Real-Time Algal Monitoring Using Novel Machine Learning Approaches Article Swipe
Seyit Uğuz
,
Yavuz Selim Şahin
,
Pradeep Kumar
,
Xufei Yang
,
Gary L. Anderson
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc9060153
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc9060153
Monitoring algal growth rates and estimating microalgae concentration in photobioreactor systems are critical for optimizing production efficiency. Traditional methods—such as microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic approaches—while accurate, are often costly, time-consuming, labor-intensive, and susceptible to contamination or production interference. To overcome these limitations, this study proposes an automated, real-time, and cost-effective solution by integrating machine learning with image-based analysis. We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of Scenedesmus dimorphus cultures. Ground truth data were obtained via manual cell enumeration under a microscope and dry biomass measurements. Among the models tested, DTS achieved the highest accuracy for cell count prediction (R2 = 0.77), while RF demonstrated superior performance for dry biomass estimation (R2 = 0.66). Compared to conventional methods, the proposed ML-based approach offers a low-cost, non-invasive, and scalable alternative that significantly reduces manual effort and response time. These findings highlight the potential of machine learning–driven imaging systems for continuous, real-time monitoring in industrial-scale microalgae cultivation.
Related Topics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bdcc9060153
- https://www.mdpi.com/2504-2289/9/6/153/pdf?version=1749450522
- OA Status
- gold
- Cited By
- 7
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411157788
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4411157788Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/bdcc9060153Digital Object Identifier
- Title
-
Real-Time Algal Monitoring Using Novel Machine Learning ApproachesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-09Full publication date if available
- Authors
-
Seyit Uğuz, Yavuz Selim Şahin, Pradeep Kumar, Xufei Yang, Gary L. AndersonList of authors in order
- Landing page
-
https://doi.org/10.3390/bdcc9060153Publisher landing page
- PDF URL
-
https://www.mdpi.com/2504-2289/9/6/153/pdf?version=1749450522Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2504-2289/9/6/153/pdf?version=1749450522Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Real-time computingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7Per-year citation counts (last 5 years)
- References (count)
-
46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4411157788 |
|---|---|
| doi | https://doi.org/10.3390/bdcc9060153 |
| ids.doi | https://doi.org/10.3390/bdcc9060153 |
| ids.openalex | https://openalex.org/W4411157788 |
| fwci | 19.7026005 |
| type | article |
| title | Real-Time Algal Monitoring Using Novel Machine Learning Approaches |
| biblio.issue | 6 |
| biblio.volume | 9 |
| biblio.last_page | 153 |
| biblio.first_page | 153 |
| topics[0].id | https://openalex.org/T14249 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9835000038146973 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2311 |
| topics[0].subfield.display_name | Industrial and Manufacturing Engineering |
| topics[0].display_name | Water Quality Monitoring and Analysis |
| topics[1].id | https://openalex.org/T12697 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.945900022983551 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2312 |
| topics[1].subfield.display_name | Water Science and Technology |
| topics[1].display_name | Water Quality Monitoring Technologies |
| is_xpac | False |
| apc_list.value | 1400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1515 |
| apc_paid.value | 1400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1515 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.5708886384963989 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.35001665353775024 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C79403827 |
| concepts[2].level | 1 |
| concepts[2].score | 0.33271703124046326 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[2].display_name | Real-time computing |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.5708886384963989 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.35001665353775024 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/real-time-computing |
| keywords[2].score | 0.33271703124046326 |
| keywords[2].display_name | Real-time computing |
| language | en |
| locations[0].id | doi:10.3390/bdcc9060153 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210238752 |
| locations[0].source.issn | 2504-2289 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2504-2289 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Big Data and Cognitive Computing |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2504-2289/9/6/153/pdf?version=1749450522 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Big Data and Cognitive Computing |
| locations[0].landing_page_url | https://doi.org/10.3390/bdcc9060153 |
| locations[1].id | pmh:oai:doaj.org/article:4eb191c5ef824106b8b701d38ca66dcd |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Big Data and Cognitive Computing, Vol 9, Iss 6, p 153 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/4eb191c5ef824106b8b701d38ca66dcd |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5004043143 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-3994-8099 |
| authorships[0].author.display_name | Seyit Uğuz |
| authorships[0].countries | TR, US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I2799303410 |
| authorships[0].affiliations[0].raw_affiliation_string | Biosystems Engineering, Faculty of Engineering-Architecture, Yozgat Bozok University, Yozgat 66100, Turkey |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I177156846 |
| authorships[0].affiliations[1].raw_affiliation_string | Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA |
| authorships[0].institutions[0].id | https://openalex.org/I2799303410 |
| authorships[0].institutions[0].ror | https://ror.org/04qvdf239 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I2799303410 |
| authorships[0].institutions[0].country_code | TR |
| authorships[0].institutions[0].display_name | Bozok Universitesi |
| authorships[0].institutions[1].id | https://openalex.org/I177156846 |
| authorships[0].institutions[1].ror | https://ror.org/015jmes13 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I177156846 |
| authorships[0].institutions[1].country_code | US |
| authorships[0].institutions[1].display_name | South Dakota State University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Seyit Uguz |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Biosystems Engineering, Faculty of Engineering-Architecture, Yozgat Bozok University, Yozgat 66100, Turkey, Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA |
| authorships[1].author.id | https://openalex.org/A5001126381 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6848-1849 |
| authorships[1].author.display_name | Yavuz Selim Şahin |
| authorships[1].countries | TR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I131835042 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Plant Protection, Faculty of Agriculture, Bursa Uludag University, Gorukle, Bursa 16240, Turkey |
| authorships[1].institutions[0].id | https://openalex.org/I131835042 |
| authorships[1].institutions[0].ror | https://ror.org/03tg3eb07 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I131835042 |
| authorships[1].institutions[0].country_code | TR |
| authorships[1].institutions[0].display_name | Bursa Uludağ Üni̇versi̇tesi̇ |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yavuz Selim Sahin |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Plant Protection, Faculty of Agriculture, Bursa Uludag University, Gorukle, Bursa 16240, Turkey |
| authorships[2].author.id | https://openalex.org/A5100755840 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8640-4350 |
| authorships[2].author.display_name | Pradeep Kumar |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I177156846 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA |
| authorships[2].institutions[0].id | https://openalex.org/I177156846 |
| authorships[2].institutions[0].ror | https://ror.org/015jmes13 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I177156846 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | South Dakota State University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Pradeep Kumar |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA |
| authorships[3].author.id | https://openalex.org/A5033142731 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6735-4597 |
| authorships[3].author.display_name | Xufei Yang |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I177156846 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA |
| authorships[3].institutions[0].id | https://openalex.org/I177156846 |
| authorships[3].institutions[0].ror | https://ror.org/015jmes13 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I177156846 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | South Dakota State University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Xufei Yang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA |
| authorships[4].author.id | https://openalex.org/A5044643998 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5466-3438 |
| authorships[4].author.display_name | Gary L. Anderson |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I177156846 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA |
| authorships[4].institutions[0].id | https://openalex.org/I177156846 |
| authorships[4].institutions[0].ror | https://ror.org/015jmes13 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I177156846 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | South Dakota State University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Gary Anderson |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2504-2289/9/6/153/pdf?version=1749450522 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Real-Time Algal Monitoring Using Novel Machine Learning Approaches |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T14249 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9835000038146973 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2311 |
| primary_topic.subfield.display_name | Industrial and Manufacturing Engineering |
| primary_topic.display_name | Water Quality Monitoring and Analysis |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W4391913857, https://openalex.org/W2358668433, https://openalex.org/W4396701345, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W4396696052 |
| cited_by_count | 7 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 7 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/bdcc9060153 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210238752 |
| best_oa_location.source.issn | 2504-2289 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2504-2289 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Big Data and Cognitive Computing |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2504-2289/9/6/153/pdf?version=1749450522 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Big Data and Cognitive Computing |
| best_oa_location.landing_page_url | https://doi.org/10.3390/bdcc9060153 |
| primary_location.id | doi:10.3390/bdcc9060153 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210238752 |
| primary_location.source.issn | 2504-2289 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2504-2289 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Big Data and Cognitive Computing |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2504-2289/9/6/153/pdf?version=1749450522 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Big Data and Cognitive Computing |
| primary_location.landing_page_url | https://doi.org/10.3390/bdcc9060153 |
| publication_date | 2025-06-09 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2998766999, https://openalex.org/W4224307858, https://openalex.org/W4392241126, https://openalex.org/W2026977142, https://openalex.org/W3089174329, https://openalex.org/W2495723071, https://openalex.org/W4207081061, https://openalex.org/W2165229257, https://openalex.org/W3094730993, https://openalex.org/W6771758557, https://openalex.org/W2050543955, https://openalex.org/W2141959525, https://openalex.org/W3178714987, https://openalex.org/W6766576792, https://openalex.org/W3206550493, https://openalex.org/W4398784579, https://openalex.org/W2125570117, https://openalex.org/W4407831433, https://openalex.org/W4376455353, https://openalex.org/W2139946326, https://openalex.org/W4407040232, https://openalex.org/W6628316452, https://openalex.org/W3095659725, https://openalex.org/W3194005255, https://openalex.org/W4389004577, https://openalex.org/W1965295747, https://openalex.org/W3080257186, https://openalex.org/W3091219271, https://openalex.org/W3133514426, https://openalex.org/W2739900084, https://openalex.org/W2800618803, https://openalex.org/W2970893300, https://openalex.org/W3001657276, https://openalex.org/W2999855274, https://openalex.org/W3200809108, https://openalex.org/W4313420829, https://openalex.org/W3041023030, https://openalex.org/W2565961081, https://openalex.org/W3092112152, https://openalex.org/W4404125492, https://openalex.org/W4386884832, https://openalex.org/W4382402963, https://openalex.org/W4404269883, https://openalex.org/W1237469442, https://openalex.org/W2995956992, https://openalex.org/W2967663220 |
| referenced_works_count | 46 |
| abstract_inverted_index.= | 122, 134 |
| abstract_inverted_index.a | 102, 145 |
| abstract_inverted_index.RF | 125 |
| abstract_inverted_index.To | 41 |
| abstract_inverted_index.We | 61 |
| abstract_inverted_index.an | 48 |
| abstract_inverted_index.as | 19 |
| abstract_inverted_index.by | 54 |
| abstract_inverted_index.in | 8, 173 |
| abstract_inverted_index.of | 65, 88, 164 |
| abstract_inverted_index.or | 38 |
| abstract_inverted_index.to | 36, 137 |
| abstract_inverted_index.(R2 | 121, 133 |
| abstract_inverted_index.DTS | 112 |
| abstract_inverted_index.RGB | 82 |
| abstract_inverted_index.and | 4, 25, 34, 51, 76, 104, 148, 156 |
| abstract_inverted_index.are | 11, 29 |
| abstract_inverted_index.dry | 105, 130 |
| abstract_inverted_index.for | 13, 117, 129, 169 |
| abstract_inverted_index.the | 63, 109, 114, 140, 162 |
| abstract_inverted_index.via | 97 |
| abstract_inverted_index.cell | 99, 118 |
| abstract_inverted_index.data | 94 |
| abstract_inverted_index.flow | 22 |
| abstract_inverted_index.from | 86 |
| abstract_inverted_index.that | 151 |
| abstract_inverted_index.this | 45 |
| abstract_inverted_index.were | 95 |
| abstract_inverted_index.with | 58 |
| abstract_inverted_index.(RF), | 71 |
| abstract_inverted_index.Among | 108 |
| abstract_inverted_index.These | 159 |
| abstract_inverted_index.Trees | 67 |
| abstract_inverted_index.algal | 1 |
| abstract_inverted_index.color | 83 |
| abstract_inverted_index.count | 119 |
| abstract_inverted_index.often | 30 |
| abstract_inverted_index.rates | 3 |
| abstract_inverted_index.study | 46 |
| abstract_inverted_index.these | 43 |
| abstract_inverted_index.time. | 158 |
| abstract_inverted_index.truth | 93 |
| abstract_inverted_index.under | 101 |
| abstract_inverted_index.using | 81 |
| abstract_inverted_index.while | 124 |
| abstract_inverted_index.(DTS), | 68 |
| abstract_inverted_index.(GBM), | 75 |
| abstract_inverted_index.(k-NN) | 79 |
| abstract_inverted_index.0.66). | 135 |
| abstract_inverted_index.0.77), | 123 |
| abstract_inverted_index.Ground | 92 |
| abstract_inverted_index.Random | 69 |
| abstract_inverted_index.effort | 155 |
| abstract_inverted_index.growth | 2 |
| abstract_inverted_index.images | 87 |
| abstract_inverted_index.manual | 98, 154 |
| abstract_inverted_index.models | 110 |
| abstract_inverted_index.offers | 144 |
| abstract_inverted_index.Forests | 70 |
| abstract_inverted_index.biomass | 106, 131 |
| abstract_inverted_index.costly, | 31 |
| abstract_inverted_index.highest | 115 |
| abstract_inverted_index.imaging | 167 |
| abstract_inverted_index.machine | 56, 165 |
| abstract_inverted_index.reduces | 153 |
| abstract_inverted_index.systems | 10, 168 |
| abstract_inverted_index.tested, | 111 |
| abstract_inverted_index.Boosting | 73 |
| abstract_inverted_index.Compared | 136 |
| abstract_inverted_index.Decision | 66 |
| abstract_inverted_index.Gradient | 72 |
| abstract_inverted_index.ML-based | 142 |
| abstract_inverted_index.Machines | 74 |
| abstract_inverted_index.accuracy | 116 |
| abstract_inverted_index.achieved | 113 |
| abstract_inverted_index.approach | 143 |
| abstract_inverted_index.critical | 12 |
| abstract_inverted_index.findings | 160 |
| abstract_inverted_index.learning | 57 |
| abstract_inverted_index.methods, | 139 |
| abstract_inverted_index.obtained | 96 |
| abstract_inverted_index.overcome | 42 |
| abstract_inverted_index.proposed | 141 |
| abstract_inverted_index.proposes | 47 |
| abstract_inverted_index.response | 157 |
| abstract_inverted_index.scalable | 149 |
| abstract_inverted_index.solution | 53 |
| abstract_inverted_index.superior | 127 |
| abstract_inverted_index.K-Nearest | 77 |
| abstract_inverted_index.Neighbors | 78 |
| abstract_inverted_index.accurate, | 28 |
| abstract_inverted_index.analysis. | 60 |
| abstract_inverted_index.cultures. | 91 |
| abstract_inverted_index.dimorphus | 90 |
| abstract_inverted_index.evaluated | 62 |
| abstract_inverted_index.extracted | 85 |
| abstract_inverted_index.highlight | 161 |
| abstract_inverted_index.low-cost, | 146 |
| abstract_inverted_index.potential | 163 |
| abstract_inverted_index.real-time | 171 |
| abstract_inverted_index.Monitoring | 0 |
| abstract_inverted_index.algorithms | 80 |
| abstract_inverted_index.automated, | 49 |
| abstract_inverted_index.cytometry, | 23 |
| abstract_inverted_index.estimating | 5 |
| abstract_inverted_index.estimation | 132 |
| abstract_inverted_index.histograms | 84 |
| abstract_inverted_index.microalgae | 6, 175 |
| abstract_inverted_index.microscope | 103 |
| abstract_inverted_index.monitoring | 172 |
| abstract_inverted_index.optimizing | 14 |
| abstract_inverted_index.prediction | 120 |
| abstract_inverted_index.production | 15, 39 |
| abstract_inverted_index.real-time, | 50 |
| abstract_inverted_index.Scenedesmus | 89 |
| abstract_inverted_index.Traditional | 17 |
| abstract_inverted_index.alternative | 150 |
| abstract_inverted_index.continuous, | 170 |
| abstract_inverted_index.efficiency. | 16 |
| abstract_inverted_index.enumeration | 100 |
| abstract_inverted_index.image-based | 59 |
| abstract_inverted_index.integrating | 55 |
| abstract_inverted_index.macroscopic | 26 |
| abstract_inverted_index.microscopy, | 20 |
| abstract_inverted_index.performance | 64, 128 |
| abstract_inverted_index.susceptible | 35 |
| abstract_inverted_index.conventional | 138 |
| abstract_inverted_index.cultivation. | 176 |
| abstract_inverted_index.demonstrated | 126 |
| abstract_inverted_index.limitations, | 44 |
| abstract_inverted_index.concentration | 7 |
| abstract_inverted_index.contamination | 37 |
| abstract_inverted_index.fluorescence, | 21 |
| abstract_inverted_index.interference. | 40 |
| abstract_inverted_index.measurements. | 107 |
| abstract_inverted_index.non-invasive, | 147 |
| abstract_inverted_index.significantly | 152 |
| abstract_inverted_index.spectroscopy, | 24 |
| abstract_inverted_index.cost-effective | 52 |
| abstract_inverted_index.methods—such | 18 |
| abstract_inverted_index.photobioreactor | 9 |
| abstract_inverted_index.time-consuming, | 32 |
| abstract_inverted_index.industrial-scale | 174 |
| abstract_inverted_index.labor-intensive, | 33 |
| abstract_inverted_index.learning–driven | 166 |
| abstract_inverted_index.approaches—while | 27 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5044643998 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I177156846 |
| citation_normalized_percentile.value | 0.985246 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |