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Kirjeid leitud 42
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ekspordi kõik päringu tulemused (42)
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26

artikkel kogumikus

Machine learning and deep learning techniques for residential load forecasting : a comparative analysisShabbir, Noman; Kütt, Lauri; Raja, Hadi Ashraf; Ahmadiahangar, Roya; Rosin, Argo; Husev, Oleksandr2021 IEEE 62nd International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON): conference proceedings2021 / p. 1-5 https://doi.org/10.1109/RTUCON53541.2021.9711741

artikkel kogumikus
Seotud publikatsioonid 1
- Residential energy management system to support increased renewable penetration = Taastuvenergiaallikate kasutustihedust toetav energiahaldussüsteem
27

artikkel kogumikus EST / artikkel kogumikus ENG

Machine learning techniques for wireless-powered ambient backscatter communications : enabling intelligent iot networks in 6g eraJameel, Furqan; Sharma, Navuday; Khan, Muhammad Awais; Khan, Imran; Alam, Muhammad Mahtab; Alam, Muhammad Mahtab; Mavromoustakis, Constandinos X.Internet of Things / 2020 https://doi.org/10.1007/978-3-030-44907-0_8 Article collection metrics at Scopus Article at Scopus

artikkel kogumikus EST / artikkel kogumikus ENG
28

artikkel ajakirjas

Multi-algorithm-based urban tree information extraction and Its applications in urban planning : research noteYao, Chaowen; Fabritius, Eeva Henna Helena; Fricker, Pia; Dembski, FabianLandscape and urban planning2025 / art. 105226 https://doi.org/10.1016/j.landurbplan.2024.105226

artikkel ajakirjas
29

artikkel ajakirjas EST / artikkel ajakirjas ENG

Multi-stage deep learning networks for automated assessment of electricity transmission infrastructure using fly-by imagesManninen, Henri; Ramlal, Craig J.; Singh, Arvind; Kilter, Jako; Landsberg, MartElectric power systems research2022 / art. 107948 https://doi.org/10.1016/j.epsr.2022.107948 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
30

artikkel ajakirjas EST / artikkel ajakirjas ENG

A new efficient ALignment-driven Neural Network for mortality prediction from irregular multivariate time series data [Formula presented]Bignoumba, Nzamba; Mellouli, Nedra; Ben Yahia, SadokExpert systems with applications2024 / art. 122148 https://doi.org/10.1016/j.eswa.2023.122148 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
Seotud publikatsioonid 1
- Predictive systems using machine learning tools to forecast adverse events during medical stays = Ennustavad süsteemid, mis kasutavad masinõppe vahendeid kõrvalekallete prognoosimiseks haiglas viibimise ajal
31

artikkel kogumikus

Non-determinism in nowadays computing and ITHenno, Jaak; Jaakkola, Hannu; Mäkelä, Jukka2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), 28 Sept.-2 Oct. 2020, Opatija, Croatia : proceedings2020 / p. 794-801 https://doi.org/10.23919/MIPRO48935.2020.9245377

artikkel kogumikus
32

artikkel kogumikus EST / artikkel kogumikus ENG

Ontology modeling of the Estonian Traffic Act for self-driving busesNogales, Alberto; Täks, Ermo; Taveter, KuldarInformation Management and Big Data : 5th International Conference : SIMBig 2018, Lima, Peru, September 3–5, 2018 : proceedings2019 / p. 249-256 https://doi.org/10.1007/978-3-030-11680-4_24 Conference proceedings at Scopus Article at Scopus

artikkel kogumikus EST / artikkel kogumikus ENG
33

artikkel ajakirjas EST / artikkel ajakirjas ENG

Orthophoto classification for UGV pth planning using heterogeneous computingHudjakov, Robert; Tamre, MartInternational journal of advanced robotic systems2013 / [7] p. : ill https://doi.org/10.5772/56545 https://www.researchgate.net/publication/269785081_Orthophoto_Classification_for_UGV_Path_Planning_using_Heterogeneous_Computing Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
34

artikkel ajakirjas EST / artikkel ajakirjas ENG

Pavement defect segmentation in orthoframes with a pipeline of three convolutional neural networksLõuk, Roland; Riid, Andri; Pihlak, Rene; Tepljakov, AlekseiAlgorithms2020 / art. 198, 27 p. : ill https://doi.org/10.3390/a13080198 Journal metrics at scopus Article at Scopus Journal metrics at WOS Article at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
35

artikkel kogumikus

Preliminary results in using attention for increasing attack identification efficiencyAhmad, Tanwir; Truscan, Dragos; Vain, Jüri2023 IEEE 16th International Conferenceon Software Testing, Verification andValidation Workshops, ICSTW 2023, 16-20 April 2023, Dublin, Ireland, Proceedings2023 / p. 159 - 164 https://doi.org/10.1109/ICSTW58534.2023.00038

artikkel kogumikus
36

artikkel kogumikus

Residential load forecasting using recurrent neural networksShabbir, Noman; Ahmadiahangar, Roya; Raja, Hadi Ashraf; Kütt, Lauri; Rosin, Argo2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) : proceedings2020 / p. 478-481 : ill https://doi.org/10.1109/CPE-POWERENG48600.2020.9161565

artikkel kogumikus
37

artikkel ajakirjas EST / artikkel ajakirjas ENG

A robust pathological voices recognition system based on DCNN and scattering transformSouli, Sameh; Amam, Rimah; Ben Yahia, SadokApplied acoustics2021 / art. 107854 : ill https://doi.org/10.1016/j.apacoust.2020.107854 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
38

artikkel kogumikus

Scene parsing using Fully Convolutional Network for Semantic SegmentationAli, Nisar; Ijaz, Ali Zeeshan; Ali, Raja Hashim; Abideen, Zain Ul; Bais, Abdul2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Regina, SK, Canada, 20232023 / p. 180-185 https://doi.org/10.1109/CCECE58730.2023.10288934

artikkel kogumikus
39

artikkel kogumikus EST / artikkel kogumikus ENG

Semi-parametric approach to random forests for high-dimensional bayesian optimisationKuzmanovski, Vladimir; Hollmén, JaakkoDiscovery Science : 25th International Conference, DS 2022, Montpellier, France, October 10–12, 2022, Proceedings2022 / p. 418 - 428 https://doi.org/10.1007/978-3-031-18840-4_30 Conference Proceedings at Scopus Article at Scopus Conference Proceedings at WOS Article at WOS

artikkel kogumikus EST / artikkel kogumikus ENG
40

artikkel ajakirjas

Short-term PV energy generation forecasting using deep learningShabbir, Noman; Ahmadiahangar, Roya; Rosin, Argo; Astapov, Victor; Kilter, JakoTelematique2022 https://www.provinciajournal.com/index.php/telematique/article/view/813

artikkel ajakirjas
41

artikkel kogumikus

The validation of graph model-based, gate level low-dimensional feature data for machine learning applicationsBalakrishnan, Aneesh; Lange, Thomas; Glorieux, Maximilien; Alexandrescu, Dan; Jenihhin, Maksim2019 IEEE Nordic Circuits and Systems Conference (NORCAS) : NORCHIP and International Symposium of System-on-Chip (SoC), 29-30 October 2019, Helsinki, Finland : proceedings in IEEE Xplore2019 / 7 p https://doi.org/10.1109/NORCHIP.2019.8906974

artikkel kogumikus
Seotud publikatsioonid 1
- A synthetic, hierarchical approach for modelling and managing complex systems' quality and reliability = Sünteetiline, hierarhiline lähenemine keerukate süsteemide kvaliteedi ja töökindluse modelleerimiseks ja haldamiseks
42

artikkel ajakirjas EST / artikkel ajakirjas ENG

Toward automatic condition assessment of high-voltage transmission infrastructure using deep learning techniquesManninen, Henri; Ramlal, Craig J.; Singh, Arvind; Rocke, Sean; Kilter, Jako; Landsberg, MartInternational journal of electrical power & energy systems2021 / art. 106726 https://doi.org/10.1016/j.ijepes.2020.106726 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
Seotud publikatsioonid 1
- Data-driven asset management and condition assessment methodology for transmission overhead lines = Ülekandevõrgu õhuliinide andmepõhine varahalduse ja seisundi hindamise metoodika

Kirjeid leitud 42, kuvan 26 - 42

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1. deep learning
2. deep learning (DL)
3. deep learning classifier
4. deep learning method
5. deep learning methods
6. deep learning model
7. deep learning model quantization
8. deep learning network
9. Deep-learning
10. deep-learning (DL)
11. embedded deep learning
12. deep reinforcement learning
13. Deep reinforcing learning SDN
14. deep donor-deep acceptor pairs
15. deep brain stimulation
16. deep convolutional neural network
17. Deep defects
18. deep energy levels
19. deep energy renovation
20. deep eutectic solvents
21. deep fake
22. deep fake recognition
23. deep habits
24. deep heat well
25. deep inference
26. deep layer
27. deep level
28. deep level traps
29. deep levels
30. deep neural network
31. deep neural network compression
32. deep neural networks
33. deep neural networks (DNNs)
34. deep offshore
35. deep packet inspection
36. deep renovation
37. deep understanding
38. deep-sea mining
39. natural deep eutectic solvents
40. nZEB deep energy renovation
41. nZEB deep renovation
42. active blended learning
43. active learning
44. active learning and engagement
45. active learning methods
46. adaptive learning
47. adversarial machine learning
48. adversarial machine-learning
49. algorithm controlling the learning process
50. appetitive associative learning
51. approach to learning
52. Attitude to learning
53. authoritarian learning
54. automata learning
55. automatic machine learning (autoML)
56. bayesian learning
57. BIM-enabled learning
58. BIM-enabled learning environment
59. biophilic learning environments
60. blended learning
61. business school as learning organization
62. business school learning organization questionnaire (BSLOQ)
63. business school learning rate dependence on GPD
64. business school learning rate measurement instrument
65. challenge-based learning
66. Challenge-Based Learning (CBL)
67. client learning
68. Collaborative e-learning
69. collaborative learning
70. competence based learning
71. competence learning
72. competence-based learning
73. competency based learning
74. Component organised learning (COL)
75. computer assisted language learning
76. computer based learning
77. computer-assisted language learning (CALL)
78. contrastive learning
79. cooperative reinforcement learning
80. coping with online teaching and learning
81. customer learning
82. data analytics and machine learning
83. dictionary learning
84. digital learning module
85. digital remote learning
86. Dimensions of Learning Organisation Questionnaire (DLOQ)
87. distance learning
88. distant learning
89. distributed independent reinforcement learning
90. double Q-learning
91. effective teaching and learning
92. e-learning
93. e‐learning
94. e-Learning course
95. e-Learning database
96. e-learning environment
97. electronic learning
98. Emergency remote learning
99. ensemble learning
100. enterprising and entrepreneurial learning
101. entrepreneurial learning
102. entrepreneurial teaching and learning
103. ESG-learning
104. experiential learning
105. Federated learning
106. federated learning (FL)
107. flexible learning
108. game based learning
109. game-based learning
110. graph machine learning
111. guided in-service learning
112. hands-on-learning
113. history learning
114. horizontal federated learning
115. human-centred learning
116. hybrid learning
117. Immersive Learning
118. incremental learning
119. inductive learning
120. inductive learning algorithm
121. interactive learning objects
122. interdisciplinary learning
123. interpretable machine learning
124. inverse reinforcement learning
125. investor learning
126. iterative learning
127. Kolb’s experiential learning cycle
128. language learning
129. Learner-centered learning environment
130. learning
131. learning (artificial intelligence)
132. learning activities
133. learning adaptation in autonomous vehicles
134. learning algorithm
135. learning analysis
136. learning analytics
137. learning and experimentation
138. learning and health outcomes
139. learning and memory
140. learning business school
141. learning by teaching
142. learning concept
143. learning content
144. learning design
145. learning difficulties
146. Learning environment
147. learning experience
148. learning factory
149. Learning from Demonstration
150. learning infrastructures
151. Learning Lab
152. learning management
153. learning management system
154. learning management systems
155. learning methodology
156. learning metrics
157. learning models
158. learning motivation
159. learning objectives
160. learning objects
161. learning organization
162. learning orientation
163. learning outcomes
164. learning process
165. learning processes
166. learning rate
167. learning rate dependence on ownership form
168. learning rate of organization
169. learning region
170. learning situation
171. learning software agents
172. learning styles
173. learning systems
174. learning technologies
175. learning theories
176. learning through the questionnaires
177. learning university
178. learning-to-rank
179. lifelong learning
180. long-short term learning
181. machine learning
182. machine learning (ML)
183. machine learning algorithms
184. machine learning approach
185. Machine Learning Explainable model
186. machine learning for environmental applications
187. machine learning models
188. machine learning systems
189. machine-learning
190. machine-learning predictive model
191. mathematics students learning habits
192. Meaningful learning
193. measuring learning
194. mobile learning
195. model learning
196. Multi-agent reinforcement learning
197. multi-task learning
198. mutual learning
199. new learning models
200. node embedding learning
201. NREN e-learning
202. on-device transfer learning
203. online learning
204. ontologies for learning systems
205. ontology learning
206. Open Learning Resources (OLR)
207. organisational learning
208. organizational learning
209. organizational learning measurement instruments
210. organizational learning rate dependence on management
211. organizational learning rate measurement
212. outcome oriented learning
213. Parkinson’s disease machine learning
214. peer‐to‐peer learning group
215. personalised learning
216. Personalized learning
217. policy learning
218. problem-based learning
219. Problem-based learning (PBL)
220. project learning
221. project-based learning
222. Q-learning
223. Q-learning algorithm
224. query learning
225. regional learning
226. reinforcement learning
227. Reinforcement Learning (RL)
228. relative learning performance
229. Remote learning
230. research-oriented learning
231. reusable learning objects
232. robust machine learning
233. scholarship of teaching and learning
234. self learning software
235. self learning system
236. self-learning
237. self-regulated learning
238. semi-supervised learning
239. sequence of learning activities
240. SIS problem, learning with errors
241. small data machine learning
242. smart learning
243. smart learning environment
244. social learning
245. socio-emotional learning
246. stochastic learning theory
247. student centred learning
248. supervised contrastive learning
249. supervised learning
250. supervised learning data
251. supervised machine learning
252. synchronous hybrid learning
253. zero-shot learning
254. task based learning
255. team learning
256. Technological learning
257. technology-enhanced learning
258. the dimensions of the learning organisation
259. the dimensions of the learning organization questionnaire (DLOQ)
260. tools for interactive learning and teaching
261. trans-disciplinary social-learning
262. transfer learning
263. true online reinforcement learning
264. ubiquitous learning
265. unsupervised learning
266. unsupervised machine learning
267. urban learning center
268. value of learning
269. web and computerbased learning
270. Web and computer-based learning
271. virtual collaborative learning environment (VCLE)
272. virtual learning environment
273. virtual learning factory
274. work-based learning
275. workplace learning

1. ELIXIR Machine Learning Focus Group

1. UNDP Urban Learning Center

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