Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
The proliferation of erroneous information on social media has a deleterious effect on both people and society. In order to mitigate the drawbacks of social media, it is crucial to distinguish between authentic and mi...
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The proliferation of erroneous information on social media has a deleterious effect on both people and society. In order to mitigate the drawbacks of social media, it is crucial to distinguish between authentic and misleading information. The proposed research presents a novel method to tackle the issue of identifying misinformation on social media. The main objective is to reframe the false news detection issue as an optimization problem and use two specialized metaheuristic algorithms, salp swarm optimization and grey wolf optimization, to address it. The proposed detection method is a three-step model wherein pre-processing the data is the fundamental step, the second step involves modifying grey wolf optimization and salp swarm optimization thereby creating a new false news detection model while the final step involves the testing of the proposed false news detection model. Three separate real-world datasets have been used for training the proposed false news detection model, conducting the data analysis, performing the statistical tests, benchmarking the proposed algorithms, and generating fruitful insights through reporting and visualization. The findings demonstrate that amongst the existing artificial intelligence algorithms tested so far, the grey wolf optimization algorithm outperforms (accuracy=0.97, precision=0.97, recall=1.0,f-score=0.98) salp swarm optimization in addressing various social media issues.
Recent years have witnessed the increasing prevalence of smart home applications, where digital twin (DT) is popularly employed for creating virtual models that interact with physical devices in real time. Empowered b...
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Recent years have witnessed the increasing prevalence of smart home applications, where digital twin (DT) is popularly employed for creating virtual models that interact with physical devices in real time. Empowered by artificial intelligence (AI), these DT-created virtual models have more intelligent decision-making capabilities to ensure reliable performance of a smart home system. In this paper, a DT based smart home framework is investigated. It is capable of achieving intelligent control, healthcare prediction and graphical monitoring. First, the human body and device are individually modeled, and then assembled into a DT system, and the corresponding model interfaces are provided for visual monitoring. Then, an intelligent algorithm fusing VGG, LSTM and attention mechanism is developed for healthcare monitoring, i.e., the screening out of the irregular ECG rhythms. The system results are provided, including various high-fidelity interactive DT interfaces as well as the effectiveness and advantages of the intelligent algorithms for arrhythmia detection.
This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning,held December 7–10, 2008, in Melbourne, Australia. SEAL is a prestigious international c...
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ISBN:
(数字)9783540896944
ISBN:
(纸本)9783540896937
This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning,held December 7–10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was ?rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and Hefei, China (2006). SEAL 2008 received 140 paper submissions from more than 30 countries. After a rigorous peer-review process involving at least 3 reviews for each paper (i.e., over 420 reviews in total), the best 65 papers were selected to be presented at the conference and included in this volume, resulting in an acceptance rate of about 46%. The papers included in this volume cover a wide range of topics in simulated evolution and learning: from evolutionarylearning to evolutionary optimization, from hybrid systems to adaptive systems, from theoretical issues to real-world applications. They represent some of the latest and best research in simulated evolution and learning in the world.
This volume contains a selection of revised papers that were presented at the software Aspects of Robotic Systems, SARS 2011 Workshop and the Machine Learning for System Construction, MLSC 2011 Workshop, held during O...
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ISBN:
(数字)9783642347818
ISBN:
(纸本)9783642347801
This volume contains a selection of revised papers that were presented at the software Aspects of Robotic Systems, SARS 2011 Workshop and the Machine Learning for System Construction, MLSC 2011 Workshop, held during October 17-18 in Vienna, Austria, under the auspices of the International Symposium Series on Leveraging Applications of Formal Methods, Verification, and Validation, ISoLA. The topics covered by the papers of the SARS and the MLSC workshop demonstrate the breadth and the richness of the respective fields of the two workshops stretching from robot programming to languages and compilation techniques, to real-time and fault tolerance, to dependability, software architectures, computer vision, cognitive robotics, multi-robot-coordination, and simulation to bio-inspired algorithms, and from machine learning for anomaly detection, to model construction in software product lines to classification of web service interfaces. In addition the SARS workshop hosted a special session on the recently launched KOROS project on collaborating robot systems that is borne by a consortium of researchers of the faculties of architecture and planning, computerscience, electrical engineering and information technology, and mechanical and industrial engineering at the Vienna University of Technology. The four papers devoted to this session highlight important research directions pursued in this interdisciplinary research project.
This book constitutes the refereed proceedings of the 6th Mexican Conference on Pattern Recognition, MCPR 2014, held in Cancun, Mexico, in June 2014. The 39 revised full papers presented were carefully reviewed and se...
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ISBN:
(数字)9783319074917
ISBN:
(纸本)9783319074900
This book constitutes the refereed proceedings of the 6th Mexican Conference on Pattern Recognition, MCPR 2014, held in Cancun, Mexico, in June 2014. The 39 revised full papers presented were carefully reviewed and selected from 68 submissions and are organized in topical sections on pattern recognition and artificial intelligence; computer vision; image processing and analysis; animal biometric recognition and applications of pattern recognition.
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