{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:02:45Z","timestamp":1763643765652,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the development of mobile payment, the Internet of Things (IoT) and artificial intelligence (AI), smart vending machines, as a kind of unmanned retail, are moving towards a new future. However, the scarcity of data in vending machine scenarios is not conducive to the development of its unmanned services. This paper focuses on using machine learning on small data to detect the placement of the spiral rack indicated by the end of the spiral rack, which is the most crucial factor in causing a product potentially to get stuck in vending machines during the dispensation. To this end, we propose a k-means clustering-based method for splitting small data that is unevenly distributed both in number and in features due to real-world constraints and design a remarkably lightweight convolutional neural network (CNN) as a classifier model for the benefit of real-time application. Our proposal of data splitting along with the CNN is visually interpreted to be effective in that the trained model is robust enough to be unaffected by changes in products and reaches an accuracy of 100%. We also design a single-board computer-based handheld device and implement the trained model to demonstrate the feasibility of a real-time application.<\/jats:p>","DOI":"10.3390\/s23041935","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T02:09:59Z","timestamp":1675994999000},"page":"1935","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Real-Time Application for the Analysis of Multi-Purpose Vending Machines with Machine Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5194-0225","authenticated-orcid":false,"given":"Yu","family":"Cao","sequence":"first","affiliation":[{"name":"Program of Intelligence and Control, Cluster of Electronics and Mechanical Engineering, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudai","family":"Ikenoya","sequence":"additional","affiliation":[{"name":"Program of Intelligence and Control, Cluster of Electronics and Mechanical Engineering, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4460-8694","authenticated-orcid":false,"given":"Takahiro","family":"Kawaguchi","sequence":"additional","affiliation":[{"name":"Program of Intelligence and Control, Cluster of Electronics and Mechanical Engineering, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seiji","family":"Hashimoto","sequence":"additional","affiliation":[{"name":"Program of Intelligence and Control, Cluster of Electronics and Mechanical Engineering, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takayuki","family":"Morino","sequence":"additional","affiliation":[{"name":"SANDEN RETAIL SYSTEMS CORPORATION, Tokyo 101-8583, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"ref_1","first-page":"32","article-title":"The commodity vending machine","volume":"2","author":"Gruber","year":"2005","journal-title":"InForum Ware Int."},{"key":"ref_2","first-page":"1","article-title":"History of the development of beverage vending machine technology in Japan","volume":"7","author":"Higuchi","year":"2007","journal-title":"Natl. Mus. Nat. Sci. Surv. Rep. Syst. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yokouchi, T. (2010, January 28\u201330). Today and tomorrow of vending machine and its services in Japan. Proceedings of the 2010 7th International Conference on Service Systems and Service Management, Tokyo, Japan.","DOI":"10.1109\/ICSSSM.2010.5530240"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/j.jbankfin.2006.10.003","article-title":"How the Internet affects output and performance at community banks","volume":"31","author":"DeYoung","year":"2007","journal-title":"J. Bank. Financ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1108\/02652329510082942","article-title":"The effects of free banking on overall satisfaction: The use of automated teller machines","volume":"13","author":"Goode","year":"1995","journal-title":"Int. J. Bank Mark."},{"key":"ref_6","first-page":"178","article-title":"Consumers\u2019 Experiences, Opinions, Attitudes, Satisfaction, Dissatisfaction, and Complaining Behavior with Vending Machines","volume":"16","author":"Lee","year":"2003","journal-title":"J. Consum. Satisf. Dissatisfaction Complain. Behav."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9129","DOI":"10.1016\/j.eswa.2011.01.051","article-title":"Recommendation system for localized products in vending machines","volume":"38","author":"Lin","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_8","unstructured":"National Automatic Merchandising Association (2022, November 10). Economic Impact of the Convenience Services Industry. Available online: https:\/\/namanow.org\/voice\/economic-research."},{"key":"ref_9","unstructured":"Japan Vending Machine Manufacturers Association (2022, November 10). Annual Report on the Popularity of Vending Machines 2021. Available online: https:\/\/www.jvma.or.jp\/information."},{"key":"ref_10","first-page":"8","article-title":"Vending machine purchasing experience among students in the university\u2019s residential college","volume":"3","author":"Badrolhisam","year":"2018","journal-title":"J. Int. Bus. Econ. Entrep. (JIBE)"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Young, S.D., Daniels, J., Chiu, C.J., Bolan, R.K., Flynn, R.P., Kwok, J., and Klausner, J.D. (2014). Acceptability of using electronic vending machines to deliver oral rapid HIV self-testing kits: A qualitative study. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0103790"},{"key":"ref_12","first-page":"19","article-title":"Gathering information based on focus groups: Consumer\u2019s Involvement in the use of vending machines","volume":"21","author":"Fernandes","year":"2016","journal-title":"Qual. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1177\/1094670509333789","article-title":"Predicting the likelihood of voiced complaints in the self-service technology context","volume":"12","author":"Robertson","year":"2009","journal-title":"J. Serv. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7776","DOI":"10.1109\/ACCESS.2017.2696365","article-title":"Machine Learning With Big Data: Challenges and Approaches","volume":"5","author":"Grolinger","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","first-page":"1","article-title":"A survey of machine learning for big data processing","volume":"2016","author":"Qiu","year":"2016","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neucom.2017.01.026","article-title":"Machine learning on big data: Opportunities and challenges","volume":"237","author":"Zhou","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7722","DOI":"10.1109\/TII.2019.2954956","article-title":"Toward new retail: A benchmark dataset for smart unmanned vending machines","volume":"16","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1111\/emip.12472","article-title":"Machine Learning and Small Data","volume":"40","author":"Cui","year":"2021","journal-title":"Educ. Meas. Issues Pract."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, S., and Li, C. (2022). A Rolling Bearing Fault Diagnosis Based on Conditional Depth Convolution Countermeasure Generation Networks under Small Samples. Sensors, 22.","DOI":"10.3390\/s22155658"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"00368504211029777","DOI":"10.1177\/00368504211029777","article-title":"Machine learning on small size samples: A synthetic knowledge synthesis","volume":"105","author":"Kokol","year":"2022","journal-title":"Sci. Prog."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ju, Y.C., Kraljevski, I., Neun\u00fcbel, H., Tsch\u00f6pe, C., and Wolff, M. (2022). Acoustic Resonance Testing of Small Data on Sintered Cogwheels. Sensors, 22.","DOI":"10.3390\/s22155814"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"147728","DOI":"10.1109\/ACCESS.2020.3014047","article-title":"Design of smart unstaffed retail shop based on IoT and artificial intelligence","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MCE.2021.3060722","article-title":"A Design of Smart Unmanned Vending Machine for New Retail Based on Binocular Camera and Machine Vision","volume":"11","author":"Liu","year":"2022","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"113063","DOI":"10.1016\/j.eswa.2019.113063","article-title":"Real-time purchase behavior recognition system based on deep learning-based object detection and tracking for an unmanned product cabinet","volume":"143","author":"Kim","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"012017","DOI":"10.1088\/1757-899X\/336\/1\/012017","article-title":"Integration k-means clustering method and elbow method for identification of the best customer profile cluster","volume":"Volume 336","author":"Syakur","year":"2018","journal-title":"Proceedings of the IOP Conference Series: Materials Science and Engineering"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lehotay-K\u00e9ry, P., and Kiss, A. (2022). Membrane Clustering of Coronavirus Variants Using Document Similarity. Genes, 13.","DOI":"10.3390\/genes13111966"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_33","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2014). Striving for simplicity: The all convolutional net. arXiv."},{"key":"ref_34","unstructured":"Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., and Lipson, H. (2015). Understanding neural networks through deep visualization. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_36","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2020, January 7\u201312). Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Goutte, C., and Gaussier, E. (2005, January 21\u201323). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. Proceedings of the European Conference on Information Retrieval, Santiago de Compostela, Spain.","DOI":"10.1007\/978-3-540-31865-1_25"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1935\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:28:17Z","timestamp":1760120897000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1935"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23041935"],"URL":"https:\/\/doi.org\/10.3390\/s23041935","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,2,9]]}}}