Selected work

Representative Papers

Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

Jun 24, 2026

This study addresses the challenge of simultaneously detecting sentiment and spam in Arabic tweets from customers of Saudi Telecom Company (STC), where fine-grained sentiment analysis is complicated by linguistic nuances and noisy user-generated content. For the first time, the authors apply the Transformer-based MARBERT pre-trained language model to a five-class sentiment classification task—encompassing positive, negative, neutral, sarcastic, and uncertain sentiments—within an end-to-end joint detection framework for sentiment and spam. Evaluated on a dataset of 24,513 tweets, the proposed approach significantly outperforms existing methods, achieving state-of-the-art results across multiple metrics including F1 score, precision, and recall. This work bridges a critical gap in Arabic natural language processing by demonstrating effective deployment in a real-world customer service context.

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The Impact of Data Compression in Real-Time and Historical Data Acquisition Systems on the Accuracy of Analytical Solutions

Oct 30, 2025

In industrial and IoT applications, high-ratio compression of real-time/historical process data reduces storage costs and improves efficiency but risks degrading the accuracy of statistical analysis, anomaly detection, and machine learning models. This paper systematically evaluates the impact of mainstream time-series compression algorithms—including PCA, SAX, and Delta encoding—on the preservation of critical data features, integrating theoretical analysis, controlled simulation experiments, and multi-scenario empirical validation. We first quantify the nonlinear relationship between compression ratio and analytical bias, identifying safety thresholds that guarantee analysis fidelity. Building on these findings, we propose a hierarchical compression strategy and engineering best practices that jointly optimize storage efficiency and analytical reliability. Results demonstrate that moderate compression preserves over 95% of model performance, whereas compression beyond the identified thresholds severely distorts statistical metrics and causes sharp declines in prediction accuracy.

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Recent publications

Latest Papers

Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

Jun 24, 2026

This study addresses the challenge of simultaneously detecting sentiment and spam in Arabic tweets from customers of Saudi Telecom Company (STC), where fine-grained sentiment analysis is complicated by linguistic nuances and noisy user-generated content. For the first time, the authors apply the Transformer-based MARBERT pre-trained language model to a five-class sentiment classification task—encompassing positive, negative, neutral, sarcastic, and uncertain sentiments—within an end-to-end joint detection framework for sentiment and spam. Evaluated on a dataset of 24,513 tweets, the proposed approach significantly outperforms existing methods, achieving state-of-the-art results across multiple metrics including F1 score, precision, and recall. This work bridges a critical gap in Arabic natural language processing by demonstrating effective deployment in a real-world customer service context.

0 citationsRead paper

The Impact of Data Compression in Real-Time and Historical Data Acquisition Systems on the Accuracy of Analytical Solutions

Oct 30, 2025

In industrial and IoT applications, high-ratio compression of real-time/historical process data reduces storage costs and improves efficiency but risks degrading the accuracy of statistical analysis, anomaly detection, and machine learning models. This paper systematically evaluates the impact of mainstream time-series compression algorithms—including PCA, SAX, and Delta encoding—on the preservation of critical data features, integrating theoretical analysis, controlled simulation experiments, and multi-scenario empirical validation. We first quantify the nonlinear relationship between compression ratio and analytical bias, identifying safety thresholds that guarantee analysis fidelity. Building on these findings, we propose a hierarchical compression strategy and engineering best practices that jointly optimize storage efficiency and analytical reliability. Results demonstrate that moderate compression preserves over 95% of model performance, whereas compression beyond the identified thresholds severely distorts statistical metrics and causes sharp declines in prediction accuracy.

0 citationsRead paper