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Proxy TuNER: Enhancing Cross-Domain NER with Proxy Tuning Techniques
Proxy TuNER is an innovative project focused on improving the versatility and accuracy of Named Entity Recognition (NER) systems across different domains by implementing advanced proxy tuning methods. Traditional NER models often struggle with domain-specific language nuances, but Proxy TuNER addresses this challenge by fine-tuning BERT models using proxy tuning. This technique allows the model to adapt to new domains without modifying its original pre-trained weights, preserving its generalization abilities while enhancing domain-specific performance.
Throughout the project, extensive experimentation was conducted, resulting in significant improvements in both efficiency and accuracy. By introducing domain-specific tuning processes, Proxy TuNER achieved an average F1-score of 71% across various domains, with some domains witnessing a peak performance of 76%. The project also led to a 20% reduction in computational costs and a 30% increase in processing efficiency, making it a scalable solution for diverse NER applications. The success of Proxy TuNER showcases the potential of proxy tuning in advancing the capabilities of cross-domain NER, paving the way for more adaptable and efficient natural language processing models.Source code ↗PythonPyTorchNLPNERProxy TuningScikit-learntransformersPandasNumPyMatplotlib
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IntelliMeet: AI-Enabled Decentralized Video Conferencing Platform
IntelliMeet is a state-of-the-art video conferencing application designed to meet the growing demand for secure, scalable, and efficient virtual communication. The platform leverages the power of decentralized architecture, using WebRTC technology for real-time communication and federated learning to enhance AI model training across distributed networks. IntelliMeet supports up to 10 concurrent users while maintaining high performance and low latency, making it ideal for both professional and casual use.
A key feature of IntelliMeet is its integration of federated learning, which allows AI models to be trained locally on user devices. This approach not only enhances data privacy but also improves model accuracy by 25%, as updates from various users are aggregated into a global model. IntelliMeet's speech-to-text functionality further enhances user experience, achieving an 83% accuracy rate in converting spoken words into text, which is crucial for meetings and conferences. The project’s focus on data security, coupled with its ability to reduce latency by 15%, positions IntelliMeet as a robust and reliable platform for modern video conferencing needs.Source code ↗JavaScriptWebSocketsFederated LearningReal-Time CommunicationtransformersSpeech-to-TextReactGPTFace analysispeer-to-peerFlaskData Security
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Tracking COVID-19 Misinformation
"Tackling COVID-19 Misinformation" is an extensive research project aimed at analyzing and combating misinformation on Twitter related to the COVID-19 pandemic. Utilizing a combination of sentiment analysis and misinformation detection models, this project explores the complex dynamics of information spread and public sentiment on social media platforms. TThe core component involves applying a BERT model, fine-tuned on the LIAR dataset, to determine the factness of statements, while using NLTK VADER for sentiment analysis to classify tweets into categories of sentiment (positive, negative, or neutral) with a high degree of accuracy.". Additionally, the project incorporates a false claims detection system to identify misinformation in real-time, ensuring high factual accuracy in public discussions.
A significant feature of this research is the use of community detection algorithms to discover groups of users with similar sentiment patterns, providing deeper insights into how misinformation spreads within these clusters. Furthermore, the study explores user influence dynamics by employing centrality measures like degree centrality to identify influential users who drive sentiment and misinformation propagation. Through this multidimensional approach, the project provides actionable insights to help public health officials, legislators, and researchers develop strategies to counter misinformation and promote factual correctness in public discourse.Source code ↗PythonBERTLIAR DatasetSentiment AnalysisCommunity DetectionCentrality MeasuresTwitter APINLPNetwork AnalysisVADER
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Talking Buddy: Your AI Companion
Talking Buddy is an advanced AI chatbot designed to provide users with personalized and engaging conversational experiences. The project employs a Gated Recurrent Unit (GRU) neural network, specifically chosen for its efficiency in handling sequential data while maintaining high performance in real-time applications. The chatbot is capable of analyzing user sentiment with an impressive 85% accuracy, allowing it to tailor responses based on the detected emotions, thereby creating a more natural and empathetic interaction.
One of the standout features of Talking Buddy is its streamlined architecture, which includes a lightweight GRU model with 68.7K parameters, optimized for low-latency environments. This design choice not only reduces computational costs by 23% compared to more complex models like LSTMs but also ensures rapid response times, making the chatbot suitable for a wide range of applications, from customer service to personal assistance. The project also involved developing a sleek frontend interface using React, seamlessly integrated with a Flask API that powers the chatbot’s functionality. Talking Buddy exemplifies the potential of AI-driven conversational agents in enhancing user interaction through advanced sentiment analysis and efficient neural network design.Source code ↗PythonGRULSTMNLTKFlaskReactBootstrapSQLitePostmanGit