Context-aware recommendation systems are essential in many e-commerce streaming and smart city applications for alleviating information overload such as providing personalized recommendations for a user's changing context and for items with rich attributes. But current multimodal methods lose considerable information in the heterogeneous features, the one-size-fits-all fusion strategies, and the lack of consideration of explicit fine-grained personalization preferences and global collaborative signals leads to suboptimal personalization performance in the presence of data sparsity and modality interference. We propose a new multimodal approach that jointly improves the modalities-specific item attributes through modality-specific graphs models multiple user preferences by performing dynamic context injection, captures hierarchical global signals using attention-based mechanisms and fuses the modalities using a heterogeneous cross-attention approach to make robust prediction. The superiority of the proposed method is also extensively verified on Amazon Baby dataset with the result of Recall@10 = 0.213 and NDCG@20 = 0.185, which is 9.2% and 7.6% better than the best baseline respectively and consistent effects are observed on the ablation studies showing the benefit of the proposed context awareness, hierarchy and attention modules. These results highlight the effectiveness of the framework to improve the accuracy, novelty and explainability of the generated multimodal recommendations, towards scalable recommendations in real-world, sparse and dynamic contexts.
S. Priya, Indhumathi C, Insozhan N et al.· 2026 7th International Confe...· 0 citations
Surveillance systems have experienced rapid growth which results in production of large video data streams. The monitoring process for this data becomes challenging because its volume exceeds human capacity and this situation creates potential for errors. Our research presents a hybrid intelligent surveillance system which conducts automatic video analysis through its two core operational components. The system employs two primary components to achieve its objectives. The SlowFast-based model enables users to track activities through their development across various time intervals. The system employs YOLO-based models to identify critical objects which include fire and weapons and road accidents through real-time monitoring. The system achieves improved stability through the implementation of a temporal debouncing method. The system uses multiple frame detection checks to improve detection accuracy which helps prevent false alarms. The system includes a module dedicated to video summarization which creates a summary from detected activities and visual changes. The system discards unneeded video content while retaining essential information through this process. The model uses a dataset that contains 4758 video clips which display various classification types. The system reaches 85% validation accuracy which demonstrates its ability to handle new data successfully. The system operates on devices with limited resources while providing an immediate alert system to inform users about essential incidents. The system delivers an easy-to-use and effective solution for intelligent video surveillance operations.
Abdul Haq Nalband, R. U, Shashwat Dodamani et al.· 2026 7th International Confe...· 0 citations
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