Data Sheet 1_Bundled assessment to replace on-road test on driving function in stroke patients: a binary classification model via random forest.docx by Lu Huang (211625)

“…The subject was classified as either Success or Unsuccess group according to whether they had completed the on-road test. A random forest algorithm was then applied to construct a binary classification model based on the data obtained from the two groups.…”

Parameters of the experiments. by Enrico Zardini (17382523)

“…Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood of the considered element. …”

Quantum pipeline workflow overview. by Enrico Zardini (17382523)

“…Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood of the considered element. …”

Related studies on IDS using deep learning. by Arshad Hashmi (13835488)

“…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …”

The architecture of the BI-LSTM model. by Arshad Hashmi (13835488)

“…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …”

Comparison of accuracy and DR on UNSW-NB15. by Arshad Hashmi (13835488)

“…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …”

Comparison of DR and FPR of UNSW-NB15. by Arshad Hashmi (13835488)

“…The suggested model’s accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. …”

DataSheet_1_Histopathology image classification: highlighting the gap between manual analysis and AI automation.pdf by Refika Sultan Doğan (17799677)

“…Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. …”

Table 1_Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.docx by Yanbo Sun (2202439)

“…Five machine learning models were deployed for the binary classification task of DM, and their performance was evaluated using the area under the curve (AUC). …”

Participants’ demographic characteristics. by Reihaneh Hassanzadeh (11986041)

“…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …”

Imaging parameters. by Reihaneh Hassanzadeh (11986041)

“…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …”

Table_1_Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.docx by Lizhao Yan (11774354)

“…</p>Materials and methods<p>Patients with the diagnosis of chondrosarcoma between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. Three algorithms—two based on neural networks (DeepSurv, neural multi-task logistic regression [NMTLR]) and one on ensemble learning (random survival forest [RSF])—were selected for training. …”

Datasheet1_Machine learning-based predictor for neurologic outcomes in patients undergoing extracorporeal cardiopulmonary resuscitation.docx by Tae Wan Kim (140536)

“…We trained and tested eight ML algorithms for a binary classification task involving the neurological outcomes of survivors after ECPR.…”