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Titlebook: Innovative Methods in Computer Science and Computational Applications in the Era of Industry 5.0; Proceedings of the 5 D. Jude Hemanth,Utku

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樓主: 召喚
11#
發(fā)表于 2025-3-23 12:54:53 | 只看該作者
Developing a Comprehensive Emotion Lexicon for Turkish,n were generated individually. A cross-validation study was conducted to compare the emotion assignments from the study and ChatGPT. The results of the study indicated that the emotion lexicon displayed a high accuracy when applied to textual data. A consensus was reached by four individuals regardi
12#
發(fā)表于 2025-3-23 17:31:29 | 只看該作者
A Hyper-Heuristic Approach to Solving Vehicle Routing Problem in Military Logistics Distribution,apping and determining the soldier’s location, RoboFlow API was applied for learning and processing the physical conditions of the current location, and C# along with Microsoft SQL Server program was used for the development of the web application. The application consists of two stages. In the firs
13#
發(fā)表于 2025-3-23 20:25:28 | 只看該作者
Electricity Price Forecasting Using Automatic Programming Methods,y honeybees. Other methods are Genetic Programming (GP), which is developed by genetic operators such as crossover and mutation and Immune Plasma Programming (IPP) inspired by plasma therapy. The main findings of the study show that AP methods can be effectively applied to complex problems such as E
14#
發(fā)表于 2025-3-24 01:40:59 | 只看該作者
Ethical Considerations and Challenges of AI Adoption in Project Management,anagement, with 81% of professionals acknowledging its impact. Furthermore, Gartner predicts that AI will significantly reduce the project management workload by 80% by 2030. To delve deeper into the ethical considerations and challenges linked to AI adoption in project management, the author conduc
15#
發(fā)表于 2025-3-24 06:01:00 | 只看該作者
16#
發(fā)表于 2025-3-24 10:10:06 | 只看該作者
Analyzing Bias in Machine Learning Models: Insights from the AlexNet Architecture on Balanced and Ir to create a test environment suitable for the study, MNIST data sets were used as a balanced dataset and MNIST data sets as an unbalanced data set. The AlexNet algorithm was used to work with these data sets and the obtained mathematical results were examined. The recognition performance of AlexNe
17#
發(fā)表于 2025-3-24 14:27:33 | 只看該作者
Ghost-Free High Dynamic Range Imaging Based on Two-Stage Dense Image Alignment,igh exposure images as input and uses a convolutional neural network (CNN) to generate HDR images. To evaluate our method, we performed experiments by merging datasets from SIG17 and ICCP19. Quantitative experiments show a significant improvement over the state-of-the-art method, with an increase of
18#
發(fā)表于 2025-3-24 15:31:50 | 只看該作者
19#
發(fā)表于 2025-3-24 22:48:05 | 只看該作者
20#
發(fā)表于 2025-3-25 01:28:58 | 只看該作者
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