The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identification of protein subGolgi localizations may assist in drug development and understanding the mechanisms of the GA involved in various cellular proces...

Sampling is a widely used graph reduction technique to accelerate computations and simplify visualizations. By comprehensively analyzing the literature on sampling, we assume that existing algorithms cannot effectively preserve minority structures are rare small in but very important analysis. In this work, initially conduct pilot user study investigate representative most appealing human viewe...

The development of mineral prospectivity mapping (MPM), which aims to outline and prioritize exploration targets, has been spurred by advances in data-driven machine learning algorithms. Supervised MPM is a typical few-shot task, suffering from scarcity labeled data, the over-fitting models an uncertainty predictions. main objective this contribution propose robust framework (FSL), combining da...

Code <span>smells refers to any symptoms or anomalies in the source code that shows violation of design principles implementation. Early detection bad smells improves software quality. Nowadays several artificial neural network (ANN) models have been used for different topics engineering: defect prediction, vulnerability detection, and clone detection. It is not necessary know data when u...

Known mineralized locations and randomly chosen non-mineralized are used traditionally as training samples in data-driven mineral prospectivity mapping (MPM). In this paper, we took advantage of (a) the variable importance partial dependence plot, which enable interpretation random forest (RF) modeling, (b) synthetic minority over-sampling technique, investigated efficacy outlier-based for MPM ...

Risk identification and management are the two most important parts of construction project management. Better risk can help in determining future consequences, but identifying possible factors has a direct indirect impact on process. In this paper, prediction system based cross analytical-machine learning model was developed for megaprojects. A total 63 pertaining to cost, time, quality, scope...

This research is aimed at predicting the physical stability for amorphous solid dispersion by utilizing deep learning methods. We propose a prediction model that effectively learns from small dataset imbalanced in terms of class. In order to overcome imbalance problem, our performs hybrid sampling which combines synthetic minority oversampling technique (SMOTE) algorithm with edited nearest nei...

As the global elderly population continues to rise, risk of severe crashes among drivers has become a pressing concern. This study presents comprehensive examination crash severity this demographic, employing machine learning models and data gathered from Virginia, United States America, between 2014 2021. The analysis integrates parametric models, namely logistic regression linear discriminant...