Titlebook: Intelligent Computing; Proceedings of the 2 Kohei Arai Conference proceedings 2024 The Editor(s) (if applicable) and The Author(s), under e

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書目名稱Intelligent Computing影響因子(影響力)




書目名稱Intelligent Computing影響因子(影響力)學(xué)科排名




書目名稱Intelligent Computing網(wǎng)絡(luò)公開度




書目名稱Intelligent Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Intelligent Computing被引頻次




書目名稱Intelligent Computing被引頻次學(xué)科排名




書目名稱Intelligent Computing年度引用




書目名稱Intelligent Computing年度引用學(xué)科排名




書目名稱Intelligent Computing讀者反饋




書目名稱Intelligent Computing讀者反饋學(xué)科排名

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Cindy van den Berg,Sunet Eybersrgie von Einflu? sind, gewisse Grunds?tze zur Verteilung der Selbstkosten aufgestellt werden k?nnen. Damit ist aber die Aufgabe der Tarif bildung keineswegs ersch?pft; es handelt sich jetzt vielmehr noch darum, die gew?hlten Prinzipien in der eigentlichen Preisstellung zum Ausdruck zu bringen, d. h.

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Faster Lock-Free Atomic Shared Pointers,an gain more throughput and reduce latency, which is crucial to complex real-time applications like audio and video processing, robotics, or real-time sensor data processing in embedded hardware. Parallel algorithms, however, come with the price of concurrency and synchronization. For example, the p

wreathe

,CICO,e: A Compute Carbon Footprint Estimation Tool Based on?Time Series Data,ivities. Generally, the presented carbon footprint estimation methods calculate the product of the compute hardware’s energy consumption and a factor expressing the emissions for the corresponding energy produced in the region where the compute hardware is located. However, there are three open issu

linear

Image Classification Method Based on Chaos Neural Network,ck Propagation Neural Network (BPNN) due to the fact that behavior of the solution from CHAOS NN is chaotic so that the solution may get out from local minima. In order to determine the parameters required for CHAOS NN, training samples are used. The required parameters for the simulation are calcul

ineluctable

,Human-Created and AI-Generated Text: What’s Left to Uncover?,arning models have created remarkably sophisticated language generators, blurring the line between human-authored content and AI-generated text. This poses a challenge for educators and professionals in distinguishing authentic work from instances of plagiarism. This study investigates the fundament

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AraXLM: New XLM-RoBERTa Based Method for Plagiarism Detection in Arabic Text,ication beyond identification of plagiarized articles and research papers in academia, safeguarding authors’ literary rights across various domains, including digital marketing and libraries. However, the emergence of cross-language plagiarism poses both a significant challenge and a crucial area fo