{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:50:15Z","timestamp":1743025815103,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031622687"},{"type":"electronic","value":"9783031622694"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-62269-4_18","type":"book-chapter","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T14:02:22Z","timestamp":1718892142000},"page":"249-261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Weak Relation Enforcement for\u00a0Kinematic-Informed Long-Term Stock Prediction with\u00a0Artificial Neural Networks"],"prefix":"10.1007","author":[{"given":"Stanislav","family":"Selitskiy","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"18_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"18_CR2","first-page":"467","volume":"2","author":"L Beheim","year":"2004","unstructured":"Beheim, L., Zitouni, A., Belloir, F., de la Housse, C.D.M.: New RBF neural network classifier with optimized hidden neurons number. WSEAS Trans. Syst. 2, 467\u2013472 (2004)","journal-title":"WSEAS Trans. Syst."},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Bender, E.M., Koller, A.: Climbing towards NLU: on meaning, form, and understanding in the age of data. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5185\u20135198. Association for Computational Linguistics (2020). https:\/\/aclanthology.org\/2020.acl-main.463","DOI":"10.18653\/v1\/2020.acl-main.463"},{"key":"18_CR4","unstructured":"Blodgett, S.L., Madaio, M.: Risks of AI foundation models in education. CoRR abs\/2110.10024 (2021). https:\/\/arxiv.org\/abs\/2110.10024"},{"key":"18_CR5","unstructured":"Bommasani, R., et\u00a0al.: On the opportunities and risks of foundation models. CoRR abs\/2108.07258 (2021). https:\/\/arxiv.org\/abs\/2108.07258"},{"key":"18_CR6","unstructured":"Broomhead, D.S., Lowe, D.: Radial basis functions, multi-variable functional interpolation and adaptive networks. Technical report, Royal Signals and Radar Establishment Malvern (United Kingdom) (1988)"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Cai, S., Mao, Z., Wang, Z., Yin, M., Karniadakis, G.E.: Physics-informed neural networks (pinns) for fluid mechanics: a review. Acta Mechanica Sinica 1\u201312 (2022)","DOI":"10.1007\/s10409-021-01148-1"},{"issue":"5","key":"18_CR8","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1109\/72.712151","volume":"9","author":"P Frasconi","year":"1998","unstructured":"Frasconi, P., Gori, M., Sperduti, A.: A general framework for adaptive processing of data structures. IEEE Trans. Neural Netw. 9(5), 768\u2013786 (1998)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"4","key":"18_CR9","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1162\/neco.1989.1.4.465","volume":"1","author":"F Girosi","year":"1989","unstructured":"Girosi, F., Poggio, T.: Representation properties of networks: Kolmogorov\u2019s theorem is irrelevant. Neural Comput. 1(4), 465\u2013469 (1989)","journal-title":"Neural Comput."},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, vol.\u00a02, pp. 729\u2013734 (2005)","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"18_CR11","doi-asserted-by":"publisher","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics, Springer New York Inc., New York (2001). https:\/\/doi.org\/10.1007\/978-0-387-21606-5","DOI":"10.1007\/978-0-387-21606-5"},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1109\/TSMC.1971.4308320","volume":"4","author":"AG Ivakhnenko","year":"1971","unstructured":"Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. Syst. Man Cybern. 4, 364\u2013378 (1971)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Kolmogorov, A.N.: On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables. American Mathematical Society (1961)","DOI":"10.1090\/trans2\/017\/12"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Kurkin, S.A., Pitsik, E.N., Musatov, V.Y., Runnova, A.E., Hramov, A.E.: Artificial neural networks as a tool for recognition of movements by electroencephalograms. In: ICINCO (1), pp. 176\u2013181 (2018)","DOI":"10.5220\/0006860201760181"},{"issue":"4","key":"18_CR15","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1162\/neco.1991.3.4.617","volume":"3","author":"V K\u016frkov\u00e1","year":"1991","unstructured":"K\u016frkov\u00e1, V.: Kolmogorov\u2019s theorem is relevant. Neural Comput. 3(4), 617\u2013622 (1991). https:\/\/doi.org\/10.1162\/neco.1991.3.4.617","journal-title":"Neural Comput."},{"key":"18_CR16","unstructured":"Lake, B.M., Murphy, G.L.: Word meaning in minds and machines (2020). https:\/\/arxiv.org\/abs\/2008.01766"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"key":"18_CR18","unstructured":"Marcus, G.: Deep learning: a critical appraisal. CoRR abs\/1801.00631 (2018). http:\/\/arxiv.org\/abs\/1801.00631"},{"key":"18_CR19","unstructured":"Meyer, L., Kl\u00fcpfel, L., Durner, M., Triebel, R.: Robust probabilistic robot arm keypoint detection exploiting kinematic knowledge. In: IROS 2022 Workshop Probabilistic Robotics in the Age of Deep Learning (2022). https:\/\/openreview.net\/forum?id=x1vvQ1M0MlB"},{"issue":"2","key":"18_CR20","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1162\/neco.1991.3.2.246","volume":"3","author":"J Park","year":"1991","unstructured":"Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Comput. 3(2), 246\u2013257 (1991)","journal-title":"Neural Comput."},{"key":"18_CR21","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1017\/S0962492900002919","volume":"8","author":"A Pinkus","year":"1999","unstructured":"Pinkus, A.: Approximation theory of the MLP model in neural networks. Acta Numer 8, 143\u2013195 (1999)","journal-title":"Acta Numer"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0021999118307125","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"18_CR23","unstructured":"Ren, S., Sun, J., He, K., Zhang, X.: Deep residual learning for image recognition. In: CVPR, vol.\u00a02, p.\u00a04 (2016)"},{"issue":"1","key":"18_CR24","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Selitskiy, S.: Kolmogorov\u2019s gate non-linearity as a step toward much smaller artificial neural networks. In: Proceedings of the 24th International Conference on Enterprise Information Systems, vol.\u00a01, pp. 492-499 (2022)","DOI":"10.5220\/0011060700003179"},{"key":"18_CR26","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1007\/978-3-031-53969-5_26","volume-title":"Machine Learning, Optimization, and Data Science","author":"S Selitskiy","year":"2024","unstructured":"Selitskiy, S.: \u201cit looks all the same to me\": cross-index training for long-term financial series prediction. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds.) LOD 2023. LNCS, vol. 14505, pp. 348\u2013363. Springer, Cham (2024)"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Selitskiy, S., Inoue, C., Schetinin, V., Jakaite, L.: The batch primary components transformer and auto-plasticity learning linear units architecture: synthetic image generation case. In: 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1\u20139 (2023)","DOI":"10.1109\/SNAMS60348.2023.10375471"},{"issue":"2","key":"18_CR28","first-page":"192","volume":"17","author":"S Selitsky","year":"2022","unstructured":"Selitsky, S.: Hybrid convolutional-multilayer perceptron artificial neural network for person recognition by high gamma EEG features. Medicinskiy Vestnik Severnogo Kavkaza 17(2), 192\u2013196 (2022)","journal-title":"Medicinskiy Vestnik Severnogo Kavkaza"},{"issue":"3","key":"18_CR29","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1109\/72.572108","volume":"8","author":"A Sperduti","year":"1997","unstructured":"Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Trans. Neural Netw. 8(3), 714\u2013735 (1997)","journal-title":"IEEE Trans. Neural Netw."},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"18_CR31","unstructured":"Vaswani, A., et al.: Attention is all you need. CoRR abs\/1706.03762 (2017). http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"18_CR32","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62269-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T08:51:01Z","timestamp":1732265461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62269-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031622687","9783031622694"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62269-4_18","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Science and Information Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/Computing","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}