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Non Technical Loses Detection - Experts Labels vs. Inspection Labels in the Learning Stage.

, , and . ICPRAM, page 624-628. SciTePress, (2014)

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Non Technical Loses Detection - Experts Labels vs. Inspection Labels in the Learning Stage., , and . ICPRAM, page 624-628. SciTePress, (2014)Semisupervised Approach to Non Technical Losses Detection., , , , and . CIARP, volume 8827 of Lecture Notes in Computer Science, page 698-705. Springer, (2014)Comparing Different Labeling Strategies in Anomalous Power Consumptions Detection., , and . ICPRAM (Selected Papers), volume 9443 of Lecture Notes in Computer Science, page 196-205. Springer, (2014)Towards Effective Blended Learning Through the Eyes of Students: A Survey Study in Transition into Face-to-Face Education., , , and . EC-TEL, volume 13450 of Lecture Notes in Computer Science, page 492-499. Springer, (2022)Optimal and Linear F-Measure Classifiers Applied to Non-technical Losses Detection., , , , , and . CIARP, volume 9423 of Lecture Notes in Computer Science, page 83-91. Springer, (2015)Contribution to privacy-enhancing tecnologies for machine learning applications.. Polytechnic University of Catalonia, Spain, (2020)Approximate Entropy and Densely Connected Neural Network in the Early Diagnostic of Patients with Chagas Disease., , , , , , , and . CinC, page 1-4. IEEE, (2022)MOOCs as a Remedial Complement: Students' Adoption and Learning Outcomes., , , and . IEEE Trans. Learn. Technol., 12 (1): 133-141 (2019)Does Taking a MOOC as a Complement for Remedial Courses Have an Effect on My Learning Outcomes? A Pilot Study on Calculus., , , , and . EC-TEL, volume 9891 of Lecture Notes in Computer Science, page 221-233. Springer, (2016)Optimal Artificial Neural Network for the Diagnosis of Chagas Disease Using Approximate Entropy and Data Augmentation., , , , , , , and . CinC, page 1-4. IEEE, (2023)