Computational Intelligence最新文献

In the current era, improving the intelligence level of vessels and ensuring the construction of a safer and more reliable maritime traffic environment has become an extremely crucial task. And intelligent vessel trajectory prediction undoubtedly impacts the intelligent navigation and collision avoidance systems of vessels. However, unfortunately, in the past few decades, the analysis work on massive trajectory data has been relatively scarce. At the same time, whether the current research focus on vessel trajectory prediction is short-term or long-term, it has led to the situation that the accuracy of trajectory prediction is far from satisfactory. In view of this, this study innovatively introduces a data-driven Long Short-Term Memory (LSTM) approach for the Automatic Identification System (AIS). This method realizes the accurate prediction of the entire vessel trajectory through the fusion of forward and reverse sub-networks (named FRA-LSTM here). Specifically, the forward sub-network in our proposed method cleverly combines LSTM with an attention mechanism to accurately extract key factors from the forward past trajectory data. Correspondingly, the reverse sub-network organically integrates the attention mechanism with a Bidirectional LSTM (BiLSTM) to simultaneously mine the unique characteristics of the backward historical trajectory data. Finally, the features output by the forward and reverse sub-networks are combined so as to successfully construct the final expected trajectory. After a large number of comprehensive and in-depth tests, we are delighted to find that compared with BiLSTM and Seq2Seq, the method proposed in this study has achieved an average increase of 96.8% and 86.5% regarding the accuracy of short-term and mid-term trajectory prediction respectively. More importantly, in the domain of long-term trajectory prediction, the average accuracy of our method is as high as 90.1% higher than that of BiLSTM and Seq2Seq, showing excellent performance advantages.

Micro-level prediction of information diffusion aims to predict the next user to participate in the diffusion process, and it is an important task in the field of social network analysis. However, the existing research has two main issues. On one hand, they rely solely on social relationships to learn users' social homophily, leading to an insufficient capture of complex diffusion relationships among users. On the other hand, they overlook the impact of group influence on cascade diffusion, which limits predictive performance. To address the above issues, this study proposes a predictive model for Information Diffusion combining Individual Association and Group Influence, denoted by IGIDP. First, a user diffusion association graph is constructed based on cascade sequences, using a Graph Convolutional Network (GCN) to learn users' structural features. A gated fusion mechanism is then employed to enhance feature representation for better learning of the impact of user diffusion relationships on cascades. Next, a hypergraph is built through user-cascade interactions, and a hypergraph attention network is introduced to learn users' global interaction feature representations. Then, a novel Transformer variant is designed to capture both individual user and group effects on cascade diffusion. Finally, a decoder provides the diffusion probability for each user. Experimental results on four public, real-world datasets show that the IGIDP model achieves improvements in Hits@$��k�$ and Map@$��k�$ by 0.42%–20.94% and 1.66%–25.20%, respectively.

In general, the object detection mechanism recognizes target objects in the image frames, and the tracking mechanism helps to capture the movement of target objects in diverse frames. Recent developments in Artificial Intelligence (AI) have enabled us to build computers, robots, and automated tools that are mostly designed for performing tasks and generating decisions without human assistance. The drones called Unmanned Aerial Vehicles (UAVs) are employed for many kinds of objectives, including parcel shipping, rescue operations, recognizing objects, and monitoring. Object detection and tracking systems are crucial for UAVs because they help to optimally capture moving objects, areas, and threats for enhancing security, surveillance, and awareness at an earlier stage. Also, they help to ultimately predict the category and location of UAVs from the video frames. Many researchers have developed diverse object detection and tracking methods on UAVs; yet, it is complex for continuously monitoring small objects in the gathered data, and it is affected by noise and blurriness due to the motion of UAVs. One of the greatest challenging duties for UAVs is object recognition and tracking since it needs precise, swift, and cost-effective object detection and tracking. Pre-trained networks are required for the detection of objects based on deep learning. Mismatches between the pre-trained and the target domain network areas create problems in object detection. With the aim of resolving these issues, a deep learning-assisted UAV control mechanism is developed in this research work by performing detection and tracking of objects. The developed model is helpful in improving rescue operations in disaster areas and security surveillance. At first, the input videos are accumulated from benchmark sources. From the videos, the resolution of the images is studied for an accurate object detection procedure. Next, the detection and tracking of the object are done via the developed Region Vision Transformer-based Adaptive Multi-scale You Only Look Once v8 (RV-AMYOLOv8). The parameters fromYOLOv8 are optimized using the Fitness-based Random Variable for Elk Herd Optimizer (FRVEHO) for enhancing the performance. The quantitative outcomes of the implemented approach help to analyze the suggested network's performance with conventional techniques. Here, the developed method attains 96% accuracy and 95% of precision measure to demonstrate its better performance than the existing methods. This object detection is helpful for analyzing the surrounding obstacles while controlling the UAVs.