The high rate at which telecommunication services are changing has yielded a volume of heterogeneous data never seen before such as call detail records, signaling data, and subscriber activity traces. These datasets are crucial in relation to optimization and business intelligence, but effective use of such datasets requires quality metadata. Traditional metadata management is mostly manual and centralized, which cannot support the pace and scale of the new telecom big data systems. As a solution to these shortcomings, the present article suggests a scalable metadata mining strategy that is founded on agentic intelligence. In comparison to more traditional machine learning pipelines, agentic intelligence enables the use of autonomous goal-oriented AI agents, capable of reasoning in a hard data space and relevant to new network topologies and network schemas. The proposed architecture will include a multiagent architecture at a distributed level, having specialized agents: (1) schema drifting ingestion and monitoring agents of data flows; (2) semantic mining actors based on natural language processing to infer contextual metadata; (3) process-aware agents for process mining to determine lineage and dependencies between data; and (4) compliance with policy and metadata retrospect: government agents. A major effect of the work is the integration of agentic intelligence and scalable infrastructures of big data such as cloud-native analytics platforms and distributed data lakes. These agents coordinate themselves using lightweight coordination mechanisms to provide a persistent metadata knowledge layer that facilitates system interoperability and robust downstream analytics. The design concepts are geared toward horizontal scalability and fault tolerance, enabling the distribution of the agents at dynamically evolving manner regarding the workload peak. Besides, agents also enhance their strategies upon continuous feedback to handle concept drift as well. Typical examples of telecom applications such as monitoring network performance and fraud detection have demonstrated that such an approach far outperforms a rule-based approach in the accuracy, coverage, and timeliness of metadata. And lastly, the framework will be capable of offering more dynamic and sustainable data management within the telecom sector.
Embodied intelligence is rapidly evolving toward natural human–machine interaction, where real-time cross-lingual speech processing is essential for robots to understand and execute commands across languages. This study reviews research progress from January 2010 to November 1, 2025, using CiteSpace to analyze knowledge structure, hotspots, and trends. The year 2010 was selected as the start date because it marks the early emergence of foundational theoretical discussions on embodied intelligence internationally, providing a comprehensive baseline to observe the entire evolutionary trajectory of the field, even though substantive domestic research emerged later around 2015. Results show a sharp rise in research interest after 2023, driven by multilingual interaction and robotics applications. Current hotspots include multimodal representation learning, low-latency speech-to-text models, cross-lingual semantic alignment, and embodied control. The field has progressed through three stages: conceptual foundations, real-time neural integration, and large-model-driven general embodiment. We summarize the technical framework spanning speech recognition, semantic grounding, and action execution, and identify challenges such as latency reduction, noise robustness, and cross-lingual knowledge transfer. Future work should focus on unified speech–action representation, scalable low-latency architectures, adaptive learning, and ethical governance. This study outlines development trends and prospects for cross-lingual real-time speech interaction in embodied intelligence.
Real-time product carbon footprint (PCF) reporting is increasingly required in global supply chains, yet conventional life cycle assessment (LCA) remains difficult to scale due to fragmented multitier data, heterogeneous formats, and substantial manual effort. This article proposes Auto-LCA, a system-level architecture for continuous, product-level carbon estimation that integrates (i) automated supply-chain data acquisition, (ii) low-latency multilingual natural language processing for extracting life-cycle-relevant attributes from unstructured documents, (iii) a structured product carbon data model, and (iv) machine-learning-based completion of missing life cycle inventory parameters with provenance and confidence tags. A central mechanism is the "carbon header," which packages accumulated embodied and operational emissions as transferable, version-controlled metadata associated with product identifiers and propagated incrementally across supply-chain tiers. Auto-LCA is designed as an open Application Programming Interface (API) layer to interface with enterprise systems (e.g., ERP/PLM/MES) and supports update-driven recomputation that recalculates only affected nodes in the supply-chain graph, improving scalability for large product portfolios and deep supplier networks. Case studies in the steel, cement, and automotive sectors demonstrate that Auto-LCA yields estimates close to representative conventional LCA benchmarks (typically within ±4%), while enabling broader coverage and more frequent updates under realistic data availability constraints. The framework also aligns with emerging digital product passport and sustainability reporting needs by enabling traceable, machine-readable carbon metadata exchange across organizations.
This research proposes a secure, explainable, and context-aware governance framework for blockchain-based digital media contracts in multimodal artificial intelligence-enabled AIoT interactive systems. As digital licensing, NFT copyright management, royalty distribution, and cross-chain content circulation become increasingly embedded in smart media ecosystems, existing contract auditing approaches remain limited by unimodal analysis, weak explainability, black-box decision processes, and insufficient cross-platform generalization. To address these challenges, the proposed framework integrates cross-modal data fusion, graph neural networks, and evolutionary expert knowledge fusion to model smart contract code, abstract syntax trees, control-flow graphs, intercontract transactions, cross-chain records, licensing metadata, NFT copyright attributes, and multimodal IoT interaction logs as unified graph representations. By incorporating causal priors and differentiable rules, the framework supports transparent risk reasoning, verifiable explanations, and trustworthy decision support. The study contributes to explainable blockchain security, multimodal AI governance, and intelligent media systems by enabling more robust detection of copyright misuse, unauthorized licensing, abnormal content distribution, and cross-chain transaction risks.
Short-term traffic flow forecasting is essential for travel safety, congestion avoidance, and effective traffic management as an integral part of intelligent transport systems. Long short-term memory (LSTM) has become a promising technique for forecasting traffic flow. Unfortunately, the LSTM model does not achieve adequate forecast accuracy due to noisy traffic data and poor selection of hyperparameter optimization values. To address these limitations, this study proposes a composite SFFSDF-LSTM model that integrates sinusoidal firefly feature selection (SFFS) and dragonfly (DF) algorithms with an LSTM network. The SFFS algorithm performs adaptive feature selection, while the DF algorithm optimizes hyperparameters to improve convergence and prediction accuracy. The proposed model efficiently identifies the optimal set of LSTM features, weights, biases, and hyperparameters for domain traffic flow prediction while minimizing training errors. From the perspective of error analysis and predictive analytics, the predictive accuracy of the combined model is evaluated and found to be superior to traditional nonparametric and baseline deep learning models.
