Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al., 2016. {TensorFlow}: a system for {Large-Scale} machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation. OSDI 16, pp. 265–283.
AI, A., Alexa AI. 2023 URL: https://developer.amazon.com/en-US/alexa/.
AI, A., Amazon AI. 2023 URL: https://aws.amazon.com/ai/.
AI, A., Azure AI. 2023 URL: https://azure.microsoft.com/.
AI, G., Google AI. 2023 URL: https://ai.google/.
Ali, A.H., Green AI for sustainability: leveraging machine learning to drive a circular economy. Babylon. J. Artif. Intell. 2023 (2023), 15–16.
Allix, K., Bissyandé, T.F., Klein, J., Le Traon, Y., Androzoo: Collecting millions of android apps for the research community. 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories, MSR, 2016, IEEE, 468–471.
Amos, B., Ludwiczuk, B., Satyanarayanan, M., et al. Openface: A general-purpose face recognition library with mobile applications. CMU Sch. Comput. Sci., 6(2), 2016, 20.
Battineni, G., Chintalapudi, N., Ricci, G., Ruocco, C., Amenta, F., Exploring the integration of artificial intelligence (AI) and augmented reality (AR) in maritime medicine. Artif. Intell. Rev., 57(4), 2024, 100.
Bilyk, Z.I., Shapovalov, Y.B., Shapovalov, V.B., Megalinska, A.P., Zhadan, S.O., Andruszkiewicz, F., Dołhańczuk-Śródka, A., Antonenko, P.D., 2020. Comparing Google Lens recognition accuracy with other plant recognition apps. In: Proceedings of the Symposium on Advances in Educational Technology. AET.
Chen, Z., Yao, H., Lou, Y., Cao, Y., Liu, Y., Wang, H., Liu, X., An empirical study on deployment faults of deep learning based mobile applications. 2021 IEEE/ACM 43rd International Conference on Software Engineering, ICSE, 2021, IEEE, 674–685.
Cheng, Y., Wang, D., Zhou, P., Zhang, T., A survey of model compression and acceleration for deep neural networks. 2017 arXiv preprint arXiv:1710.09282.
Dang, X., Li, Y., Ma, W., Guo, Y., Hu, Q., Papadakis, M., Cordy, M., Traon, Y.L., Towards exploring the limitations of test selection techniques on graph neural networks: An empirical study. Empir. Softw. Eng., 29(5), 2024, 112.
Dang, X., Li, Y., Papadakis, M., Klein, J., Bissyandé, T.F., Le Traon, Y., Graphprior: mutation-based test input prioritization for graph neural networks. ACM Trans. Softw. Eng. Methodol. 33:1 (2023), 1–40.
Dang, X., Li, Y., Papadakis, M., Klein, J., Bissyandeé, T.F., Le Traon, Y., Test input prioritization for machine learning classifiers. IEEE Trans. Softw. Eng., 2024.
David, R., Duke, J., Jain, A., Janapa Reddi, V., Jeffries, N., Li, J., Kreeger, N., Nappier, I., Natraj, M., Wang, T., et al. Tensorflow lite micro: Embedded machine learning for tinyml systems. Proc. Mach. Learn. Syst. 3 (2021), 800–811.
DeepLearning4J, R., DeepLearning4J. 2023 URL: https://github.com/eclipse/deeplearning4j/.
Deng, Z., Chen, K., Meng, G., Zhang, X., Xu, K., Cheng, Y., 2022. Understanding real-world threats to deep learning models in android apps. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. pp. 785–799.
Dospinescu, O., Popa, I., Face detection and face recognition in android mobile applications. Inform. Econ., 20(1), 2016, 20.
Gamble, A., Artificial intelligence and mobile apps for mental healthcare: a social informatics perspective. Aslib J. Inf. Manag. 72:4 (2020), 509–523.
He, Y., Lin, J., Liu, Z., Wang, H., Li, L.-J., Han, S., 2018. Amc: Automl for model compression and acceleration on mobile devices. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 784–800.
Hjelmås, E., Low, B.K., Face detection: A survey. Comput. Vis. Image Underst. 83:3 (2001), 236–274.
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., Mobilenets: Efficient convolutional neural networks for mobile vision applications. 2017 arXiv preprint arXiv:1704.04861.
Huang, Y., Hu, H., Chen, C., Robustness of on-device models: Adversarial attack to deep learning models on android apps. 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP, 2021, IEEE, 101–110.
Hub, T.L., TensorFlow lite hub. 2023 URL: https://tfhub.dev/s?deployment-format=lite&subtype=module,placeholder/.
Jones, H.G., What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora. AoB Plants, 12(6), 2020, plaa052.
Joshi, R.D., Dhakal, C.K., Predicting type 2 diabetes using logistic regression and machine learning approaches. Int. J. Environ. Res. Public Health, 18(14), 2021, 7346.
Locke, S., Bashall, A., Al-Adely, S., Moore, J., Wilson, A., Kitchen, G.B., Natural language processing in medicine: a review. Trends Anaesth. Crit. Care 38 (2021), 4–9.
Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G., Recommender system application developments: a survey. Decis. Support Syst. 74 (2015), 12–32.
Omar, L.I., Salih, A.A., Systematic review of english/arabic machine translation postediting: Implications for AI application in translation research and pedagogy. Informatics, Vol. 11, 2024, MDPI, 23.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32 (2019), 8026–8037.
Pham, V., Bluche, T., Kermorvant, C., Louradour, J., Dropout improves recurrent neural networks for handwriting recognition. 2014 14th International Conference on Frontiers in Handwriting Recognition, 2014, IEEE, 285–290.
Ribeiro, M., Grolinger, K., Capretz, M.A., Mlaas: Machine learning as a service. 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA, 2015, IEEE, 896–902.
Rokach, L., Maimon, O., Decision trees. Data Mining and Knowledge Discovery Handbook, 2005, Springer, 165–192.
Searcher, T.S., The silver searcher. 2023 URL: https://github.com/ggreer/the_silver_searcher/.
Shokri, R., Stronati, M., Song, C., Shmatikov, V., Membership inference attacks against machine learning models. 2017 IEEE Symposium on Security and Privacy, SP, 2017, IEEE, 3–18.
SNPE, R., SNPE. 2023 URL: https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk/.
Sun, Z., Sun, R., Lu, L., Mislove, A., 2021. Mind your weight (s): A large-scale study on insufficient machine learning model protection in mobile apps. In: 30th {USENIX} Security Symposium. {USENIX} Security 21.
Wang, H., Wang, N., Yeung, D.-Y., 2015. Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1235–1244.
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T.L., Gugger, S., Drame, M., Lhoest, Q., Rush, A.M., Transformers: State-of-the-art natural language processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020, Association for Computational Linguistics, Online, 38–45 URL: https://www.aclweb.org/anthology/2020.emnlp-demos.6.
Xu, M., Liu, J., Liu, Y., Lin, F.X., Liu, Y., Liu, X., 2019. A first look at deep learning apps on smartphones. In: The World Wide Web Conference. pp. 2125–2136.
Xu, D., Tian, Y., A comprehensive survey of clustering algorithms. Ann. Data Sci. 2 (2015), 165–193.
Yao, Y., Xiao, Z., Wang, B., Viswanath, B., Zheng, H., Zhao, B.Y., 2017. Complexity vs. performance: empirical analysis of machine learning as a service. In: Proceedings of the 2017 Internet Measurement Conference. pp. 384–397.
Zhang, C., Patras, P., Haddadi, H., Deep learning in mobile and wireless networking: A survey. IEEE Commun. Surv. Tutor. 21:3 (2019), 2224–2287.
Zhang, X., Zhou, X., Lin, M., Sun, J., 2018. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. CVPR.
Zhao, N., Wu, M., Chen, J., Android-based mobile educational platform for speech signal processing. Int. J. Electr. Eng. Educ. 54:1 (2017), 3–16.
