Publications | Lund University Publications

251 – 260 of 393

show: 10
|
sort: year (new to old)

News feed Embed this list Save this search Mark all Export

Cancel

Embed this list

copy and paste the html snippet below into your own page:

<iframe src="https://lup.lub.lu.se/search/publication?embed=1&q=keywords+exact+%22Machine+learning%22&amp;hide_pagination=1&amp;hide_info=1&amp;hide_options=1"
  width="100%"
  height="400"
  allowtransparency="true"
  frameborder="0">
</iframe>

« previous
1
…
25
26
27
28
…
40
next »

2022
Mark Predicting the Solubility of Inorganic Ion Pairs in Water
Rahman, Tasnim ; Petrus, Enric ; Segado, Mireia ; Martin, Nicolas P. ; Palys, Lauren N. ; Rambaran, Mark A. ^LU ; Ohlin, C. Andre ; Bo, Carles and Nyman, May (2022) In Angewandte Chemie - International Edition 61(19).
- Contribution to journal › Article
Mark Comparing algorithms for assessing upper limb use with inertial measurement units
Subash, Tanya ; David, Ann ; ReetaJanetSurekha, Stephen Sukumaran ; Gayathri, Sankaralingam ; Samuelkamaleshkumar, Selvaraj ; Magimairaj, Henry Prakash ; Malesevic, Nebojsa ^LU ; Antfolk, Christian ^LU ; SKM, Varadhan and Melendez-Calderon, Alejandro , et al. (2022) In Frontiers in Physiology 13.
- Contribution to journal › Article
Mark A machine learning based approach for predicting usage efficiency of shared e-scooters using vehicle availability data
Zhao, Pengxiang ^LU ; Li, Aoyong and Mansourian, A ^LU (2022) 25th AGILE Conference on Geographic Information Science In AGILE: GIScience Series, 3, 20, 2022 3.
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
Mark Regeringarna antog förhandlingsmandat för AI-förordningen
Selberg, Niklas ^LU (2022) In EU & arbetsrätt p.5-5
- Contribution to specialist publication or newspaper › Specialist publication article
Mark A review of explainable AI in the satellite data, deep machine learning, and human poverty domain
Hall, Ola ^LU ; Ohlsson, Mattias ^LU and Rögnvaldsson, Thorsteinn (2022) In Patterns 3(10).
- Contribution to journal › Scientific review
Mark Roadmap on signal processing for next generation measurement systems
Iakovidis, Dimitris K. ; Ooi, Melanie ; Kuang, Ye Chow ; Demidenko, Serge ; Shestakov, Alexandr ; Sinitsin, Vladimir ; Henry, Manus ; Sciacchitano, Andrea ; Discetti, Stefano and Donati, Silvano , et al. (2022) In Measurement Science and Technology 33(1).
- Contribution to journal › Scientific review
Mark Critical appraisal of technologies to assess electrical activity during atrial fibrillation : a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology
De Groot, Natasja M.S. ; Shah, Dipen ; Boyle, Patrick M. ; Anter, Elad ; Clifford, Gari D. ; Deisenhofer, Isabel ; Deneke, Thomas ; Van Dessel, Pascal ; Doessel, Olaf and Dilaveris, Polychronis , et al. (2022) In Europace 24(2). p.313-330
- Contribution to journal › Scientific review
Mark Converting the genomic knowledge base to build protein specific machine learning prediction models; a classification study on thermophilic serine protease
Sunny, Jithin S. ; Kumar, Atul ^LU ; Nisha, Khairun and Saleena, Lilly M. (2022) In Biologia 77(12). p.3615-3622
- Contribution to journal › Article
Mark The effect of intra-workplace pay inequality on employee trust in managers: Assessing a multilevel moderated mediation effect model
Schulz, Felix ^LU ; Valizade, Danat and Charlwood, Andy (2022) In Human Relations 75(4). p.705-733
- Contribution to journal › Article
2021
Mark Nutritional markers of undiagnosed type 2 diabetes in adults : Findings of a machine learning analysis with external validation and benchmarking
De Silva, Kushan ; Lim, Siew ; Mousa, Aya ; Teede, Helena ; Forbes, Andrew ; Demmer, Ryan T ; Jönsson, Daniel ^LU and Enticott, Joanne (2021) In PLoS ONE 16(5). p.1-21
- Contribution to journal › Article

« previous
1
…
25
26
27
28
…
40
next »

your query:: Remove keywords: Machine learning