Algorithm of model development and validation. by Bethan Cracknell Daniels (20469396)

Performance of machine learning algorithms. by Wenxue Liu (6877754)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Landslide susceptibility maps of the study area were developed using the RF algorithm for (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. by Raja Das (1841107)

Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

<b>Discrimination of gold deposit types based on big data analysis and machine learning techniques</b> by Lihao Feng (20837036)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

Data Sheet 1_Development and validation of an endoscopic diagnostic model for sessile serrated lesions based on machine learning algorithms.docx by Xinying Yu (16400811)

“…Background and aims<p>Sessile serrated lesions (SSLs) are morphologically subtle and often misclassified as hyperplastic polyps (HPs), increasing colorectal cancer risks. We developed a machine learning (ML) model to improve endoscopic SSL diagnosis.…”

Supporting data for “Developing algorithms to optimize recommender system for self-regulated learning promotion in online contexts: a design-based study” by Long Zhang (11129139)

Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

Subjects:

Risk element category diagram. by Yao Hu (3479972)

“…This article used these data to establish an LSTM model, which trained LSTM to identify potential risks and provide early warning by learning patterns and trends in historical data. …”

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)

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

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

Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms by Jie Zhu (126574)

“…Six different machine learning algorithms were employed to develop prognostic models based on the clinical features during the acute phase, which were reduced using Lasso regression.…”

Data Sheet 1_Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm – a 10-year multicenter retrospective study.xlsx by Yuan Liu (88411)

“…</p>Methods<p>We gathered 34 feature variables from a cohort of 1,097 colon cancer patients, including 87 individuals who developed gastroparesis post-surgery, across multiple hospitals, and applied a range of machine learning algorithms to construct the predictive model. …”

Machine learning algorithms hyperparameter values. by Mahbub E. Sobhani (22278967)

Types of machine learning algorithms. by Md. Merajul Islam (12646837)

“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.</p><p>Materials and methods</p><p>This study used the latest nationally representative cross-sectional Bangladesh demographic health survey (BDHS), 2017–18 data. …”

Description of the dataset used for the machine learning algorithm. by Tri Ngo Quang (20390189)