Titlebook: Advances in Computing and Data Sciences; 6th International Co Mayank Singh,Vipin Tyagi,Tuncer ?ren Conference proceedings 2022 The Editor(s

Sparkle

書目名稱Advances in Computing and Data Sciences影響因子(影響力)




書目名稱Advances in Computing and Data Sciences影響因子(影響力)學(xué)科排名




書目名稱Advances in Computing and Data Sciences網(wǎng)絡(luò)公開度




書目名稱Advances in Computing and Data Sciences網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Computing and Data Sciences被引頻次




書目名稱Advances in Computing and Data Sciences被引頻次學(xué)科排名




書目名稱Advances in Computing and Data Sciences年度引用




書目名稱Advances in Computing and Data Sciences年度引用學(xué)科排名




書目名稱Advances in Computing and Data Sciences讀者反饋




書目名稱Advances in Computing and Data Sciences讀者反饋學(xué)科排名

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1865-0929 ly reviewed and selected from 411 submissions. The papers focus on advances of next generation computing technologies in the areas of advanced computing and data sciences..978-3-031-12640-6978-3-031-12641-3Series ISSN 1865-0929 Series E-ISSN 1865-0937

圓錐體

https://doi.org/10.1007/978-981-15-7514-3y railways use cases enabling application-initiated and manipulated data sessions. Using the service interfaces, on-board and trackside applications can establish data sessions e.g. in case of emergency. The service is described by typical use cases, data types, resources and supported methods, and some implementation issues are discussed.

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吞下

Adel Sarea,Ghilan Al-Madhagy Taufiq-Hail predicting the occurrence of cybercrime. We have considered four major categories of cybercrime, namely cyber bullying, cyber harassment, cyber fraud and cyber hacking for performing various experiments. In the proposed model we have achieved an accuracy of 97.6% while predicting cyber-criminal activities.

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opportune

https://doi.org/10.1007/978-981-15-7514-3d predict the output as ALL infected or not. According to the experimental results, it is observed that the performance of CNN supersites other machine learning classifiers for the proposed classification in terms of accuracy.

中世紀(jì)

Unsanhame Mawkhlieng,Abhijit Majumdarat, these were then classified and categorized based on the principal preserved component analysis (PPCA) method into four main emotions; positive, negative, ambiguous, and neutral successfully. And for this task, Natural Language Processing (NLP) pre-training was applied, which was a transformer based machine learning technique.

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