F1 score of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

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Examples of ground-truth graph structures with (<i>p</i>, <i>n</i>_≠0) = (10, 10). by Tomokaze Shiratori (9635271)

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Number of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Number of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

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Graphs of the regularization terms. by Tomokaze Shiratori (9635271)

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F1 score of a given number of selected edges on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Computation time as a function of the number of variables on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Computation time as a function of the number of variables on the random graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of a given number of selected edges on the random graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

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Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis by Alireza Enshaeian (20360253)

The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset. by Abdullah Asım Yılmaz (20715311)

PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection by Mehdi Khalil (20625037)

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Normalized Load and Price Data (First Week of October 2024). by Nasir Nauman (22272299)

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ISO-NE Load Data (January 2024–October 2024). by Nasir Nauman (22272299)

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Model Performance Comparison on European and Asian Electricity Market Data. by Nasir Nauman (22272299)

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ISO-NE Price Data (January 2024–October 2024). by Nasir Nauman (22272299)

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Model’s measure methods. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Back-trajectories from a) Valladolid, b) Madrid and c) Granada stations, calculated by HYSPLIT, at altitude level of 3 km on the 3rd October 10:00 UTC. by Celia Herrero del Barrio (20479635)