineering 41–52. doi:10.1109/MCSE.2014.52.
   Web of Science ®Google Scholar
• Wang, Y.-M., and T. M. S. Elhag. 2008. “An Adaptive Neuro-fuzzy Inference System for Bridge Risk Assessment.” Expert Systems with Applications 34 (4): 3099–3106. .
   Google Scholar
• Ware, C. 2012. Information Visualization: Perception for Design. Morgan Kaufmann.
   Google Scholar
• Workman, M., and D. Riding. 2016. “Semantic Cognition and the Ontological to Epistemic Transformation: Using Technologies to Facilitate Understanding.” In Semantic Web, edited by M. Workman and D. Riding, 7–30, Springer, Kobe, Japan.
   Google Scholar
• Wright, W. 1998. “Business Visualization Adds Value.” IEEE Computer Graphics and Applications 18 (4): 39. doi:10.1109/38.689660.
   Web of Science ®Google Scholar
• Wu, X., X. Zhu, G.-Q. Wu, and W. Ding. 2014. “Data Mining with Big Data.” IEEE Transactions on Knowledge and Data Engineering 26 (1): 97–107. doi:10.1109/TKDE.2013.109.
   Web of Science ®Google Scholar
• Xu, Y., Z. Musgrave, B. Noble, and M. Bailey. 2014. “Workload-Aware Provisioning in Public Clouds.” IEEE Internet Computing 18 (4): 15–21. doi:10.1109/MIC.2014.38.
   Web of Science ®Google Scholar
• Yadav, C., J. Rajeev, A. R. Singh, and P. K. Mishra. 2016. “Kano Integrated Robust Design Approach for Aesthetical Product Design: A Case Study of a Car Profile.” Journal of Intelligent Manufacturing 28 (7): 1–19.
   Google Scholar
• Yanagisawa, H., and S. Fukuda. 2005. “Interactive Reduct Evolutional Computation for Aesthetic Design.” Journal of Computing and Information Science in Engineering 5 (1): 1–7. doi:10.1115/1.1846055.
   Web of Science ®Google Scholar
• Yang, C. C. 2011a. “Constructing a Hybrid Kansei Engineering System Based on Multiple Affective Responses: Application to Product Form Design.” Computers & Industrial Engineering 60 (4): 760–768. doi:10.1016/j.cie.2011.01.011.
   Web of Science ®Google Scholar
• Yang, C. C. 2011b. “A Classification-Based Kansei Engineering System for Modeling Consumers’ Affective Responses and Analyzing Product Form Features.” Expert Systems with Applications 38: 11382–11393. doi:10.1016/j.eswa.2011.03.008.
   Web of Science ®Google Scholar
• Yang, C. C., and M. D. Shieh. 2010. “A Support Vector Regression Based Prediction Model of Affective Responses for Product Form Design.” Computers & Industrial Engineering 59 (4): 682–689. doi:10.1016/j.cie.2010.07.019.
   Web of Science ®Google Scholar
• Yang, H., and S. Fong. 2012. “Incrementally Optimized Decision Tree for Noisy Big Data.” 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, Beijing, China.
   Google Scholar
• Yaqoob, I., V. Chang, A. Gani, S. Mokhtar, I. A. T. Hashem, E. Ahmed, N. B. Anuar, and S. U. Khan. 2016. “Information Fusion in Social Big Data: Foundations, State-Of-The-Art, Applications, Challenges, and Future Research Directions.” International Journal of Information Management. doi:10.1016/j.ijinfomgt.2016.04.014.
   Google Scholar
• Yi, J. S., Y. A. Kang, and J. Stasko. 2007. “Toward a Deeper Understanding of the Role of Interaction in Information Visualization.” IEEE Transactions on Visualization and Computer Graphics 13 (6): 1224–1231. doi:10.1109/TVCG.2007.70515.
   PubMed Web of Science ®Google Scholar
• Zhai, L. Y., L. P. Khoo, and Z. W. Zhong. 2009a. “A Dominance-Based Rough Set Approach to Kansei Engineering in Product Development.” Expert Systems with Applications 36 (1): 393–402. doi:10.1016/j.eswa.2007.09.041.
   Web of Science ®Google Scholar
• Zhai, L. Y., L. P. Khoo, and Z. W. Zhong. 2009b. “A Rough Set Based Decision Support Approach to Improving Consumer Affective Satisfaction in Product Design.” International Journal of Industrial Ergonomics 39: 295–302. doi:10.1016/j.ergon.2008.11.003.
   Web of Science ®Google Scholar
• Zhang, P., and N. Li. 2005. “The Importance of Affective Quality.” Communications of the ACM 48 (9): 105–108. doi:10.1145/1081992.
   Web of Science ®Google Scholar
• Zhou, F., B. Lei, Y. Liu, and R. J. Jiao. 2017. “Affective Parameter Shaping in User Experience Prospect Evaluation Based on Hierarchical Bayesian Estimation.” Expert Systems with Applications 78: 1–15. doi:10.1016/j.eswa.2017.02.003.
   Web of Science ®Google Scholar
• Zhou, F., J. R. Jiao, D. Schaefer, and S. Chen. 2009. “Rough Set Based Rule Mining for Affective Design.” Proceedings, International Conference on Engineering Design, Stanford University, Stanford, CA, USA.
   Google Scholar
• Zhou, F., X. Qu, J. R. Jiao, and M. G. Helander. 2014. “Emotion Prediction from Physiological Signals: A Comparison Study between Visual and Auditory Elicitors.” Interacting with Computers 26: 285–302. doi:10.1093/iwc/iwt039.
   Web of Science ®Google Scholar