Training -validation accuracy & lost for ANN. by Gaurav Sandeep Dave (21519884)

“…This approach leverages recent advancements in data science to derive actionable insights from CPM data, facilitating more informed decision-making and optimization of pump operations.…”

DATASET AI by Elena Stamate (18836305)

“…Performance metrics include accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC).</p><p dir="ltr">All data have been de-identified and processed in accordance with institutional ethical standards.…”

Distribution of different numbers (<i>k</i>) of FMs in H&N tumor model. by Haolin Liu (789542)

Comparison of robust discriminant methods. by Shaojuan Ma (12750125)

“…The MRCD algorithm enhances robustness by regularizing the covariance matrix, making it suitable for high-dimensional data where the number of variables exceeds the number of observations. …”

Image 1_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.tif by Xuehui Fan (10762662)

The four areas of interest to the patients. by Anne Lübbeke (3918686)

“…</p><p>Conclusion</p><p>The tool based on a survey of patients’ perceived concerns and interests and the corresponding long-term data from a large institutional registry makes past patients’ experience accessible, understandable, and visible for today’s patients and their clinicians. …”

An example of a complication. by Anne Lübbeke (3918686)

“…</p><p>Conclusion</p><p>The tool based on a survey of patients’ perceived concerns and interests and the corresponding long-term data from a large institutional registry makes past patients’ experience accessible, understandable, and visible for today’s patients and their clinicians. …”

Baseline characteristics. by Anne Lübbeke (3918686)

“…</p><p>Conclusion</p><p>The tool based on a survey of patients’ perceived concerns and interests and the corresponding long-term data from a large institutional registry makes past patients’ experience accessible, understandable, and visible for today’s patients and their clinicians. …”

Building blocks of the project. by Anne Lübbeke (3918686)

“…</p><p>Conclusion</p><p>The tool based on a survey of patients’ perceived concerns and interests and the corresponding long-term data from a large institutional registry makes past patients’ experience accessible, understandable, and visible for today’s patients and their clinicians. …”

Octree spatial visualization. by Gaia Marangon (22582137)

“…The model, implemented within a finite element environment, captures essential bone characteristics, including orthotropic elasticity, viscoplasticity, and progressive damage accumulation. …”

Reduced yield-deformation parameters. by Gaia Marangon (22582137)

“…The model, implemented within a finite element environment, captures essential bone characteristics, including orthotropic elasticity, viscoplasticity, and progressive damage accumulation. …”

Breakdown of respondents. by Qunita Brown (19751520)

“…Recorded interviews were transcribed, cleaned, coded and managed through Atlas.ti.22. Thematic Analysis was used to analyse the data. …”

Preprocessed Spatial Dynamic Wind Power Forecasting (SDWPF) Dataset from Baidu KDD Cup 2022 Challenge by Yuan Shi (22779782)

Video 2_Replicating associative learning of rodents with a neuromorphic robot in an open-field arena.mp4 by Tianze Liu (13922196)

Video 1_Replicating associative learning of rodents with a neuromorphic robot in an open-field arena.mp4 by Tianze Liu (13922196)

Calculate the MRCD matrix flowchart. by Shaojuan Ma (12750125)

Seismic-Acoustic Dataset of Coastal Bryde’s Whales in the Beibu Gulf by Yue Wang (22435645)

The proposed framework. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. …”

Number of recognised words. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. …”

Word2Vec models [3]. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. …”