Real-time global event detection particularly catastrophic events has benefited significantly due to the ubiquitous adoption of social media platforms and advancements in image classification and natural language processing. Social media is a rich repository of multimedia content during disasters, encompassing reports on casualties, infrastructure damage, and information about missing individuals. While previous research has predominantly concentrated on textual or image analysis, the proposed study presents a multimodal middle fusion paradigm that includes Cross-modal attention and Self-attention to improve learning from both image and text modalities. Through rigorous experimentation, we validate the effectiveness of our proposed middle fusion paradigm in leveraging complementary information from both textual and visual sources.The proposed intermediate design outperforms current late and early fusion structures, achieving an accuracy of 91.53% and 91.07% in the informativeness and disaster type recognition categories, respectively. This study is among the few that examine all three tasks in the CrisisMMD dataset by combining textual and image analysis, demonstrating an approximate improvement of about 2% in prediction accuracy compared to similar studies on the same dataset.Additionally, ablation studies indicate that it outperforms the best-selected unimodal classifiers, with a 3-5% increase in prediction accuracies across various tasks. Thus, the method aims to bolster emergency response capabilities by offering more precise insights into evolving events.

Background: Gestational Diabetes Mellitus (GDM) is a metabolic condition that develops in course of pregnancy. The World Health Organization describes it as carbohydrate intolerance that causes hyperglycemia of varying severity and manifests itself or is first noticed during pregnancy. Early prediction is now possible, owing to the application of cutting-edge methods like machine learning. Objective: In the proposed empirical study, different machine-learning algorithms are applied to predict the prospective risk factors influencing the progression of GDM in gestating mothers. Materials and methods: The performance of these algorithms is evaluated through accuracy, precision, f1-score, etc. The lifestyle interventions and medications listed in Ayurveda literature are discussed for effective management of the disease. Results: Most of the proposed classifiers achieved a reasonable accuracy range of 75-82 %. Appropriate lifestyle changes, herbal remedies, decoctions, and churnas have all been shown to be useful in lowering the risk of GDM. Early detection using machine learning models can significantly reduce disease severity by facilitating timely Ayurvedic interventions. Conclusion: The proposed work is more focused on the identification of factors impacting GDM in expectant women. A balanced diet with physical exercise, proper medication, and better lifestyle management (through Garbini Paricharya) can control the perils of GDM if diagnosed prematurely.

Multi-label attribute recognition is a critical task in computer vision, with applications ranging across diverse fields. This problem often involves detecting objects with multiple attributes, necessitating sophisticated models capable of both high-level differentiation and fine-grained feature extraction. The integration of object detection and attribute recognition typically relies on approaches such as dual-stage networks, where accurate predictions depend on advanced feature extraction techniques, such as Region of Interest (RoI) pooling. To meet these demands, an efficient method that achieves both reliable detection and attribute classification in a unified framework is essential. This study introduces an innovative MTL framework designed to incorporate Multi-Person Attribute Recognition (MPAR) within a single-model architecture. Named MPAR-RCNN, this framework unifies object detection and attribute recognition tasks through a spatially aware, shared backbone, facilitating efficient and accurate multi-label prediction. Unlike the traditional Fast Region-based Convolutional Neural Network (R-CNN), which separately manages person detection and attribute classification with a dual-stage network, the MPAR-RCNN architecture optimizes both tasks within a single structure. Validated on the WIDER (Web Image Dataset for Event Recognition) dataset, the proposed model demonstrates an improvement over current state-of-the-art (SOTA) architectures, showcasing its potential in advancing multi-label attribute recognition.