Predictive performance of algorithms for multiclass classification by serogroup of bacterial meningitis: weighted average per algorithm (AUC-ROC). by Audêncio Victor (10400699)

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.…”

DMTD algorithm. by Yunhu Huang (21402795)

“…Compared with previous works, EITO enables the control of continuous train operation without reference to offline speed profiles and optimizes several key performance indicators online. Finally, we conducted comparative tests of the manual driving, intelligent driving algorithm (ITOR, STON), and the algorithms proposed in this paper, EITO, using real line data from the Yizhuang Line of Beijing Metro (YLBS). …”

Flow diagram of the MAO algorithm. by Mohamed Irhuma (20818274)

“…In recent years, numerous methods were developed for Supply Chain (SC) demand forecasting, which posed several limitations related to inadequate handling of dynamic time series patterns and data requirement problems. …”

Predictive performance of algorithms for multiclass classification by serogroup of bacterial meningitis (AUC-ROC). by Audêncio Victor (10400699)

Confusion matrix of the proposed algorithm and state of the art (part 1). by Meshari Alazmi (452881)

Confusion matrix of the proposed algorithm and state of the art (part 2). by Meshari Alazmi (452881)

Performance metrics of best algorithms for multiclass classification by serogroup of bacterial meningitis with hyperparameter tuning. by Audêncio Victor (10400699)

A comparison of the performance of several S-boxes including the proposed one. by Nayeem Ahmad Khan (11727377)

Subjects:

Predictive performance of algorithms for binary classification of bacterial vs. non-bacterial meningitis (AUC-ROC). by Audêncio Victor (10400699)

Performance metrics of best algorithms for binary classification of bacterial vs. non-bacterial meningitis with hyperparameter tuning. by Audêncio Victor (10400699)

Predictive performance of algorithms for binary classification of bacterial vs. non-bacterial meningitis: weighted average (AUC-ROC). by Audêncio Victor (10400699)

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)

<i>Wolfset: A High-Quality Underwater Acoustic Dataset for Algorithm Development and Analysis</i> by Nuno Pessanha Santos (7381244)

“…<p dir="ltr">As data becomes increasingly available, relying on quality datasets for algorithm analysis and development is essential. …”

Table 2_Intelligent grading of sugarcane leaf disease severity by integrating physiological traits with the SSA-XGBoost algorithm.docx by Xinrui Wang (488330)

“…</p>Methods<p>In this study, we propose an intelligent method for identifying sugarcane foliar disease severity based on physiological traits. Field-collected data—including Soil and Plant Analyzer Development (SPAD) values, leaf surface temperature, and nitrogen content—were acquired using a plant nutrient analyzer (TYS-4N) from sugarcane leaves infected with brown stripe disease, ring spot disease, and mosaic disease at four severity levels (mild, moderate, moderately severe, and severe). …”

Table 1_Intelligent grading of sugarcane leaf disease severity by integrating physiological traits with the SSA-XGBoost algorithm.xlsx by Xinrui Wang (488330)

“…</p>Methods<p>In this study, we propose an intelligent method for identifying sugarcane foliar disease severity based on physiological traits. Field-collected data—including Soil and Plant Analyzer Development (SPAD) values, leaf surface temperature, and nitrogen content—were acquired using a plant nutrient analyzer (TYS-4N) from sugarcane leaves infected with brown stripe disease, ring spot disease, and mosaic disease at four severity levels (mild, moderate, moderately severe, and severe). …”

The general processing diagram used to create OSSL. by José L. Safanelli (20552654)

Subjects:

Supplementary data for "Algorithm-level data-guided correction for class imbalance in biological machine learning predictions: Protein interactions as a case" by Ebrahim Barzegari (21152114)

“…Correct and efficient use of algorithm-level methods, on the other hand, needs paying heed to data structure and content. …”

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)

“…In the present study, four advanced machine learning algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and k-nearest neighbor (KNN)—were employed to develop predictive models. …”

Process followed to develop and validate the MAMELI algorithm. by Valentina Bollati (106778)

Subjects: