Umair Shabbir

TV commercials show the business value for the host company of the specific commercial. From the business point of view a system is required that can automatically detect the commercial for third-party for business analysis. Review of the work about detection of TV commercials guide that the work is done for specific videos supported by frame level with high computing cost. This paper proposed a system that works on TV broadcast/Online videos to automatically detect commercials. Instead of relying on frames we are trying to detect commercials with the information of shot-level. The first module works on shot detection other works on its classification. Videos data is split into shots and classify in commercial class and noncommercial class. For shot feature extraction we are using ANN and SVM classifier is trained to complete the classification of specific shots. Using a traditional technique of the commercial detection with Artificial Neural Network enable to handle a variety of program types, unclear commercials, and result good precision and recall not only in TV broadcasting but also it work for online videos.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually. Accurate and timely diagnosis demands the synthesis of heterogeneous clinical data streams including electrocardiograms (ECG), electronic health records (EHR), echocardiographic imaging, laboratory biomarkers, and unstructured clinical notes. In this paper, we propose MMCardio, a novel multimodal deep learning framework that integrates five distinct data modalities through a cross-modal transformer-based fusion mechanism augmented with a dynamic attention gating (DAG) module. Our architecture employs modality-specific encoders a 1-D residual convolutional network for ECG signals, a clinical language model fine-tuned on MIMIC-IV for EHR text, and a 3-D convolutional encoder for echocardiographic video. Their representations are fused via a hierarchical cross-attention mechanism. Evaluated on a combined cohort of 87,243 patients across four public and institutional datasets, MMCardio achieves an AUC-ROC of 0.971, accuracy of 94.7%, and F1-score of 0.943, outperforming the best unimodal baselines by +9.8% AUC and state-of-the-art multimodal methods by +3.8% AUC. An extensive ablation study confirms the additive contribution of each modality. Explainability analysis using SHAP and attention visualization reveals clinically meaningful feature attributions aligned with established cardiology guidelines. This framework demonstrates strong potential for real-time deployment in clinical decision support systems

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