Institution · Arcada University of Applied Sciences
Most cited papers from Arcada University of Applied Sciences
What are the most cited papers from Arcada University of Applied Sciences?
The most cited paper from Arcada University of Applied Sciences is "Recycling potential of post-consumer plastic packaging waste in Finland" (Helena Dahlbo, Valeria Poliakova, Ville Mylläri et al., 2017), with 362 citations. Next come "Predicting distress in European banks" (279) and "High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications" (275).
Showing 50 of 60 papers. Every paper with more than 38 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Recycling potential of post-consumer plastic packaging waste in Finland Helena Dahlbo, Valeria Poliakova, Ville Mylläri et al. · Waste Management | 31 Oct 2017 | 362 |
| 2 | Predicting distress in European banks Frank Betz, Silviu Opricǎ, Tuomas A. Peltonen et al. · Journal of Banking & Finance | 7 Dec 2013 | 279 |
| 3 | High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications Anton Akusok, Kaj-Mikael Björk, Yoan Miché et al. · IEEE Access | 2015 | 275 |
| 4 | To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems Julia Amann, Dennis Vetter, Stig Nikolaj Fasmer Blomberg et al. · PLOS Digital Health | 17 Feb 2022 | 244 |
| 5 | Quality of life in old age described as a sense of well‐being, meaning and value Anneli Sarvimäki, Bettina Stenbock‐Hult · Journal of Advanced Nursing | Oct 2000 | 226 |
| 6 | Applying configurational analysis to IS behavioural research: a methodological alternative for modelling combinatorial complexities Liu Yong, József Mezei, Vassilis Kostakos et al. · Information Systems Journal | Dec 2015 | 222 |
| 7 | The compendium of self-enactable techniques to change and self-manage motivation and behaviour v.1.0 Keegan Phillip Knittle, Matti T. J. Heino, Marta Moreira Marques et al. · Nature Human Behaviour | 13 Jan 2020 | 203 |
| 8 | Rice Husk Ash in Concrete Solomon Asrat Endale, Woubishet Zewdu Taffese, Duy‐Hai Vo et al. · Sustainability | 22 Dec 2022 | 160 |
| 9 | The Natural History of Idiopathic Frozen Shoulder: A 2- to 27-year Followup Study Heidi Vastamäki, Jyrki A. Kettunen, Martti Vastamäki · Clinical Orthopaedics and Related Research | 17 Nov 2011 | 160 |
| 10 | Z-Inspection ® : A Process to Assess Trustworthy AI Roberto V. Zicari, John Brodersen, James Brusseau et al. · IEEE Transactions on Technology and Society | 17 Mar 2021 | 131 |
| 11 | Care as a matter of courage: vulnerability, suffering and ethical formation in nursing care Charlotte Brun Thorup, Ewa Rundqvist, Christel Roberts et al. · Scandinavian Journal of Caring Sciences | 10 Nov 2011 | 122 |
| 12 | Extreme learning machine for missing data using multiple imputations Dušan Sovilj, Emil Eirola, Yoan Miché et al. · Neurocomputing | 10 Aug 2015 | 115 |
| 13 | All-cause and disease-specific mortality among male, former elite athletes: an average 50-year follow-up Jyrki A. Kettunen, Urho M. Kujala, Jaakko A. Kaprio et al. · British Journal of Sports Medicine | 2 Sep 2014 | 115 |
| 14 | Variation and adaptation: learning from success in patient safety-oriented simulation training Peter Dieckmann, Mary Patterson, Saadi Lahlou et al. · Advances in Simulation | 31 Oct 2017 | 101 |
| 15 | The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation Thomas Hellstén, Jonny Karlsson, Muhammed Shamsuzzaman et al. · Rehabilitation Process and Outcome | 2021 | 96 |
| 16 | Adaptive and online network intrusion detection system using clustering and Extreme Learning Machines S. Antony Roshan, Yoan Miché, Anton Akusok et al. · Journal of the Franklin Institute | 13 Jul 2017 | 95 |
| 17 | Untreated Scheuermann’s disease: a 37-year follow-up study Leena Ristolainen, Jyrki A. Kettunen, Markku Heliövaara et al. · European Spine Journal | 21 Nov 2011 | 95 |
| 18 | A machine learning method for predicting the chloride migration coefficient of concrete Woubishet Zewdu Taffese, Leonardo Espinosa-Leal · Construction and Building Materials | 8 Aug 2022 | 93 |
| 19 | Improving Supervised Learning Classification Methods Using Multigranular Linguistic Modeling and Fuzzy Entropy Juan Antonio Morente-Molinera, József Mezei, Christer P. O. Carlsson et al. · IEEE Transactions on Fuzzy Systems | 27 Jul 2016 | 88 |
| 20 | Nordic walking decreased circulating chemerin and leptin concentrations in middle-aged men with impaired glucose regulation Mika Venojärvi, Niko Wasenius, Sirpa Manderoos et al. · Annals of Medicine | 30 Oct 2012 | 88 |
| 21 | Prediction of chloride resistance level of concrete using machine learning for durability and service life assessment of building structures Woubishet Zewdu Taffese, Leonardo Espinosa-Leal · Journal of Building Engineering | 29 Aug 2022 | 87 |
| 22 | Toward robust early-warning models: a horse race, ensembles and model uncertainty Markus Holopainen, Peter Sarlin · Quantitative Finance | 20 Sep 2017 | 87 |
| 23 | Two sides of the mirror: parents' and service providers' view on the family‐centredness of care for children with cerebral palsy Ira Jeglinsky, I. Autti‐Rämö, E. Brogren Carlberg · Child Care Health and Development | 9 Sep 2011 | 75 |
| 24 | Sport injuries as the main cause of sport career termination among Finnish top‐level athletes Leena Ristolainen, Jyrki A. Kettunen, Urho M. Kujala et al. · European Journal of Sport Science | 28 Oct 2011 | 75 |
| 25 | Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier Roberto V. Zicari et al. · Frontiers in Human Dynamics | 13 Jul 2021 | 72 |
| 26 | Minimal Learning Machine: A novel supervised distance-based approach for regression and classification Amauri Holanda de Souza Junior, Francesco Corona, Guilherme A. Barreto et al. · Neurocomputing | 6 Apr 2015 | 69 |
| 27 | Bank distress in the news: Describing events through deep learning Samuel Rönnqvist, Peter Sarlin · Neurocomputing | 17 Jun 2017 | 67 |
| 28 | Investigating the impact of Internet of Things services from a smartphone app on grocery shopping Asle Fagerstrøm, Niklas Eriksson, Valdimar Sigurðsson · Journal of Retailing and Consumer Services | 2 Sep 2019 | 66 |
| 29 | The meaning of vulnerability to older persons Anneli Sarvimäki, Bettina Stenbock‐Hult · Nursing Ethics | 30 Dec 2014 | 64 |
| 30 | Evidence of clinical competence Gun‐Britt Lejonqvist, Katie Eriksson, Riitta Meretoja · Scandinavian Journal of Caring Sciences | 8 Nov 2011 | 63 |
| 31 | European Union directives and clinical practice in nursing education in the Nordic countries Jette Henriksen, Anna Löfmark, Eivor Wallinvirta et al. · Nordic journal of nursing research | 17 Jun 2019 | 62 |
| 32 | Estimation of the baking quality of wheat flours based on rheological parameters of the mixolab curve Iuliana Banu, Georgeta Stoenescu, Violeta Ionescu et al. · Czech Journal of Food Sciences | 28 Feb 2011 | 62 |
| 33 | Leading indicators of systemic banking crises: Finland in a panel of EU countries Patrizio Lainà, Juho Nyholm, Peter Sarlin · Review of Financial Economics | 2015 | 61 |
| 34 | Movement as a basic concept in physiotherapy – A human science approach Camilla Wikström‐Grotell, Katie Eriksson · Physiotherapy Theory and Practice | 5 Jul 2012 | 61 |
| 35 | The meaning of vulnerability to nurses caring for older people Bettina Stenbock‐Hult, Anneli Sarvimäki · Nursing Ethics | 2011 | 60 |
| 36 | Network linkages to predict bank distress Andreea Constantin, Tuomas A. Peltonen, Peter Sarlin · Journal of Financial Stability | 23 Nov 2016 | 59 |
| 37 | Evaluating clinical competence during nursing education: A comprehensive integrative literature review Gun‐Britt Lejonqvist, Katie Eriksson, Riitta Meretoja · International Journal of Nursing Practice | 15 Sep 2015 | 58 |
| 38 | On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls Roberto V. Zicari et al. · Frontiers in Human Dynamics | 8 Jul 2021 | 57 |
| 39 | A qualitative study of consumer resistance to mobile payments for in-store purchases Niklas Eriksson, Ates Gökhan, Minna Stenius · Procedia Computer Science | 2021 | 57 |
| 40 | Extreme learning machine towards dynamic model hypothesis in fish ethology research Rui Nian, Bo He, Bing Zheng et al. · Neurocomputing | 6 Nov 2013 | 55 |
| 41 | Edge MLOps: An Automation Framework for AIoT Applications Emmanuel Raj, David Buffoni, Magnus Westerlund et al. | Oct 2021 | 51 |
| 42 | Performance of biocomposites from surface modified regenerated cellulose fibers and lactic acid thermoset bioresin Sunil Kumar Ramamoorthy, Fatimat Oluwatoyin Bakare, R. Herrmann et al. · Cellulose | 5 May 2015 | 51 |
| 43 | Online grocery shoppers due to the Covid-19 pandemic - An analysis of demographic and household characteristics Niklas Eriksson, Minna Stenius · Procedia Computer Science | 2022 | 50 |
| 44 | Teaching anaesthesia induction to medical students: comparison between full-scale simulation and supervised teaching in the operating theatre Juhana Hallikainen, Olli Väisänen, Tarja Randell et al. · European Journal of Anaesthesiology | Feb 2009 | 48 |
| 45 | Embedded Online Fish Detection and Tracking System via YOLOv3 and Parallel Correlation Filter Shasha Liu, Xiaoyu Li, Mingshan Gao et al. | Oct 2018 | 47 |
| 46 | New Classification Models for Detecting Hate and Violence Web Content Shuhua Liu, Thomas Forss | 2015 | 46 |
| 47 | Bank networks from text: interrelations, centrality and determinants Samuel Rönnqvist, Peter Sarlin · Quantitative Finance | 16 Sep 2015 | 46 |
| 48 | A machine-learning-enhanced hierarchical multiscale method for bridging from molecular dynamics to continua Shaoping Xiao, Renjie Hu, Zhen Li et al. · Neural Computing and Applications | 18 Sep 2019 | 45 |
| 49 | Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study via Machine Learning Meeshanthini V. Dogan, Steven R. H. Beach, Ronald L. Simons et al. · Genes | 18 Dec 2018 | 43 |
| 50 | Combining N-gram based Similarity Analysis with Sentiment Analysis in Web Content Classification Shuhua Liu, Thomas Forss | 2014 | 43 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for an institution when any of its authors gave it as an affiliation; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.