With the rise of artificial intelligence in university financial management, big data based artificial intelligence is gradually being applied to related fields of university financial management. Based on business bi...
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With the speedy prosperity of artificial intelligence technology, the utilization of computationalintelligence technology in various fields is gradually deepening. To raise the level and efficiency of education and o...
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This study explores the methods of economic system modeling and analysis based on artificial intelligence algorithms. By preprocessing and performing feature engineering on China's quarterly GDP data from 2010 to ...
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HEMT devices have potential to handle the fast processing in applications such as real-time diagnostics utilizing the artificial-intelligence (AI) enhanced abilities. Along-with this HEMT devices can be extensively us...
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This paper situates itself within the broader medical imaging field, focusing on applying generative modelling techniques for anomaly detection in retinal images. Given the complexity of retinal structures, detecting ...
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Machine learning algorithms provide new ways to solve the problem of stock prediction. Among them, the decision tree algorithm has been widely used in financial market prediction because of its strong interpretability...
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With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for indi...
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A dynamic computational Thinking resolution can be likely in a 6G environment by way of Green-IoT and Artificial intelligence (AI), important parts of computational thinking. This order requires lowering difficult pro...
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Generative artificial intelligence offers a more efficient solution for the design of structures. However, an inverse generation of structures, which meet multiple design objectives, remains an open problem. This arti...
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software dominates modern enterprises, affecting numerous functions. software firms constantly experiment with new methodologies to define and assess software quality to stay competitive and ensure excellence. Softwar...
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software dominates modern enterprises, affecting numerous functions. software firms constantly experiment with new methodologies to define and assess software quality to stay competitive and ensure excellence. softwareengineering uses fundamentals and cutting-edge technology to develop great software. In recent decades, Data-mining techniques and machine learning for classifying problematic software projects have emerged to improve software quality. ML approaches, especially ensemble learning models, are becoming fundamental to software engineers' daily jobs. This work created a binary white shark optimizer (WSO) to optimize standard ensemble learning models. The objective is to identify the most suitable ensemble number for weak learners to maximize accuracy on benchmark datasets. The EM model uses 14 weak learners. Twenty-one experimental runs are performed on 15 software-defective module datasets. The optimized ensemble model outperforms the standard Ensemble learning model in AUC-ROC, Accuracy, Precision, Recall, F1-Score, and Specificity. The enhanced model has an average accuracy of 86%, compared to 76% for the standard ensemble model across all datasets. The optimized model outperformed the conventional ensemble for the same datasets, with an average AUC of 72% compared to 61% for the standard ensemble. The optimized model was more stable than the standard model, with an STD of 5.53E-03 vs 7.24E-02 for the ensemble model. The WSO optimization process strengthens and generalizes optimizeels. The study suggests that evolutionary metaheuristic approaches can enhance EM models' accuracy, trustworthiness, and adaptability.
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