{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T07:10:05Z","timestamp":1748589005314,"version":"3.41.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key Project of National Natural Science Foundation of China","award":["U1908212"],"award-info":[{"award-number":["U1908212"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["N2017013, N2017014"],"award-info":[{"award-number":["N2017013, N2017014"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004772","name":"Ningxia Natural Science Foundation","doi-asserted-by":"crossref","award":["2024AAC03349"],"award-info":[{"award-number":["2024AAC03349"]}],"id":[{"id":"10.13039\/501100004772","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s11760-025-04123-6","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T06:07:45Z","timestamp":1747030065000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An influence study of task-irrelevant factors on the neural network accuracy"],"prefix":"10.1007","volume":"19","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[]},{"given":"Changsheng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Haitong","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wenjing","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"4123_CR1","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249\u2013259 (2018)","journal-title":"Neural Netw."},{"key":"4123_CR2","doi-asserted-by":"crossref","unstructured":"\u015een, S.Y., \u00d6zkurt, N.: Convolutional neural network hyperparameter tuning with adam optimizer for ECG classification. In 2020 Innovations in Intelligent Systems and Applications Conference (2020)","DOI":"10.1109\/ASYU50717.2020.9259896"},{"key":"4123_CR3","doi-asserted-by":"crossref","unstructured":"Wawrzy\u0144ski, P., Zawistowski, P., Lepak, \u0141.: Automatic hyperparameter tuning in on-line learning: classic Momentum and ADAM. In 2020 International Joint Conference on Neural Networks (2020)","DOI":"10.1109\/IJCNN48605.2020.9207204"},{"key":"4123_CR4","doi-asserted-by":"crossref","unstructured":"Nguyen, Q., Teku, N., Bose, T.: Epsilon greedy strategy for hyper parameters tuning of a neural network equalizer. In 2021 12th International Symposium on Image and Signal Processing and Analysis (2021)","DOI":"10.1109\/ISPA52656.2021.9552055"},{"key":"4123_CR5","doi-asserted-by":"crossref","unstructured":"Vulpe-Grigora\u015fi, A., Grigore, O.: Convolutional neural network hyperparameters optimization for facial emotion recognition. In 2021 12th International Symposium on Advanced Topics in Electrical Engineering (2021)","DOI":"10.1109\/ATEE52255.2021.9425073"},{"key":"4123_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, A., Yao, F.: Analysis of effective e-commerce coordination big data processing strategies under infinite deep neural network topology. In 2021 Asia-Pacific Conference on Communications Technology and Computer Science (2021)","DOI":"10.1109\/ACCTCS52002.2021.00067"},{"key":"4123_CR7","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, J., Liu, C.: Reconstructing neural network topology from firing activity. In 2020 39th Chinese Control Conference (CCC) (2020)","DOI":"10.23919\/CCC50068.2020.9189382"},{"key":"4123_CR8","doi-asserted-by":"crossref","unstructured":"Kucuk, M., Uysal, I.: Performance analysis of neural network topologies and hyperparameters for deep clustering. In 2020 International Joint Conference on Neural Networks (2020)","DOI":"10.1109\/IJCNN48605.2020.9207110"},{"key":"4123_CR9","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should i trust you?\u201d Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"4123_CR10","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.74"},{"issue":"1","key":"4123_CR11","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1038\/s41467-019-08987-4","volume":"10","author":"S Lapuschkin","year":"2019","unstructured":"Lapuschkin, S., W\u00e4ldchen, S., Binder, A., Montavon, G., Samek, W., M\u00fcller, K.R.: Unmasking Clever Hans predictors and assessing what machines really learn. Nat. Commun. 10(1), 1096 (2019)","journal-title":"Nat. Commun."},{"key":"4123_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst., 25 (2012)"},{"key":"4123_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"4123_CR14","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In Proceedings of the AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"4123_CR15","doi-asserted-by":"publisher","first-page":"91916","DOI":"10.1109\/ACCESS.2020.2994762","volume":"8","author":"A Waheed","year":"2020","unstructured":"Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-Turjman, F., Pinheiro, P.R.: Covidgan: data augmentation using auxiliary classifier gan for improved Covid-19 detection. IEEE Access 8, 91916\u201391923 (2020)","journal-title":"IEEE Access"},{"key":"4123_CR16","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, F., Lim, S.N., Belongie, S., Weinberger, K.Q.: On feature normalization and data augmentation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01220"},{"key":"4123_CR17","unstructured":"Hinton, G.E.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)"},{"issue":"1","key":"4123_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J Big Data 6(1), 1\u201348 (2019)","journal-title":"J Big Data"},{"key":"4123_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"4123_CR20","unstructured":"Inoue, H.: Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929 (2018)"},{"key":"4123_CR21","unstructured":"Zhang, H.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"4123_CR22","doi-asserted-by":"crossref","unstructured":"Phankokkruad, M.: Convolutional neural network models for deep face recognition on limitation and interfering factors in image dataset. In 2018 IEEE\/ACIS 17th International Conference on Computer and Information Science (ICIS) (2018)","DOI":"10.1109\/ICIS.2018.8713547"},{"key":"4123_CR23","doi-asserted-by":"publisher","first-page":"86966","DOI":"10.1109\/ACCESS.2021.3077629","volume":"9","author":"T Kim","year":"2021","unstructured":"Kim, T., Kim, Y.: Suppressing spoof-irrelevant factors for domain-agnostic face anti-spoofing. IEEE Access 9, 86966\u201386974 (2021)","journal-title":"IEEE Access"},{"issue":"1","key":"4123_CR24","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1109\/TAES.2020.3031435","volume":"57","author":"C Belloni","year":"2020","unstructured":"Belloni, C., Balleri, A., Aouf, N., Le Caillec, J.M., Merlet, T.: Explainability of deep SAR ATR through feature analysis. IEEE Transact Aerosp Electron. Syst. 57(1), 659\u2013673 (2020)","journal-title":"IEEE Transact Aerosp Electron. Syst."},{"key":"4123_CR25","doi-asserted-by":"crossref","unstructured":"Han, P., Chen, Z., Wan, Y., Cheng, Z.: PolSAR image classification based on optimal feature and convolution neural network. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (2020)","DOI":"10.1109\/IGARSS39084.2020.9324670"},{"issue":"1","key":"4123_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2011.181","volume":"25","author":"Q Song","year":"2011","unstructured":"Song, Q., Ni, J., Wang, G.: A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Transact. Knowl. Data Eng. 25(1), 1\u201314 (2011)","journal-title":"IEEE Transact. Knowl. Data Eng."},{"key":"4123_CR27","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88, 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."},{"key":"4123_CR28","unstructured":"Lecun, Y., Cortes, C.: The mnist database of handwritten digits, http:\/\/yann.lecun.com\/exdb\/mnist (2010)"},{"key":"4123_CR29","unstructured":"rizhevsky, A., Hinton, G.: Learning Multiple Layers of Features from Tiny Images (2009)"},{"key":"4123_CR30","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"4123_CR31","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European Conference on Computer Vision (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"4123_CR32","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"4123_CR33","unstructured":"Jocher, G., Nishimura, K., Mineeva, T.: Yolov5, https:\/\/github.com\/ultralytics\/yolov5 (2020)"},{"key":"4123_CR34","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Jin, X., Sattlegger, D., Steger, C.: The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization. Proceedings of the 17th International Joint Conference on Computer Vision (2021)","DOI":"10.5220\/0010865000003124"},{"key":"4123_CR35","doi-asserted-by":"crossref","unstructured":"Lailesh, A.K., Richi, J.A., Preethi, N.: A pre-trained yolo-v5 model and an image subtraction approach for printed circuit board defect detection. In 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) (2023)","DOI":"10.1109\/IITCEE57236.2023.10090861"},{"issue":"21","key":"4123_CR36","doi-asserted-by":"publisher","first-page":"12044","DOI":"10.3390\/su132112044","volume":"13","author":"YY Liau","year":"2021","unstructured":"Liau, Y.Y., Ryu, K.: Status recognition using pre-trained YOLOv5 for sustainable human-robot collaboration (HRC) system in mold assembly. Sustainability 13(21), 12044 (2021)","journal-title":"Sustainability"},{"key":"4123_CR37","doi-asserted-by":"publisher","first-page":"8085","DOI":"10.1109\/JSTARS.2022.3206399","volume":"15","author":"W Liu","year":"2022","unstructured":"Liu, W., Quijano, K., Crawford, M.M.: YOLOv5-tassel: detecting tassels in RGB UAV imagery with improved YOLOv5 based on transfer learning. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 15, 8085\u20138094 (2022)","journal-title":"IEEE J. Select. Top. Appl. Earth Observ. Remote Sens."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04123-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04123-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04123-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T06:39:55Z","timestamp":1748587195000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04123-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,12]]},"references-count":37,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["4123"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04123-6","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2025,5,12]]},"assertion":[{"value":"1 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"561"}}