Computer-aided diagnosis techniques based on deep learning in skin cancer classification have disadvantages such as unbalanced datasets, redundant information in the extracted features and ignored int...
As modeling tools and approaches become more advanced, ecological models are becoming more complex. Traditional sensitivity analyses can struggle to identify the nonlinearities and interactions emerge...
The growing use of digitized mental health applications requires new reliable early screening tools to identify user suicide risk. We used a lexicon-based random forest machine learning algorithm to p...
The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifyi...
We present a new approach to segment and classify bacterial spore layers from Transmission Electron Microscopy (TEM) images using a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) cla...
Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to incr...
In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course g...
Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importan...
Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken....
Random Forest (RF) is a widely used machine learning method with good performance on classification and regression tasks. It works well under low sample size situations, which benefits applications in...
Aggregate tests of rare variants are often employed to identify associated regions compared to sequentially testing each individual variant. When an aggregate test is significant, it is of interest to...
Functional mobility tests, such as the L test of functional mobility, are recommended to provide clinicians with information regarding the mobility progress of lower-limb amputees. Smartphone inertial...
Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal pl...
Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography-mass spectrometry (LC-MS) was used to perform lipidomic ...
Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16-18 c...
LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necro...
