This study presents a novel resource scheduling framework for cloud computing environments that incorporates the Age of Information (AOI) metric into the decision-making process, enabling precise quantification and optimization of information freshness. The proposed framework leverages an enhanced deep reinforcement learning algorithm to adaptively learn optimal scheduling policies in dynamic cloud settings. We introduce a multidimensional reward function that not only considers traditional metrics such as resource utilization and task completion time but also integrates AOI as a core indicator, thereby achieving holistic optimization of information freshness at the system level. The method incorporates prioritized experience replay and n-step learning mechanisms, which enhance learning efficiency and policy stability. Extensive simulation experiments demonstrate that the framework maintains low average AOI under varying workloads while adhering to resource capacity and energy consumption constraints. This approach provides novel theoretical foundations and practical guidelines for improving real-time cloud service quality and facilitating timely decision-making in edge computing scenarios.

Mobile banking has been experiencing unsustainable growth in the last ten years, as the volume of digital payments worldwide is currently more than USD 8.49 trillion and is estimated to be more than USD 20 trillion by 2026. At the same time, fraudulent attacks have been directed toward mobile banking platforms to a significant extent, causing losses amounting to USD 485.6 billion worldwide alone in 2023. The current fraud detection architectures have been heavily based on a centralized cloud-based model which is inherently associated with a latency delay of between 200 to 800 milliseconds that introduces a time delay that advanced attackers can use to transact fraudulent transactions before the defensive countermeasures are activated. The paper will suggest a new Edge-to-Cloud Collaborative Fraud Detection (EC-CFD) model that allocates inference workloads to three hierarchical levels. The structure is a combination of differential privacy, mutual authentication, and federated learning to allow joints in improving the model without having to centralize sensitive financial information. The feature engineering has 18 dimensions which include transaction statistics, geo-location anomalies, device fingerprinting and behavioral biometrics. Synthetic dataset of 2.4 million transactions, experimented on with samples of PaySim and IEEE-CIS benchmarks on fraud detection methods to provide realistic behavioural diversification. The findings indicate that the suggested EC-CFD model is characterized by a 98.7 % detection rate of fraud, where the F1-score is 0.974 and the AUC-ROC value is 0.992. It is important to note that the hierarchical structure allows accomplishing 87.2% raw transactions at the edge level and achieves a mean detection latency of 8.3 ms. The proposed framework lowers false positives by 31.4 per cent, the average end-to-end latency by 73, and could be extended to support 50,000 active users without lowered performance, which proves the effectiveness of hierarchical edge intelligence as the primary paradigm of next-generation financial cybersecurity.

Autonomous ground robots are increasingly relied upon in dynamic environments where reliable navigation and context-aware interaction are essential. However, existing robotic control systems often rely on cloud-based reinforcement learning (RL) frameworks or static algorithms that fail to adapt in real-time to noisy, unpredictable scenarios. These models typically overlook the constraints of edge deployment and lack robust integration with human interaction modalities such as voice and semantic object awareness. To address these limitations, this work proposes a fully embedded, IoT-enabled ground robot powered by a reinforcement learning-based adaptive control framework. The system leverages Raspberry Pi 4B+ as its core computational unit, integrating MQTT-driven communication, multimodal interaction through speech and vision, and lightweight policy convergence for obstacle-aware navigation. A novel RL-based state-action pipeline is trained and deployed entirely on-device, ensuring real-time responsiveness without external computation. Experimental evaluations show that the proposed framework reduces navigation errors by 22% and improves interaction latency by 37% over traditional PID and A*-based systems. The RL model converges in under 2200 episodes, with stable reward curves and high reliability across variable acoustic and physical terrains. This study showcases how low-cost, edge-based robots can achieve high autonomy and situational awareness contributing to future advancements in resilient, self-adaptive robotic systems within smart and resource-constrained environments.

This bibliometric survey examines 200 highly cited publications spanning from 2009 to 2025, retrieved from the Scopus database using search terms targeting neuromorphic computing and its applications in healthcare engineering and biomedical systems. The analysis reveals a rapidly maturing interdisciplinary field at the convergence of materials science, device physics, circuit design, computer architecture, and biomedical engineering. Key findings indicate that memristive devices, particularly those based on metal oxides and two-dimensional materials, constitute the dominant hardware platform for neuromorphic computing, with phase-change materials, spintronic devices, and ferroelectric tunnel junctions representing significant alternative approaches. The United States, China, Germany, South Korea, and Switzerland emerge as the most productive nations, with extensive international collaboration networks reflecting the field's multidisciplinary nature. Thematic clustering identifies five major research domains: (1) memristive devices and resistive switching mechanisms, (2) two-dimensional materials for neuromorphic applications, (3) spiking neural networks and neuromorphic architectures, (4) optical and photonic neuromorphic computing, and (5) biomedical and healthcare applications including electronic skin, neural prosthetics, and biosensing. Emerging trends include the integration of neuromorphic computing with edge artificial intelligence for wearable healthcare devices, the development of reservoir computing for real-time biomedical signal processing, and the convergence of neuromorphic systems with biohybrid interfaces. This survey provides a comprehensive mapping of the intellectual landscape, identifies persistent challenges including device variability, scalability, and biocompatibility, and proposes future directions for healthcare-oriented neuromorphic engineering.