Scientific Reports最新文献

Aerial image classification is considered an open challenge due to its properties and the presence of various complex images. Given the complexity and variation in aerial images, this paper proposes two hybrid models for classification. The first hybrid model combines features extracted from ResNet-50 and the Vision Transformer (ViT), followed by the application of multi-head attention (MHA) to detect the most informative features. The second hybrid model also extracts features from ResNet-50 and ViT, then applies cross-attention. Both hybrid models are assessed using the benchmark Sikkim Aerial Images Dataset for Object Detection (SAIOD). The efficacy of the two hybrid models is assessed using the well-established performance metrics, including precision, recall, F1-score, and the ROC curve. The results indicate that the first model, which employs MHA, achieves superior performance with an accuracy of 95.80%. Both models outperform the best existing methods, achieving accuracies of 95.80% and 95.52%, respectively.

The rapid growth of the Internet of Medical Things (IoMT) has increased the adoption of remote healthcare applications and telemedicine services. A Massive amount of sensitive healthcare care is being gathered daily by IoT devices. . Managing the continuous flow of data streams while maintaining low latency, scalability and security remains as a challenge in traditional IoMT architectures. These problems lead to delays in real-time healthcare decision-making which is critical and increases system overhead. . To address these problems, the proposed work introduces a novel framework that integrates machine learning with blockchain-based federated IoT clouds, customised for an efficient and secure platform for handling healthcare data. The framework includes Gradient Boosting Machines (GBM) for Intelligent data storage optimisation, which analyses historical access patterns and real-time data, improving cache hit rates by 25% and reducing read latency by 30%. The system performance is improved by Deep Q-Learning (DQN), ensuring resource management. Due to optimisation, the maximum CPU load is reduced by 20% and improved management by 15%. Convolutional Autoencoders are used to improve privacy and security. These helped improve anomaly detection by 95% and reduce false positives by 10%. Long Short-Term Memory (LSTM) network improves the rate ofresource utilisation prediction to 90%, and proactive resource management achieves a 25% reduction in latency spikes. The Adaptive Byzantine Fault Tolerance (ABFT) consensus protocol with Reinforcement Learning(RL), is integrated to improve transaction efficiency and dynamically adapts the consensus parameters. The proposed integration results in a 40% improvement in transaction throughput and a 20% reduction in transaction latency. In comparison of ABFT-RL consensus with PBFT and Raft consensus under similar workloads, the proposed ABFT-RL enhanced throughput by 43% and decreased end-to-end latency by 31%, offering improved scalability and responsiveness. A private blockchain network called Hyperledger Fabric is considered. In this proposed work, the optimised output of each layer is fed into the next layer, and this seamless flow of data gives an efficient architecture managing the complexities of the blockchain-based federated IoT cloud.

The rapid advancement of artificial intelligence (AI) has reshaped the employment market, triggering widespread anxiety among college students about their future careers and posing a potential threat to their career decisions. Grounded in Career Construction Theory, this study investigated the impact mechanism of AI anxiety on career decisions among 315 Chinese college students, utilising a questionnaire survey and structural equation modeling (SEM). The analysis specifically examined the mediating role of career adaptability and the moderating role of self-efficacy. The results indicated that AI anxiety not only directly and negatively predicted career decisions but also exerted an adverse indirect effect by undermining career adaptability, with this mediating effect accounting for 63.35% of the total effect. However, the moderating effect of self-efficacy was insignificant, indicating limited buffering capacity. These findings suggest that higher education institutions should promote outcome-based education (OBE) reforms, enhance students' career adaptability by universalising AI literacy and career planning courses, and deepen industry-education integration. Such measures can help students make more confident and clear-sighted career decisions in the AI era.

This paper presents the first application of the Modified Extended Direct Algebraic (MEDA) method to the (2+1)-dimensional Wazwaz-Kaur-Boussinesq equation, a model governing wave dynamics in shallow waters. The approach successfully uncovers previously unreported classes of exact solutions, including combo dark-singular solitons and Jacobi elliptic function solutions. The spectrum of obtained solutions-which also encompasses bright, dark, and singular solitons, as well as hyperbolic, periodic, exponential, and rational functions-reveals rich and complex soliton dynamics. A comprehensive stability analysis confirms the robustness of these solutions under perturbation. These results significantly advance the understanding of wave propagation in nonlinear systems, providing valuable insights for applications in fluid dynamics, nonlinear optics, and plasma physics, while demonstrating the efficacy of the MEDA method for tackling complex nonlinear evolution equations.