Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes...
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In many countries, recent boosts in the construction and renovation sectors and energy efficiency directives are driving a growing interest in the built environment among designers and maintainers. In this context, cu...
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The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four s...
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The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.
A smart city incorporates infrastructure methods that are environmentally responsible,such as smart communications,smart grids,smart energy,and smart *** city administration has prioritized the use of cutting-edge tec...
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A smart city incorporates infrastructure methods that are environmentally responsible,such as smart communications,smart grids,smart energy,and smart *** city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality,with energy resources taking *** achieve optimal energy management in themultidimensional system of a city tribe,it is necessary not only to identify and study the vast majority of energy elements,but also to define their implicit *** is because optimal energy management is required to reach this *** lighting index is an essential consideration when evaluating the comfort *** order to realize the concept of a smart city,the primary objective of this research is to create a system for managing and monitoring the lighting *** is possible to identify two distinct phaseswithin the intelligent *** data collection concludes,the monitoring system will be *** the second step,the operation of the control system is analyzed and its effect on the performance of the numerical model is *** evaluation is based on the proposed *** optimized resultswere deemed satisfactory because they maintained the brightness index value(79%)while consuming less *** intelligent implementation system generated satisfactory outcomes,which were observed 1.75 times on average.
The increasing prevalence of liver disease necessitates the development of accurate predictive models to aid in early diagnosis and treatment. This study investigates the effectiveness of several machine learning clas...
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ISBN:
(数字)9798350369106
ISBN:
(纸本)9798350369113
The increasing prevalence of liver disease necessitates the development of accurate predictive models to aid in early diagnosis and treatment. This study investigates the effectiveness of several machine learning classifiers, Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), and Logistic Regression, in predicting liver disease outcomes based on clinical and biochemical features. Each model's performance was rigorously evaluated using multiple metrics, including F1-score, accuracy, root mean square error (RMSE), mean absolute error (MAE), precision, recall, and cross-validation ROC-AUC scores. The Random Forest Classifier emerged as the most effective model, achieving a perfect accuracy of 100% and an F1-score of 1.00, suggesting its robustness in handling complex, high-dimensional data. The Gradient Boosting Classifier also demonstrated commendable performance with an accuracy of 89% and an F1-score of 0.85, indicating its efficacy in capturing the underlying patterns of liver disease. Conversely, Logistic Regression and SVM exhibited lower performance metrics, pointing to potential limitations in their ability to model the intricacies of liver disease data effectively. The insights from this study not only enhance our understanding of liver disease prediction using machine learning but also emphasize the need for further research to validate these models in diverse clinical populations. This study contributes to the growing body of literature advocating for the integration of machine learning techniques in clinical decision-making processes, ultimately aiming to improve patient outcomes in liver disease management.
Measuring the auditory lateralization elicited by interaural time difference (ITD) cues involves the estimation of a psychometric function (PF). The shape of this function usually follows from the analysis of the subj...
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The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical ...
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As the backbone of Industry 4.0, Cyber-Physical Systems (CPSs) have attracted extensive attention from industry, academia, and government. Missing data is a common problem in CPS data processing and may cause incorrec...
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The deterioration of transmission lines has a profound effect on the reliability and safety of the power grid. Accurate estimation of their age is critical for effective maintenance and investment planning. This paper...
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This preliminary study explored how to make otherwise passive bystanders into an integral part of a virtual reality (VR) experience using asymmetric game design. We present a fully implemented collaborative VR game, c...
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