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

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https://doi.org/10.1007/978-3-662-07591-3 are Training and Testing accuracy, Precision, recall, and F1 score. The study proves to provide the best results with the highest training accuracy of 98.80% and testing accuracy of 94.74% with the InceptionV3 models compared to ResNet152V2 and VGG19.
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https://doi.org/10.1007/b138878Forest, Naïve Bayes and Neural Network were implemented. Besides these methods a lexicon-based approach was used to see the overall variation in the results. The lexicon resource for Benali was created for this implementation.
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Machine Learning Analysis on Hate Speech Against Asiansyes, LSTM and CNN, the latter presented the better results and was later used for the development of a Twitter bot, able to consult whether or not any given thread had a tendency to racism. Thus, by the end of this study, racism messages classification was proven to be possible, opening possibilities to deepening on this subject.
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Deep Transfer Learning for Enhanced Blackgram Disease Detection: A Transfer Learning - Driven Approa are Training and Testing accuracy, Precision, recall, and F1 score. The study proves to provide the best results with the highest training accuracy of 98.80% and testing accuracy of 94.74% with the InceptionV3 models compared to ResNet152V2 and VGG19.
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Sustainable Natural Gas Price Forecasting with DEEPARa Root Mean Squared Error (RMSE) of 0.2021. This model provides valuable insights for stakeholders and serves as a tool to estimate natural gas market prices, assisting in decision-making within the competitive market. The approach used in this study enhances forecasting performance, enabling efficient management of the energy system.
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Analyzing the Performance of BERT for the Sentiment Classification Task in Bengali TextForest, Naïve Bayes and Neural Network were implemented. Besides these methods a lexicon-based approach was used to see the overall variation in the results. The lexicon resource for Benali was created for this implementation.
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