The ongoing data revolution is being reshaped by the convergence of Agentic Arti fi cial Intelligence (Agentic AI), explainable data analytics, and visual reasoning frameworks. As Big Data continues to expand across scienti fi c, industrial, and social domains, the ability to autonomously explore massive datasets while maintaining explainability, interpretability, and transparency has become a critical challenge. Traditional data analytics systems, although powerful in computation, often operate as “ black boxes, ” limiting human understanding, trust, and accountability in high-stakes decision-making processes. This Special Issue explores new theoretical foundations, models, and architectures that empower explain-able Big Data analytics through Agentic AI, focusing on interpretable reasoning, knowledge graphs, causal inference, human-in-the-loop learning, and visual data storytelling. It seeks to highlight interdisciplinary research spanning AI, data science, cognitive computing, human – computer interaction, and visualization, with applications across health care, fi nance, smart cities, Industry 5.0, and climate informatics. The fi rst article suggests a Big Data – Driven Person-alized Instruction framework powered by Explainable Agentic AI (X-AI). This work advances a scalable and culturally responsive paradigm for personalized education by combining big data analytics, agentic autonomy, and explainable AI. The authors of the second article have developed an Agentic AI pipeline integrating genome-wide association study (GWAS) data from FinnGen Release 12 (6854 cases; 384,461 controls). GWAS identi fi ed a strongly associated locus on chromosome 19q13.32, with lead variant rs429358 (P = 2.79
Sunil Prajapat, Mohammad Shabaz, Muhammad Asim Saleem et al.· Big Data· 0 citations
Automatic skin lesion segmentation is a pivotal problem in the medical domain and an indispensable component in the computer-aided diagnosis program. Most convolutional neural network-based segmentation algorithms have demonstrated promising performance due to their ability to encode detail and semantic features efficiently. However, they fail to capture the long-range contextual information at the global level. Therefore, researchers employ Transformer architecture to address this issue. Unfortunately, these methods fail to learn sufficient pixel information at the local level. Motivated by this, some researchers attempt to design a hybrid architecture based on CNN and Transformer. However, the large number of parameters and high computational cost make them challenging to train and use. To alleviate these problems, we propose an effective Lightweight Multi-scale Deformation Aggregation U-Net (LMDAU-Net), which consists of a Lightweight Local-global Learning Module (LLM) and an Adaptive Interactive Fusion Module (AIF). Specifically, we utilize the two branches of the proposed LLM to efficiently learn local fine-grained and global coarse-grained features that assist the model in capturing the complementary feature representations. Moreover, we employ the AIF to selectively learn semantic and detail features at different scales, which can dynamically explore variable feature cues. Extensive experiments on four skin benchmarks, including ISIC2016, ISIC 2017, ISIC2018, and PH2, demonstrate that LMDAU-Net achieves state-of-the-art performance in both qualitative and quantitative aspects. We have released our code on https://github.com/Lm0611/LMDAU-Net.
Jun-Han Hu, Ming Liu, Jing Yang et al.· IEEE journal of biomedical a...· 0 citations
A scalable and adaptable paradigm for implementing fine-grained authorization in distributed systems, such as smart healthcare, is Attribute-Based Access Control (ABAC). ABAC offers dynamic data sharing capabilities, which are crucial for modern healthcare systems, by allowing access decisions based on user attributes. ABAC is frequently combined with cryptographic techniques to further improve the security of data transfer and protect private medical records in untrusted settings. However, it is still difficult to ensure both effective management of massive amounts of medical data and robust security against new quantum threats. In this paper, we present a hybrid quantum-safe ABAC framework for secure smart healthcare data sharing. The proposed technique enables secure and efficient access control over encrypted medical records by combining fine-grained attribute-based policy enforcement with lightweight cryptographic primitives. The proposed scheme is appropriate for practical smart healthcare settings, as it supports efficient access verification and dynamic attribute management. Security analysis shows that the framework maintains data secrecy and access control correctness while offering resistance against quantum adversaries. Performance evaluation shows that the proposed framework achieves lower computational, communication, and storage overhead compared to existing approaches. Thus, the proposed framework integrates fine-grained attribute-based access control with quantum-safe communication techniques and blockchain-assisted attribute management in order to enable secure, efficient, and scalable data sharing in smart healthcare systems.
Vehicular consumer electronics increasingly support computation-intensive and latency-sensitive services, imposing stringent efficiency, reliability, and security requirements on vehicular edge computing (VEC) systems. In dynamic vehicular environments, inference-based information leakage and anomalous communication behaviors further threaten system performance and data privacy. To address these challenges, this paper proposes a UAV-assisted cooperative VEC architecture that integrates threat-aware task offloading with intelligent spatiotemporal caching across roadside units (RSUs) and UAV edge nodes. A security-aware uplink transmission model is developed to capture potential information leakage risks and abnormal communication patterns, enabling adaptive offloading decisions. We formulate a joint optimization problem to minimize end-to-end task execution delay while improving cache utilization under limited computing and storage resources. To efficiently solve this problem, a Threat-Aware Joint Optimization (TAGO) framework is designed by combining proximal policy optimization for adaptive task offloading and a gradient-based caching update derived from the Frank-Wolfe algorithm to capture spatiotemporal service popularity. Simulation results demonstrate that the proposed approach significantly reduces task delay and improves cache efficiency compared with several baseline strategies, showing its effectiveness for secure and efficient UAV-assisted vehicular consumer electronics systems.
A large language model (LLM) enhanced differentiable trajectory planning framework for IoT-enabled autonomous driving is proposed and a surrounding agent centric data augmentation strategy is introduced to reorganize sur rounding agent trajectories as additional planning supervision, thereby improving the training distribution without collecting additional raw data.
Shihao Zhang, Jing Yang, Ziyu Song et al.· IEEE Internet of Things Jour...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
