Abnormal behavior detection in dense crowd, during the Hajj pilgrimage is vital to public security. Existing approaches face challenges due to factors like occlusions, illumination variations, and uniform attire. This research introduces the Crowd Anomaly Detection Framework (CADF), an improved YOLOv8-based model, integrating Soft-NMS to improve detection accuracy under complex conditions. CADF extensively evaluated on the Hajjv2 dataset, delivering an AUC of 88.27%, a 13.09% improvement over YOLOv2 and 12.19% over YOLOv5, with an Accuracy of 91.6%. To validate its generalizability, the framework is also tested on UCSD and ShanghaiTech datasets. Comparisons with state-of-the-art models, including VGG19 and EfficientDet, demonstrated CADF’s superiority in accuracy, AUC, precision, recall, and mAP metrics. By addressing the unique challenges of Hajj crowd and achieving strong performance across diverse datasets, CADF highlights its potential for real-time crowd anomaly detection, contributing to enhanced safety in large-scale public gatherings and aligning with Sustainable Development Goals 3 and 11.

In the age of big data, there is increasing growth of data on the Internet. It becomes frustrating for users to locate the desired data. Therefore, text summarization emerges as a solution to this problem. It summarizes and presents the users with the gist of the provided documents. However, summarizer systems face challenges, such as poor grammaticality, missing important information, and redundancy, particularly in multi-document summarization. This study involves the development of a graph-based extractive generic MDS technique, named Grapharizer (GRAPH-based summARIZER), focusing on resolving these challenges. Grapharizer addresses the grammaticality problems of the summary using lemmatization during pre-processing. Furthermore, synonym mapping, multi-word expression mapping, and anaphora and cataphora resolution, contribute positively to improving the grammaticality of the generated summary. Challenges, such as redundancy and proper coverage of all topics, are dealt with to achieve informativity and representativeness. Grapharizer is a novel approach which can also be used in combination with different machine learning models. The system was tested on DUC 2004 and Recent News Article datasets against various state-of-the-art techniques. Use of Grapharizer with machine learning increased accuracy by up to 23.05% compared with different baseline techniques on ROUGE scores. Expert evaluation of the proposed system indicated the accuracy to be more than 55%.