The exchange of information in Wireless Sensor Networks (WSNs) across different environments, whether they are above the ground, underground, underwater, or in space has advanced significantly over time. Among these advancements, precise localization of nodes within the network remains a key and vital challenge. In the context of Underwater Wireless Sensor Networks (UWSNs), localization plays a pivotal role in enabling the efficient execution of diverse underwater applications such as environmental monitoring, disaster management, military surveillance and many more. This review article is focusing on three primary aspects, the first section focuses on the fundamentals of localization in UWSNs, providing an in depth and comprehensive discussion on various localization methods. Where we have highlighted the two main categories that are anchor based and anchor free localization along with their respective subcategories. The second section of this article examines the diverse challenges that may emerge during the implementation of the localization process. To enhance clarity and structure, these challenges have been carefully analyzed and categorized into three main groups and that are, (i) Algorithmic challenges, (ii) Technical challenges, and (iii) Environmental challenges. The third section of this article begins by presenting the latest advancements in UWSNs localization, followed by an exploration of how Machine Learning (ML) and Deep Learning (DL) models can contribute in enhancing the localization process. To evaluate the potential benefits of the ML and DL techniques, we have assessed their performance through simulations, focusing on metrics such as localization error, velocity estimation error, Root Mean Square Error (RMSE), and energy consumption. This review also aims to provide actionable insights and a guideline for future research directions and opportunities for practitioners in the field of UWSNs localization. Which will ultimately help in enhancing the performance and reliability of underwater applications by advancing localization techniques and promoting seamless integration.

In the realm of emotion recognition, understanding the intricate relationships between emotions and their underlying causes remains a significant challenge. This paper presents MultiCauseNet, a novel framework designed to effectively extract emotion-cause pairs by leveraging multimodal data, including text, audio, and video. The proposed approach integrates advanced multimodal feature extraction techniques with attention mechanisms to enhance the understanding of emotional contexts. The key text, audio, and video features are extracted using BERT, Wav2Vec, and Vision transformers (ViTs), which are then employed to construct a comprehensive multimodal graph. The graph encodes the relationships between emotions and potential causes, and Graph Attention Networks (GATs) are used to weigh and prioritize relevant features across the modalities. To further improve performance, Transformers are employed to model intra-modal and inter-modal dependencies through self-attention and cross-attention mechanisms. This enables a more robust multimodal information fusion, capturing the global context of emotional interactions. This dynamic attention mechanism enables MultiCauseNet to capture complex interactions between emotional triggers and causes, improving extraction accuracy. Experiments on emotion benchmark datasets, including IEMOCAP and MELD achieved a WFI score of 73.02 and 53.67 respectively. The results for cause pair analysis are evaluated on ECF and ConvECPE with a Cause recognition F1 score of 65.12 and 84.51, and a Pair extraction F1 score of 55.12 and 51.34.

Artificial intelligence (AI) has become a critical tool for risk management across industries such as insurance, healthcare, business, and finance. It enables risk quantification, improves predictive accuracy, and supports decision-making in dynamic and uncertain environments. This paper examines models, methods, and frameworks for AI-based risk assessment, while addressing concerns of ethics, regulation, and explainability. Key technologies, including machine learning, deep learning, and reinforcement learning, are highlighted for their ability to transform traditional approaches by enhancing prediction, optimization, and decision processes. The second part focuses on AI-driven risk modeling techniques. Supervised learning methods such as support vector machines, random forests, and decision trees demonstrate strong predictive capacity from historical data. Unsupervised learning, including clustering methods, uncovers hidden patterns in risk datasets. Reinforcement learning is gaining prominence for adaptive risk optimization under changing conditions. Deep learning, particularly neural networks, offers significant improvements in handling large-scale data and achieving higher predictive accuracy. Finally, the paper outlines the future of AI in risk management, recognizing both its transformative potential and persistent challenges. With the rapid advancement of AI and increasing availability of big data, risk management practices are undergoing fundamental change. Yet, successful adoption requires careful attention to ethical, legal, and technological considerations. Organizations must continue to adapt to ensure that AI technologies are deployed transparently, responsibly, and to the benefit of enterprises and society as a whole.

In this article, we present a novel Transformer-Based Aspect-Level Sentiment Classification with Intent (TASCI) model, designed to enhance sentiment analysis by integrating aspect-level sentiment classification with intent analysis. Traditional sentiment analysis methods often overlook the nuanced relationship between the intent behind a statement and the sentiment expressed toward specific aspects of an entity. TASCI addresses this gap by first extracting aspects using a self-attention mechanism and then employing a Transformer-based model to infer the speaker’s intent from preceding sentences. This dual approach allows TASCI to contextualize sentiment analysis, providing a more accurate reflection of user opinions. We validate TASCI’s performance on three benchmark datasets: Restaurant, Laptop, and Twitter, achieving state-of-the-art results with an accuracy of 89.10% and a macro-F1 score of 83.38% on the Restaurant dataset, 84.81% accuracy and 78.63% macro-F1 score on the Laptop dataset, and 79.08% accuracy and 77.27% macro-F1 score on the Twitter dataset. These results demonstrate that incorporating intent analysis significantly enhances the model’s ability to capture complex sentiment expressions across different domains, thereby setting a new standard for aspect-level sentiment classification.

The widespread adoption of IoT technology across various domains has introduced several challenges, particularly in the areas of authentication and data storage. This research paper presents a novel approach utilizing blockchain technology to address these challenges in the context of IoT. The proposed scheme introduces a hierarchical network structure comprising of base stations (BS), cluster heads (CHs), and remote sensors (RS) to ensure seamless communication between nodes. To tackle latency and throughput issues, a consortium blockchain is employed, with adaptive proof-of-work (PoW) and proof-of-authority (PoA) consensus mechanisms implemented at BS and CHs, respectively. The scheme incorporates multiple protocols including initialization, registration, authentication, node classification, trust computation, and revocation strategies to handle malicious nodes effectively. Smart contracts and a distributed hash table (DHT) are utilized to reduce certification delays and maintain crucial authentication data, trust values, and transaction history. Extensive simulation analysis is conducted to evaluate the proposed scheme, considering factors such as certification delay, accuracy in detecting malicious nodes, communication efficiency, and computation cost. Comparative assessments against existing schemes demonstrate the superior performance, efficiency, and effectiveness of the proposed scheme.