Alien species have the potential to significantly threaten and harm the living beings and the environment. The search for cheap and easy-to-deploy solutions has been among the priorities of environmentalists and resea...
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The department of the Internet of Things is developing very rapidly. We interact with its other fields in our daily life in one way or another way like smart vehicle systems, smart homes, smart medical systems, and mo...
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The implementation of the Stimuli-Organism-Response (SOR) framework introduced by Mehrabian and Russell in 1974 is described in this paper. The framework as an environmental psychology framework specifies that the org...
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The Internet of Things (IoT) is a cutting-edge concept that unites the Internet with actual physical objects from a variety of industries, such as home automation, manufacturing, human health, and environmental monito...
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WebAssembly is a fast, safe, and portable low-level language suitable for diverse application scenarios. And The WebAssembly virtual machines are widely used by Web browsers or Blockchain platforms as execution engine...
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Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meani...
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Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.
Gray code, a voltage-level-to-data-bit translation scheme, is widely used in QLC SSDs. However, it causes the four data bits in QLC to exhibit significantly different read and write performance with up to 8 × lat...
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Tile-based streaming and super resolution are two representative technologies adopted to improve bandwidth efficiency of immersive video steaming. The former allows selective download of contents in the user viewport ...
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This paper presents a multimodal interaction system designed for speech-based autism intervention in Sinhala-speaking Sri Lankan children, utilizing the NAO robot. The system integrates four components, language conte...
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Machine learning techniques, particularly fuzzy Support Vector Machines (fuzzy SVM), have demonstrated effectiveness in sentiment classification within social networks. However, the evolving complexity of social netwo...
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Machine learning techniques, particularly fuzzy Support Vector Machines (fuzzy SVM), have demonstrated effectiveness in sentiment classification within social networks. However, the evolving complexity of social networks content and the necessity to handle diverse opinions prompt ongoing advancements in sentiment analysis methodologies. In this study, we propose an innovative approach by integrating a Convolutional Neural Network (CNN) model with fuzzy SVM. Our key contribution lies in the fusion of these two methodologies, capitalizing on the strengths of fuzzy SVM for opinion mining and CNN for extracting intricate features. Additionally, we extend this method to enable multi-class opinion classification, allowing for a finer understanding of nuances in expressed sentiments. Through evaluation on benchmark datasets, we highlight a notable improvement in performance compared to baseline methods. The results underscore the enhanced generalization capabilities and increased accuracy in opinion classification offered by our hybrid model, rendering it a promising asset for decision-making applications based on multi-opinion analysis.
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