BACKGROUND AND OBJECTIVES: Hematologic toxicity (HT) is a common and serious side effect for advanced cervical cancer patients undergoing chemoradiotherapy. Accurately predicting HT can significantly improve patient management and treatment outcomes....
This study aims to develop accurate and efficient machine learning models to predict the concentrations of phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) in 10 legume species naturally growing in the Çamlıhemşin district of Rize prov...
Despite the critical role of biofilters in water quality and sustainability, predicting their performance remains challenging due to the complexity of microbial interactions and limitations of sparse, high-dimensional datasets. Here, we introduce Env...
Integrating disease severity with real-time meteorological variables and advanced machine learning techniques has provided valuable predictive insights for assessing disease severity in wheat. This study emphasizes the potential of machine learning m...
Since 2019, humanity has been suffering from the negative impact of COVID-19, and the virus did not stop in its usual state but began to pivot to become more harmful until it reached its form now, which is the omicron variant. Therefore, in an attemp...
R1.C1: The dairy industry is a cornerstone of global food production and economic development; yet, its productivity is frequently hindered by common bovine health issues, including lameness, mastitis, metritis, and foot-and-mouth disease. These cond...
To identify clinical features that predict the risk of meeting difficult-to-treat (D2T) rheumatoid arthritis (RA) definition in advance. This retrospective analysis included RA patients from the ATTRA registry who initiated biologic (b-) or targeted ...
In panic-buying situations, individuals suddenly purchase excessive quantities of goods, leading to a massive crisis of essential goods in the market. As a result, many consumers cannot access the required products, creating an unstable societal situ...
This study aimed to identify candidate diagnostic miRNAs from the serum of colorectal cancer (CRC) patients using Boruta, a wrapper-based feature selection technique, in combination with decision tree-based machine learning methods. We analyzed three...
Current machine learning systems consume vastly more energy than biological brains. Neuromorphic systems aim to overcome this difference by mimicking the brain's information coding via discrete voltage spikes. However, it remains unclear how both art...
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