Connecting firm's web scraped textual content to body of science: Utilizing microsoft academic graph hierarchical topic modeling

Autor: Arash Hajikhani, Lukas Pukelis, Arho Suominen, Sajad Ashouri, Torben Schubert, Ad Notten, Scott W. Cunningham

Publikováno v: MethodsX, Vol 9, Iss , Pp 101650- (2022)

This paper demonstrates a method to transform and link textual information scraped from companies' websites to the scientific body of knowledge. The method illustrates the benefit of Natural Language Processing (NLP) in creating links between establi

Externí odkaz: https://doaj.org/article/55e6bf6c89c9441592a31278b74e9751

The role of product digitization for productivity: Evidence from web-scraping European high-tech company websites

Autor: Torben Schubert, Sajad Ashouri, Matthias Deschryvere, Angela Jäger, Fabiana Visentin, Cunningham, Scott W., Arash Hajikhani, Lukas Pukelis, Arho Suominen

Publikováno v: VTT Technical Research Centre of Finland-PURE

Digitalization is considered an important driver of the unravelling societal and economic transformations. However, holding both promises and challenges, its effects on the performance of individual firms are still underexplored. In this paper, we re

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4799b24cc27201f4182fc6cf305c9912

Indicators on firm level innovation activities from web scraped data

Autor: Cees van Beers, Serdar Türkeli, Sajad Ashouri, Arash Hajikhani, Arho Suominen, Torben Schubert, Scott Cunninghum, Lukas Pukelis

Publikováno v: Data in brief, 42:108246. Elsevier Science
Data in Brief, 42
Ashouri, S, Suominen, A, Hajikhani, A, Pukelis, L, Schubert, T, Türkeli, S, Van Beers, C & Cunningham, S 2022, ' Indicators on firm level innovation activities from web scraped data ', Data in Brief, vol. 42, 108246 . https://doi.org/10.1016/j.dib.2022.108246

This article presents data on companies' innovative behavior measured at the firm-level based on web scraped firm-level data derived from medium-high and high-technology companies in the European Union and the United Kingdom. The data are retrieved f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d17cd97e9bc65cf455e8d2f34420213c
https://doi.org/10.2139/ssrn.3938767

Using a Motion Sensor to Categorize Low Back Pain Patients: A Machine Learning Approach

Autor: Ehsan Rashedi, Sajad Ashouri, Mohamad Parnianpour, Mohsen Abedi, Nasibeh Azadeh-Fard, Masoud Abdollahi

Low back pain (LBP) remains a critical health issue impacting literally millions of people worldwide. Currently, clinical practitioners rely on subjective measures such as the STarT Back Screening Tool to categorize LBP patients, which then informs s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb0273dc7624d4ab1ad4ea51a1751dc8
https://doi.org/10.1101/803155

A novel approach to spinal 3-D kinematic assessment using inertial sensors: Towards effective quantitative evaluation of low back pain in clinical settings

Autor: Kinda Khalaf, Mohsen Abedi, Mohamad Parnianpour, Sajad Ashouri, Masoud Abdollahi, Farideh Dehghan Manshadi

Publikováno v: Computers in biology and medicine. 89

This paper presents a novel approach for evaluating LBP in various settings. The proposed system uses cost-effective inertial sensors, in conjunction with pattern recognition techniques, for identifying sensitive classifiers towards discriminate iden

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b37fe46588f920b7f9b8e6453736cacf
https://pubmed.ncbi.nlm.nih.gov/28800443