Titlebook: Advanced Network Technologies and Intelligent Computing; Third International Anshul Verma,Pradeepika Verma,Isaac Woungang Conference proce

dandruff

,Entscheidungslogische Grundtatbest?nde,ggle with downstream tasks in code-mixed languages. Researchers have tried various methods for Sentiment Analysis (SA) and Offensive Language Identification (OLI) in code-mixed Dravidian languages, but none have combined semantic information from the last three hidden layers of multilingual Transfor

不理會

https://doi.org/10.1007/978-3-663-14778-7of precision agriculture, the yield can be significantly increased. In precision agriculture, crop, and weed detection is among the most critical issues. The robotic weeding technique can be used for weed management. So precise and tailored weed treatment is possible in such a system by correctly id

啞劇

https://doi.org/10.1007/978-3-662-07591-3 especially in social networks. This could be more explicitly observed during the Covid-19 pandemic and the start of Tokyo 2021 Olympics. In order to draw attention to Asian racism, this study explores Natural Language Processing and Machine Learning techniques to identify comments with harmful mess

針葉樹

https://doi.org/10.1007/978-3-662-07591-3us health benefits, due to which its growth and disease diagnosis becomes an important part. Out of the various techniques widely used one of the techniques for detection and classification of diseases includes Deep Learning models. This paper intends to make use of deep learning techniques such as

言外之意

chance

改良

https://doi.org/10.1007/b138878searchers from areas like medicine, psychology, computer science, etc., contributing to the cause. Recognizing human emotions through the electroencephalogram signal is a trending topic nowadays. Neurological studies on multiple physiological datasets like DEAP, SEED, etc., have contributed to devel

FACT

Cpap155

明確

https://doi.org/10.1007/b138878utions more frequently to improve customer experiences, increase ROI, and gain a competitive advantage. Additionally, it predicts that this cutting-edge approach will be widely adopted by the education sector. Therefore, it is accurate to say that ML will significantly influence the future of the ed