{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T18:00:33Z","timestamp":1777658433177,"version":"3.51.4"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"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":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s13198-022-01801-3","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T05:00:21Z","timestamp":1669870821000},"page":"353-366","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Prediction of stock price growth for novel greedy heuristic optimized multi-instances quantitative (NGHOMQ)"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2496-1581","authenticated-orcid":false,"given":"Subba Rao","family":"Polamuri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Srinnivas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. Krishna","family":"Mohan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"1801_CR1","doi-asserted-by":"publisher","first-page":"81105","DOI":"10.1016\/j.artint.2013.06.003","volume":"201","author":"J Amores","year":"2013","unstructured":"Amores J (2013) Multiple instance classication: review, taxonomy and comparative study. Artif Intell 201:81105","journal-title":"Artif Intell"},{"key":"1801_CR2","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.eswa.2016.02.006","volume":"55","author":"RC Cavalcante","year":"2016","unstructured":"Cavalcante RC, Brasileiro RC, Souza VL, Nobrega JP, Oliveira AL (2016) Computational intelligence and financial markets: a survey and future directions. Expert Syst Appl 55:194\u2013211","journal-title":"Expert Syst Appl"},{"key":"1801_CR3","doi-asserted-by":"publisher","first-page":"100015","DOI":"10.1016\/j.dajour.2021.100015","volume":"2","author":"P Chhajer","year":"2022","unstructured":"Chhajer P, Shah M, Kshirsagar A (2022) The applications of artificial neural networks, support vector machines, and long\u2013short term memory for stock market prediction. Decis Anal J 2:100015. https:\/\/doi.org\/10.1016\/j.dajour.2021.100015","journal-title":"Decis Anal J"},{"issue":"12","key":"1801_CR4","first-page":"3171","volume":"89","author":"TG Dietterich","year":"1997","unstructured":"Dietterich TG, Lathrop RH, Lozano-P\u00e9rez T (1997) Solving the multiple instance problem with axis-parallel rectangles. Artif Intell 89(12):3171","journal-title":"Artif Intell"},{"key":"1801_CR5","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1016\/j.jrmge.2015.06.008","volume":"7","author":"A Ebrahimabadi","year":"2015","unstructured":"Ebrahimabadi A, Azimipour M, Bahreini A (2015) Prediction of roadheaders\u2019 performance using artificial neural network approaches (MLP and KOSFM). J Rock Mech Geotech Eng 7:573\u2013583. https:\/\/doi.org\/10.1016\/j.jrmge.2015.06.008","journal-title":"J Rock Mech Geotech Eng"},{"key":"1801_CR6","doi-asserted-by":"crossref","unstructured":"Feng J, Zhou Z-H (2017) Deep MIML network. In: Proceedings of 21st AAAI conference on artificial intelligence (AAAI), pp. 18841890","DOI":"10.1609\/aaai.v31i1.10890"},{"issue":"8","key":"1801_CR7","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"1801_CR8","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/978-3-319-62701-4_2","volume-title":"Advances in data mining. Applications and theoretical aspects","author":"F Jin","year":"2017","unstructured":"Jin F, Wang W, Chakraborty P, Self N, Chen F, Ramakrishnan N (2017) Tracking multiple social media for stock market event prediction. In: Perner P (ed) Advances in data mining. Applications and theoretical aspects. Springer, London, pp 16\u201330"},{"key":"1801_CR9","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.dt.2014.12.001","volume":"11","author":"N Kilic","year":"2015","unstructured":"Kilic N, Ekici B, Hartomacioglu S (2015) Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools. Def Technol 11:110\u2013122. https:\/\/doi.org\/10.1016\/j.dt.2014.12.001","journal-title":"Def Technol"},{"key":"1801_CR10","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/j.jempfin.2011.08.002","volume":"18","author":"JH Kim","year":"2011","unstructured":"Kim JH, Shamsuddin A, Lim KP (2011) Stock return predictability and the adaptive markets hypothesis: evidence from century-long US data. J Empir Finan 18:868\u2013879","journal-title":"J Empir Finan"},{"key":"1801_CR11","doi-asserted-by":"crossref","unstructured":"Kotzias D, Denil M, de Freitas N, Smyth P (2015) From group to individual labels using deep features. In: Proceedings of 21st ACM SIGKDD international conference on knowledge discovery data mining (KDD), pp. 597606","DOI":"10.1145\/2783258.2783380"},{"key":"1801_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-020-09413-5","author":"G Kumar","year":"2020","unstructured":"Kumar G, Jain S, Singh UP (2020) Stock market forecasting using computational intelligence: a survey. Arch Comput Methods Eng. https:\/\/doi.org\/10.1007\/s11831-020-09413-5","journal-title":"Arch Comput Methods Eng"},{"key":"1801_CR13","doi-asserted-by":"crossref","unstructured":"Kumar DA, Murugan S (2013) Performance analysis of Indian stock market index using neural network time series model. In: Proceedings of the international conference on pattern recognition, informatics and mobile engineering, Salem, India, 21\u201322. pp. 72\u201378","DOI":"10.1109\/ICPRIME.2013.6496450"},{"key":"1801_CR14","unstructured":"Liu G, Wu J, Zhou Z-H (2012) Key instance detection in multiinstance learning. In: Proceedings of Asian conference on machine learning, pp. 253268"},{"key":"1801_CR15","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.asoc.2017.03.007","volume":"56","author":"L Luo","year":"2017","unstructured":"Luo L, You S, Xu Y, Peng H (2017) Improving the integration of piece wise linear representation and weighted support vector machine for stock trading signal prediction. Appl Soft Comput 56:199\u2013216","journal-title":"Appl Soft Comput"},{"key":"1801_CR16","doi-asserted-by":"crossref","unstructured":"Ma F, Chitta R, Zhou J, You Q, Sun T, Gao J (2017) Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, KDD '17. ACM, pp. 1903\u20131911","DOI":"10.1145\/3097983.3098088"},{"key":"1801_CR17","unstructured":"Nau R (2014) Mathematical structure of ARIMA models, vol 1, pp 1\u20138"},{"key":"1801_CR18","doi-asserted-by":"crossref","unstructured":"Ning Y, Muthiah S, Rangwala H, Ramakrishnan N (2016) Modeling precursors for event forecasting via nested multi-instance learning. In: Proceedings of 22nd ACM SIGKDD international conference on knowledge discovery data mining (KDD), pp. 10951104","DOI":"10.1145\/2939672.2939802"},{"issue":"3","key":"1801_CR19","first-page":"1224","volume":"8","author":"SR Polamuri","year":"2019","unstructured":"Polamuri SR, Srinivas K, Mohan AK (2019) Stock market prices prediction using random forest and extra tree regression. Int J Recent Tech Eng 8(3):1224\u20131228","journal-title":"Int J Recent Tech Eng"},{"key":"1801_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-020-04782-2","author":"SR Polamuri","year":"2020","unstructured":"Polamuri SR, Srinivas K, Mohan AK (2020) Multi model-based hybrid prediction algorithm (MM-HPA) for stock market prices prediction framework (SMPPF). Arab J Sci Eng. https:\/\/doi.org\/10.1007\/s13369-020-04782-2","journal-title":"Arab J Sci Eng"},{"key":"1801_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2021.07.001","author":"SR Polamuri","year":"2021","unstructured":"Polamuri SR, Srinivas K, Mohan AK (2021) Multi-model generative adversarial network hybrid prediction algorithm (MMGAN-HPA) for stock market prices prediction. J King Saud Univ Comput Inf Sci. https:\/\/doi.org\/10.1016\/j.jksuci.2021.07.001","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"1","key":"1801_CR22","doi-asserted-by":"publisher","first-page":"e0227222","DOI":"10.1371\/journal.pone.0227222","volume":"15","author":"J Qiu","year":"2020","unstructured":"Qiu J, Wang B, Zhou C (2020) Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS ONE 15(1):e0227222. https:\/\/doi.org\/10.1371\/journal.pone.0227222","journal-title":"PLoS ONE"},{"key":"1801_CR23","doi-asserted-by":"publisher","first-page":"970","DOI":"10.2307\/1912100","volume":"40","author":"JNK Rao","year":"1972","unstructured":"Rao JNK, Box GEP, Jenkins GM (1972) Time series analysis forecasting and control. Econometrica 40:970","journal-title":"Econometrica"},{"key":"1801_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1420-3_101","volume-title":"ICDSMLA 2019. Lecture notes in electrical engineering","author":"PS Rao","year":"2020","unstructured":"Rao PS, Srinivas K, Mohan AK (2020) A survey on stock market prediction using machine learning techniques. In: Kumar A, Paprzycki M, Gunjan V (eds) ICDSMLA 2019. Lecture notes in electrical engineering, vol 601. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-15-1420-3_101"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-022-01801-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-022-01801-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-022-01801-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T10:26:05Z","timestamp":1674901565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-022-01801-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["1801"],"URL":"https:\/\/doi.org\/10.1007\/s13198-022-01801-3","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]},"assertion":[{"value":"18 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All Authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This article was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}