The popularity of wireless sensor networks for establishing different communication systems is increasing daily. A wireless network consists of sensors prone to various security threats. These sensor nodes make a wireless network vulnerable to denial-of-service attacks. One of them is a wormhole attack that uses a low latency link between two malicious sensor nodes and affects the routing paths of the entire network. This attack is brutal as it is resistant to many cryptographic schemes and hard to observe within the network. This paper provides a comprehensive review of the literature on the subject of the detection and mitigation of wormhole attacks in wireless sensor networks. The existing surveys are also explored to find gaps in the literature. Several existing schemes based on different methods are also evaluated critically in terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end delay. As artificial intelligence and machine learning have massive potential for the efficient management of sensor networks, this paper provides AI- and ML-based schemes as optimal solutions for the identified state-of-the-art problems in wormhole attack detection. As per the author’s knowledge, this is the first in-depth review of AI- and ML-based techniques in wireless sensor networks for wormhole attack detection. Finally, our paper explored the open research challenges for detecting and mitigating wormhole attacks in wireless networks.

With the tremendous growth in the number of electronic documents, it is becoming challenging to manage the volume of information. Much research has focused on automatically summarizing the information available in the documents. Multi-Document Summarization (MDS) is one approach that aims to extract the information from the available documents in such a concise way that none of the important points are missed from the summary while avoiding the redundancy of information at the same time. This study presents an extensive survey of extractive MDS over the last decade to show the progress of research in this field. We present different techniques of extractive MDS and compare their strengths and weaknesses. Research work is presented by category and evaluated to help the reader understand the work in this field and to guide them in defining their own research directions. Benchmark datasets and standard evaluation techniques are also presented. This study concludes that most of the extractive MDS techniques are successful in developing salient and information-rich summaries of the documents provided.

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%.

Advancements in technology have placed global news at our fingertips, anytime, anywhere, through social media and online news sources. Analyzing the extensive electronic text collections is urgently needed. According to the scholars, combining the topic and word embedding models could improve text representation and help with downstream tasks related to natural language processing. However, the field of news topic recognition lacks a standardized approach to integrating topic models and word embedding models. This presents an exciting opportunity for research, as existing algorithms tend to be overly complex and miss out on the potential benefits of fusion. To overcome limitations in news text topic recognition, this research suggests a new technique word embedding latent Dirichlet allocation that combines topic models and word embeddings for better news topic recognition. This framework seamlessly integrates probabilistic topic modeling using latent Dirichlet allocation with Gibbs sampling, semantic insights from Word2Vec embeddings, and syntactic relationships to extract comprehensive text representations. Popular classifiers leverage these representations to perform automatic and precise news topic identification. Consequently, our framework seamlessly integrates document-topic relationships and contextual information, enabling superior performance, enhanced expressiveness, and efficient dimensionality reduction. Our word embedding method significantly outperforms existing approaches, reaching 88% and 97% accuracy on 20NewsGroup and BBC News in news topic recognition.

Abnormal behavior detection in dense crowds, during the Hajj pilgrimage, is critical for public safety. Existing approches faces challenges due to factors like illumination variations, occlusion, and uniform attire. This research introduces the Crowd Anomaly Detection Framework (CADF), an enhanced version of YOLOv8, designed to address these challenges. The framework features a modular backbone, multi-scale feature fusion, and soft-NMS to improve detection accuracy in over crowded scenes. Applied to the HAJJv2 dataset, the CADF shows significant improvements in detection accuracy, achieving an AUC of 88.27% performing better then YOLOv2 by 13.09% and YOLOv5 by12.19% with a high F1 score of 94.7%. The framework is reliable for enhancing public safety during large-scale events, aligning with Sustainable Development Goals 3 and 11 to improve health, well-being, and urban safety