In real-world Industrial Internet of Things (IIoT) scenarios, due to the limited storage capacity of IIoT devices, fresh data continuously received by diverse devices will overwrite the outdated data and change the lo...
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This study introduces a novel approach to traffic congestion detection using Reinforcement Learning (RL) of machine learning classifiers enhanced by Explainable Artificial Intelligence (XAI) techniques in Smart City (...
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This study introduces a novel approach to traffic congestion detection using Reinforcement Learning (RL) of machine learning classifiers enhanced by Explainable Artificial Intelligence (XAI) techniques in Smart City (SC). Conventional traffic management systems rely on static rules, and heuristics face challenges in dynamically addressing urban traffic problems' complexities. This study explains the novel Reinforcement Learning (RL) framework integrated with an Explainable Artificial Intelligence (XAI) approach to deliver more transparent results. The model significantly reduces the missing data rate and improves overall prediction accuracy by incorporating RL for real-time adaptability and XAI for clarity. The proposed method enhances security, privacy, and prediction accuracy for traffic congestion detection by using Machine Learning (ML). Using RL for adaptive learning and XAI for interpretability, the proposed model achieves improved prediction and reduces the missing data rate, with an accuracy of 98.10, which is better than the existing methods.
Accurate segmentation of power line targets helps quickly locate faults, evaluate line conditions, and provides key image data support and analysis for the safe and stable operation of the power *** aerial power line ...
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Accurate segmentation of power line targets helps quickly locate faults, evaluate line conditions, and provides key image data support and analysis for the safe and stable operation of the power *** aerial power line in segmentation due to the target is small, and the imaging reflected energy is weak, so the Unmanned Aerial Vehicle (UAV) aerial power line image is very susceptible to the interference of the environment line elements and noise, resulting in the detection of the power line target in the image of the defective, intermittent, straight line interferences and other low accuracy and real-time efficiency is not high. For this reason, this paper designs a pure amplitude stretching kernel function to form a Fourier amplitude vector field and uses this amplitude vector field to implement the stretching transformation of the amplitude field of the aerial power line image, so that the angular field after the Fourier inverse transformation can better react to the spatial domain line targets, and finally, after the Relative Total Variation (RTV) processing, the power line can be well detected. The proposed algorithm is compared with the main power line segmentation algorithms, such as Region Convolutional Neural Networks(R-CNN) and Phase Stretch Transform(PST). The average values of evaluation indicators PPA, MMPA and MMIoU of the image segmentation results of the proposed algorithm reach 0.96, 0.96 and 0.95 respectively, and the average time lag of detection is less than 0.2s, indicating that the accuracy and real-time performance of the segmentation results of the proposed algorithm are significantly better than those of the above algorithms.
The partial set covering problem (PSCP) is a significant combinatorial optimization problem that finds applications in numerous real-world scenarios. The objective of PSCP is to encompass a minimum number of subsets w...
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The partial set covering problem (PSCP) is a significant combinatorial optimization problem that finds applications in numerous real-world scenarios. The objective of PSCP is to encompass a minimum number of subsets while ensuring the coverage of at least n elements. Due to its NP-hard nature, solving large-scale PSCP efficiently remains a critical issue in computational intelligence. To effectively tackle this challenge, we delve into a population-based approach that incorporates a modified tabu search, thereby striking a delicate balance between exploration and exploitation. To further enhance its efficacy, we employ the multiple path-relinking strategy and the fix-and-optimize process. Finally, the dynamic resource allocation scheme is utilized to save computing efforts. Comparative experiments of the proposed algorithm were conducted against three state-of-the-art competitors, across two distinct categories, encompassing 150 instances. The results significantly underscore the profound effectiveness of our proposed algorithm, as evidenced by the updating of 67 best-known solutions. Moreover, we conduct an in-depth analysis of the key components inherent to the algorithm, shedding light on their respective influences on the whole performance.
Dear editor,The problem of answering queries using views,where a view is a set of predefined queries,arises in a variety of data management *** formalize the fact that a set of views V contains enough information for ...
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Dear editor,The problem of answering queries using views,where a view is a set of predefined queries,arises in a variety of data management *** formalize the fact that a set of views V contains enough information for answering a specific query
Zenith Tropospheric Delay (ZTD) is a significant source of atmospheric error in the Global Navigation Satellite System (GNSS). Developing a high-accuracy ZTD prediction model is essential for both GNSS positioning and...
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Zenith Tropospheric Delay (ZTD) is a significant source of atmospheric error in the Global Navigation Satellite System (GNSS). Developing a high-accuracy ZTD prediction model is essential for both GNSS positioning and GNSS meteorology. To address the challenges of incomplete information extraction and gradient explosion present in current single and combined neural network models that utilize serial ensemble learning, this study proposes a VMD-Informer-BiLSTM-EAA hybrid model based on a parallel ensemble learning strategy. Additionally, it takes into account the non-stationarity of the ZTD sequence. The model employs the Variational Mode Decomposition (VMD) method to address the non-stationarity of ZTD. It utilizes both the informer and Bidirectional Long Short-Term Memory (BiLSTM) architectures to learn ZTD data in parallel, effectively capturing both long-term trends and short-term dynamic changes. The features are then fused using the Efficient Additive Attention (EAA) mechanism, which assigns weights to create a more comprehensive representation of the ZTD data. This enhanced representation ultimately leads to improved predictions of ZTD values. We fill in the missing parts of the GNSS-derived ZTD using the ZTD data from ERA5, sourced from the IGS stations in the Australian region, specifically at 12 IGS stations. These interpolated data are then used to develop a VMD-Informer-BiLSTM-EAA hybrid model for ZTD predictions with a one-year forecast horizon. We applied this model to predict the ZTD for each IGS station in our study area for the year 2021. The numerical results indicate that our model outperforms several comparative models, such as VMD-Informer, Transformer, BiLSTM and GPT3, based on the following key metrics: a Root Mean Square Error (RMSE) of 1.43 cm, a Mean Absolute Error (MAE) of 1.15 cm, a Standard Deviation (STD) of 1.33 cm and a correlation coefficient (R) of 0.96. Furthermore, our model reduces the training time by 8.2% compared to the Transfo
—Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be proce...
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Fuzzy multi-view modeling inevitably involves the following challenges: 1) effectively utilizing the shared decision information among views to facilitate the training of fuzzy systems and 2) optimizing the fuzzy trai...
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Fuzzy multi-view modeling inevitably involves the following challenges: 1) effectively utilizing the shared decision information among views to facilitate the training of fuzzy systems and 2) optimizing the fuzzy training structure through mutual approximation among views. To this end, a two-view mutual approximation fuzzy classifier (Sdivf-FC) is proposed herein. First, by integrating the knowledge decision matrices of each view, a joint-view shared decision subspace is constructed, which guides the decision-making process of each single view in every layer. This design enhances the learning efficiency of single views and maximizes the utilization of shared information among views. Second, a two-view mutual approximation-based optimization strategy is proposed to optimize the consequent parameters of fuzzy rules through intra-view and inter-view approximations. Additionally, a novel stacked-like structure is designed, which combines the projection of the previous layer's input space and the prediction errors of the two views to generate a new input space, thereby accelerating the training process and increasing the diversity of training samples. To maintain the interpretability of Sdivf-FC, an antecedent parameter inheritance mechanism is also proposed. Experimental results demonstrate that Sdivf-FC outperforms similar classifiers on both benchmark UCI datasets and epilepsy electroencephalogram datasets.
The increasing demand for higher bandwidth and reliable communication networks driven by next-generation mobile technologies (5G/6G) and the Internet of Things (IoT) poses challenges for existing communication infrast...
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Script event prediction is the task of predicting the subsequent event given a sequence of events that already took place. It benefits task planning and process scheduling for event-centric systems including enterpris...
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