Data Mining and Fusion Framework for In-Home Monitoring Applications Article Swipe
Idongesit Ekerete
,
Matías García-Constantino
,
Chris Nugent
,
Paul McCullagh
,
James McLaughlin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23218661
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23218661
Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23218661
- https://www.mdpi.com/1424-8220/23/21/8661/pdf?version=1698130530
- OA Status
- gold
- Cited By
- 1
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387908603
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387908603Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23218661Digital Object Identifier
- Title
-
Data Mining and Fusion Framework for In-Home Monitoring ApplicationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-24Full publication date if available
- Authors
-
Idongesit Ekerete, Matías García-Constantino, Chris Nugent, Paul McCullagh, James McLaughlinList of authors in order
- Landing page
-
https://doi.org/10.3390/s23218661Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/21/8661/pdf?version=1698130530Direct 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/1424-8220/23/21/8661/pdf?version=1698130530Direct OA link when available
- Concepts
-
Computer science, Data mining, Sensor fusion, Support vector machine, Software, Machine learning, Random forest, Naive Bayes classifier, Artificial intelligence, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- References (count)
-
65Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4387908603 |
|---|---|
| doi | https://doi.org/10.3390/s23218661 |
| ids.doi | https://doi.org/10.3390/s23218661 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37960361 |
| ids.openalex | https://openalex.org/W4387908603 |
| fwci | 0.18196834 |
| type | article |
| title | Data Mining and Fusion Framework for In-Home Monitoring Applications |
| biblio.issue | 21 |
| biblio.volume | 23 |
| biblio.last_page | 8661 |
| biblio.first_page | 8661 |
| topics[0].id | https://openalex.org/T10036 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9787999987602234 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Advanced Neural Network Applications |
| topics[1].id | https://openalex.org/T12120 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.968999981880188 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2305 |
| topics[1].subfield.display_name | Environmental Engineering |
| topics[1].display_name | Air Quality Monitoring and Forecasting |
| topics[2].id | https://openalex.org/T12697 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9686999917030334 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2312 |
| topics[2].subfield.display_name | Water Science and Technology |
| topics[2].display_name | Water Quality Monitoring Technologies |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7112983465194702 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C124101348 |
| concepts[1].level | 1 |
| concepts[1].score | 0.618187665939331 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[1].display_name | Data mining |
| concepts[2].id | https://openalex.org/C33954974 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5570011138916016 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q486494 |
| concepts[2].display_name | Sensor fusion |
| concepts[3].id | https://openalex.org/C12267149 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5454460382461548 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[3].display_name | Support vector machine |
| concepts[4].id | https://openalex.org/C2777904410 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5192785263061523 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7397 |
| concepts[4].display_name | Software |
| concepts[5].id | https://openalex.org/C119857082 |
| concepts[5].level | 1 |
| concepts[5].score | 0.508922815322876 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[5].display_name | Machine learning |
| concepts[6].id | https://openalex.org/C169258074 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4842969477176666 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[6].display_name | Random forest |
| concepts[7].id | https://openalex.org/C52001869 |
| concepts[7].level | 3 |
| concepts[7].score | 0.4821150302886963 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q812530 |
| concepts[7].display_name | Naive Bayes classifier |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4697844088077545 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C199360897 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[9].display_name | Programming language |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7112983465194702 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/data-mining |
| keywords[1].score | 0.618187665939331 |
| keywords[1].display_name | Data mining |
| keywords[2].id | https://openalex.org/keywords/sensor-fusion |
| keywords[2].score | 0.5570011138916016 |
| keywords[2].display_name | Sensor fusion |
| keywords[3].id | https://openalex.org/keywords/support-vector-machine |
| keywords[3].score | 0.5454460382461548 |
| keywords[3].display_name | Support vector machine |
| keywords[4].id | https://openalex.org/keywords/software |
| keywords[4].score | 0.5192785263061523 |
| keywords[4].display_name | Software |
| keywords[5].id | https://openalex.org/keywords/machine-learning |
| keywords[5].score | 0.508922815322876 |
| keywords[5].display_name | Machine learning |
| keywords[6].id | https://openalex.org/keywords/random-forest |
| keywords[6].score | 0.4842969477176666 |
| keywords[6].display_name | Random forest |
| keywords[7].id | https://openalex.org/keywords/naive-bayes-classifier |
| keywords[7].score | 0.4821150302886963 |
| keywords[7].display_name | Naive Bayes classifier |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.4697844088077545 |
| keywords[8].display_name | Artificial intelligence |
| language | en |
| locations[0].id | doi:10.3390/s23218661 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101949793 |
| locations[0].source.issn | 1424-8220 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8220 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Sensors |
| 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/1424-8220/23/21/8661/pdf?version=1698130530 |
| 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 | Sensors |
| locations[0].landing_page_url | https://doi.org/10.3390/s23218661 |
| locations[1].id | pmid:37960361 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| 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 | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Sensors (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37960361 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:10650580 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | cc-by |
| locations[2].pdf_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10650580/pdf/sensors-23-08661.pdf |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors (Basel) |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10650580 |
| locations[3].id | pmh:oai:doaj.org/article:4767978924544c38891183d4548b23c8 |
| locations[3].is_oa | False |
| locations[3].source.id | https://openalex.org/S4306401280 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[3].source.host_organization | |
| locations[3].source.host_organization_name | |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors, Vol 23, Iss 21, p 8661 (2023) |
| locations[3].landing_page_url | https://doaj.org/article/4767978924544c38891183d4548b23c8 |
| locations[4].id | pmh:oai:pure.atira.dk:publications/4ff9ec69-b301-466c-b2f6-88322953a9ea |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S4306400216 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | Research Portal (King's College London) |
| locations[4].source.host_organization | https://openalex.org/I183935753 |
| locations[4].source.host_organization_name | King's College London |
| locations[4].source.host_organization_lineage | https://openalex.org/I183935753 |
| locations[4].license | cc-by |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | article |
| locations[4].license_id | https://openalex.org/licenses/cc-by |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Ekerete, I, Garcia-Constantino, M, McCullagh, P, Nugent, CD & McLaughlin, J 2023, 'Data Mining and Fusion Framework for In-Home Monitoring Applications', Sensors, vol. 23, no. 21, 8661, pp. 1-16. https://doi.org/10.3390/s23218661 |
| locations[4].landing_page_url | https://pure.ulster.ac.uk/en/publications/4ff9ec69-b301-466c-b2f6-88322953a9ea |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5057430295 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4072-7178 |
| authorships[0].author.display_name | Idongesit Ekerete |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I138801177 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Computing, Ulster University, Belfast BT15 1ED, UK |
| authorships[0].institutions[0].id | https://openalex.org/I138801177 |
| authorships[0].institutions[0].ror | https://ror.org/01yp9g959 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I138801177 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | University of Ulster |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Idongesit Ekerete |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | School of Computing, Ulster University, Belfast BT15 1ED, UK |
| authorships[1].author.id | https://openalex.org/A5002575060 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3420-0532 |
| authorships[1].author.display_name | Matías García-Constantino |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I138801177 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Computing, Ulster University, Belfast BT15 1ED, UK |
| authorships[1].institutions[0].id | https://openalex.org/I138801177 |
| authorships[1].institutions[0].ror | https://ror.org/01yp9g959 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I138801177 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | University of Ulster |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Matias Garcia-Constantino |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Computing, Ulster University, Belfast BT15 1ED, UK |
| authorships[2].author.id | https://openalex.org/A5021247547 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0882-7902 |
| authorships[2].author.display_name | Chris Nugent |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I138801177 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Computing, Ulster University, Belfast BT15 1ED, UK |
| authorships[2].institutions[0].id | https://openalex.org/I138801177 |
| authorships[2].institutions[0].ror | https://ror.org/01yp9g959 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I138801177 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | University of Ulster |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Christopher Nugent |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Computing, Ulster University, Belfast BT15 1ED, UK |
| authorships[3].author.id | https://openalex.org/A5006478284 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-9060-6262 |
| authorships[3].author.display_name | Paul McCullagh |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I138801177 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Computing, Ulster University, Belfast BT15 1ED, UK |
| authorships[3].institutions[0].id | https://openalex.org/I138801177 |
| authorships[3].institutions[0].ror | https://ror.org/01yp9g959 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I138801177 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | University of Ulster |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Paul McCullagh |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Computing, Ulster University, Belfast BT15 1ED, UK |
| authorships[4].author.id | https://openalex.org/A5045151799 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6026-8971 |
| authorships[4].author.display_name | James McLaughlin |
| authorships[4].countries | GB |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I138801177 |
| authorships[4].affiliations[0].raw_affiliation_string | School of Engineering, Ulster University, Belfast BT15 1ED, UK |
| authorships[4].institutions[0].id | https://openalex.org/I138801177 |
| authorships[4].institutions[0].ror | https://ror.org/01yp9g959 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I138801177 |
| authorships[4].institutions[0].country_code | GB |
| authorships[4].institutions[0].display_name | University of Ulster |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | James McLaughlin |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | School of Engineering, Ulster University, Belfast BT15 1ED, UK |
| 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/1424-8220/23/21/8661/pdf?version=1698130530 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Data Mining and Fusion Framework for In-Home Monitoring Applications |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10036 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9787999987602234 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Advanced Neural Network Applications |
| related_works | https://openalex.org/W4200112873, https://openalex.org/W2955796858, https://openalex.org/W4224941037, https://openalex.org/W4367336074, https://openalex.org/W4379620016, https://openalex.org/W3154045278, https://openalex.org/W3210764983, https://openalex.org/W4367335949, https://openalex.org/W3089416646, https://openalex.org/W2004826645 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3390/s23218661 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101949793 |
| best_oa_location.source.issn | 1424-8220 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8220 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Sensors |
| 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/1424-8220/23/21/8661/pdf?version=1698130530 |
| 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 | Sensors |
| best_oa_location.landing_page_url | https://doi.org/10.3390/s23218661 |
| primary_location.id | doi:10.3390/s23218661 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
| primary_location.source.issn | 1424-8220 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8220 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Sensors |
| 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/1424-8220/23/21/8661/pdf?version=1698130530 |
| 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 | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s23218661 |
| publication_date | 2023-10-24 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2766117169, https://openalex.org/W6729186010, https://openalex.org/W1588876236, https://openalex.org/W2159793651, https://openalex.org/W2750656378, https://openalex.org/W4385677058, https://openalex.org/W4254655263, https://openalex.org/W6712221595, https://openalex.org/W6711937490, https://openalex.org/W2132178275, https://openalex.org/W2544954526, https://openalex.org/W1987552279, https://openalex.org/W1680797894, https://openalex.org/W2499581503, https://openalex.org/W2038274858, https://openalex.org/W2140460921, https://openalex.org/W3009579421, https://openalex.org/W3203920588, https://openalex.org/W3217250071, https://openalex.org/W2978477452, https://openalex.org/W2184190733, https://openalex.org/W4251324575, https://openalex.org/W2163496225, https://openalex.org/W2772206077, https://openalex.org/W3016336630, https://openalex.org/W2296938967, https://openalex.org/W2790700562, https://openalex.org/W3039992996, https://openalex.org/W2885482618, https://openalex.org/W2787218119, https://openalex.org/W1525476464, https://openalex.org/W2535475821, https://openalex.org/W1997386971, https://openalex.org/W2766718576, https://openalex.org/W2914525916, https://openalex.org/W3041549894, https://openalex.org/W2897083303, https://openalex.org/W3013292489, https://openalex.org/W2580949616, https://openalex.org/W6920958753, https://openalex.org/W1512748932, https://openalex.org/W2291624323, https://openalex.org/W3194850724, https://openalex.org/W4238765590, https://openalex.org/W4385693906, https://openalex.org/W4306722552, https://openalex.org/W3003292653, https://openalex.org/W2555613390, https://openalex.org/W3046398819, https://openalex.org/W2982580298, https://openalex.org/W2947263797, https://openalex.org/W2953971078, https://openalex.org/W2853971004, https://openalex.org/W4236707026, https://openalex.org/W1966119405, https://openalex.org/W4239762439, https://openalex.org/W3199790572, https://openalex.org/W3022538979, https://openalex.org/W2900872689, https://openalex.org/W2398242360, https://openalex.org/W3087968351, https://openalex.org/W3214623224, https://openalex.org/W2395501333, https://openalex.org/W3102035217, https://openalex.org/W4243187317 |
| referenced_works_count | 65 |
| abstract_inverted_index.A | 73 |
| abstract_inverted_index.a | 44, 65 |
| abstract_inverted_index.It | 48 |
| abstract_inverted_index.an | 114 |
| abstract_inverted_index.as | 56, 139 |
| abstract_inverted_index.in | 11 |
| abstract_inverted_index.of | 15, 19, 75, 99, 118, 131, 159, 168, 173, 187 |
| abstract_inverted_index.on | 97, 122, 166 |
| abstract_inverted_index.to | 23, 42 |
| abstract_inverted_index.574 | 76 |
| abstract_inverted_index.CN2 | 155 |
| abstract_inverted_index.One | 14 |
| abstract_inverted_index.SDT | 20, 46 |
| abstract_inverted_index.The | 33, 185 |
| abstract_inverted_index.and | 5, 27, 39, 61, 63, 80, 86, 101, 120, 124, 134, 154, 181, 193, 199, 201 |
| abstract_inverted_index.for | 70, 175, 179, 183, 196 |
| abstract_inverted_index.how | 22 |
| abstract_inverted_index.the | 16, 94, 109, 129, 160, 188 |
| abstract_inverted_index.1722 | 81 |
| abstract_inverted_index.Data | 1, 162 |
| abstract_inverted_index.been | 8 |
| abstract_inverted_index.both | 37 |
| abstract_inverted_index.cost | 192 |
| abstract_inverted_index.data | 50, 66, 103, 132, 197 |
| abstract_inverted_index.deal | 24 |
| abstract_inverted_index.from | 84 |
| abstract_inverted_index.have | 7 |
| abstract_inverted_index.help | 130 |
| abstract_inverted_index.main | 17 |
| abstract_inverted_index.such | 55, 138 |
| abstract_inverted_index.that | 108 |
| abstract_inverted_index.used | 10 |
| abstract_inverted_index.were | 91 |
| abstract_inverted_index.with | 25, 30, 128 |
| abstract_inverted_index.work | 35 |
| abstract_inverted_index.(SDT) | 3 |
| abstract_inverted_index.84.7% | 119 |
| abstract_inverted_index.94.4% | 174 |
| abstract_inverted_index.95.7% | 121, 178 |
| abstract_inverted_index.96.4% | 182 |
| abstract_inverted_index.Radar | 87 |
| abstract_inverted_index.Tree, | 143 |
| abstract_inverted_index.Weka, | 60 |
| abstract_inverted_index.based | 165 |
| abstract_inverted_index.fused | 92 |
| abstract_inverted_index.novel | 45 |
| abstract_inverted_index.total | 74 |
| abstract_inverted_index.using | 93 |
| abstract_inverted_index.Bayes, | 141 |
| abstract_inverted_index.Fusion | 2, 163 |
| abstract_inverted_index.Naïve | 140 |
| abstract_inverted_index.Neural | 144 |
| abstract_inverted_index.Random | 146 |
| abstract_inverted_index.Sensor | 0, 161 |
| abstract_inverted_index.Vector | 152 |
| abstract_inverted_index.fusion | 52, 67, 111 |
| abstract_inverted_index.images | 79 |
| abstract_inverted_index.mining | 133 |
| abstract_inverted_index.models | 6, 137 |
| abstract_inverted_index.values | 172 |
| abstract_inverted_index.widely | 9 |
| abstract_inverted_index.Forest, | 147 |
| abstract_inverted_index.Further | 157 |
| abstract_inverted_index.Orange, | 62 |
| abstract_inverted_index.Recall. | 184 |
| abstract_inverted_index.Studio, | 58 |
| abstract_inverted_index.Support | 151 |
| abstract_inverted_index.average | 115, 171 |
| abstract_inverted_index.complex | 28 |
| abstract_inverted_index.diverse | 12 |
| abstract_inverted_index.feature | 202 |
| abstract_inverted_index.gleaned | 83 |
| abstract_inverted_index.in-home | 71 |
| abstract_inverted_index.machine | 135 |
| abstract_inverted_index.novelty | 186 |
| abstract_inverted_index.present | 34 |
| abstract_inverted_index.propose | 43 |
| abstract_inverted_index.results | 106 |
| abstract_inverted_index.sensing | 88 |
| abstract_inverted_index.thermal | 85 |
| abstract_inverted_index.(binary) | 78 |
| abstract_inverted_index.Accuracy | 117 |
| abstract_inverted_index.Decision | 142 |
| abstract_inverted_index.Descent, | 150 |
| abstract_inverted_index.Gradient | 149 |
| abstract_inverted_index.Machine, | 153 |
| abstract_inverted_index.Network, | 145 |
| abstract_inverted_index.achieved | 113 |
| abstract_inverted_index.compares | 49 |
| abstract_inverted_index.datasets | 29, 41, 82 |
| abstract_inverted_index.features | 169 |
| abstract_inverted_index.formats. | 32 |
| abstract_inverted_index.includes | 21, 191 |
| abstract_inverted_index.learning | 136 |
| abstract_inverted_index.packages | 54, 96 |
| abstract_inverted_index.proposed | 110, 189 |
| abstract_inverted_index.proposes | 64 |
| abstract_inverted_index.software | 53, 95 |
| abstract_inverted_index.suitable | 69 |
| abstract_inverted_index.utilised | 36 |
| abstract_inverted_index.Accuracy, | 177 |
| abstract_inverted_index.Anaconda, | 59 |
| abstract_inverted_index.datasets, | 126 |
| abstract_inverted_index.different | 31 |
| abstract_inverted_index.framework | 68, 112, 164, 190 |
| abstract_inverted_index.indicated | 107, 170 |
| abstract_inverted_index.instances | 98 |
| abstract_inverted_index.labelling | 198 |
| abstract_inverted_index.Induction. | 156 |
| abstract_inverted_index.Precision, | 180 |
| abstract_inverted_index.RapidMiner | 57 |
| abstract_inverted_index.Stochastic | 148 |
| abstract_inverted_index.advantages | 195 |
| abstract_inverted_index.algorithms | 4 |
| abstract_inverted_index.challenges | 18 |
| abstract_inverted_index.evaluation | 158 |
| abstract_inverted_index.framework. | 47 |
| abstract_inverted_index.homogenous | 38 |
| abstract_inverted_index.solutions, | 89 |
| abstract_inverted_index.timesaving | 194 |
| abstract_inverted_index.extraction. | 203 |
| abstract_inverted_index.homogeneous | 100, 123 |
| abstract_inverted_index.Experimental | 105 |
| abstract_inverted_index.aggregation. | 104 |
| abstract_inverted_index.mining-based | 51 |
| abstract_inverted_index.preparation, | 200 |
| abstract_inverted_index.applications. | 13, 72 |
| abstract_inverted_index.heterogeneous | 26, 40, 102, 125 |
| abstract_inverted_index.respectively, | 90, 127 |
| abstract_inverted_index.Classification | 116, 176 |
| abstract_inverted_index.cross-validation | 167 |
| abstract_inverted_index.privacy-friendly | 77 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5057430295 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I138801177 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.46556674 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |