Feature Importance Analysis of the Algorithms expressed by SHAP indices. by M. A. Habib (16839161)

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Statistical analysis of the prediction results from the ML algorithms. by M. A. Habib (16839161)

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Benchmark data and code for evaluating and enhancing spatial cognition abilities of large language models (LLMs) by Yang (16534473)

Data processing for multi-sensor synchronization. by Kathylee Pinnock Branford (18291038)

“…</b> All processed sensor data for the day is combined into a single dataset, <b>viii.…”

Evaluating and mapping spatial drought in northeast Thailand: utilizing analytic hierarchy process and random forest algorithms by Zahid Naeem Qaisrani (21567311)

“…The AHP model achieved an overall accuracy (OA) of 85.19%, aligning closely with the RF model, which had an OA of 88.89%. The RF algorithm effectively mapped spatial drought events with high accuracy, as confirmed by comparisons with rainfall data. …”

Temperature sensor 2 sample data and data curve after noise reduction. by Tang Ruipeng (20141334)

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Temperature sensor 1 sample data and data curve after noise reduction. by Tang Ruipeng (20141334)

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Temperature sensor 3 sample data and data curve after noise reduction. by Tang Ruipeng (20141334)

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Accuracy of the Random forest algorithm for different values of test samples and different choice of random states. by Abeer Aljohani (18497914)

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Accuracy of the Random forest algorithm for different values of test samples and different choice window indexes. by Abeer Aljohani (18497914)

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Comparison between pyAKI’s generated labels and the labels generated by the MIMIC-code implementation of the KDIGO criteria. by Christian Porschen (12967868)

Details of multiple GWO algorithm variants. by Genliang Li (5816264)

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Performance of different machine learning algorithms. by Apichat Suratanee (634281)

Proposed hybrid GA- ML algorithms. by Ankur Kumar (1357440)

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Proposed GA-ML algorithms pseudocode. by Ankur Kumar (1357440)

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The structure of the data sample. by Wenbing Shi (5806160)

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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)

Training and validation loss process of SE-MAConvLSTM on two datasets. by Rong Zhu (172093)

Screening criteria applied for the study selection process. by Yong Lin Lai (20854961)

Subjects: “…underlying degradation processes…”

The accuracy of all employed learning algorithms. by Quan Shao (20148209)