Web-Scale Multimodal Summarization using CLIP-Based Semantic Alignment

We introduce Web-Scale Multimodal Summarization, a lightweight framework for generating summaries by combining retrieved text and image data from web sources. Given a user-defined topic, the system performs parallel web, news, and image searches. Retrieved images are ranked using a fine-tuned CLIP model to measure semantic alignment with topic and text. Optional BLIP captioning enables image-only summaries for stronger multimodal coherence.The pipeline supports features such as adjustable fetch limits, semantic filtering, summary styling, and downloading structured outputs. We expose the system via a Gradio-based API with controllable parameters and preconfigured presets.Evaluation on 500 image-caption pairs with 20:1 contrastive negatives yields a ROC-AUC of 0.9270, an F1-score of 0.6504, and an accuracy of 96.99%, demonstrating strong multimodal alignment. This work provides a configurable, deployable tool for web-scale summarization that integrates language, retrieval, and vision models in a user-extensible pipeline.

Accuracy-Constrained CNN Pruning for Efficient and Reliable EEG-Based Seizure Detection

Deep learning models, especially convolutional neural networks (CNNs), have shown considerable promise for biomedical signals such as EEG-based seizure detection. However, these models come with challenges, primarily due to their size and compute requirements in environments where real-time detection or limited resources are available. In this study, we present a lightweight one-dimensional CNN model with structured pruning to improve efficiency and reliability. The model was trained with mild early stopping to address possible overfitting, achieving an accuracy of 92.78% and a macro-F1 score of 0.8686. Structured pruning of the baseline CNN involved removing 50% of the convolutional kernels based on their importance to model predictions. Surprisingly, after pruning the weights and memory by 50%, the new network was still able to maintain predictive capabilities, while modestly increasing precision to 92.87% and improving the macro-F1 score to 0.8707. Overall, we present a convincing case that structured pruning removes redundancy, improves generalization, and, in combination with mild early stopping, achieves a promising way forward to improve seizure detection efficiency and reliability, which is clear motivation for resource-limited settings.