Artificial Intelligence (AI) can now make very real images and videos. This helps create digital content, but it also brings big security risks. People can make fake news easily. The people need ways to find these fakes. Passive detection is a good method. It does not need watermarks added before making the image. Instead, it looks directly at the media files to find mistakes made by the computer. This paper reviews different passive detection methods. For images, this paper looks at pixel patterns and frequency data. For videos, this paper checks if frames connect smoothly over time and looks for body signals like heartbeats. Right now, detection programs work well on things they have seen before. However, they usually fail on new types of AI fakes. Future work must fix this problem so detectors can find any fake media, no matter how it was made. This paper aims to classify and review existing passive detection methods, reveal the common shortcomings of current algorithms in generalization ability, and point out the necessary path to build a general forgery detector in the future to address the security challenges brought about by the continuous evolution of deepfake technology.

The low-altitude economy, as an emerging industry form covering multiple fields such as aviation manufacturing, logistics transportation, and urban services, has become an important engine driving regional economic upgrading and industrial structure optimization. Accurately identifying low-altitude concept listed companies is an important foundation for conducting industry trend analysis, enterprise value assessment, and precise policy support. The machine learning classification algorithm, with its outstanding data mining and pattern recognition capabilities, has been widely applied in areas such as enterprise attribute prediction and industry classification, providing strong technical support for the identification of low-altitude concept listed companies. In response to the problem of low classification accuracy of low-altitude concept listed companies under high-dimensional imbalanced data, this paper proposes the KPCA-ISSA-RF classification algorithm. First, it conducts correlation analysis and violin plot analysis, and then uses multiple machine learning algorithms for comparative research. Experimental results show that the KPCA-ISSA-RF algorithm proposed in this paper has significantly better comprehensive performance than the AdaBoost, GBDT, decision tree, CatBoost, random forest, ExtraTrees, XGBoost, KNN, and logistic regression algorithms. Its accuracy and recall rate reach 92.9%, and the precision and F1 value are both 91.6%, ranking first among all algorithms. It demonstrates strong classification discrimination ability and comprehensive fitting effect, providing reliable technical support for related research and practice in the low-altitude economy.

In the wave of Industry 4.0, industrial Internet of Things (iot) devices are increasingly widely used in manufacturing, energy, chemical and other fields. The scale and operational complexity of these devices are constantly rising, and their stable operation is crucial for production efficiency and safety. To make up for the shortcomings of existing algorithms in long time series feature extraction and complex association mining, this paper proposes the Transformer-BiGRU classification and regression algorithm, and first conducts correlation analysis and violin plot analysis. Experiments show that the core evaluation indicators of the classification algorithm have significant advantages. The accuracy rate, recall rate, and precision rate all reach 84%, 84%, and 86% respectively. The F1 value is 83%, all higher than all comparison machine learning algorithms. The AUC reaches 93%, although slightly lower than CatBoost's 94%, it is higher than other algorithms such as Random Forest. Strong generalization and category discrimination capabilities; The MSE of the regression algorithm is 16.598, the RMSE is 4.074, and the MAE is 2.615, all of which are the lowest. The MAPE is 51.468, which is in a relatively low range. The R² reaches 0.442, which is significantly better than the traditional algorithm. This algorithm integrates the global dependency capture capability of Transformer with the temporal feature extraction advantages of BiGRU, providing a reliable solution for the precise analysis and prediction of the operating status of industrial Internet of Things devices, which is of great significance for ensuring the efficiency and safety of industrial production.

This study aims to improve the early prediction of academic paper impact. To this end, a dual-pathway dynamic heterogeneous graph neural network model is proposed. The model constructs a temporal heterogeneous graph consisting of multiple types of nodes, including papers, authors, institutions, and journals. Citation and non-citation pathways are designed to model knowledge diffusion relations and social-semantic associations, respectively. In addition, a life-cycle-aware mechanism is introduced to capture feature variations across the emerging, growing, and mature stages of papers. On this basis, the prediction outcome is further disentangled into three independent components, namely diffusion effect, social bias effect, and intrinsic value effect, thereby enhancing the interpretability of the model. Experimental results on datasets from multiple disciplines, including computer science, chemistry, and psychology, demonstrate that the proposed method outperforms existing mainstream models in terms of mean absolute log error and log-transformed coefficient of determination. It is especially more accurate and stable in cold-start scenarios. The results indicate that the proposed method can provide effective support for the early identification of high-potential papers.

Mild cognitive impairment (MCI) is a crucial prodromal phase of dementia but it is challenging to screen early MCI in community or home contexts due to the fact that traditional cognitive testing requires time and is prone to educational, linguistic, and experimenter biases. The proposed framework is an explainable multiple instance learning (MIL) of wearable EEG-based early MCI screening. All participants are modeled as bags of short EEG clips and bag level diagnosis is learned by gated attention aggregation enabling the model to concentrate on informative examples without having to annotate them at the clip level. The framework combines multi-scale temporal convolution, spectral-connectivity representation and evidence attribution based on attention to enhance both its classification accuracy and its clinical plausibility. On an independent subject experiment with 126 elderly subjects, the suggested model had a score of 0.892 ± 0.021, a score of 0.887 ± 0.024, an area under the curve of 0.934 ± 0.018, and a Matthews correlation coefficient of 0.781 ± 0.031, which surpasses the conventional machine learning baselines and non-explainable deep models. Strong attention segments were found to be constantly associated with increased frontal theta activity, reduced posterior alpha power, and reduced frontoparietal coherence. These results imply explainable MIL might offer a practical and understandable solution to the scalable wearable EEG-based MCI screening problem.

With the rapid expansion of e-commerce logistics, traditional fixed-route navigation methods used in logistics robots are increasingly constrained by low flexibility, high maintenance costs, and limited robustness in dynamic environments. To address these challenges, this paper reviews the application and development of image recognition technology in the navigation stage of logistics robots. The study first analyses the limitations of conventional navigation methods, including magnetic stripe, QR code, and track-guided navigation, and then examines the role of image recognition in key navigation stages such as localisation and mapping, path planning, and obstacle avoidance. On this basis, the paper summarises the iterative evolution of image recognition technologies from traditional image processing methods to deep learning approaches represented by convolutional neural networks and YOLO-based object detection. Their respective advantages in perception accuracy, environmental adaptability, and real-time performance are discussed. The paper further explores future development trends, including multi-sensor fusion, 5G-enabled communication, edge-cloud collaborative architectures, and path-planning optimisation. It concludes that image recognition has become a crucial technical foundation for improving the intelligence, autonomy, and operational efficiency of logistics robots, although challenges remain in complex lighting conditions, irregular object recognition, and environmental adaptability.

This study examines cooperation strategies between family farms and e-commerce enterprises under rural revitalization, constructing a tripartite evolutionary game model involving family farms, e-commerce enterprises, and local government to analyze equilibrium point stability, using sensitivity analysis and Matlab simulation. The system contains eight pure strategy equilibrium points, five being conditionally stable. Decision-making depends on relative net benefits; higher benefits drive strategy selection. Increasing central policy subsidies and reducing policy costs promote positive government evolution. Local government can accelerate positive evolution by widening the subsidy gap between family farms and e-commerce enterprises, as this gap significantly influences government decisions and proper adjustment enables steady progress toward ideal states. Higher cooperation benefits and lower cooperation costs encourage positive e-commerce enterprise evolution. Meanwhile, increased renovation benefits and reduced renovation costs facilitate positive family farm evolution. Finally, corresponding recommendations are proposed for each stakeholder.

The situation of cyber threats is becoming increasingly complex and changeable. The traditional cyber threat intelligence system, due to its excessive reliance on closed data sources, has problems such as lagging updates and limited coverage. It is not only difficult to meet the real-time defense requirements but also unable to comprehensively capture new attack methods and potential threat trends. Open-source threat intelligence, with its advantage of multiple sources, can expose attack trends in advance and has become an important force to supplement the traditional intelligence system. However, the existing single machine learning algorithm has obvious shortcomings in the classification and evaluation of open-source threat intelligence and is difficult to take into account the multi-dimensional data features. To this end, this paper proposes the LSTM-KELM-Transformer classification algorithm. Firstly, data mining is carried out through correlation analysis and violin graph analysis, and then comparative experiments are conducted with multiple machine learning algorithms. The results show that this algorithm achieves 99% in accuracy, recall rate, precision rate and F1 score, with an AUC value of 99%. All evaluation indicators significantly outperform other algorithms, demonstrating excellent classification performance. This research provides a new technical solution for the efficient classification of open-source threat intelligence, which is of great practical significance for strengthening the real-time defense capability against network threats and improving the threat intelligence system construction.

This paper base on KNX bus tech, start from home life and office scene need, unite science innovation and human care, aim at different use scene, design full digital distribute control system for different place and people. Full digital distribute control system, to all kind light, air con, curtain and other electric device in area do auto and center control manage, realize energy monitor, not only can effective manage building electric device, give flexible use function and effect, also can keep and longer lamp and electric device use life, reach safe, save energy, human, smart effect, and can easy expand according to user need in future use.