Abstract
In recent years, the design of social software UI has become a design research focus in the field of design. Color affects many factors in UI design. However, there is currently no suitable method for effectively selecting colors in social software. In this paper, the color of social software UI design based on BP neural network is selected. The traditional BP neural network (BP), genetic algorithm improved BP neural network (GA-BP) and Mind Evolution Algorithm improved BP neural network (MEA-BP) are analyzed and summarized. Finally, the strong predictor and thought evolution method are used to improve MEA-BP-Adaboost. The experiment proves that the training results of the MEA-BP-Adaboost neural network are very good, and the color difference is reduced by about 30, 26.5 and 35.3%, respectively, compared to the three different BP neural networks. The color selection method based on MEA-BP-Adaboost can more effectively improve the accuracy of color selection in the UI design of social software, while reducing the number of experiments. In the color selection algorithm, the color accuracy rate and recall rate of the seven different colors are basically between 90 and 95%, which can basically achieve the desired effect. This also proves that the usability of BP neural network in social software UI design is very high. The methods involved in this article can be applied to other color space conversion and other image acquisition, display, processing and output devices. It is believed that these research works have certain theoretical guiding significance and practical application value to promote the development of color image color restoration technology.
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References
Liu Q, Wang C (2017) Within-component and between-component multi-kernel discriminating correlation analysis for color face recognition. IET Comput Vis 11(8):663–674
Xu M, Dai S, Wu Z, Shi X, Qiao Y (2017) Rapid analysis of dyed safflowers by color objectification and pattern recognition methods. J Tradit Chin Med Sci 3(4):234–241
Shen Y, Ai T, Zhao R (2018) A method for color raster map annotation recognition. Wuhan Daxue Xuebao 43(1):145–151
Parra A, Boutin M, Delp EJ (2017) Automatic gang graffiti recognition and interpretation. J Electron Imaging 26(5):1
Bora DJ (2017) Importance of image enhancement techniques in color image segmentation: a comprehensive and comparative study. Indian J Sci Res 15(1):115–131
Nosovskiy G (2018) Geometrical coding of color images. Publications De L Institut Mathematique 103(117):159–173
Chidambaram C, Lopes HS (2017) An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching. Int J Nat Comput Res 1(2):54–70
Sun Z, Tong G, Zhao B, Zhang Q, Lyu X (2017) Research of adaptive color classification method for solar cells. Acta Energiae Solaris Sinica 38(6):1546–1552
Poco J, Mayhua A, Heer J (2017) Extracting and retargeting color mappings from bitmap images of visualizations. IEEE Trans Vis Comput Graph 24(1):637–646
Cai C, Song X, He J (2017) Algorithm and realization for cattle face contour extraction based on computer vision. Trans Chin Soc Agric Eng 33(11):171–177
Wang C, Li Z, Dey N, Li Z, Ashour AS, Fong SJ (2018) Histogram of oriented gradient based plantar pressure image feature extraction and classification employing fuzzy support vector machine. J Med Imaging Health Inform 8(4):842–854
Ahsan M, Cai Y, Zhang W (2020) Information extraction of bionic camera-based polarization navigation patterns under noisy weather conditions. J Shanghai Jiao Tong Univ Sci 25(1):18–26
Dong L, Dong W, Feng N, Mao M, Chen L, Kong G (2017) Color space quantization-based clustering for image retrieval. Front Comput Sci 11(6):1023–1035
Alsallakh B, Jourabloo A, Ye M, Liu X, Ren L (2017) Do convolutional neural networks learn class hierarchy? IEEE Trans Vis Comput Graph 24(1):152–162
Yu K, Salzmann M (2017) Second-order convolutional neural networks. Clin Immunol Immunopathol 66(3):230–238
Hu S, Wang J (2017) Global stability of a class of discrete-time recurrent neural networks. IEEE Trans Circuits Syst I Fundam Theory Appl 49(8):1104–1117
Rakkiyappan R, Dharani S, Cao J (2017) Synchronization of neural networks with control packet loss and time-varying delay via stochastic sampled-data controller. IEEE Trans Neural Netw Learn Syst 26(12):3215–3226
Segler MHS, Kogej T, Tyrchan C, Waller MP (2017) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 4(1):120–131
Cheng L, Liu Y (2018) Spiking neural networks: model, learning algorithms and applications. Kongzhi Yu Juece Control Decis 33(5):923–937
Hassan M, Hamada M (2017) A neural networks approach for improving the accuracy of multi-criteria recommender systems. Appl Sci 7(9):868
Nogueira RF, de Alencar Lotufo R, Machado RC (2017) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf Forensics Secur 11(6):1206–1213
Chen L, Bentley P, Rueckert D (2017) Fully automatic acute ischemic lesion segmentation in dwi using convolutional neural networks. Neuroimage Clin 15(C):633–643
Luo Y, Cheng Y, Uzuner Ö, Szolovits P, Starren J (2017) Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes. J Am Med Inform Assoc 25(1):93
Chaudhry MT, Hasan Jamal M, Gillani Z, Anwar W, Khan MS (2020) Thermal-benchmarking for cloud hosting green data centers. Sustain Comput Inform Syst 25:100357
Anwar SM, Majid M, Qayyum A, Awais M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42(11):226
Eres R, Louis WR, Molenberghs P (2017) Common and distinct neural networks involved in fMRI studies investigating morality: an ALE meta-analysis. Soc Neurosci 13(4):1–15
Abpeykar S, Ghatee M (2019) An ensemble of RBF neural networks in decision tree structure with knowledge transferring to accelerate multi-classification. Neural Comput Appl 31:7131–7151
Wang Q, Zhao J, Gong D, Shen Y, Li M, Lei Y (2017) Parallelizing convolutional neural networks for action event recognition in surveillance videos. Int J Parallel Prog 45(4):734–759
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Li, X., Li, Y. & Jae, M.H. Neural network’s selection of color in UI design of social software. Neural Comput & Applic 33, 1017–1027 (2021). https://doi.org/10.1007/s00521-020-05422-4
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DOI: https://doi.org/10.1007/s00521-020-05422-4