T. Porntaveetus

Intelligent Business Document Processing Using AI- and NLP-Based Techniques: A Systematic Literature Review

This systematic literature review examines the application of artificial intelligence (AI) and natural language processing (NLP) techniques in intelligent business document processing. The study systematically analyses 46 peer-reviewed articles published between 2014 and 2025 and indexed in the Scopus database. The reviewed literature was grouped into six core NLP-based analytical tasks: semantic search, question answering, summarisation, text data integration and matching, event extraction, and business process management. The findings show that AI- and NLP-based methods have significantly improved the automation, retrieval, interpretation, and structuring of business documents. Semantic search methods enhance information retrieval by moving beyond keyword matching, while question-answering systems and summarisation techniques support automated knowledge discovery and content reduction. Deep learning and transformer-based models have also improved entity matching, event extraction, and predictive business process monitoring. However, the review identifies several persistent limitations, including the continued dominance of extractive approaches, limited adoption of abstractive summarisation, insufficient integration of knowledge graphs, fragmented system development, limited enterprise-scale validation, and a lack of reusable code and shared resources. The findings further indicate that large language models (LLMs), particularly when combined with prompt engineering, retrieval-augmented generation, knowledge graphs, and agent-based architectures, offer promising opportunities to address these gaps. Overall, this review highlights both the progress and remaining challenges in developing scalable, explainable, and domain-adaptable AI-driven systems for intelligent business document processing.

ASGRR: Adaptive Swarm-Guided Graph Policy Routing for energy-efficient WSN-IoT networks

TLQ-Geo: a two-level q-learning-based geographic routing protocol for flying ad hoc networks

In flying ad hoc networks (FANETs), high node mobility, dynamic topology, and limited resources, such as energy and bandwidth, lead to unstable links and short-lived routes. In such an environment, although Q-learning-based routing methods are adaptable, they face serious challenges in practice due to large state space, high computational load, and slow convergence. To address these issues, this paper proposes a two-level Q-learning-based geographic routing protocol called TLQ-Geo for FANETs. This protocol integrates hierarchical decision-making with adaptive reinforcement learning. TLQ-Geo divides the routing process into two layers: the guided region selection (GRS) layer and the Q-learning-based routing (QRL) layer. The GRS layer determines a bounded search corridor between the source and the destination using a chain of intelligent decision points (IDPs), while the QRL layer performs distributed path optimization within this virtual corridor via Q-learning. By restricting the state space to the region guided by IDPs, TLQ-Geo significantly reduces convergence time and computational overhead. In addition, a dynamic inter-layer feedback mechanism periodically evaluates the performance of each IDP chain and adaptively reconfigures it under topology variations. Extensive simulations demonstrate that when the node density varies, TLQ-Geo achieves higher network lifespan (approximately 4.51%), improved packet delivery ratio (about 1.25%), lower routing overhead (around 3.38%), and better energy efficiency (about 20.79%), while the delay increases by about 13.84%, compared to three basic routing methods, namely QRCF, QRF, and QFAN. Also, when the node speed changes, TLQ-Geo yields better network lifespan (approximately 5.46%), higher packet delivery ratio (about 1.69%), lower overhead (around 2.80%), and better energy efficiency (about 6.42%), while the delay increases by about 9.09%.