{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:39:44Z","timestamp":1767339584914,"version":"3.37.3"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:00:00Z","timestamp":1598227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:00:00Z","timestamp":1598227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Fiber optics cable has been adopted by telecommunication companies worldwide as the primary medium of transmission. The cable is steadily replacing long-haul microwave, copper cable, and satellite transmissions systems. Fiber cable has been deployed in an underground, submarine, and aerial architecture to transmit high-speed signals in intercontinental, inter countries, inter cities and intra-cities. Underground fiber cable transmission has experienced major failures as compared to other mediums of fiber transmission infrastructure. The failure is rampant, and especially the cable get cuts frequently in areas where there are road constructions, road road expansion projects, and other developmental projects. The cost of repairing these failures is enormous, and it largely depends on the cause of failure and the geographical area the faults occurred. The main aim of this paper was to investigate the cost of repairing underground fiber cable failures, clustered the cause of faults, and then used feedforward neural networks (FFNN) and linear regression to predict the cost of repairing future faults. The result of the predictive model is significant to the telecommunications industry, which means the cost of repairing an underground optical networks will be known to the industry players before the fault occurs. depending on which area, the cause of the failure and the mean time to repair (MTTR), the predictive model tells the mobile network operators the cost involved to repair the damaged cable. The accuracy of the result obtained indicates the predictive model is good for predicting the cost of repairing fiber cable cut in underground optical networks.<\/jats:p>","DOI":"10.1186\/s40537-020-00343-4","type":"journal-article","created":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T11:03:02Z","timestamp":1598266982000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks"],"prefix":"10.1186","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0300-2469","authenticated-orcid":false,"given":"Owusu","family":"Nyarko-Boateng","sequence":"first","affiliation":[]},{"given":"Adebayo Felix","family":"Adekoya","sequence":"additional","affiliation":[]},{"given":"Benjamin Asubam","family":"Weyori","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,24]]},"reference":[{"unstructured":"Allafrica. Ghana: Fiber cuts\u2014root of poor service quality and negative user experience. 2020. https:\/\/allafrica.com\/stories\/201902040673.html. Accessed 14th Feb 2020.","key":"343_CR1"},{"key":"343_CR2","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.osn.2017.12.006","volume":"28","author":"J Mata","year":"2018","unstructured":"Mata J, de Miguel I, Dur\u00e1n RJ, Merayo N, Singh SK, Jukan A, Chamania M. Artificial intelligence (AI) methods in optical networks: a comprehensive survey; 1573\u20134277. Opt Switch Netw. 2018;28:43\u201357.","journal-title":"Opt Switch Netw"},{"issue":"2","key":"343_CR3","doi-asserted-by":"crossref","first-page":"e12121","DOI":"10.1002\/eng2.12121","volume":"2","author":"O Nyarko-Boateng","year":"2020","unstructured":"Nyarko-Boateng O, Xedagbui FEB, Adekoya AF, Weyori BA. Fiber optic deployment challenges and their management in a developing country: a tutorial and case study in Ghana. Eng Rep. 2020;2(2):e12121.","journal-title":"Eng Rep"},{"issue":"13","key":"343_CR4","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1016\/j.ifacol.2019.11.326","volume":"52","author":"P Alavian","year":"2019","unstructured":"Alavian P, Eun Y, Liu K, Meerkov SM, Zhang L. The (\u03b1, \u03b2)-Precise estimates of MTBF and MTTR: definitions, calculations, and induced effect on machine efficiency evaluation. IFAC-PapersOnLine. 2019;52(13):1004\u20139. https:\/\/doi.org\/10.1016\/j.ifacol.2019.11.326.","journal-title":"IFAC-PapersOnLine"},{"issue":"9","key":"343_CR5","doi-asserted-by":"publisher","first-page":"5760","DOI":"10.1007\/s11227-019-02797-7","volume":"75","author":"H Ahmadvand","year":"2019","unstructured":"Ahmadvand H, Goudarzi M. SAIR: significance-aware approach to improve QoR of big data processing in case of budget constraint. J Supercomput. 2019;75(9):5760\u201381.","journal-title":"J Supercomput"},{"key":"343_CR6","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/LCA.2016.2636293","volume":"16","author":"H Ahmadvand","year":"2017","unstructured":"Ahmadvand H, Goudarzi M. Using data variety for efficient progressive big data processing in warehouse-scale computers. IEEE Comput Archit Lett. 2017;16:166\u20139.","journal-title":"IEEE Comput Archit Lett"},{"key":"343_CR7","first-page":"1","volume":"9","author":"N Padhy","year":"2018","unstructured":"Padhy N, Singh RP, Satapathy SC. Cost-effective and fault-resilient reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications. Clust Comput. 2018;9:1\u201323.","journal-title":"Clust Comput"},{"unstructured":"Ahn E, Kumar A, Feng D, Fulham M, Kim J. Unsupervised feature learning with K-means and an ensemble of deep convolutional neural networks for medical image classification. 2019. arXiv preprint arXiv:1906.03359.","key":"343_CR8"},{"unstructured":"Z\u00f6ller MA, Huber MF. Survey on automated machine learning. 2019. arXiv preprint arXiv:1904.12054.","key":"343_CR9"},{"key":"343_CR10","isbn-type":"print","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7138-7","volume-title":"An introduction to statistical learning with applications in R","author":"G James","year":"2013","unstructured":"James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning with applications in R. New York: Springer Science+Business Media; 2013. https:\/\/doi.org\/10.1007\/978-1-4614-7138-7. ISBN 978-1-4614-7138-7","ISBN":"https:\/\/id.crossref.org\/isbn\/9781461471387"},{"key":"343_CR11","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.eswa.2017.09.006","volume":"91","author":"S Ouellet","year":"2018","unstructured":"Ouellet S, Michaud F. Enhanced automated body feature extraction from a 2D image using anthropomorphic measures for silhouette analysis. Expert Syst Appl. 2018;91:270\u20136. https:\/\/doi.org\/10.1016\/j.eswa.2017.09.006.","journal-title":"Expert Syst Appl"},{"key":"343_CR12","doi-asserted-by":"publisher","first-page":"112828","DOI":"10.1016\/j.eswa.2019.112828","volume":"139","author":"LO Orimoloye","year":"2020","unstructured":"Orimoloye LO, Sung M-C, Ma T, Johnson JEV. Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices. Expert Syst Appl. 2020;139:112828. https:\/\/doi.org\/10.1016\/j.eswa.2019.112828.","journal-title":"Expert Syst Appl."},{"issue":"3","key":"343_CR13","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1364\/JOCN.10.000162","volume":"10","author":"T Panayiotou","year":"2018","unstructured":"Panayiotou T, Chatzis SP, Ellinas G. Leveraging statistical machine learning to address failure localization in optical networks. IEEE\/OSA J Opt Commun Netw. 2018;10(3):162\u201373.","journal-title":"IEEE\/OSA J Opt Commun Netw"},{"doi-asserted-by":"crossref","unstructured":"Engelbrecht AP. Computational intelligence: an introduction. Hoboken: Wiley. 2007. SN - 9780470512500. https:\/\/books.google.com.gh\/books?id=IZosIcgJMjUC","key":"343_CR14","DOI":"10.1002\/9780470512517"},{"doi-asserted-by":"crossref","unstructured":"M\u00e4kinen M, Iosifidis A, Gabbouj M, Kanniainen J. Predicting jump arrivals in stock prices using neural networks with limit order book data. 2018. SSRN 3165408.","key":"343_CR15","DOI":"10.2139\/ssrn.3165408"},{"doi-asserted-by":"publisher","unstructured":"Ghobadi M, Mahajan R. Optical layer failures in a large backbone. In: IMC '16: proceedings of the 2016 internet measurement conference November 2016. 2016. pp. 461\u20137. https:\/\/doi.org\/10.1145\/2987443.2987483","key":"343_CR16","DOI":"10.1145\/2987443.2987483"},{"doi-asserted-by":"crossref","unstructured":"Tran DT, Magris M, Kanniainen J, Gabbouj M, Iosifidis A. Tensor representation in high-frequency financial data for price change prediction. In: 2017 IEEE symposium series on computational intelligence (SSCI). IEEE. 2017. pp. 1\u20137.","key":"343_CR17","DOI":"10.1109\/SSCI.2017.8280812"},{"doi-asserted-by":"crossref","unstructured":"Ntakaris A, Mirone G, Kanniainen J, Gabbouj M, Iosifidis A. Feature engineering for mid-price prediction forecasting with deep learning. 2019. arXiv preprint arXiv:1904.05384.","key":"343_CR18","DOI":"10.1109\/ACCESS.2019.2924353"},{"doi-asserted-by":"publisher","unstructured":"Christensen MH, Nozal MH, Kavadakis I, Pinson P. Data-driven learning from dynamic pricing data\u2014classification and forecasting. In: 2019 IEEE Milan PowerTech, Milan, Italy, 2019. pp. 1\u20136. https:\/\/doi.org\/10.1109\/PTC.2019.8810769","key":"343_CR19","DOI":"10.1109\/PTC.2019.8810769"},{"key":"343_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.02.074","author":"Di Wang","year":"2020","unstructured":"Wang Di, Guo X, Guan C, Li S, Jinhui Xu. Estimating stochastic linear combination of non-linear regressions efficiently and scalably. Neurocomputing. 2020. https:\/\/doi.org\/10.1016\/j.neucom.2020.02.074.","journal-title":"Neurocomputing"},{"key":"343_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecosta.2020.01.004","author":"B Funke","year":"2020","unstructured":"Funke B, Hirukawa M. Bias correction for local linear regression estimation using asymmetric kernels via the skewing method. Econometr Stat. 2020. https:\/\/doi.org\/10.1016\/j.ecosta.2020.01.004.","journal-title":"Econometr Stat"},{"key":"343_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2019.12.011","author":"P Anitha","year":"2019","unstructured":"Anitha P, Patil MM. RFM model for customer purchase behavior using the K-Means algorithm. J King Saud Univ Comput Inf Sci. 2019. https:\/\/doi.org\/10.1016\/j.jksuci.2019.12.011.","journal-title":"J King Saud Univ Comput Inf Sci"},{"doi-asserted-by":"publisher","unstructured":"Rahouma KH, Ali A. Applying machine learning technology to optimize the operational cost of the egyptian optical network. In: 16th international learning & technology conference 2019. Procedia Comput Sci. 2019;163:502\u201317. https:\/\/doi.org\/10.1016\/j.procs.2019.12.133. 1877\u20130509 \u00a9 2019 The Authors. Published by Elsevier B.V.","key":"343_CR23","DOI":"10.1016\/j.procs.2019.12.133"},{"issue":"33","key":"343_CR24","first-page":"588","volume":"24","author":"Y Hong","year":"2019","unstructured":"Hong Y, Hammad AW, Akbarnezhad A. Forecasting the net costs to organizations of Building Information Modelling (BIM) implementation at different levels of development (LOD). J Inf Technol Constr. 2019;24(33):588\u2013603.","journal-title":"J Inf Technol Constr"},{"key":"343_CR25","series-title":"Springer Proceedings in Business and Economics","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1208-3_1","volume-title":"Advances in analytics and applications","author":"S Utatunda","year":"2019","unstructured":"Utatunda S. Machine learning: an introduction. In: Laha A, editor. Advances in analytics and applications., Springer Proceedings in Business and EconomicsSingapore: Springer; 2019. https:\/\/doi.org\/10.1007\/978-981-13-1208-3_1."},{"key":"343_CR26","doi-asserted-by":"publisher","first-page":"64722","DOI":"10.1109\/ACCESS.2019.2916793","volume":"7","author":"P Nousi","year":"2019","unstructured":"Nousi P, Tsantekidis A, Passalis N, Ntakaris A, Kanniainen J, Tefas A, GabboujIosifidis MA. Machine learning for forecasting mid-price movements using limit order book data. IEEE Access. 2019;7:64722\u201336.","journal-title":"IEEE Access"},{"issue":"8","key":"343_CR27","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1002\/for.2543","volume":"37","author":"A Ntakaris","year":"2018","unstructured":"Ntakaris A, Magris M, Kanniainen J, Gabbouj M, Iosifidis A. Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods. J Forecast. 2018;37(8):852\u201366.","journal-title":"J Forecast"},{"unstructured":"Tsantekidis A, Passalis N, Tefas A, Kanniainen J, Gabbouj M, Iosifidis A. Using deep learning for price prediction by exploiting stationary limit order book features. 2018. arXiv preprint arXiv:1810.0","key":"343_CR28"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00343-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-020-00343-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00343-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T06:30:37Z","timestamp":1696573837000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-020-00343-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,24]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["343"],"URL":"https:\/\/doi.org\/10.1186\/s40537-020-00343-4","relation":{},"ISSN":["2196-1115"],"issn-type":[{"type":"electronic","value":"2196-1115"}],"subject":[],"published":{"date-parts":[[2020,8,24]]},"assertion":[{"value":"19 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests\" in this section.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"64"}}