Journal articles on the topic 'Learning with pre-built models'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Learning with pre-built models.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
Fauszt, Tibor, László Bognár, and Ágnes Sándor. "Increasing the Prediction Power of Moodle Machine Learning Models with Self-defined Indicators." International Journal of Emerging Technologies in Learning (iJET) 16, no. 24 (December 21, 2021): 23–39. http://dx.doi.org/10.3991/ijet.v16i24.23923.
Full textErian, Karim H., Pedro H. Regalado, and James M. Conrad. "Missing data handling for machine learning models." IAES International Journal of Robotics and Automation (IJRA) 10, no. 2 (June 1, 2021): 123. http://dx.doi.org/10.11591/ijra.v10i2.pp123-132.
Full textCinar, Eyup. "A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring." Sensors 22, no. 18 (September 8, 2022): 6791. http://dx.doi.org/10.3390/s22186791.
Full textLage, Isaac, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman, and Finale Doshi-Velez. "Human Evaluation of Models Built for Interpretability." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 (October 28, 2019): 59–67. http://dx.doi.org/10.1609/hcomp.v7i1.5280.
Full textYimam, Seid Muhie, Abinew Ali Ayele, Gopalakrishnan Venkatesh, Ibrahim Gashaw, and Chris Biemann. "Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets." Future Internet 13, no. 11 (October 27, 2021): 275. http://dx.doi.org/10.3390/fi13110275.
Full textWang, Chengyu, Mengli Cheng, Xu Hu, and Jun Huang. "EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16111–13. http://dx.doi.org/10.1609/aaai.v35i18.18028.
Full textSingh, Hrithik, Shambhavi Kaushik, Shruti Talyan, and Kartikeya Dwivedi. "Skin Cancer Detection Using Deep Learning techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4296–305. http://dx.doi.org/10.22214/ijraset.2022.43090.
Full textPedditzi, Maria L., and Marcello Nonnis. "Pre-service Teachers' Representations About Children's Learning: A Pilot Study." Open Psychology Journal 13, no. 1 (November 13, 2020): 315–20. http://dx.doi.org/10.2174/1874350102013010315.
Full textZhang, Li, Haimeng Fan, Chengxia Peng, Guozheng Rao, and Qing Cong. "Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning." Healthcare 8, no. 3 (August 28, 2020): 307. http://dx.doi.org/10.3390/healthcare8030307.
Full textAchicanoy, Harold, Deisy Chaves, and Maria Trujillo. "StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications." Symmetry 13, no. 8 (August 16, 2021): 1497. http://dx.doi.org/10.3390/sym13081497.
Full textMars, Mourad. "From Word Embeddings to Pre-Trained Language Models: A State-of-the-Art Walkthrough." Applied Sciences 12, no. 17 (September 1, 2022): 8805. http://dx.doi.org/10.3390/app12178805.
Full textAn, Yaxin, and Sanket A. Deshmukh. "Machine learning approach for accurate backmapping of coarse-grained models to all-atom models." Chemical Communications 56, no. 65 (2020): 9312–15. http://dx.doi.org/10.1039/d0cc02651d.
Full textPallepati, Manvith. "Network Intrusion Detection System Using Machine Learning with Data Preprocessing and Feature Extraction." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2360–65. http://dx.doi.org/10.22214/ijraset.2022.44326.
Full textOh, Seungmin, Sangwon Oh, Hyeju Shin, and Jinsul Kim. "Pre-processing optimization for learning power consumption prediction models." Journal of Contents Computing 4, no. 1 (June 30, 2022): 431–37. http://dx.doi.org/10.9728/jcc.2022.06.4.1.431.
Full textIyer-Raniga, Usha, and Pekka Huovila. "Learning through sharing: beyond the traditional North-South learning models for a circular built environment." IOP Conference Series: Earth and Environmental Science 588 (November 21, 2020): 022023. http://dx.doi.org/10.1088/1755-1315/588/2/022023.
Full textKadam, Vaibhav, Satish Kumar, Arunkumar Bongale, Seema Wazarkar, Pooja Kamat, and Shruti Patil. "Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products." Applied System Innovation 4, no. 2 (May 14, 2021): 34. http://dx.doi.org/10.3390/asi4020034.
Full textEkung, Samuel, and Isaac Odesola. "LEARNING MODELS FOR EFFECTIVE PROPAGATION OF SUSTAINABLE CONSTRUCTION PRACTICES IN THE BUILT ENVIRONMENT." Malaysian Journal of Sustainable Environment 4, no. 1 (September 30, 2018): 113. http://dx.doi.org/10.24191/myse.v4i1.5610.
Full textFahey-Gilmour, J., B. Dawson, P. Peeling, J. Heasman, and B. Rogalski. "Multifactorial analysis of factors influencing elite Australian football match outcomes: a machine learning approach." International Journal of Computer Science in Sport 18, no. 3 (December 1, 2019): 100–124. http://dx.doi.org/10.2478/ijcss-2019-0020.
Full textToba, Hapnes, Hendra Bunyamin, Juan Elisha Widyaya, Christian Wibisono, and Lucky Surya Haryadi. "Masking preprocessing in transfer learning for damage building detection." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (June 1, 2023): 552. http://dx.doi.org/10.11591/ijai.v12.i2.pp552-559.
Full textQiao, Yanhua, Xiaolei Zhu, and Haipeng Gong. "BERT-Kcr: prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models." Bioinformatics 38, no. 3 (October 13, 2021): 648–54. http://dx.doi.org/10.1093/bioinformatics/btab712.
Full textKubera, Elżbieta, Agnieszka Kubik-Komar, Krystyna Piotrowska-Weryszko, and Magdalena Skrzypiec. "Deep Learning Methods for Improving Pollen Monitoring." Sensors 21, no. 10 (May 19, 2021): 3526. http://dx.doi.org/10.3390/s21103526.
Full textAnjomshoaa, Amin, and Edward Curry. "Transfer Learning in Smart Environments." Machine Learning and Knowledge Extraction 3, no. 2 (March 29, 2021): 318–32. http://dx.doi.org/10.3390/make3020016.
Full textKhalil, Enas A. H., Enas M. F. El Houby, and Hoda K. Mohamed. "Machine Learning for Multilabel Emotion Classification in Arabic Tweets." International Journal of Computer Science and Mobile Computing 11, no. 5 (May 30, 2022): 222–33. http://dx.doi.org/10.47760/ijcsmc.2022.v11i05.010.
Full textHammad, Mahmoud, Mohammed Al-Smadi, Qanita Bani Baker, and Sa’ad A. Al-Zboon. "Using deep learning models for learning semantic text similarity of Arabic questions." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 3519. http://dx.doi.org/10.11591/ijece.v11i4.pp3519-3528.
Full textOh, Seo Hyun, Min Kang, and Youngho Lee. "Protected Health Information Recognition by Fine-Tuning a Pre-training Transformer Model." Healthcare Informatics Research 28, no. 1 (January 31, 2022): 16–24. http://dx.doi.org/10.4258/hir.2022.28.1.16.
Full textZhao, Mingjun, Haijiang Wu, Di Niu, and Xiaoli Wang. "Reinforced Curriculum Learning on Pre-Trained Neural Machine Translation Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9652–59. http://dx.doi.org/10.1609/aaai.v34i05.6513.
Full textReza, Ahmed Wasif, Md Mahamudul Hasan, Nazla Nowrin, and Mir Moynuddin Ahmed Shibly. "Pre-trained deep learning models in automatic COVID-19 diagnosis." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (June 1, 2021): 1540. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1540-1547.
Full textRotman, Guy, and Roi Reichart. "Multi-task Active Learning for Pre-trained Transformer-based Models." Transactions of the Association for Computational Linguistics 10 (2022): 1209–28. http://dx.doi.org/10.1162/tacl_a_00515.
Full textBaranov, Mykola, Yurii Shcherbyna, and Oles Khodych. "Exploit computer vision inpainting approach to boost deep learning models." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 12 (December 15, 2022): 1–6. http://dx.doi.org/10.23939/sisn2022.12.001.
Full textKim, Gyunyeop, and Sangwoo Kang. "Effective Transfer Learning with Label-Based Discriminative Feature Learning." Sensors 22, no. 5 (March 4, 2022): 2025. http://dx.doi.org/10.3390/s22052025.
Full textVerbruggen, Gust, Vu Le, and Sumit Gulwani. "Semantic programming by example with pre-trained models." Proceedings of the ACM on Programming Languages 5, OOPSLA (October 20, 2021): 1–25. http://dx.doi.org/10.1145/3485477.
Full textShadiev, Rustam, Xueying Wang, Ting-Ting Wu, and Yueh-Min Huang. "Review of Research on Technology-Supported Cross-Cultural Learning." Sustainability 13, no. 3 (January 29, 2021): 1402. http://dx.doi.org/10.3390/su13031402.
Full textWeng, Rongxiang, Heng Yu, Weihua Luo, and Min Zhang. "Deep Fusing Pre-trained Models into Neural Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11468–76. http://dx.doi.org/10.1609/aaai.v36i10.21399.
Full textJeon, Joohyeong, Byungohk Rhee, and Jinsu Gim. "Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding." Polymers 14, no. 24 (December 18, 2022): 5548. http://dx.doi.org/10.3390/polym14245548.
Full textBulla, Premamayudu, Lakshmipathi Anantha, and Subbarao Peram. "Deep Neural Networks with Transfer Learning Model for Brain Tumors Classification." Traitement du Signal 37, no. 4 (October 10, 2020): 593–601. http://dx.doi.org/10.18280/ts.370407.
Full textPatel, Tatsat R., Muhammad Waqas, Seyyed M. M. J. Sarayi, Zeguang Ren, Cesario V. Borlongan, Rimal Dossani, Elad I. Levy, et al. "Revascularization Outcome Prediction for A Direct Aspiration-First Pass Technique (ADAPT) from Pre-Treatment Imaging and Machine Learning." Brain Sciences 11, no. 10 (October 5, 2021): 1321. http://dx.doi.org/10.3390/brainsci11101321.
Full textB, Naveen Kumar, C. Akshay, Abhishek S P, Ganadakshaka K, and K. Jayanth. "An Automated System for Fruit Adulteration and Fruit Grading." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1223–27. http://dx.doi.org/10.22214/ijraset.2022.44011.
Full textKhalifa, Noorain, Leela Sarath Kumar Konda, and Rajendra Kristam. "Machine learning-based QSAR models to predict sodium ion channel (Nav 1.5) blockers." Future Medicinal Chemistry 12, no. 20 (October 2020): 1829–43. http://dx.doi.org/10.4155/fmc-2020-0156.
Full textAlelyani, Salem. "Detection and Evaluation of Machine Learning Bias." Applied Sciences 11, no. 14 (July 7, 2021): 6271. http://dx.doi.org/10.3390/app11146271.
Full textDidkovskyi, Oleksandr, Vladislav Ivanov, Alessio Radice, Monica Papini, Laura Longoni, and Alessandra Menafoglio. "A Comparison Between Machine Learning and Functional Geostatistics Approaches for Data-Driven Analyses of Sediment Transport in a Pre-Alpine Stream." Mathematical Geosciences 54, no. 3 (March 16, 2022): 467–506. http://dx.doi.org/10.1007/s11004-022-09995-9.
Full textBechelli, Solene, and Jerome Delhommelle. "Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images." Bioengineering 9, no. 3 (February 27, 2022): 97. http://dx.doi.org/10.3390/bioengineering9030097.
Full textLi, Y., Y. Tong, K. Lu, S. Yu, and J. Qian. "P213 Can artificial intelligence help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neuron network." Journal of Crohn's and Colitis 14, Supplement_1 (January 2020): S247—S248. http://dx.doi.org/10.1093/ecco-jcc/jjz203.342.
Full textIdrissi, Idriss, Mostafa Azizi, and Omar Moussaoui. "Accelerating the update of a DL-based IDS for IoT using deep transfer learning." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (August 1, 2021): 1059. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp1059-1067.
Full textBassier, M., M. Vergauwen, and B. Van Genechten. "AUTOMATED CLASSIFICATION OF HERITAGE BUILDINGS FOR AS-BUILT BIM USING MACHINE LEARNING TECHNIQUES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W2 (August 16, 2017): 25–30. http://dx.doi.org/10.5194/isprs-annals-iv-2-w2-25-2017.
Full textSaraubon, Kobkiat, Nuttapong Wiriyanuruknakon, and Natdanai Tangthirasunun. "Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 11 (June 4, 2021): 34. http://dx.doi.org/10.3991/ijim.v15i11.20753.
Full textAbbas, Ather, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho. "AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods." Geoscientific Model Development 15, no. 7 (April 8, 2022): 3021–39. http://dx.doi.org/10.5194/gmd-15-3021-2022.
Full textHuang, Yefei, Tianlai Xu, Zexu Zhang, Hutao Cui, and Yu Su. "Satellite Segmentation with Pre-trained CNN Models." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2171/1/012003.
Full textMiyazawa, Kazuki, Yuta Kyuragi, and Takayuki Nagai. "Simple and Effective Multimodal Learning Based on Pre-Trained Transformer Models." IEEE Access 10 (2022): 29821–33. http://dx.doi.org/10.1109/access.2022.3159346.
Full textShoaib, Mohamed R., Mohamed R. Elshamy, Taha E. Taha, Adel S. El-Fishawy, and Fathi E. Abd El-Samie. "Efficient Brain Tumor Detection Based on Deep Learning Models." Journal of Physics: Conference Series 2128, no. 1 (December 1, 2021): 012012. http://dx.doi.org/10.1088/1742-6596/2128/1/012012.
Full textDang, Cach N., María N. Moreno-García, and Fernando De la Prieta. "Hybrid Deep Learning Models for Sentiment Analysis." Complexity 2021 (August 12, 2021): 1–16. http://dx.doi.org/10.1155/2021/9986920.
Full text