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Intelligent employment rate prediction model based on a neural computing framework and human–computer interaction platform

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Abstract

An intelligent employment rate prediction model based on a neural computing framework and human–computer interaction platform is demonstrated in this manuscript. Predictive analytics is the future of things, and its significance is manifested in two main aspects: understanding the future so that people can prepare for its arrival, and predicting the current decision so that people can understand the possible consequences, and by the consequences of the analysis to determine the current decision, and strive to make the current decision. However, there are lots of challenges for the prediction tasks. The novelty of this research is mainly concentrated on two major aspects: (1) the neural network model is optimized and enhanced. The proposed nerve tree network model is essentially based on a tree-structured code for a multi-layered feed-forward sparse neural network; with the tree-structured code, the nerve tree network model does not require interconversion between its genotype and phenotype in the coding and decoding operations, and also effectively reduces the computing time. (2) The human–computer interaction is integrated to construct a user-friendly system. In interactive technology, the interactive contact surface and the model, interactive methods and social acceptance have also given rise to many questions that must be solved and problems that require further research and technological innovation. Through numerical verification, the performance of the proposed framework is validated, and the simulation proves the overall performance of the proposed model. Compared with other models, the proposed algorithms can achieve higher prediction accuracy.

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References

  1. Fateh MF, Zameer A, Mirza NM, Mirza SM, Raja MAZ (2017) Biologically inspired computing framework for solving two-point boundary value problems using differential evolution. Neural Comput Appl 28(8):2165–2179

    Article  Google Scholar 

  2. Triloka J, Senanayake SA, Lai D (2017) Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles. Neural Comput Appl 28(1):65–77

    Article  Google Scholar 

  3. Umuroglu Y, Fraser NJ, Gambardella G, Blott M, Leong P, Jahre M, Vissers K (2017) FINN: a framework for fast, scalable binarized neural network inference. In: Proceedings of the 2017 ACM/SIGDA international symposium on field-programmable gate arrays, ACM, Pennsylvania, pp 65–74

  4. Moro S, Cortez P, Rita P (2017) A framework for increasing the value of predictive data-driven models by enriching problem domain characterization with novel features. Neural Comput Appl 28(6):1515–1523

    Article  Google Scholar 

  5. Li P, Li J, Huang Z, Li T, Gao CZ, Yiu SM, Chen K (2017) Multi-key privacy-preserving deep learning in cloud computing. Future Gener Comput Syst 74:76–85

    Article  Google Scholar 

  6. Zhang Y, Wang H, Xie Y (2017) An intelligent hybrid model for power flow optimization in the cloud-IOT electrical distribution network. Clust Comput. https://doi.org/10.1007/s10586-017-1270-0

    Article  Google Scholar 

  7. Teerapittayanon S, McDanel B, Kung HT (2017) Distributed deep neural networks over the cloud, the edge and end devices. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS), IEEE, pp 328–339

  8. Gopalakrishnan R, Chua Y, Iyer LR (2018) Classifying neuromorphic data using a deep learning framework for image classification. arXiv preprint arXiv:1807.00578

  9. Gursoy O, Sharif MH (2018) Parallel computing for artificial neural network training. Period Eng Nat Sci 6(1):1–10

    Google Scholar 

  10. Kim C, Lee J, Han T, Kim YM (2018) A hybrid framework combining background subtraction and deep neural networks for rapid person detection. J Big Data 5(1):22

    Article  Google Scholar 

  11. Zhao M, Tian Z, Chow TWS (2018) Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation. Neural Comput Appl 1:1–9. https://doi.org/10.1007/s00521-018-3342-3

    Article  Google Scholar 

  12. Wen W, Zhang Y, Fang Y, Fang Z (2018) Image salient regions encryption for generating visually meaningful ciphertext image. Neural Comput Appl 29(3):653–663

    Article  Google Scholar 

  13. Sikora C, Burleson W (2017) The dance of emotion: Demonstrating ubiquitous understanding of human motion and emotion in support of human computer interaction. In: 2017 seventh international conference on affective computing and intelligent interaction (ACII), IEEE, pp 548–555

  14. Kearney P, Li WC, Braithwaite G, Greaves M, (2017) The investigation human–computer interaction on multiple remote tower operations. In: International conference on engineering psychology and cognitive ergonomics: performance, emotion and situation awareness

  15. Janlert LE, Stolterman E (2017) The meaning of interactivity—some proposals for definitions and measures. Hum Comput Interact 32(3):103–138

    Article  Google Scholar 

  16. Manogaran G, Lopez D (2017) A Gaussian process based big data processing framework in cluster computing environment. Clust Comput. https://doi.org/10.1007/s10586-017-0982-5

    Article  Google Scholar 

  17. Li P, Li J, Huang Z et al (2017) AI-based software-defined virtual network function scheduling with delay optimization. Clust Comput. https://doi.org/10.1007/s10586-017-0849-9

    Article  Google Scholar 

  18. Suresh A, Varatharajan R (2017) Competent resource provisioning and distribution techniques for cloud computing environment. Clust Comput. https://doi.org/10.1007/s10586-017-1293-6

    Article  Google Scholar 

  19. Li J (2017) A synthetic research on the multimedia data encryption based mobile computing security enhancement model and multi-channel mobile human computer interaction framework. Multimed Tools Appl 76(16):16963–16987

    Article  Google Scholar 

  20. Ahmad J, Muhammad K, Ahmad I, Ahmad W, Smith ML, Smith LN, Jain DK, Wang H, Mehmood I (2018) Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems. Comput Ind 98:23–33

    Article  Google Scholar 

  21. Kersten-van Dijk ET, Westerink JH, Beute F, IJsselsteijn WA (2017) Personal informatics, self-insight, and behavior change: a critical review of current literature. Hum Comput Interact 32(5–6):268–296

    Article  Google Scholar 

  22. Sakamoto D, Kanda T, Ono T, Ishiguro H, Hagita N (2018) Androids as a telecommunication medium with a humanlike presence. In: Geminoid studies: science and technologies for humanlike teleoperated androids, pp 39–56

  23. Zhang S, Wang H, Huang W, You Z (2018) Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Opt Int J Light Electron Opt 157:866–872

    Article  Google Scholar 

  24. Mansouri I, Gholampour A, Kisi O, Ozbakkaloglu T (2018) Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques. Neural Comput Appl 29(3):873–888

    Article  Google Scholar 

  25. Cui K, Qin X (2018) Virtual reality research of the dynamic characteristics of soft soil under metro vibration loads based on BP neural networks. Neural Comput Appl 29(5):1233–1242

    Article  Google Scholar 

  26. Khan M, Asghar Z (2018) A novel construction of substitution box for image encryption applications with Gingerbreadman chaotic map and S 8 permutation. Neural Comput Appl 29(4):993–999

    Article  Google Scholar 

  27. Chatterjee S, Sarkar S, Dey N, Ashour AS, Sen S (2018) Hybrid non-dominated sorting genetic algorithm: II-neural network approach. In: Advancements in applied metaheuristic computing, IGI Global, pp 264–286

  28. Bellini R, Strohmayer A, Alabdulqader E, Ahmed AA, Spiel K, Bardzell S, Balaam M (2018) Feminist HCI: taking stock, moving forward, and engaging community. In: Extended abstracts of the 2018 CHI conference on human factors in computing systems, ACM, Pennsylvania, p SIG02

  29. Carpendale S, Bardzell S, Burnett M, Kumar N, Balaam M (2018) Panel: extending conversations about gender and HCI. In: Extended abstracts of the 2018 CHI conference on human factors in computing systems, ACM, Pennsylvania, p panel03

  30. Law E, Oudeyer PY, Yin M, Schaekermann M, Williams AC (2017) Designing for curiosity: an interdisciplinary workshop. In: Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems, ACM, pp 586–592

  31. Moore EB, Smith TL, Greenberg J (2018) Keyboard and screen reader accessibility in complex interactive science simulations: design challenges and elegant solutions. In: International conference on universal access in human-computer interaction. Springer, Cham, pp 385–400

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Acknowledgements

The research is supported by the following projects: (1) National Natural Science Foundation of China “Research on the high quality Employment of College Graduates in China Based on the Data Mining: Evaluation System, Influencing Factors and Realization Path” (71874205); (2) Beijing Social Science Fund Project “Research on the Effectiveness Evaluation of Beijing Promoting Employment and Entrepreneurship Policy” (17YJB008); (3) China University of Political Science and Law School-level Scientific Research Project “Research on the Influence of Human capital and Social capital on the Employment Quality of College Graduates: A Multilevel Data Mining Approach Based on Machine Learning” (16ZFG79002); (4) China University of Political Science and Law “Excellent Young and Middle-aged Teacher Development Support Program” (01146511).

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  1. Business School of China University of Political Science and Law, No. 25 Road Xitucheng, Haidian District, Beijing, 100088, China

    Ting Wang

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  1. Ting Wang
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Wang, T. Intelligent employment rate prediction model based on a neural computing framework and human–computer interaction platform. Neural Comput & Applic 32, 16413–16426 (2020). https://doi.org/10.1007/s00521-019-04019-w

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