Titlebook: Digital Technologies and Applications; Proceedings of ICDTA Saad Motahhir,Badre Bossoufi Conference proceedings 2024 The Editor(s) (if appl

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書目名稱Digital Technologies and Applications影響因子(影響力)




書目名稱Digital Technologies and Applications影響因子(影響力)學(xué)科排名




書目名稱Digital Technologies and Applications網(wǎng)絡(luò)公開度




書目名稱Digital Technologies and Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Digital Technologies and Applications被引頻次




書目名稱Digital Technologies and Applications被引頻次學(xué)科排名




書目名稱Digital Technologies and Applications年度引用




書目名稱Digital Technologies and Applications年度引用學(xué)科排名




書目名稱Digital Technologies and Applications讀者反饋




書目名稱Digital Technologies and Applications讀者反饋學(xué)科排名

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https://doi.org/10.1007/978-3-031-68650-4ICDTA; Machine Learning; Artificial Intelligence; Big Data; Internet of Things; Blockchain; Embedded Syste

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978-3-031-68649-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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eativity and problem-solving capabilities. This paper carries out an extensive literature review to explore the current state of the art of the most relevant trends addressing how artificial intelligence supports idea generation. The findings are split into two sections. The descriptive analysis sec

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techniques, including statistical analysis, physical examinations, and in-course data mining are employed to determine these rates. This paper implements an educational data mining approach, which examines large data sets, in order to extricate important information that will enable to optimize stu

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Illustrating Talks and Articles,n effectively predict flood hazard, employing a regression model proves instrumental in estimating and predicting flood characteristics within urban areas. This approach reduces the computational time associated with the hydrodynamic models. This paper provides a systematic literature review (SLR) o

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gly embedded into how investors calculate risk and opportunities, alongside traditional financial metrics. But incorporating environmental data into investment analysis faces a whole host of challenges, complexity because of inherent diversity, lack of standardization and the list goes on..The prese

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ems powered by deep learning. This study thoroughly investigates this transformative evolution, delving into the com- plex workings of recommender systems..Beyond theoretical discussions, our investigation involves a practical evaluation of these techniques on a variety of datasets, including the Am