Image preprocessing effect. by Juquan Tan (20642365)

The preprocessed Tara data set. by Zhibo Chen (1759765)

Electrocardiogram signal preprocessing diagram. by Yu Fang (219603)

“…<div><p>Conventional heart sound classification methods often rely on single-channel, one-dimensional feature extraction, which inadequately captures pathological relationships across different auscultation zones, thereby limiting the accuracy of heart disease detection. …”

Flowchart of the local outlier detection method. by Xiaoyang Liu (1815211)

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Data screening and preprocessing flow chart. by Ting Qin (1931218)

X-CRISP: Domain-Adaptable and Interpretable CRISPR Repair Outcome Prediction - Preprocessed CRISPR repair outcomes and train/test target splits by Colm Seale (21499823)

Proposed method flow diagram. by Irshad Ali (5562320)

“…However, conventional inversion methods face limitations such as noise sensitivity, low resolution, and reduced effectiveness in geologically complex areas, often resulting in oversimplified subsurface models. …”

Entropy results analysis of MFST-DAD via temporary condition. by Aya Tareef (22170633)

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Response of MFST-DAD’s RDP algorithm to data captured from (a) WPA3-SAE only and (b) WPA3-TM attack scenarios. by Aya Tareef (22170633)

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Benchmarking with state-of-the-art related works. by Aya Tareef (22170633)

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Confusion matrix of the Naive Bayes classifier used in MFST-DAD. by Aya Tareef (22170633)

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Analysis of frame types and their frequency of occurrence. by Aya Tareef (22170633)

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MFST-DAD system model. by Aya Tareef (22170633)

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Real-time prevention response of MFST-DAD with adaptive blacklist mechanism. by Aya Tareef (22170633)

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Experimental setup. by Aya Tareef (22170633)

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MFST-DAD design architecture. by Aya Tareef (22170633)

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Evaluation results of ML-based classifier within MFST-DAD framework. by Aya Tareef (22170633)

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Entropy results analysis of MFST-DAD with (a) WPA3-SAE and (b) WPA3-TM. by Aya Tareef (22170633)

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Analysis of time gaps between Wi-Fi packets. by Aya Tareef (22170633)

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Data Sheet 1_Using preprocessed datasets to construct and interpret multiclass identification models.docx by Cong Wang (107450)

“…Therefore, developing alternative approaches for constructing interpretable and robust models using these data types is crucial.</p>Methods<p>This study proposes using preprocessed data—specifically, morphological features extracted from images and chemical component concentrations predicted from NIR spectra—to build multiclass identification models. …”