{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:54:23Z","timestamp":1782842063978,"version":"3.54.5"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information Communication Technology Planning and Evaluation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Korea Ministry of Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electrical Engineering"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.compeleceng.2026.111340","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T12:50:22Z","timestamp":1782737422000},"page":"111340","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["DRIVERDAPP: Driver\u2019s distraction record using deep learning and blockchain"],"prefix":"10.1016","volume":"139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9595-921X","authenticated-orcid":false,"given":"Odinachi Udemezuo","family":"Nwankwo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6973-530X","authenticated-orcid":false,"given":"Simeon Okechukwu","family":"Ajakwe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5704-9134","authenticated-orcid":false,"given":"Muhammad Rasyid Redha","family":"Ansori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1129-0727","authenticated-orcid":false,"given":"Gifar Arif","family":"Haryadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2977-5964","authenticated-orcid":false,"given":"Dong-Seong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6885-5185","authenticated-orcid":false,"given":"Jae Min","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.compeleceng.2026.111340_b1","doi-asserted-by":"crossref","DOI":"10.3390\/s23083835","article-title":"EFFNet-CA: An efficient driver distraction detection based on multiscale features extractions and channel attention mechanism","volume":"23","author":"Khan","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.compeleceng.2026.111340_b2","doi-asserted-by":"crossref","unstructured":"Baheti B, Gajre S, Talbar S. Detection of Distracted Driver Using Convolutional Neural Network. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition workshops. CVPRW, 2018, p. 1145\u201311456.","DOI":"10.1109\/CVPRW.2018.00150"},{"key":"10.1016\/j.compeleceng.2026.111340_b3","doi-asserted-by":"crossref","unstructured":"Adochiei I-R, \u015etirbu O-I, Adochiei NI, Pericle-Gabriel M, Larco C-M, Mustata S-M, Costin D. Drivers\u2019 Drowsiness Detection and Warning Systems for Critical Infrastructures. In: 2020 international conference on e-health and bioengineering. EHB, 2020, p. 1\u20134.","DOI":"10.1109\/EHB50910.2020.9280192"},{"issue":"7","key":"10.1016\/j.compeleceng.2026.111340_b4","doi-asserted-by":"crossref","first-page":"8823","DOI":"10.1109\/TITS.2021.3086411","article-title":"Driver distraction detection using octave-like convolutional neural network","volume":"23","author":"Li","year":"2022","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10.1016\/j.compeleceng.2026.111340_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.healthplace.2022.102896","article-title":"Disability and pedestrian road traffic injury: A scoping review","volume":"77","author":"Schwartz","year":"2022","journal-title":"Health Place"},{"key":"10.1016\/j.compeleceng.2026.111340_b6","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1049\/itr2.12579","article-title":"Facets of security and safety problems and paradigms for smart aerial mobility and intelligent logistics","volume":"18","author":"Ajakwe","year":"2024","journal-title":"IET Intell Transp Syst"},{"issue":"8","key":"10.1016\/j.compeleceng.2026.111340_b7","doi-asserted-by":"crossref","first-page":"590","DOI":"10.3390\/drones9080590","article-title":"i BANDA: A blockchain-assisted defense system for authentication in drone-based logistics","volume":"9","author":"Ajakwe","year":"2025","journal-title":"Drones"},{"key":"10.1016\/j.compeleceng.2026.111340_b8","doi-asserted-by":"crossref","unstructured":"Nandhini P, Kuppuswami S, Malliga S, Srinath P, Veeramanikandan P. Driver Drowsiness Detection using Deep Learning. In: 2022 6th international conference on computing methodologies and communication. ICCMC, 2022, p. 1031\u20136.","DOI":"10.1109\/ICCMC53470.2022.9754053"},{"key":"10.1016\/j.compeleceng.2026.111340_b9","doi-asserted-by":"crossref","unstructured":"Sinha A, Aneesh RP, Gopal SK. Drowsiness Detection System Using Deep Learning. In: 2021 seventh international conference on bio signals, images, and instrumentation. ICBSII, 2021, p. 1\u20136.","DOI":"10.1109\/ICBSII51839.2021.9445132"},{"key":"10.1016\/j.compeleceng.2026.111340_b10","unstructured":"Mansur V, Shambavi K. Highway Drivers Drowsiness Detection System Model with R-Pi and CNN technique. In: 2021 12th international conference on computing communication and networking technologies. ICCCNT, 2021, p. 1\u20136."},{"key":"10.1016\/j.compeleceng.2026.111340_b11","doi-asserted-by":"crossref","unstructured":"Li Y, Wang L, Mi W, Xu H, Hu J, Li H. Distracted Driving Detection by Combining ViT and CNN. In: 2022 IEEE 25th international conference on computer supported cooperative work in design. CSCWD, 2022, p. 908\u201313.","DOI":"10.1109\/CSCWD54268.2022.9776082"},{"key":"10.1016\/j.compeleceng.2026.111340_b12","unstructured":"Ganguly B, Dey D, Munshi S. An Integrated System for Drivers\u2019 Drowsiness Detection Using Deep Learning Frameworks. In: 2022 IEEE VLSI device circuit and system. VLSI DCS, 2022, p. 55\u20139."},{"key":"10.1016\/j.compeleceng.2026.111340_b13","doi-asserted-by":"crossref","unstructured":"I SS, Ramli R, Azri MA, Aliff M, Mohammad Z. Raspberry Pi Based Driver Drowsiness Detection System Using Convolutional Neural Network (CNN). In: 2022 IEEE 18th international colloquium on signal processing & applications. CSPA, 2022, p. 30\u20134.","DOI":"10.1109\/CSPA55076.2022.9781879"},{"key":"10.1016\/j.compeleceng.2026.111340_b14","doi-asserted-by":"crossref","unstructured":"Majdi MS, Ram S, Gill JT, Rodriguez JJ. Drive-Net: Convolutional Network for Driver Distraction Detection. In: 2018 IEEE southwest symposium on image analysis and interpretation. SSIAI, 2018, p. 1\u20134.","DOI":"10.1109\/SSIAI.2018.8470309"},{"key":"10.1016\/j.compeleceng.2026.111340_b15","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.patrec.2017.12.023","article-title":"Detecting distraction of drivers using Convolutional Neural Network","volume":"139","author":"Masood","year":"2020","journal-title":"Pattern Recognit Lett"},{"key":"10.1016\/j.compeleceng.2026.111340_b16","doi-asserted-by":"crossref","unstructured":"Jamsheed V. A, Janet B, Reddy US. Real Time Detection of driver distraction using CNN. In: 2020 third international conference on smart systems and inventive technology. ICSSIT, 2020, p. 185\u201391.","DOI":"10.1109\/ICSSIT48917.2020.9214233"},{"key":"10.1016\/j.compeleceng.2026.111340_b17","first-page":"4109","article-title":"Performance comparison of deep CNN models for detecting driver\u2019s distraction","volume":"68","author":"Srinivasan","year":"2021","journal-title":"Comput Mater Contin"},{"key":"10.1016\/j.compeleceng.2026.111340_b18","article-title":"Automatic driver distraction detection using deep convolutional neural networks","volume":"14","author":"Hossain","year":"2022","journal-title":"Intell Syst Appl"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111340_b19","article-title":"Distraction detection to predict vehicle crashes: A deep learning approach","volume":"26","author":"Bekka","year":"2022","journal-title":"Comput Sist"},{"issue":"8","key":"10.1016\/j.compeleceng.2026.111340_b20","doi-asserted-by":"crossref","first-page":"3835","DOI":"10.3390\/s23083835","article-title":"EFFNet-CA: an efficient driver distraction detection based on multiscale features extractions and channel attention mechanism","volume":"23","author":"Khan","year":"2023","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111340_b21","doi-asserted-by":"crossref","first-page":"6916","DOI":"10.1038\/s41598-025-91293-5","article-title":"An intelligent network framework for driver distraction monitoring based on RES-SE-CNN","volume":"15","author":"Lei","year":"2025","journal-title":"Sci Rep"},{"issue":"17","key":"10.1016\/j.compeleceng.2026.111340_b22","doi-asserted-by":"crossref","DOI":"10.3390\/su16177642","article-title":"A new approach to detect driver distraction to ensure traffic safety and prevent traffic accidents: Image processing and MCDM","volume":"16","author":"Alemdar","year":"2024","journal-title":"Sustainability"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111340_b23","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s12243-023-00973-8","article-title":"On the performance and scalability of consensus mechanisms in privacy-enabled decentralized renewable energy marketplace","volume":"79","author":"Tkachuk","year":"2024","journal-title":"Ann Telecommun"},{"issue":"8","key":"10.1016\/j.compeleceng.2026.111340_b24","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3390\/bdcc9080196","article-title":"Leadership uniformity in timeout-based quorum Byzantine fault tolerance (QBFT) consensus","volume":"9","author":"Delladetsimas","year":"2025","journal-title":"Big Data Cogn Comput"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111340_b25","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3390\/fi17010031","article-title":"Exploiting blockchain technology for enhancing digital twins\u2019 security and transparency","volume":"17","author":"Ferone","year":"2025","journal-title":"Future Internet"},{"key":"10.1016\/j.compeleceng.2026.111340_b26","series-title":"State farm distracted driver detection","author":"Montoya","year":"2016"},{"key":"10.1016\/j.compeleceng.2026.111340_b27","series-title":"Ultralytics YOLO11","author":"Jocher","year":"2024"},{"key":"10.1016\/j.compeleceng.2026.111340_b28","series-title":"Intro to web3.py: Ethereum for Python developers","author":"McCubbin","year":"2025"},{"key":"10.1016\/j.compeleceng.2026.111340_b29","series-title":"Using convolutional neural networks to perform classification on state farm insurance driver images","author":"Singh","year":"2016"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111340_b30","article-title":"Driver distraction identification with an ensemble of convolutional neural networks","volume":"2019","author":"Eraqi","year":"2019","journal-title":"J Adv Transp"},{"key":"10.1016\/j.compeleceng.2026.111340_b31","series-title":"Real-time distracted driver posture classification","author":"Abouelnaga","year":"2017"},{"issue":"8","key":"10.1016\/j.compeleceng.2026.111340_b32","article-title":"Leadership uniformity in timeout-based quorum Byzantine fault tolerance (QBFT) consensus","volume":"9","author":"Delladetsimas","year":"2025","journal-title":"Big Data Cogn Comput"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111340_b33","doi-asserted-by":"crossref","DOI":"10.3390\/fi17010031","article-title":"Exploiting blockchain technology for enhancing digital twins\u2019 security and transparency","volume":"17","author":"Ferone","year":"2025","journal-title":"Future Internet"},{"key":"10.1016\/j.compeleceng.2026.111340_b34","series-title":"Driver distraction in commercial vehicle operations","author":"Olson","year":"2009"},{"issue":"6","key":"10.1016\/j.compeleceng.2026.111340_b35","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1080\/15389588.2012.683841","article-title":"An assessment of commercial motor vehicle driver distraction using naturalistic driving data","volume":"13","author":"Hickman","year":"2012","journal-title":"Traffic Inj Prev"}],"container-title":["Computers and Electrical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790626004106?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790626004106?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:31:48Z","timestamp":1782840708000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0045790626004106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":35,"alternative-id":["S0045790626004106"],"URL":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111340","relation":{},"ISSN":["0045-7906"],"issn-type":[{"value":"0045-7906","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DRIVERDAPP: Driver\u2019s distraction record using deep learning and blockchain","name":"articletitle","label":"Article Title"},{"value":"Computers and Electrical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111340","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"111340"}}