CHIGA's impact on defect prediction performance by Sheunopa Charumbira (21011003)

“…CHIGA achieves this by combining the chi-square technique for metric ranking and a binary-encoded genetic algorithm for feature subset selection.…”

Supplementary materials for PhD thesis 'Characterisation Of The Blazhko Effect In RR Lyrae Stars Using SuperWASP Data' by Paul Greer (22619328)

“…<br><br>The classification and investigation of Blazhko effect and binary candidates contained herein provide the opportunity for further study with both existing, and future, ground- and space-based missions such as Gaia, the LSST and PLATO.…”

Models and Dataset by M RN (9866504)

“…Its simplicity and lack of algorithm-specific parameters make it computationally efficient and easy to apply in high-dimensional problems such as gene selection for cancer classification.…”

Table 1_Non-obtrusive monitoring of obstructive sleep apnea syndrome based on ballistocardiography: a preliminary study.docx by Biyong Zhang (20906192)

“…</p>Results<p>Cross-validated on 32 subjects, the proposed approach achieved an accuracy of 71.9% for four-class severity classification and 87.5% for binary classification (AHI less than 15 or not).…”

Integrating terahertz time-domain spectroscopy with XGBoost for rapid and interpretable species-level wood identification of <i>Pterocarpus</i> by Min Yu (120607)

“…The results showed that the XGBoost model performed best, achieving 100% accuracy in binary classification (<i>P</i>. <i>santalinus</i> and <i>P</i>. …”

Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model by Ramya Chinnasamy (21633527)

“…A dynamic attention-based ensemble (DA_Ensemble) comprising Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Feedforward Neural Network (FNN) models is employed to boost classification performance. The proposed model was evaluated on benchmark datasets including UNSW-NB15, CIC-IDS2017, and H23Q under both binary and multiclass settings. …”

Image 1_From genetic data to kinship clarity: employing machine learning for detecting incestuous relations.jpeg by Dejan Šorgić (21459863)

“…</p>Results:<p>The CatBoost algorithm performed best in the binary classification of Normal Paternity vs. …”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”

An adapted Figure [10] demonstrating the process of injury prediction validation using a pattern recognition approach. by Seren Lois Evans (19942603)

“…<p>Athlete monitoring and training load refer to input variables for the pattern recognition algorithms. Injury classification is typically the binary response (injury yes/no) in labelling the training vectors. …”

Supplementary Material for: Utilizing Deep Learning to Identify Electron-Dense Deposits in Renal Biopsy Electron Microscopy Images by figshare admin karger (2628495)

“…To evaluate the model's classification capability, we created a binary classification model to identify the presence of deposits in EM images. …”

Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”

Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”

Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”

Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx by Varun Sendilraj (19732510)

“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”

Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”

Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…</p>Results<p>DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…<br> <br>Conclusion<br><br>The study concludes that the habitat variable, used in isolation, is insufficient to create a safe and reliable mushroom toxicity classification model. The consistent accuracy of 70.28% does not represent a flaw in the SVM. algorithm, but rather the predictive performance ceiling of the feature itself, whose simplicity and class overlap limit the model's discriminatory ability. …”

iNCog-EEG (ideal vs. Noisy Cognitive EEG for Workload Assessment) Dataset by Fariya Bintay Shafi (21692408)

“…</p><h3>Applications</h3><p dir="ltr">This dataset can be applied to a wide range of research areas, including:</p><ul><li>EEG signal denoising and artifact rejection</li><li>Binary and hierarchical <b>cognitive workload classification</b></li><li>Development of <b>robust Brain–Computer Interfaces (BCIs)</b></li><li>Benchmarking algorithms under <b>ideal and noisy conditions</b></li><li>Multitasking and mental workload assessment in <b>real-world scenarios</b></li></ul><p dir="ltr">By combining controlled multitasking protocols with deliberately introduced environmental noise, <b>iNCog-EEG provides a comprehensive benchmark</b> for advancing EEG-based workload recognition systems in both clean and challenging conditions.…”

<b>From street view imagery to the countryside: large-scale perception of rural China using deep learning</b> by Yao Yao (7903457)

“…The label field is a binary classification result: 0 means the left image is better than the right image, and 1 means the opposite.…”

Twitter dataset by mehdi khalil (20153943)

“…It was constructed using advanced ML algorithms and NLP techniques to analyze the language patterns in social media communications. …”