Khouloud Boukadi

Artificial intelligence (AI) is increasingly adopted in animal‐related domains such as health monitoring, behaviour analysis and welfare assessment. However, concerns about the transparency and interoperability of AI outputs are rising. This scoping review investigates how AI and explainable AI (XAI) are applied in animal‐related systems and examines the role of semantic technologies in enhancing their interoperability. In this review, we followed PRISMA‐ScR guidelines and conducted five structured searches across ScienceDirect, Springer, Scopus, IEEE Xplore and Web of Science. The searches targeted AI applications (S ₁ ), XAI applications (S ₂ ) and the use of semantic knowledge in AI (S ₃ ) and XAI (S ₄ ). Studies were screened, assessed using the QualSyst tool and selected based on Q1/Q2 SJR or CORE C–A* classification. A total of 21 review papers were selected for AI applications and 8 for XAI. No eligible papers were found regarding the use of semantics in AI or XAI. While explainability is gaining attention, it remains mostly limited to visual or statistical tools, lacking domain‐specific contextualization. We observed that AI is now pervasive in animal‐related research, yet XAI practices remain underdeveloped and lack semantic grounding. This gap calls for ontology‐based explainability to enhance trust, relevance and usability for both experts and non‐specialists.

The Proof of Stake (PoS) consensus mechanism is increasingly used in blockchain systems; however, resource allocation for PoS-based mobile blockchain networks remains underexplored, particularly given the constraints of mobile devices. This work introduces MEC-Chain, a new framework that integrates Mobile Edge Computing (MEC) with mobile blockchain to support efficient validator-node execution under PoS. MEC-Chain formalizes a multi-objective resource-allocation problem that jointly considers latency, reliability, and cost from both the validator and MEC-provider perspectives. To address this challenge, we develop a deep reinforcement learning-based allocation agent using the Proximal Policy Optimization (PPO) algorithm. Experimental results show that PPO achieves a 30–40% reduction in total execution time, 25–35% lower transmission latency, and 10–15% higher reliability compared to A2C (Advantage Actor–Critic) and DQN (Deep Q-Network), while offering comparable cost savings across all methods. These results demonstrate the effectiveness of MEC-Chain in enabling low-latency, reliable, and resource-efficient PoS validation within mobile blockchain environments.