{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:53:20Z","timestamp":1760241200633,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T00:00:00Z","timestamp":1576454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61877050"],"award-info":[{"award-number":["61877050"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program in ShaanXi Province of China","award":["2019ZDLGY03-10"],"award-info":[{"award-number":["2019ZDLGY03-10"]}]},{"name":"Major Issues of Basic Education in Shaanxi Province of China","award":["ZDKT1916"],"award-info":[{"award-number":["ZDKT1916"]}]},{"name":"Scientific research project of Education Department of Shaanxi Provincial Government","award":["19JK0808"],"award-info":[{"award-number":["19JK0808"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Personnel performance is important for the high-technology industry to ensure its core competitive advantages are present. Therefore, predicting personnel performance is an important research area in human resource management (HRM). In this paper, to improve prediction performance, we propose a novel framework for personnel performance prediction to help decision-makers to forecast future personnel performance and recruit the best suitable talents. Firstly, a hybrid convolutional recurrent neural network (CRNN) model based on self-attention mechanism is presented, which can automatically learn discriminative features and capture global contextual information from personnel performance data. Moreover, we treat the prediction problem as a classification task. Then, the k-nearest neighbor (KNN) classifier was used to predict personnel performance. The proposed framework is applied to a real case of personnel performance prediction. The experimental results demonstrate that the presented approach achieves significant performance improvement for personnel performance compared to existing methods.<\/jats:p>","DOI":"10.3390\/e21121227","type":"journal-article","created":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T02:59:01Z","timestamp":1576551541000},"page":"1227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance Prediction"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4926-386X","authenticated-orcid":false,"given":"Xia","family":"Xue","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"},{"name":"Maths and Information Technology School, Yuncheng University, Yuncheng 044000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0706-2103","authenticated-orcid":false,"given":"Jun","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"},{"name":"State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi\u2019an 710127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Economic and Management, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710016, China"},{"name":"China Aerospace Academy of Systems Science and Engineering, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aiqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shouxi","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,16]]},"reference":[{"key":"ref_1","first-page":"1068","article-title":"Knowledge Representation and Discovery Using Formal Concept Analysis: An HRM Application","volume":"2","author":"Bal","year":"2011","journal-title":"World Congress Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.eswa.2006.09.003","article-title":"Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry","volume":"2","author":"Chien","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_3","unstructured":"Karahoca, A., Karahoca, D., and Kaya, O. (2008, January 27\u201330). Data mining to cluster human performance by using online self regulating clustering method. Proceedings of the Wseas International Conference on Multivariate Analysis & Its Application in Science & Engineering, Istanbul, Turkey."},{"key":"ref_4","first-page":"104","article-title":"Leveraging Educational Data Mining for Real-Time Performance Assessment of Scientific Inquiry Skills within Microworlds","volume":"4","author":"Gobert","year":"2012","journal-title":"J. Educ. Data Mining"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s00170-016-8418-6","article-title":"Human performance modeling for manufacturing based on an improved KNN algorithm","volume":"84","author":"Li","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_6","unstructured":"Wang, Q., Li, B., and Hu, J. (2009, January 9\u201311). Feature Selection for Human Resource Selection Based on Affinity Propagation and SVM Sensitivity Analysis. Proceedings of the World Congress on Nature & Biologically Inspired Computing, Coimbatore, India."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.patcog.2018.02.026","article-title":"Breast mass classification via deeply integrating the contextual information from multi-view data","volume":"80","author":"Wang","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9180391","DOI":"10.1155\/2019\/9180391","article-title":"Weakly Supervised Deep Semantic Segmentation Using CNN and ELM with Semantic Candidate Regions","volume":"2019","author":"Xu","year":"2019","journal-title":"Complexity"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_13","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Satt, A., Rozenberg, S., and Hoory, R. (2017, January 20\u201324). Efficient Emotion Recognition from Speech Using Deep Learning on Spectrograms. Proceedings of the 18th Annual Conference of the International Speech Communication Association (Interspeech 2017), Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-200"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bartz, C., Herold, T., Yang, H., and Meinel, C. (2017, January 14\u201318). Language Identification Using Deep Convolutional Recurrent Neural Networks. Proceedings of the 24th International Conference on Neural Information Processing (ICONIP 2017), Guangzhou, China.","DOI":"10.1007\/978-3-319-70136-3_93"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.jhydrol.2018.04.065","article-title":"Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas","volume":"561","author":"Zhang","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sun, X. (2019). Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss. Entropy, 21.","DOI":"10.3390\/e21010037"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1744-6570.1991.tb00688.x","article-title":"The big five personality dimensions and job performance: A meta-analysis","volume":"44","author":"Barrick","year":"1991","journal-title":"Personnel Psychology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1207\/s15327043hup1002_3","article-title":"Task Performance and Contextual Performance: The Meaning for Personnel Selection Research","volume":"10","author":"Borman","year":"1997","journal-title":"Hum. Perform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1111\/1468-0394.00235","article-title":"Data mining for selection of insurance sales agents","volume":"20","author":"Cho","year":"2003","journal-title":"Expert Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5129","DOI":"10.1016\/j.eswa.2010.10.003","article-title":"Improving sale performance prediction using support vector machines","volume":"38","author":"Aguado","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9939","DOI":"10.1016\/j.eswa.2011.11.126","article-title":"Job performance prediction in a call center using a naive Bayes classifier","volume":"39","author":"Valle","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"53","DOI":"10.5121\/ijdkp.2015.5205","article-title":"Data Mining for Prediction of Human Performance Capability in the Software Industry","volume":"5","author":"Thakur","year":"2015","journal-title":"Int. J. Data Mining Knowl. Manag. Process"},{"key":"ref_25","first-page":"1","article-title":"Employee\u2019s Performance Analysis and Prediction Using K-means Clustering & Decision Tree Algorithm","volume":"18","author":"Sarker","year":"2018","journal-title":"Glob. J. Comput. Sci. Technol."},{"key":"ref_26","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, NV, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., and Blunsom, P. (2014, January 22\u201327). A Convolutional Neural Network for Modelling Sentences. Proceedings of the Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, Baltimore, MD, USA.","DOI":"10.3115\/v1\/P14-1062"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ren, Y., Zhang, Y., Zhang, M., and Ji, D. (2016, January 12\u201317). Context-Sensitive Twitter Sentiment Classification Using Neural Network. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.9974"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3444","DOI":"10.1093\/bioinformatics\/btw486","article-title":"Drug drug interaction extraction from biomedical literature using syntax convolutional neural network","volume":"32","author":"Zhao","year":"2016","journal-title":"Bioinformatics"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ombabi, A.H., Lazzez, O., Ouarda, W., and Alimi, A.M. (2017, January 17\u201319). Deep learning framework based on Word2Vec and CNN for users interests classification. Proceedings of the 2017 Sudan Conference on Computer Science and Information Technology (SCCSIT), Elnihood, Sudan.","DOI":"10.1109\/SCCSIT.2017.8293054"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.patcog.2017.01.030","article-title":"Accurate object detection using memory-based models in surveillance scenes","volume":"67","author":"Li","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5600","DOI":"10.1109\/TIP.2018.2855422","article-title":"Video Captioning by Adversarial LSTM","volume":"27","author":"Yang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ins.2017.06.021","article-title":"Drug\u2013drug interaction extraction from biomedical literature using support vector machine and long short term memory networks","volume":"415\u2013416","author":"Huang","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_34","unstructured":"Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., and Sohl-Dickstein, J. (2015, January 7\u201312). Deep knowledge tracing. Proceedings of the Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Su, Y., Liu, Q., Liu, Q., Huang, Z., Yin, Y., Chen, E., Ding, C.H.Q., Wei, S., and Hu, G. (2018, January 2\u20137). Exercise-Enhanced Sequential Modeling for Student Performance Prediction. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11864"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wei, X., Lin, H., Yang, L., and Yu, Y. (2017). A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification. Information, 8.","DOI":"10.3390\/info8030092"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zapata-Impata, B.S., Gil, P., and Torres, F. (2019). Learning Spatio Temporal Tactile Features with a ConvLSTM for the Direction Of Slip Detection. Sensors, 19.","DOI":"10.3390\/s19030523"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Baldominos, A., Saez, Y., and Isasi, P. (2018). Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments. Sensors, 18.","DOI":"10.3390\/s18041288"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12467","DOI":"10.1109\/ACCESS.2019.2891770","article-title":"A Single Attention-Based Combination of CNN and RNN for Relation Classification","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1037\/0021-9010.82.1.30","article-title":"The five factor model of personality and job performance in the European Community","volume":"82","author":"Salgado","year":"1997","journal-title":"J. Appl. Psychol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.asoc.2008.09.003","article-title":"A fuzzy AHP approach to personnel selection problem","volume":"9","author":"Serhadlioglu","year":"2009","journal-title":"Appl. Soft Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_44","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015, January 7\u20139). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.patrec.2011.10.021","article-title":"An affinity-based new local distance function and similarity measure for kNN algorithm","volume":"33","author":"Bhattacharya","year":"2012","journal-title":"Pattern Recognit. Lett."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/12\/1227\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:42:36Z","timestamp":1760190156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/12\/1227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,16]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["e21121227"],"URL":"https:\/\/doi.org\/10.3390\/e21121227","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2019,12,16]]}}}