Deep Learning-Based Model Using DensNet201 for Mobile User Interface Evaluation

References

• Alamoodi, A. H., Garfan, S., Zaidan, B. B., Zaidan, A. A., Shuwandy, M. L., Alaa, M., Alsalem, M. A., Mohammed, A., Aleesa, A. M., Albahri, O. S., Al-Hussein, W. A., & Alobaidi, O. R. (2020). A systematic review into the assessment of medical apps: Motivations, challenges, recommendations and methodological aspect. Health and Technology, 10(5), 1045–1061. https://doi.org/10.1007/s12553-020-00451-4
   Web of Science ®Google Scholar
• Albashish, D. (2022). Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images. PeerJ Computer Science, 8, e1031. https://doi.org/10.7717/peerj-cs.1031
   PubMed Web of Science ®Google Scholar
• Allamanis, M., & Sutton, C. (2014). Mining idioms from source code. In Proceedings of the 22nd ACM sigsoft international symposium on foundations of software engineering (pp. 472–483). https://doi.org/10.1145/2635868.2635901
   Google Scholar
• Alqahtani, F., & Orji, R. (2019). Usability issues in mental health applications. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization (pp. 343–348). https://doi.org/10.1145/3314183.3323676
   Google Scholar
• Bernhaupt, R., Mihalic, K., & Obrist, M. (2008). Usability evaluation methods for mobile applications. In Handbook of research on user interface design and evaluation for mobile technology (pp. 745–758). IGI Global.
   Google Scholar
• Bessghaier, N., & Souii, M. (2017). Towards usability evaluation of hybrid mobile user interfaces. In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) (pp. 895–900). IEEE. https://doi.org/10.1109/AICCSA.2017.10
   Google Scholar
• Buse, R. P., Weimer, W. (2012). Synthesizing API usage examples. In 2012 34th International Conference on Software Engineering (ICSE) (pp. 782–792).
   Google Scholar
• Canziani, A., Paszke, A., & Culurciello, E. (2016). An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678.
   Google Scholar
• Chouchane, M., Soui, M., & Ghedira, K. (2021). The impact of the code smells of the presentation layer on the diffuseness of aesthetic defects of Android apps. Automated Software Engineering, 28(2), 1–29. https://doi.org/10.1007/s10515-021-00297-8
   Web of Science ®Google Scholar
• Deka, B., Doosti, B., Huang, F., Franzen, C., Hibschman, J., Afergan, D., Li, Y., Kumar, R., Dong, T., & Nichols, J. (2021). An Early Rico Retrospective: Three years of uses for a mobile App Dataset. In Artificial Intelligence for Human Computer Interaction: A Modern Approach (pp. 229–256). https://doi.org/10.1007/978-3-030-82681-9_8
   Google Scholar
• Deka, B., Huang, Z., Franzen, C., Hibschman, J., Afergan, D., Li, Y., Jeffrey N., & Kumar, R. (2017). Rico: A mobile app dataset for building data-driven design applications. In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology (pp. 845–854). https://doi.org/10.1145/3126594.3126651
   Google Scholar
• Eiben, A. E., & Smit, S. K. (2011). Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation, 1(1), 19–31. https://doi.org/10.1016/j.swevo.2011.02.001
   Web of Science ®Google Scholar
• Fan, Z., Xie, J. K., Wang, Z. Y., Liu, P. C., Qu, S. J., & Huo, L. (2021). Image classification method based on improved KNN algorithm. In Journal of physics: Conference series (Vol. 1930, No. 1, p. 012009). IOP Publishing.
   Google Scholar
• Fast, E., Steffee, D., Wang, L., Brandt, J. R., & Bernstein, M. S. (2014). Emergent, crowd-scale programming practice in the IDE. In Proceedings of the SIGCHI. In Conference on human factors in computing systems (pp. 2491–2500). https://doi.org/10.1145/2556288.2556998
   Google Scholar
• Feng, B., Zhang, M., Zhu, H., Wang, L., & Zheng, Y. (2021). MRI image segmentation model with support vector machine algorithm in diagnosis of solitary pulmonary nodule. Contrast Media & Molecular Imaging, 2021, 9668836. https://doi.org/10.1155/2021/9668836
   PubMed Web of Science ®Google Scholar
• Fernandez, R. A., Deja, J. A., & Samson, B. P. V. (2018). Automating heuristic evaluation of websites using convolutional neural networks. In Proceedings of the Asian HCI Symposium’18 on Emerging Research Collection (pp. 9–12). https://doi.org/10.1145/3205851.3205854
   Google Scholar
• Fogarty, J., Tan, D., Kapoor, A., Winder, S. (2008, April). CueFlik: Interactive concept learning in image search. In Proceedings of the sigchi conference on human factors in computing systems (pp. 29–38).
   Google Scholar
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning MIT Press (2016). In Conference on information and communication systems (ICICS) (pp. 151–156).
   Google Scholar
• Han, H., Wang, W. Y., & Mao, B. H. (2005). Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In International conference on intelligent computing. Springer.
   Google Scholar
• Harrison, R., Flood, D., & Duce, D. (2013). Usability of mobile applications: Literature review and rationale for a new usability model. Journal of Interaction Science, 1(1), 1–16. https://doi.org/10.1186/2194-0827-1-1
   Google Scholar
• He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
   Google Scholar
• Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708).
   Google Scholar
• Ines, G., Makram, S., Mabrouka, C., & Mourad, A. (2017). Evaluation of mobile interfaces as an optimization problem. Procedia Computer Science, 112, 235–248. https://doi.org/10.1016/j.procs.2017.08.234
   Google Scholar
• Khani, M. G., Mazinani, M. R., Fayyaz, M., & Hoseini, M. (2016). A novel approach for website aesthetic evaluation based on convolutional neural networks. In 2016 Second International Conference on Web Research (ICWR) (pp. 48–53). https://doi.org/10.1109/ICWR.2016.7498445
   Google Scholar
• Khumsi, A. F., Almezhghwi, K., Adweb, K. (2019). Deep learning based analysis in oncological studies: Colorectal cancer staging. In International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions (pp. 573–579).
   Google Scholar
• Kowall, J., & Cappelli, W. (2012). Magic quadrant for application performance monitoring. Gartner Research ID G, 232180.
   Google Scholar
• Lee, E. (2019). Using k-nearest-neighbours (knn) machine learning technique to classify archived helicopter wear debris data. In Proceedings of the AIAC18: 18th Australian International Aerospace Congress (p. 816).
   Google Scholar
• Lee, M., Kent, T., Carswell, C. M., Seidelman, W., & Sublette, M. (2014). Zebra-striping: Visual flow in grid-based graphic design. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, (Vol. 58, No. 1, pp. 1318 –1322). Sage.
   Google Scholar
• Li, G., & Yu, Y. (2016). Visual saliency detection based on multiscale deep CNN features. IEEE Transactions on Image Processing, 25(11), 5012–5024. https://doi.org/10.1109/TIP.2016.2602079
   PubMed Web of Science ®Google Scholar
• Li, L., Wang, C., Li, W., & Chen, J. (2018). Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines. Neurocomputing, 275, 1725–1733. https://doi.org/10.1016/j.neucom.2017.09.004
   Web of Science ®Google Scholar
• Li, M., Lv, J., & Tang, C. (2019). Aesthetic assessment of paintings based on visual balance. IET Image Processing, 13(14), 2821–2828. https://doi.org/10.1049/iet-ipr.2018.6572
   Web of Science ®Google Scholar
• Li, R., & Li, S. (2022). Multimedia image data analysis based on KNN algorithm. Computational Intelligence and Neuroscience, 2022, 7963603. https://doi.org/10.1155/2022/7963603
   PubMedGoogle Scholar
• Lu, H., Wang, L., Ye, M., Yan, K., & Jin, Q. (2018). DNN-based image classification for software Gui testing. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1818–1823). https://doi.org/10.1109/SmartWorld.2018.00306
   Google Scholar
• Margetis, G., Ntoa, S., Antona, M., & Stephanidis, C. (2021). Human‐centered design of artificial intelligence. Handbook of Human Factors and Ergonomics, 1085–1106.
   Google Scholar
• Michailidou, E., Harper, S., & Bechhofer, S. (2008). Visual complexity and aesthetic perception of web pages. In Proceedings of the 26th annual ACM international conference on design of communication (pp. 215–224). https://doi.org/10.1145/1456536.1456581
   Google Scholar
• Nayebi, F., Desharnais, J. M., & Abran, A. (2012). The state of the art of mobile application usability evaluation. In 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1–4). https://doi.org/10.1109/CCECE.2012.6334930
   Google Scholar
• Nishii, R., & Eguchi, S. (2004). Supervised image classification based on adaboost with contextual weak classifiers. In IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium (Vol. 2, pp. 1467–1470).
   Google Scholar
• Niu, X. X., & Suen, C. Y. (2012). A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recognition, 45(4), 1318–1325. https://doi.org/10.1016/j.patcog.2011.09.021
   Web of Science ®Google Scholar
• Ogunseye, E. O., Adenusi, C. A., Nwanakwaugwu, A. C., Ajagbe, S. A., & Akinola, S. O. (2022). Predictive analysis of mental health conditions using AdaBoost algorithm. ParadigmPlus, 3(2), 11–26. https://doi.org/10.55969/paradigmplus.v3n2a2
   Google Scholar
• Osman, H., Lungu, M., & Nierstrasz, O. (2014). Mining frequent bug-fix code changes. In 2014 Software Evolution Week-IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE) (pp. 343–347).
   Google Scholar
• Palomba, F., Di Nucci, D., Panichella, A., Zaidman, A., & De Lucia, A. (2017). Lightweight detection of android-specific code smells: The adoctor project. In 2017 IEEE 24th international conference on software analysis, evolution and reengineering (SANER) (pp. 487–491). https://doi.org/10.1109/SANER.2017.7884659
   Google Scholar
• Sahami Shirazi, A., Henze, N., Schmidt, A., Goldberg, R., Schmidt, B., & Schmauder, H. (2013, June). Insights into layout patterns of mobile user interfaces by an automatic analysis of android apps. In Proceedings of the 5th ACM SIGCHI symposium on engineering interactive computing systems (pp. 275–284). https://doi.org/10.1145/2494603.2480308
   Google Scholar
• Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
   PubMed Web of Science ®Google Scholar
• Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
   Web of Science ®Google Scholar
• Soui, M., Chouchane, M., Gasmi, I., & Mkaouer, M. W. (2017). PLAIN: PLugin for predicting the usAbility of Mobile User Interface. In VISIGRAPP (1: GRAPP) (pp. 127–136).
   Google Scholar
• Soui, M., Chouchane, M., Mkaouer, M. W., Kessentini, M., & Ghedira, K. (2020). Assessing the quality of mobile graphical user interfaces using multi-objective optimization. Soft Computing, 24(10), 7685–7714. https://doi.org/10.1007/s00500-019-04391-8
   Web of Science ®Google Scholar
• Soui, M., Ghedira, K., & Abed, M. (2015). Evaluating user interface adaptation using the context of use. International Journal of Adaptive, Resilient and Autonomic Systems, 6(1), 1–24. https://doi.org/10.4018/IJARAS.2015010101
   Google Scholar
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).
   Google Scholar
• Thai, L. H., Hai, T. S., & Thuy, N. T. (2012). Image classification using support vector machine and artificial neural network. International Journal of Information Technology and Computer Science, 4(5), 32–38. https://doi.org/10.5815/ijitcs.2012.05.05
   Google Scholar
• Thilagavathi, B., Suthendran, K., & Srujanraju, K. (2021). Data Engineering and Communication Technology (pp. 669–678). Springer.
   Google Scholar
• Toribio, P., Alejo, R., Valdovinos, R. M., & Pacheco Sanchez, J. H. (2012). Using Gabriel graphs in borderline-SMOTE to deal with severe two-class imbalance problems on neural networks. Artificial Intelligence Research and Development, 248, 29–36. https://doi.org/10.3233/978-1-61499-139-7-29
   Google Scholar
• Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12(1), 1–11. https://doi.org/10.1038/s41598-022-10358-x
   PubMedGoogle Scholar
• Wang, T., Chen, Y., Qiao, M., & Snoussi, H. (2018). A fast and robust convolutional neural network-based defect detection model in product quality control. The International Journal of Advanced Manufacturing Technology, 94(9–12), 3465–3471. https://doi.org/10.1007/s00170-017-0882-0
   Web of Science ®Google Scholar
• Wang, X. D., & Wolfbeis, O. S. (2016). Fiber-optic chemical sensors and biosensors (2013–2015). Analytical Chemistry, 88(1), 203–227. https://doi.org/10.1021/acs.analchem.5b04298
   PubMed Web of Science ®Google Scholar
• Watzman, S. (2003). Visual design principles for usable interfaces. In J. A. Jacko, & A. Sears (Eds.), The human-computer interaction handbook: Fundamentals, evolving technologies, and emerging applications (pp. 263–285). https://doi.org/10.1201/9781410615862
   Google Scholar
• Weimer, D., Scholz-Reiter, B., & Shpitalni, M. (2016). Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Annals, 65(1), 417–420. https://doi.org/10.1016/j.cirp.2016.04.072
   Web of Science ®Google Scholar
• White, T. D., Fraser, G., & Brown, G. J. (2019). Improving random GUI testing with image-based widget detection. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 307–317). https://doi.org/10.1145/3293882.3330551
   Google Scholar
• Xu, W. (2019). Toward human-centered AI: A perspective from human-computer interaction. Interactions, 26(4), 42–46. https://doi.org/10.1145/3328485
   Google Scholar
• Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: An overview and application in radiology. Insights into Imaging, 9(4), 611–629. https://doi.org/10.1007/s13244-018-0639-9
   PubMed Web of Science ®Google Scholar
• Zhang, P. (1993). Model selection via multifold cross validation. The Annals of Statistics, 21(1), 299–313. https://doi.org/10.1214/aos/1176349027
   Web of Science ®Google Scholar
• Zhou, L., Li, Q., Huo, G., & Zhou, Y. (2017). Image classification using biomimetic pattern recognition with convolutional neural networks features. Computational Intelligence and Neuroscience, 2017, 3792805. https://doi.org/10.1155/2017/3792805
   Google Scholar