{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T15:48:20Z","timestamp":1762876100043,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T00:00:00Z","timestamp":1570147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN\/2016-04007"],"award-info":[{"award-number":["RGPIN\/2016-04007"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectures. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies to compare their characteristics by training and testing on a butterfly dataset, and determined the optimal model to deploy in an Android application. The application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from the mobile gallery.<\/jats:p>","DOI":"10.3390\/make1040059","type":"journal-article","created":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T10:54:58Z","timestamp":1570186498000},"page":"1039-1057","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Towards Image Classification with Machine Learning Methodologies for Smartphones"],"prefix":"10.3390","volume":"1","author":[{"given":"Lili","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8004-0907","authenticated-orcid":false,"given":"Petros","family":"Spachos","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.knosys.2006.11.012","article-title":"Automatic species identification of live moths","volume":"20","author":"Mayo","year":"2007","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","unstructured":"(2019, August 24). Taxonomic Keys. Available online: https:\/\/collectionseducation.org\/identify-specimen\/taxonomic-keys\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1146\/annurev.ento.51.110104.151054","article-title":"Keys and the Crisis in Taxonomy: Extinction or Reinvention?","volume":"52","author":"Walter","year":"2007","journal-title":"Annu. Rev. Entomol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1038\/356281a0","article-title":"Taxonomy of taxonomists","volume":"356","author":"Gaston","year":"1992","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1017\/S1367943002002299","article-title":"Declines in the numbers of amateur and professional taxonomists: implications for conservation","volume":"5","author":"Hopkins","year":"2002","journal-title":"Anim. Conserv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1046\/j.1439-0418.1999.00307.x","article-title":"Automating insect identification: exploring the limitations of a prototype system","volume":"123","author":"Weeks","year":"1999","journal-title":"J. Appl. Entomol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0262-8856(98)00161-9","article-title":"Species\u2013identification of wasps using principal component associative memories","volume":"17","author":"Weeks","year":"1999","journal-title":"Image Vision Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1023\/A:1018348204573","article-title":"Image analysis, neural networks, and the taxonomic impediment to biodiversity studies","volume":"6","author":"Weeks","year":"1997","journal-title":"Biodivers. Conserv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1111\/j.1095-8312.2005.00503.x","article-title":"Towards integrative taxonomy","volume":"85","author":"Dayrat","year":"2005","journal-title":"Biol. J. Linn. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networksv","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bouzalmat, A., Kharroubi, J., and Zarghili, A. (2014). Comparative Study of PCA, ICA, LDA using SVM Classifier. J. Emerg. Techol. Web Intell., 6.","DOI":"10.4304\/jetwi.6.1.64-68"},{"key":"ref_13","first-page":"47","article-title":"PCA-SVM Algorithm for Classification of Skeletal Data-Based Eigen Postures","volume":"6","author":"Hai","year":"2016","journal-title":"Am. J. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alam, S., Kang, M., Pyun, J.-Y., and Kwon, G. (2016, January 5\u20138). Performance of classification based on PCA, linear SVM, and Multi-kernel SVM. Proceedings of the Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria.","DOI":"10.1109\/ICUFN.2016.7536945"},{"key":"ref_15","first-page":"e563","article-title":"Automatic identification of species with neural networks","volume":"11","author":"Jimenez","year":"2014","journal-title":"PeerJ"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.biosystemseng.2011.10.003","article-title":"The identification of butterfly families using content-based image retrieval","volume":"111","author":"Wang","year":"2012","journal-title":"Biosyst. Eng."},{"key":"ref_17","unstructured":"Iamsa-at, S., Horata, P., Sunat, K., and Thipayang, N. (2014, January 26\u201329). Improving Butterfly Family Classification Using Past Separating Features Extraction in Extreme Learning Machine. Proceedings of the 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), Kitakyushu, Japan."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.aspen.2013.12.004","article-title":"Identification of butterfly based on their shapes when viewed from different angles using an artificial neural network","volume":"17","author":"Kang","year":"2014","journal-title":"J Asia-PAC Entomol."},{"key":"ref_19","unstructured":"Xie, J., Hou, Q., Shi, Y., Peng, L., Jing, L., Zhuang, F., Zhang, J., Tang, X., and Xu, S. (2018). The Automatic Identification of Butterfly Species. arXiv."},{"key":"ref_20","unstructured":"Lane, N., Bhattacharya, S., Mathur, A., Forlivesi, C., and Kawsar, F. (December, January 30). DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit. Proceedings of the 8th EAI International Conference on Mobile Computing, Applications and Services, Cambridge, UK."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Samangouei, P., and Chellappa, R. (2016, January 6\u20139). Convolutional neural networks for attribute-based active authentication on mobile devices. Proceedings of the IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), Niagara Falls, NY, USA.","DOI":"10.1109\/BTAS.2016.7791163"},{"key":"ref_22","unstructured":"Alsing, O. (2019, April 29). Mobile Object Detection using TensorFlow Lite and Transfer Learning (Dissertation). Available online: http:\/\/urn.kb.se\/resolve?urn=urn:nbn:se:kth:diva-233775."},{"key":"ref_23","first-page":"355","article-title":"Bagging Support Vector Machines for Leukemia Classification","volume":"9","author":"Zararsiz","year":"2012","journal-title":"IJCSI"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Schachtner, R., Lutter, D., Stadlthanner, K., Lang, E.W., Schmitz, G., Tom\u00e9, A.M., and Vilda, P.G. (2007, January 22\u201326). Routes to identify marker genes for microarray classification. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4353368"},{"key":"ref_25","first-page":"239","article-title":"Classification of EEG data using FHT and SVM based on Bayesian Network","volume":"8","author":"Deepa","year":"2011","journal-title":"IJCSI"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.4028\/www.scientific.net\/AMR.181-182.1031","article-title":"Why Can SVM Be Performed in PCA Transformed Space for Classification?","volume":"181\u2013182","author":"Zhang","year":"2011","journal-title":"Adv. Mater. Res."},{"key":"ref_27","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees, Chapman and Hall\/CRC."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_30","unstructured":"Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc."},{"key":"ref_31","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, J., Markert, K., and Everingham, M. (2009, January 7\u201310). Learning Models for Object Recognition from Natural Language Descriptions. Proceedings of the 20th British Machine Vision Conference (BMVC2009), London, UK.","DOI":"10.5244\/C.23.2"},{"key":"ref_33","unstructured":"(2018, December 05). eNature: FieldGuides. Available online: http:\/\/www.biologydir.com\/enature-fieldguides-info-33617.html."},{"key":"ref_34","unstructured":"(2019, June 30). TensorFlow Lite GPU delegate. Available online: https:\/\/www.tensorflow.org\/lite\/performance\/gpu."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2010, January 22\u201327). Large-Scale Machine Learning with Stochastic Gradient Descent. Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT\u20192010), Paris, France.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref_36","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA."},{"key":"ref_37","unstructured":"(2018, November 21). Batch normalization in Neural Networks. Available online: https:\/\/towardsdatascience.com\/batch-normalization-in-neural-networks-1ac91516821c."},{"key":"ref_38","unstructured":"Tornay, S.C. (1938). Ockham: Studies and Selections, Open Court."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/4\/59\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:27:29Z","timestamp":1760189249000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/4\/59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,4]]},"references-count":38,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["make1040059"],"URL":"https:\/\/doi.org\/10.3390\/make1040059","relation":{},"ISSN":["2504-4990"],"issn-type":[{"type":"electronic","value":"2504-4990"}],"subject":[],"published":{"date-parts":[[2019,10,4]]}}}