DMLBC: : Dependable machine learning for seaports using blockchain technology
Authors: 
Claudia Durán, 
Christian Fernández-Campusano, Raúl Carrasco, Eduardo CarrilloAuthors Info & Claims
Claudia Durán, Christian Fernández-Campusano, Raúl Carrasco, Eduardo CarrilloAuthors Info & ClaimsReferences
[1]
Ahmad R.W., Hasan H., Jayaraman R., Salah K., Omar M., Blockchain applications and architectures for port operations and logistics management, Res. Transp. Bus. Manag. (2021),.
[2]
Akhavan P., Philsoophian M., Rajabion L., Namvar M., Developing a block-chained knowledge management model (BCKMM): Beyond traditional knowledge management, in: 19th Eur. Conf. Knowl. Manag., ECKM 2018, SSRN, Italy, 2018, pp. 17–24.
[3]
Alahmadi D.H., Baothman F.A., Alrajhi M.M., Alshahrani F.S., Albalawi H.Z., Comparative analysis of blockchain technology to support digital transformation in ports and shipping, J. Intell. Syst. 31 (2022) 55–69,.
[4]
Ali A., Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns, Energy 229 (2021),.
[5]
Ali S., Guo X., Karri R., Mukhopadhyay D., Fault Attacks on AES and their Countermeasures, Springer International Publishing, Cham, 2016, pp. 163–208,.
[6]
Allende M., León D.L., Cerón S., Leal A., Pareja A., Silva M.D., Pardo A., Jones D., Worrall D., Merriman B., Gilmore J., Kitchener N., Venegas-Andraca S.E., Quantum-resistance in blockchain networks, 2021, arXiv:2106.06640.
[7]
Alyami H., Yang Z., Riahi R., Bonsall S., Wang J., Advanced uncertainty modelling for container port risk analysis, Accid. Anal. Prev. 123 (2019) 411–421,.
[8]
Anderson R., Security Engineering: A Guide to Building Dependable Distributed Systems, Wiley, 2020.
[9]
Ansorena I.L., Ansorena C.L., Managing uncertainty in ferry terminals: a machine learning approach, Int. J. Bus. Inf. Syst. 33 (2020) 285–297,.
[10]
Asariotis R., Ayala G., Assaf M., Bacrot C., Benamara H., Chantrel D., Cournoyer A., Fugazza M., Hansen P., Hoffmann J., Kulaga T., Premti A., Rodríguez L., Salo B., Tahiri K., Tokuda H., Ugaz P., Youssef F., Review of Maritime Transport 2021, Review of Maritime Transport, United Nations publication, New York, NY, USA, 2021.
[11]
Astudillo G., Carrasco R., Fernandez-Campusano C., Chacon M., Copper price prediction using support vector regression technique, Appl. Sci. 10 (2020),.
[12]
Atak Ü., Arslanoğlu Y., Machine learning methods for predicting marine port accidents: a case study in container terminal, Ships Offshore Struct. 17 (2022) 2480–2487,.
[13]
Barros J., Cunha F., Martins C., Pedrosa P., Cortez P., Predicting weighing deviations in the dispatch workflow process: A case study in a cement industry, IEEE Access 11 (2023) 8119–8135,.
[14]
Belmoukari B., Audy J.F., Forget P., Smart port: a systematic literature review, Eur. Transp. Res. Rev. 15 (2023),.
[15]
Biswas S., Sharif K., Li F., Nour B., Wang Y., A scalable blockchain framework for secure transactions in IoT, IEEE Internet Things J. 6 (2019) 4650–4659,.
[16]
Brintrup A., Pak J., Ratiney D., Pearce T., Wichmann P., Woodall P., McFarlane D., Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing, Int. J. Prod. Res. 58 (2020) 3330–3341,.
[17]
Buchman E., Kwon J., Milosevic Z., The latest gossip on BFT consensus, 2018, CoRR abs/1807.04938. arXiv:1807.04938.
[18]
Cámara Marítima y Portuaria de Chile A.G. (CAMPORT), Indicador de Desempeño Logístico “LPI” 2023, CAMPORT, Valparaíso, Chile, 2023.
[19]
Canto A.C., Kaur J., Kermani M.M., Azarderakhsh R., Algorithmic security is insufficient: A comprehensive survey on implementation attacks haunting post-quantum security, 2023, arXiv:2305.13544.
[20]
Chaharsooghi S.K., Heydari J., Zegordi S.H., A reinforcement learning model for supply chain ordering management: An application to the beer game, Decis. Support Syst. 45 (2008) 949–959,.
[21]
Chen F., Xiao Z., Cui L., Lin Q., Li J., Yu S., Blockchain for internet of things applications: A review and open issues, J. Netw. Comput. Appl. 172 (2020),.
[22]
Chen Y., Zhou Y., Machine learning based decision making for time varying systems: Parameter estimation and performance optimization, Knowl.-Based Syst. 190 (2020),.
[23]
Cintas-Canto A., Kaur J., Mozaffari-Kermani M., Azarderakhsh R., Chatgpt vs. lightweight security: First work implementing the nist cryptographic standard ascon, 2023, arXiv:2306.08178.
[24]
Comisión Estrategia 2030 de Puertos y su Logística, Logística y Puertos: Una Plataforma Estratégica de Desarrollo para Chile, Consejo Nacional de Innovación para el Desarrollo, Santiago, Chile, 2015.
[25]
Crain T., Gramoli V., Larrea M., Raynal M., Dbft: Efficient leaderless byzantine consensus and its application to blockchains, in: 2018 IEEE 17th International Symposium on Network Computing and Applications, NCA, 2018, pp. 1–8,.
[26]
Czachorowski K., Solesvik M., Kondratenko Y., The application of blockchain technology in the maritime industry, in: Kharchenko V., Kondratenko Y., Kacprzyk J. (Eds.), Green IT Eng. Soc. Bus. Ind. Appl., in: Studies in Systems, Decision and Control, vol. 171, Springer International Publishing, Cham, 2019, pp. 561–577,.
[27]
de la Peña Zarzuelo I., Freire Soeane M.J., López Bermúdez B., Industry 4.0 in the port and maritime industry: A literature review, J. Ind. Inf. Integr. 20 (2020),.
[28]
De Myttenaere A., Golden B., Le Grand B., Rossi F., Using the mean absolute percentage error for regression models, in: Proceedings of the 23th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015, ESANN, Bruges, Belgium, 2015, pp. 113–118.
[29]
Di Vaio A., Varriale L., Blockchain technology in supply chain management for sustainable performance: Evidence from the airport industry, Int. J. Inf. Manage. 52 (2020),.
[30]
Díaz R.M., Valdés Figueroa L., Pérez G., Oportunidades y desafíos para la implementación de blockchain en el ámbito logístico de américa latina y el caribe, 2021.
[31]
Dirección Nacional de Aduanas, Declaración única de salida - IVV, 2023, URL: https://www.aduana.cl/declaracion-unica-de-salida-ivv/aduana/2007-02-27/170133.html, Accessed 30 May 2023.
[32]
Dirección Nacional de Aduanas, Formulario DIN, 2023, URL: https://www.aduana.cl/formulario-din/aduana/2018-12-04/122940.html, Accessed 30 May 2023.
[33]
Dubrova E., Ngo K., Gärtner J., Wang R., Breaking a fifth-order masked implementation of crystals-kyber by copy-paste, in: Proceedings of the 10th ACM Asia Public-Key Cryptography Workshop, Association for Computing Machinery, New York, NY, USA, 2023, pp. 10–20,.
[34]
Durán C.A., Córdova F.M., Palominos F., Method for improving critical strategic and operational success factors in a port system, Procedia Comput. Sci. 139 (2018) 448–455,.
[35]
Durán C., Fernández-Campusano C., Carrasco R., Vargas M., Navarrete A., Boosting the decision-making in smart ports by using blockchain, IEEE Access 9 (2021) 128055–128068,.
[36]
Durlik I., Miller T., Dorobczyński L., Kozlovska P., Kostecki T., Revolutionizing marine traffic management: A comprehensive review of machine learning applications in complex maritime systems, Appl. Sci. 13 (2023),.
[37]
Dutta P., Choi T.M., Somani S., Butala R., Blockchain technology in supply chain operations: Applications, challenges and research opportunities, Transp. Res. E 142 (2020),.
[38]
Elkhatib R., Azarderakhsh R., Mozaffari-Kermani M., Accelerated risc-v for sike, in: 2021 IEEE 28th Symposium on Computer Arithmetic, ARITH, IEEE Computer Society, Los Alamitos, CA, USA, 2021, pp. 131–138,.
[39]
Farshidi S., Jansen S., España S., Verkleij J., Decision support for blockchain platform selection: Three industry case studies, IEEE Trans. Eng. Manage. 67 (2020) 1109–1128,.
[40]
Fayyad U.M., Piatetsky-Shapiro G., Smyth P., From data mining to knowledge discovery: An overview, in: Fayyad U.M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. (Eds.), Adv. Knowl. Discov. Data Min., American Association for Artificial Intelligence, Menlo Park, CA, USA, 1996, pp. 1–34.
[41]
Feng H., Grifoll M., Yang Z., Zheng P., Latest challenges to ports in public–private partnership: Case of Dandong Port (China)’s bankruptcy, Transp. Policy 110 (2021) 293–305,.
[42]
Fernández-Campusano C., Larrea M., Cortiñas R., Raynal M., A distributed leader election algorithm in crash-recovery and omissive systems, Inform. Process. Lett. 118 (2017) 100–104,.
[43]
Filom S., Amiri A.M., Razavi S., Applications of machine learning methods in port operations – a systematic literature review, Transp. Res. E 161 (2022),.
[44]
Franses P.H., A note on the mean absolute scaled error, Int. J. Forecast. 32 (2016) 20–22,.
[45]
Frozza T., Pinheiro de Lima E., Gouvêa da Costa S.E., Knowledge management and blockchain technology for organizational sustainability: Conceptual model, Braz. J. Oper. Prod. Manag. 20 (1354) (2023),.
[46]
Gao N., Han D., Weng T.H., Xia B., Li D., Castiglione A., Li K.C., Modeling and analysis of port supply chain system based on fabric blockchain, Comput. Ind. Eng. 172 (2022),.
[47]
García P.M., Rozemberg R., Barafani M., Suominen K., Cram Martos V., Corcuera-Santamaría S., Moreno M., Cornejo R., Carballo I.E., Radl A., Cano L., Fan Z., Lin J., Blockchain and internatinal trade: New technologies for a bigger and better latin american international insertion, Integr. Trade 128 (2020).
[48]
Gerhard W.A., Gunsch C.K., Metabarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports, Environ. Int. 124 (2019) 312–319,.
[49]
González G., Gonzalez-Cava J.M., Méndez J.A., An intelligent decision support system for production planning based on machine learning, J. Intell. Manuf. 31 (2020) 1257–1273,.
[50]
Grida M.O., Abd Elrahman S., Eldrandaly K.A., Critical success factors evaluation for blockchain & rsquo;s adoption and implementing, Systems 11 (2023) 2,.
[51]
Haidine A., Ait-Allal A., Aqqal A., Dahbi A., Networking layer for the evolution of maritime ports into a smart environment, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. (2021).
[52]
Hamner B., Frasco M., Metrics: Evaluation metrics for machine learning (Version 0.1. 4)[Computer Software], 2018, URL: https://cran.r-project.org/package=Metrics.
[53]
Hasan H., AlHadhrami E., AlDhaheri A., Salah K., Jayaraman R., Smart contract-based approach for efficient shipment management, Comput. Ind. Eng. 136 (2019) 149–159,.
[54]
Henesey L., Lizneva Y., Anwar M., A multi-agent system with blockchain for container stacking and dispatching, in: 21st International Conference on Harbor, Maritime and Multimodal Logistics Modeling and Simulation, HMS, Lisbon, 18 2019 through 20 September 2019, Dime University of Genoa, 2019, pp. 79–87.
[55]
Hirata E., Matsuda T., Uncovering the impact of COVID-19 on shipping and logistics, Marit. Bus. Rev. 7 (2022) 305–317,.
[56]
Hong H., Jeon H., Youn C., Kim H., Incorporation of shipping activity data in recurrent neural networks and long short-term memory models to improve air quality predictions around busan port, Atmosphere (Basel) 12 (2021),.
[57]
Hossain T., Adams M., Walker T.R., Role of sustainability in global seaports, Ocean Coast. Manag. 202 (2021),.
[58]
Huang D., Grifoll M., Sanchez-Espigares J.A., Zheng P., Feng H., Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic, Transp. Policy 128 (2022) 1–12,.
[59]
Hyndman R.J., Koehler A.B., Another look at measures of forecast accuracy, Int. J. Forecast. 22 (2006) 679–688,.
[60]
Idris I., Python Data Analysis Cookbook, Packt Publishing Ltd, Birmingham, UK, 2016.
[61]
Imran F., Shahzad K., Butt A., Kantola J., Digital transformation of industrial organizations: Toward an integrated framework, J. Chang. Manag. 21 (2021) 451–479,.
[62]
International Maritime Organization (IMO), Introducción a la OMI, 2023, Online. URL: https://www.imo.org/es/About/Paginas/Default.aspx, Accessed 30 Nov 2023.
[63]
Islam S., Amin S.H., Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques, J. Big Data 7 (2020) 65,.
[64]
Jin Y., Wang R., Zhuang X., Wang K., Wang H., Wang C., Wang X., Prediction of COVID-19 data using an ARIMA-LSTM hybrid forecast model, Mathematics 10 (4001) (2022),.
[65]
John A., Yang Z., Riahi R., Wang J., A risk assessment approach to improve the resilience of a seaport system using Bayesian networks, Ocean Eng. 111 (2016) 136–147,.
[66]
Kamišalić A., Kramberger R., Fister I., Synergy of blockchain technology and data mining techniques for anomaly detection, Appl. Sci. 11 (7987) (2021),.
[67]
Kaur J., Canto A.C., Kermani M.M., Azarderakhsh R., A comprehensive survey on the implementations, attacks, and countermeasures of the current nist lightweight cryptography standard, 2023, arXiv:2304.06222.
[68]
Kim J.H., Kim J., Lee G., Park J., Machine learning-based models for accident prediction at a Korean container port, Sustainability 13 (9137) (2021),.
[69]
Kuo S.Y., Huang X.R., Chen L.B., Smart ports: Sustainable smart business port operation schemes based on the Artificial Intelligence of Things and blockchain technologies, IEEE Potentials 41 (2022) 32–37,.
[70]
Lara J.A., Lizcano D., Martínez M.A., Pazos J., Data preparation for KDD through automatic reasoning based on description logic, Inf. Syst. 44 (2014) 54–72,.
[71]
Li I., Arbuzov A., Shilkin V., Shilkina I., Baryshnikova N., Application of machine learning algorithms in ensuring information security of the internet of things in water transport, in: Bieliatynskyi A., Breskich V. (Eds.), Saf. Aviat. Sp. Technol. Sel. Proc. 9th World Congr. Aviation XXI Century, in: Lecture Notes in Mechanical Engineering, Springer International Publishing, Cham, 2022, pp. 543–557.
[72]
Li M., Weng J., Yang A., Lu W., Zhang Y., Hou L., Liu J.N., Xiang Y., Deng R.H., CrowdBC: A blockchain-based decentralized framework for crowdsourcing, IEEE Trans. Parallel Distrib. Syst. 30 (2019) 1251–1266,.
[73]
Lind M., Ward R., Bergmann M., Haraldson S., Zerem A., Hoffmann J., Eklund E., Maritime informatics for increased collaboration, in: Lind M., Michaelides M., Ward R.T., Watson R. (Eds.), Marit. Informatics, in: Progress in IS, Springer International Publishing, Cham, 2021, pp. 113–136,.
[74]
Liu S., Tian L., Huang Y., A comparative study on prediction of throughput in coal ports among three models, Int. J. Mach. Learn. Cybern. 5 (2014) 125–133,.
[75]
Liu J., Zhang H., Zhen L., Blockchain technology in maritime supply chains: applications, architecture and challenges, Int. J. Prod. Res. 61 (2023) 3547–3563,.
[76]
Loh H.S., Zhou Q., Thai V.V., Wong Y.D., Yuen K.F., Fuzzy comprehensive evaluation of port-centric supply chain disruption threats, Ocean Coast. Manag. 148 (2017) 53–62,.
[77]
Lu Y., Blockchain: A survey on functions, applications and open issues, J. Ind. Integr. Manag. 03 (2018),.
[78]
Markets, Markets, Smart port market by technology (IOT, blockchain, process automation, artificial intelligence), elements (terminal automation, PCS, smart port infrastructure), throughput capacity, port type - and region - global forecast to 2027, 2022, URL: https://www.marketsandmarkets.com/Market-Reports/smart-ports-market-165784113.html, Accessed 30 Nov 2023.
[79]
Meng M.H., Qian Y., A blockchain aided metric for predictive delivery performance in supply chain management, in: 2018 IEEE Int. Conf. Serv. Oper. Logist. Informatics, IEEE, Singapore, 2018, pp. 285–290,.
[80]
van Meter H.J., Revising the DIKW pyramid and the real relationship between data, information, knowledge, and wisdom, Law Technol. Hum. 2 (2020) 69–80,.
[81]
Mozaffari-Kermani M., Reliable and High-Performance Hardware Architectures for the Advanced Encryption Standard/Galois Counter Mode, (Doctoral dissertation) The University of Western Ontario, Canada, 2011.
[82]
Mozaffari Kermani M., Azarderakhsh R., Integrating Emerging Cryptographic Engineering Research and Security Education, American Society for Engineering Education (ASEE), 2015.
[83]
Nadi A., Sharma S., Snelder M., Bakri T., van Lint H., Tavasszy L., Short-term prediction of outbound truck traffic from the exchange of information in logistics hubs: A case study for the port of Rotterdam, Transp. Res. C 127 (2021),.
[84]
Narayanam K., Dayama P., Nishad S., Accelerated carrier invoice factoring using predictive freight transport events, in: 2022 IEEE Int. Conf. Blockchain Cryptocurrency, IEEE, Shanghai, China, 2022, pp. 1–5,.
[85]
Nti I.K., Adekoya A.F., Weyori B.A., A systematic review of fundamental and technical analysis of stock market predictions, Artif. Intell. Rev. 53 (2020) 3007–3057.
[86]
Ølnes S., Ubacht J., Janssen M., Blockchain in government: Benefits and implications of distributed ledger technology for information sharing, Gov. Inf. Q. 34 (2017) 355–364,.
[87]
Oudani M., A combined multi-objective multi criteria approach for blockchain-based synchromodal transportation, Comput. Ind. Eng. 176 (2023).
[88]
Paardenkooper K., The role of data-driven logistics in arctic shipping BT - arctic maritime logistics: The potentials and challenges of the northern sea route, in: Ilin I., Devezas T., Jahn C. (Eds.), Arct. Marit. Logist. Potentials Challenges North. Sea Route, in: Contributions to Management Science, Springer International Publishing, Cham, 2022, pp. 173–191,.
[89]
Pallis P.L., Port risk management in container terminals, Transp. Res. Procedia 25 (2017) 4411–4421,.
[90]
Petersen D., Automating governance: Blockchain delivered governance for business networks, Ind. Mark. Manag. 102 (2022) 177–189,.
[91]
Port Technology Team, Cosco shipping ports launches finance framework for smart and green ports - port technology international, 2021, URL: https://www.porttechnology.org/news/cosco-shipping-ports-launches-finance-framework-for-smart-and-green-ports/, Accessed 21 Nov 2023.
[92]
Priore P., Ponte B., Rosillo R., de la Fuente D., Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments, Int. J. Prod. Res. 57 (2019) 3663–3677,.
[93]
Proto S., Corso E.D., Apiletti D., Cagliero L., Cerquitelli T., Malnati G., Mazzucchi D., REDTag: A predictive maintenance framework for parcel delivery services, IEEE Access 8 (2020) 14953–14964,.
[94]
Ranjbarzadeh R., Bagherian Kasgari A., Jafarzadeh Ghoushchi S., Anari S., Naseri M., Bendechache M., Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images, Sci. Rep. 11 (2021) 10930,.
[95]
Ranjbarzadeh R., Jafarzadeh Ghoushchi S., Anari S., Safavi S., Tataei Sarshar N., Babaee Tirkolaee E., Bendechache M., A deep learning approach for robust, multi-oriented, and curved text detection, Cogn. Comput. (2022),.
[96]
Ranjbarzadeh R., Jafarzadeh Ghoushchi S., Tataei Sarshar N., Tirkolaee E.B., Ali S.S., Kumar T., Bendechache M., ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition, Artif. Intell. Rev. 56 (2023) 10099–10136,.
[97]
Raza Z., Svanberg M., Wiegmans B., Modal shift from road haulage to short sea shipping: a systematic literature review and research directions, Transp. Rev. 40 (2020) 382–406,.
[98]
Raza Z., Woxenius J., Vural C.A., Lind M., Digital transformation of maritime logistics: Exploring trends in the liner shipping segment, Comput. Ind. 145 (2023),.
[99]
Reis C., Ruivo P., Oliveira T., Faroleiro P., Assessing the drivers of machine learning business value, J. Bus. Res. 117 (2020) 232–243,.
[100]
Rico-Peña J.J., Arguedas-Sanz R., López-Martin C., Models used to characterise blockchain features. A systematic literature review and bibliometric analysis, Technovation 123 (2023),.
[101]
Safety4sea, Port of antwerp works on blockchain pilot project, 2018, URL: https://safety4sea.com/port-of-antwerp-works-on-blockchain-pilot-project/, Accessed 21 Nov 2023.
[102]
Sanja B., Blockchain conceptual framework in shipping and port management, in: Maritime Transport Conference, Vol. 9, 2022,.
[103]
Sarker A., Canto A.C., Mozaffari Kermani M., Azarderakhsh R., Error detection architectures for hardware/software co-design approaches of number-theoretic transform, IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 42 (2023) 2418–2422,.
[104]
Sarker A., Mozaffari Kermani M., Azarderakhsh R., Efficient error detection architectures for postquantum signature falcon’s sampler and kem saber, IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 30 (2022) 794–802,.
[105]
Shani G., Gunawardana A., Evaluating recommendation systems, in: Ricci F., Rokach L., Shapira B., Kantor P.B. (Eds.), Recomm. Syst. Handb., Springer, Boston, MA, USA, 2011, pp. 257–297,.
[106]
Sicex Chile, Qué es SICEX, 2023, URL: https://www.sicexchile.cl/que-es-sicex, Accessed 30 May 2023.
[107]
da Silva M.R.F., Agostino I.R.S., Frazzon E.M., Integration of machine learning and simulation for dynamic rescheduling in truck appointment systems, Simul. Model. Pract. Theory 125 (2023),.
[108]
Singh S.K., Rathore S., Park J.H., BlockIoTIntelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence, Futur. Gener. Comput. Syst. 110 (2020) 721–743,.
[109]
Sohaib R.M., Onireti O., Sambo Y., Imran M.A., Network slicing for beyond 5 g systems: An overview of the smart port use case, Electronics 10 (2021),.
[110]
Su X., Huang S., An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain, Front. Ecol. Evol. 11 (2023),.
[111]
Su Z., Wu C., Xiao Y., He H., Study on the prediction model of accidents and incidents of cruise ship operation based on machine learning, Ocean Eng. 260 (2022),.
[112]
Tsiulin S., Hilmola O.P., Goryaev N., Barriers towards development of urban consolidation centres and their implementation: literature review, World Rev. Intermodal Transp. Res. 6 (2017) 251–272.
[113]
Tsiulin S., Reinau K.H., The role of port authority in new blockchain scenarios for maritime port management: The case of Denmark, Transp. Res. Procedia 52 (2021) 388–395,.
[114]
Tsiulin S., Reinau K.H., Hilmola O.P., Goryaev N., Karam A., Blockchain-based applications in shipping and port management: a literature review towards defining key conceptual frameworks, Rev. Int. Bus. Strateg. 30 (2020) 201–224,.
[115]
Tsolaki K., Vafeiadis T., Nizamis A., Ioannidis D., Tzovaras D., Utilizing machine learning on freight transportation and logistics applications: A review, ICT Express (2022),.
[116]
Tufano A., Accorsi R., Manzini R., Machine learning methods to improve the operations of 3PL logistics, Procedia Manuf. 42 (2020) 62–69,.
[117]
UNHCR, Unhcr, Global Report 2020, 2020, URL: https://www.acnur.org/media/informe-global-2020, Accessed 21 Nov 2023.
[118]
Vacca A., Di Sorbo A., Visaggio C.A., Canfora G., A systematic literature review of blockchain and smart contract development: Techniques, tools, and open challenges, J. Syst. Softw. 174 (2021),.
[119]
Varshini A.G.P., Kumari K.A., Predictive analytics approaches for software effort estimation : A review, Indian J. Sci. Technol. 13 (2020) 2094–2103,.
[120]
Vejvar M., Lai K.h., Lo C.K.Y., Fürst E.W.M., Strategic responses to institutional forces pressuring sustainability practice adoption: Case-based evidence from inland port operations, Transp. Res. D 61 (2018) 274–288,.
[121]
Wafi A., Hassan H., Mabrouk A., Fundamental analysis vs technical analysis in the egyptian stock exchange–empirical study, Int. J. Bus. Manag. Study–IJBMS 2 (2015) 212–218.
[122]
Wamba S.F., Queiroz M.M., Blockchain in the operations and supply chain management: Benefits, challenges and future research opportunities, Int. J. Inf. Manage. 52 (2020),.
[123]
Wong W.K., Guo Z.X., A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm, Int. J. Prod. Econ. 128 (2010) 614–624,.
[124]
Wong S., Yeung J.K., Lau Y.Y., Kawasaki T., A case study of how maersk adopts cloud-based blockchain integrated with machine learning for sustainable practices, Sustainability 15 (2023) 7305,.
[125]
Wong S., Yeung J.K.W., Lau Y.Y., So J., Technical sustainability of cloud-based blockchain integrated with machine learning for supply chain management, Sustainability 13 (2021) 8270.
[126]
Wu C., Lin C., Barnes D., Zhang Y., Partner selection in sustainable supply chains: A fuzzy ensemble learning model, J. Clean. Prod. 275 (2020),.
[127]
Yan R., Wang S., Peng C., An artificial intelligence model considering data imbalance for ship selection in port state control based on detention probabilities, J. Comput. Sci. 48 (2021),.
[128]
Zhang C., Fang J., Application research of several LSTM variants in power quality time series data prediction, in: Proc. 2nd Int. Conf. Artif. Intell. Pattern Recognit., Association for Computing Machinery, New York, NY, USA, 2019, pp. 171–175,.
[129]
Zhao J., Liu H.J., Love P.E.D., Greenwood D.J., Sing M.C.P., Public–private partnerships: A dynamic discrete choice model for road projects, Socioecon. Plan. Sci. 82 (2022),.
[130]
Zheng Z., Xie S., Dai H.N., Chen W., Chen X., Weng J., Imran M., An overview on smart contracts: Challenges, advances and platforms, Futur. Gener. Comput. Syst. 105 (2020) 475–491,.
Recommendations
Information systems in seaports: a categorization and overview
Information systems have become indispensable to the competitiveness of ports, facilitating communication and decision making for enhancing the visibility, efficiency, reliability, and security in port operations under various conditions. Providing ...
Methodologies for Sustainable Development of TEN-T/RFC Corridors and Core Ports: The Role of Governance in the Export Time Optimization
Computational Science and Its Applications – ICCSA 2023 WorkshopsAbstractPorts are a key element of the economic development of a country but have to face with many problems of different nature, above all, linked to the governance process. In particular, some ports, due to a slow and complex bureaucracy, find difficult ...
Can the Blockchain Facilitate the Development of an Interport Community?
Computational Science and Its Applications – ICCSA 2021AbstractSeaports are part of a complex and information-intensive maritime supply chain that includes a set of organizations and operators who need to exchange a large amount of information and data with each other. As supply chains become increasingly ...
Comments
Information & Contributors
Information
Published In
The Author(s).
Publisher
Elsevier Science Inc.
United States
Publication History
Published: 17 April 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 13 Jan 2025