Managing appendicitis in children is still lacking international consensus. This is due to the lack of defined international pediatric appendicitis management recommendations and the unavailability of data-driven stud...
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This paper presents a comparative study of machinelearning models for detecting abusive messages, focusing on code-mixed data in Wolof and French languages. With the increasing use of digital platforms, there has bee...
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This paper presents a deep learning-based model developed to accurately classify Parkinson's disease patients using 3D MRI scans and data augmentation techniques. The study aimed to address the research question o...
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This paper investigates the development of new energy vehicles in China using a combination of multi-model machinelearning and the ARIMA algorithm. Initially, four indicators and six influencing factors representing ...
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The network's infrastructure becomes more vulnerable to cyber-attacks as the number of services offered through the internet expands. The complexity of "Distributed Denial-of-Service (DDoS)" threats on t...
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The increasing prominence of natural disasters and the variability in climate change have underscored the significance of research in contemporary and prospective studies. This investigation zeroes in on floods, a ubi...
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The interaction between soils and geosynthetics plays an important role in the applications of these materials for reinforcement in geotechnical engineering. The complexities of soil-geosynthetic interactions vary dep...
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The interaction between soils and geosynthetics plays an important role in the applications of these materials for reinforcement in geotechnical engineering. The complexities of soil-geosynthetic interactions vary depending on the type and properties of both the geosynthetic and the soil. This paper introduces a machinelearning approach, specifically a random forest algorithm, for predicting interface friction angles. The dataset comprises 495 interfaces involving geomembranes and sand, with fourteen influencing parameters recorded for each interface, influencing the shear strength outcome. In the analysis, Pearson's correlation coefficient is employed to measure the linear interdependence between each pair of input-input and input-output variables. Following the linear regression analysis, an optimized random forest is utilized to project the interface friction angle. The random forest algorithm divides the selected data into training and testing sets, and only 3% of the training set and 6% of the testing set exceed +/- 5 degrees from the actual records. The coefficient of determination (R-2) indicates strong agreement between the predicted and laboratory study friction angles, with R-2 = 0.93 for the training set and R-2 = 0.92 for the testing set. Consequently, the random forest algorithm demonstrates effectiveness in predicting interface friction angles.
PDF malware is a significant threat to computer security. The purpose of this study is to introduce a new approach for improving the security of PDF readers. The method utilizes transfer learning by leveraging existin...
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Lack of planning and regulations around the landfills has resulted and continues to result in severe environmental damage to the immediate environment around the landfills. Our study systematically reviews the literat...
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Handwritten digit recognition has recently gained importance, attracting many researchers due to its use in various machinelearning and computer vision applications. As technology and science progressing, there is a ...
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