Hierarchical Network Research Articles

A Hierarchical Adaptive Selection Neural Network for Dynamic Impact Localization of Wind Turbine Blades

Abstract This paper focuses on the localization problem of dynamic impacts that can lead to significant damages on wind turbine blades (WTBs). Localization of dynamic impacts on WTBs is essential for wind turbines (WTs) due to their vulnerability to dynamic impacts such as birds, stones, hails. The proposed deep learning methodology contributes to accurately locate the impacted blade and specific position using the measuremnts from a limited number of sensors. In particular, a novel hierarchical adaptive selection neural network (HASNN) is proposed, which integrates a classification subnetwork and a regression subnetwork. Specifically, an adaptive blade selection mechanism (ABSM) is designed to determine the impacted blade for classification while an adaptive window selection mechanism (AWSM) is developed to highlight the representative time period for regression. By deploying a limited number of sensors to acquire measured vibration data, the proposed method can accurately identify the collision locations of transient impacts loaded on WTBs. In both simulated and real-world experiments, the proposed method achieves the mean absolute error of 0.189 centimeter and 1.088 centimeter for impact localization. The experimental results demonstrate the superior performance of the proposed model in comparison with the existing methods for localizing impulsive loads on WTBs.

Low-carbon building evaluation index system based on hierarchical analysis method

Low-carbon building is an unavoidable development trend in the construction industry, especially in the critical moment of global warming, it is necessary to make a comprehensive evaluation of low-carbon buildings. At this stage, low carbon building has become an important direction in the construction field, and whether the low carbon building reaches the corresponding standards and the advantages played by the low carbon field need to be assessed with perfect evaluation indexes. Based on this, this paper constructs a low-carbon building evaluation system from the whole life cycle of the building using the hierarchical analysis method (AHP) and BP neural network method. Firstly, the definition and influencing factors of low-carbon buildings are analyzed, secondly, the evaluation index system of low-carbon buildings is constructed, and then the evaluation index system of low-carbon buildings is verified by using the hierarchical analysis method, and the results show that the evaluation results based on the hierarchical analysis and the BP neural network method are more accurate than those of the traditional hierarchical analysis method. The results show that the evaluation results based on hierarchical analysis and BP neural network are more accurate than the traditional hierarchical analysis method. It shows that the BP neural network method can effectively reduce the influence of subjective factors in the hierarchical analysis method and improve the objectivity of the evaluation results. On this basis, this paper proposes countermeasures to promote the development of low-carbon buildings, in order to provide a certain reference for the long-term development of low-carbon buildings.

Automated sleep staging on reduced channels in children with epilepsy

ObjectivesThis study aimed to validate a sleep staging algorithm using in-hospital video-electroencephalogram (EEG) in children without epilepsy, with well-controlled epilepsy (WCE), and with drug-resistant epilepsy (DRE).MethodsOvernight video-EEG, along with electrooculogram (EOG) and chin electromyogram (EMG), was recorded in children between 4 and 18 years of age. Classical sleep staging was performed manually as a ground truth. An end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging (SeqSleepNet) was used to perform automated sleep staging using three channels: C4-A1, EOG, and chin EMG.ResultsIn 176 children sleep stages were manually scored: 47 children without epilepsy, 74 with WCE, and 55 with DRE. The 5-class sleep staging accuracy of the automatic sleep staging algorithm was 84.7% for the children without epilepsy, 83.5% for those with WCE, and 80.8% for those with DRE (Kappa of 0.79, 0.77, and 0.73 respectively). Performance per sleep stage was assessed with an F1 score of 0.91 for wake, 0.50 for N1, 0.83 for N2, 0.84 for N3, and 0.86 for rapid eye movement (REM) sleep.ConclusionWe concluded that the tested algorithm has a high accuracy in children without epilepsy and with WCE. Performance in children with DRE was acceptable, but significantly lower, which could be explained by a tendency of more time spent in N1, and by abundant interictal epileptiform discharges and intellectual disability leading to less recognizable sleep stages. REM sleep time, however, significantly affected in children with DRE, can be detected reliably by the algorithm.Clinical trial registration: ClinicalTrials.gov, identifier NCT04584385.

Mobile User Traffic Generation via Multi-Scale Hierarchical GAN

Mobile user traffic facilitates diverse applications, including network planning and optimization, whereas large-scale mobile user traffic is hardly available due to privacy concerns. One alternative solution is to generate mobile user traffic data for downstream applications. However, existing generation models cannot simulate the multi-scale temporal dynamics in mobile user traffic on individual and aggregate levels. In this work, we propose a multi-scale hierarchical generative adversarial network (MSH-GAN) containing multiple generators and a multi-class discriminator. Specifically, the mobile traffic usage behavior exhibits a mixture of multiple behavior patterns, which are called micro-scale behavior patterns and are modeled by different pattern generators in our model. Moreover, the traffic usage behavior of different users exhibits strong clustering characteristics, with the co-existence of users with similar and different traffic usage behaviors. Thus, we model each cluster of users as a class in the discriminator’s output, referred to as macro-scale user clusters. Then, the gap between micro-scale behavior patterns and macro-scale user clusters is bridged by introducing the switch mode generators, which describe the traffic usage behavior in switching between different patterns. All users share the pattern generators. In contrast, the switch mode generators are only shared by a specific cluster of users, which models the multi-scale hierarchical structure of the traffic usage behavior of massive users. Finally, we urge MSH-GAN to learn the multi-scale temporal dynamics via a combined loss function, including adversarial loss, clustering loss, aggregated loss, and regularity terms. Extensive experiment results demonstrate that MSH-GAN outperforms state-of-art baselines by at least 118.17% in critical data fidelity and usability metrics. Moreover, observations show that MSH-GAN can simulate traffic patterns and pattern switch behaviors.

Enhancing Knowledge Graph Embedding with Hierarchical Self-Attention and Graph Neural Network Techniques for Drug-Drug Interaction Prediction in Virtual Reality Environments

In biomedicine, the critical task is to decode Drug–Drug Interactions (DDIs) from complex biomedical texts. The scientific community employs Knowledge Graph Embedding (KGE) methods, enhanced with advanced neural network technologies, including capsule networks. However, existing methodologies primarily focus on the structural details of individual entities or relations within Biomedical Knowledge Graphs (BioKGs), overlooking the overall structural context of BioKGs, molecular structures, positional features of drug pairs, and their critical Relational Mapping Properties. To tackle the challenges identified, this study presents HSTrHouse an innovative hierarchical self-attention BioKGs embedding framework. This architecture integrates self-attention mechanisms with advanced neural network technologies, including Convolutional Neural Network (CNN) and Graph Neural Network (GNN), for enhanced computational modeling in biomedical contexts. The model bifurcates the BioKGs into entity and relation layers for structural analysis. It employs self-attention across these layers, utilizing PubMedBERT and CNN for position feature extraction, and a GNN for drug pair molecular structure analysis. Then, we connect the position and molecular structure features to integrate them into the self-attention calculation of entity and relation. After that, the output of the self-attention layer is combined with the connected vectors of the position feature and molecular structure feature to obtain the final representation vector, and finally, to model the Relational Mapping Properties (RMPs), the representation vector is embedded into the complex vector space using Householder projections to obtain the BioKGs model. The paper validates HSTrHouse’s efficacy by comparing it with advanced models on three standard BioKGs for DDIs research.

Intelligent Geo-Tour Route Recommendation Algorithm Based on Feature Text Mining and Spatial Accessibility Model

In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering algorithm (SSCA) based on FTM was constructed by tourists’ text evaluation data mining. Considering the spatial attributes of scenic spots, the scenic spot topology tree algorithm (SSTTA) based on dynamic buffer spatial accessibility (DBSA) was constructed. The optimal scenic spots were recommended based on interest matching and spatial accessibility optimization. As to the recommended scenic spots, this paper proposes an optimal tour route recommendation algorithm (TRRA) based on SSTTA, which aims to determine the optimal adjacent section path structure tree (ASPST) with the lowest cost under travel constraints and transportation modes. The experiment verifies that the proposed algorithm can recommend scenic spots that match tourists’ interests and have optimal spatial accessibility, and the optimal tour routes with the lowest costs under certain travel constraints. Compared with the searched sub-optimal tour routes, the optimal tour route recommended by the proposed algorithm produces the lowest travel costs, and all the scenic spots in the tour route meet the tourists’ interests. Compared with the commonly used BDMA and GDMA methods, the proposed algorithm can determine the optimal routes with lower travel costs.

Global Value Chains, Labor Rights, and the Nature of Transnational Law

Abstract This Article develops a framework to analyze the laws that bear on working conditions in global value chains (GVCs), a production structure based on hierarchical international firm networks. In so doing, it contributes to broader debates about how to conceptualize law under contemporary “globalization.” The Article argues against the widespread view that GVCs—and today’s globalized legal order—are fundamentally different from a prior regime of Westphalian state sovereignty. Instead of this vision of globalization as rupture with the past, the Article proposes a “monist” perspective according to which the state has always been embedded in a single body of conflicting and contested transnational legal rules. In this view, sovereign power was never something of which all states could have more or less. Rather, sovereignty is a bundle of rights that can only ever be distributed to specific states vis-à-vis one another and private actors like labor unions and corporations. The Article emphasizes the analytical benefits of this conceptualization of transnational law for policy projects like those of improving working conditions in GVCs and empowering workers globally.

Attention-Oriented CNN Method for Type 2 Diabetes Prediction

Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries.

Hierarchical Classification of Botnet Using Lightweight CNN

This paper addresses the persistent threat of botnet attacks on IoT devices, emphasizing their continued existence despite various conventional and deep learning methodologies developed for intrusion detection. Utilizing the Bot-IoT dataset, we propose a hierarchical CNN (HCNN) approach featuring three levels of classification. The HCNN approach, presented in this paper, consists of two networks: the non-hierarchical and the hierarchical network. The hierarchical network works by combining features obtained at a higher level with those of its descender. This combined information is subsequently fed into the following level to extract features for the descendant nodes. The overall network consists of 1790 parameters, with the hierarchical network introducing an additional 942 parameters to the existing backbone. The classification levels comprise a binary classification of normal vs attack in the first level, followed by 5 classes in the second level, and 11 classes in the third level. To assess the effectiveness of our proposed approach, we evaluate performance metrics such as Precision (P), Recall (R), F1 Score (F1), and Accuracy (Acc). Rigorous experiments are conducted to compare the performance of both the hierarchical and non-hierarchical models and existing state-of-the-art approaches, providing valuable insights into the efficiency of our proposed hierarchical CNN approach for addressing botnet attacks on IoT devices.

Performance Comparison of Various Machine Learning Models for Rainfall Prediction Using Wind Information

Forecasting rainfall, crucial for agriculture, water management, and disaster preparedness, presents significant challenges due to intricate relationships often missed by conventional statistical methods. Hence, machine learning (ML) models offer promising alternatives, enhancing the precision and dependability of rainfall predictions. This paper presents a comprehensive comparison of various ML models with diverse model structures and regularization strategies for rainfall prediction in urban metropolitan cities. The results show that the random forest model, and gradient boosting model outperform the other models such as logistic regression, support vector machine (SVM), decision tree, K-nearest neighbor (KNN), Naive Bayes, linear SVM, and neural network in terms of accuracy. Validation accuracies of 75%, 77%, 68%, 78%, 76%, 78%, 74%, 75% and 76% were achieved for logistic regression, SVM, decision tree, random forest model, KNN, gradient boosting model, Naive Bayes, linear SVM, and neural network, respectively. The choice of ML models for rainfall prediction should consider the characteristics of the data, e.g. a lag feature for 20 days was employed that uses previous time steps to predict the next time step. The paper concludes that ML models, especially the random forest and gradient boosting models are powerful and robust tools for rainfall prediction in urban metropolitan cities. Key Words: rainfall prediction, wind information, machine learning, gradient boosting, random forest

Molecular Evolution of RAMOSA1 (RA1) in Land Plants

RAMOSA1 (RA1) is a Cys2-His2-type (C2H2) zinc finger transcription factor that controls plant meristem fate and identity and has played an important role in maize domestication. Despite its importance, the origin of RA1 is unknown, and the evolution in plants is only partially understood. In this paper, we present a well-resolved phylogeny based on 73 amino acid sequences from 48 embryophyte species. The recovered tree topology indicates that, during grass evolution, RA1 arose from two consecutive SUPERMAN duplications, resulting in three distinct grass sequence lineages: RA1-like A, RA1-like B, and RA1; however, most of these copies have unknown functions. Our findings indicate that RA1 and RA1-like play roles in the nucleus despite lacking a traditional nuclear localization signal. Here, we report that copies diversified their coding region and, with it, their protein structure, suggesting different patterns of DNA binding and protein–protein interaction. In addition, each of the retained copies diversified regulatory elements along their promoter regions, indicating differences in their upstream regulation. Taken together, the evidence indicates that the RA1 and RA1-like gene families in grasses underwent subfunctionalization and neofunctionalization enabled by gene duplication.

Abstract PO4-07-04: Breast Cancer HER2 Status Prediction from Hematoxylin-Eosin Stained Images Using Point Cloud Transformer

Abstract Background: Human epidermal growth factor receptor-2 (HER2) status is a key factor in determining the treatment strategy for breast cancer patients. Patients with HER2-positive status are more likely to benefit from HER2-targeted therapy, leading to improved prognosis. In current routine diagnostic practice, pathologists utilize Hematoxylin-Eosin (HE) stained tumor tissues for histopathological assessment. Subsequently, IHC assessment and/or FISH test are performed to evaluate the HER2 status. However, manual assessment results may be affected by tissue usability and observer-subjectivity. Therefore, there is a necessity to predict the HER2 status directly from HE images to minimize time and cost while ensuring enhanced consistency. Methods: We identified 608 HE diagnostic slides with HER2 status from The Cancer Genome Atlas in breast cancer (TCGA-BRCA). It contains 474 HER2-positive slides (IHC 3+, IHC 2+ and FISH positive) and 134 HER2-negative slides (IHC 0, IHC 1+, IHC 2+ and FISH negative). To analyze these slides, we first tiled the HE images into patches with a fixed size of 256 × 256 at 20 × magnification. Then the patch-level feature was derived from a self-supervised pretrained transformer. Meanwhile, artificial intelligence (AI) methods are adopted to predict HER2 status from HE images. To capture the long-distance patch relations within a slide, we represented the patches as distinct points and utilized the Point Cloud Transformer model for HER2 status prediction. Specifically, 1024 patches (points) in each slide were randomly selected and input into the Point Cloud Transformer. This process yielded a slide-level prediction result for the HER2 status. Furthermore, Graph Attention Network (GAT), Graph Sample and Aggregate Network (Graph SAGE), and hierarchical Point Cloud Network (PointNet++) were adopted to compare the effectiveness of HER2 status prediction using Point Cloud Transformer. Of note that Point Cloud Transformer incorporated attention mechanisms for point aggregation compared to PointNet++. Results: The Point Cloud Transformer was trained with 5-fold cross-validation, and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was reported. The performance of point-based models outperformed the graph-based models. And the Point Cloud Transformer achieved the highest AUC of 0.7496 among all AI models. The detailed AUC for each AI model was shown in Table 1. Conclusion: Our study revealed that the HER2 status can be predicted directly from HE images without using IHC images. Furthermore, point-based models have demonstrated the ability to capture long-distance relations among patches, surpassing graph-based models in terms of prediction performance. To further enhance performance, we adopted a better point aggregation method, such as the Point Cloud Transformer, which held promise for further improving the accuracy of predictions in the future. Table 1: AUC of HER2 status prediction from HE images in the TCGA-BRCA dataset Method GAT Graph SAGE PointNet++ Point Cloud Transformer AUC 0.6640 0.6737 0.7201 0.7496 Citation Format: Bao Li, Zhenyu Liu, Yang Du, Jie Tian. Breast Cancer HER2 Status Prediction from Hematoxylin-Eosin Stained Images Using Point Cloud Transformer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-04.

A novel transformer-based DL model enhanced by position-sensitive attention and gated hierarchical LSTM for aero-engine RUL prediction

Accurate prediction of remaining useful life (RUL) for aircraft engines is essential for proactive maintenance and safety assurance. However, existing methods such as physics-based models, classical recurrent neural networks, and convolutional neural networks face limitations in capturing long-term dependencies and modeling complex degradation patterns. In this study, we propose a novel deep-learning model based on the Transformer architecture to address these limitations. Specifically, to address the issue of insensitivity to local context in the attention mechanism employed by the Transformer encoder, we introduce a position-sensitive self-attention (PSA) unit to enhance the model's ability to incorporate local context by attending to the positional relationships of the input data at each time step. Additionally, a gated hierarchical long short-term memory network (GHLSTM) is designed to perform regression prediction at different time scales on the latent features, thereby improving the accuracy of RUL estimation for mechanical equipment. Experiments on the C-MAPSS dataset demonstrate that the proposed model outperforms existing methods in RUL prediction, showcasing its effectiveness in modeling complex degradation patterns and long-term dependencies.

Hierarchical spatial attention-based cross-scale detection network for Digital Works Supervision System (DWSS)

Vision-based on-site detection is a crucial component of the Digital Works Supervision System (DWSS). However, accurate detecting cross-scale objects in complicated construction sites remains a challenge. In this research, a hierarchical spatial attention-based cross-scale detection network is designed to address these challenges and provide accurate on-site information for the DWSS. Firstly, a convolutional neural network is constructed to extract multi-scale feature maps with different levels of information. Furthermore, a hierarchical spatial attention mechanism is proposed to facilitate information propagation and complement between multi-scale feature maps. Secondly, the cascade detection mechanism is designed to improve the detection performance of cross-scale objects. Large-scale feature maps with more detailed information are used for the detection of small-scale objects such as workers. Small-scale feature maps with semantic features are used for the detection of large-scale objects such as construction machines. Finally, the on-site information is automatically extracted from detection results and converted into suitable data formats to generate on-site reports for the DWSS. Experiments demonstrate that the proposed method can achieve SOTA detection performance on both MOCS and SODA dataset, especially for the small-scale objects and complicated construction scenarios.

Emotion-aware hierarchical interaction network for multimodal image aesthetics assessment

Image aesthetics assessment (IAA) has attracted increasing attention recently but is still challenging due to its high abstraction and complexity. Intuitively, image emotion and aesthetics are both human subjective feelings evoked by visual content. Previous researches have demonstrated that image aesthetics has intrinsic relationships with emotion. In fact, human emotional experience has potential impact on the perception of image aesthetics. Therefore, emotion information can be exploited to enhance aesthetic representation learning. Inspired by this, this paper presents an Emotion-Aware Hierarchical Interaction NETwork (EAHI-NET) for multimodal image aesthetics assessment, which explores both intra-modal and inter-modal interactions between aesthetics and emotions hierarchically. Specifically, we first propose the intra-modal emotion-aesthetics interaction module to obtain emotion-enhanced visual and textual aesthetic representations respectively. Then we propose the inter-modal feature enhancement to obtain the cross-modal aesthetic and emotion features. Finally, we design the inter-modal emotion-aesthetics interaction module to further investigate the cross-modal interplay between aesthetics and emotion, based on which hierarchical feature representations are achieved for multimodal IAA. The extensive experiments show that the proposed method can outperform the state-of-the-arts on multimodal AVA and Photo.net datasets.

From pixels to pathology: A novel dual-pathway multi-scale hierarchical upsampling network for MRI-based prostate zonal segmentation

Prostate cancer is a prevalent and life-threatening disease characterized by abnormal cell growth within the prostate gland. Early and accurate diagnosis of prostate cancer is crucial for effective treatment planning. Magnetic Resonance Imaging (MRI) is valuable for diagnosing and assessing prostate cancer. Medical professionals use MRI to create segmentation for detecting prostate cancer. However, existing segmentation methods are limited in accurately delineating anatomical structures and tumor regions within the prostate. This research proposes an innovative methodology for advancing MRI-based prostate segmentation. The objective is to encompass anatomical and tumor zones within the prostate, facilitating precise diagnosis and treatment planning. The proposed dual-pathway multi-scale hierarchical upsampling network introduces significant modifications compared to the traditional UNet-based architecture. It outperforms previous studies, demonstrating superior performance in anatomical segmentation on both the ProstateX and Prostate158 datasets. It achieves an intersection over union of 0.8449 and a dice similarity coefficient of 0.9872 on ProstateX, as well as an intersection over union of 0.8065 and a dice similarity coefficient of 0.9831 on Prostate158, suppressing previous research by a significant margin. These results highlight the potential of this approach to advance the utility of MRI in diagnosing and planning the treatment of prostate-related pathologies, benefiting both patients and healthcare practitioners.

Synchronization in a Three Level Network of All-to-All Periodically Forced Hodgkin–Huxley Reaction–Diffusion Equations

This article focuses on the analysis of dynamics emerging in a network of Hodgkin–Huxley reaction–diffusion equations. The network has three levels. The three neurons in level 1 receive a periodic input but do not receive inputs from other neurons. The three neurons in level 2 receive inputs from one specific neuron in level 1 and all neurons in level 3. The neurons in level 3 (all other neurons) receive inputs from all other neurons in levels 2 and 3. Furthermore, the right-hand side of pre-synaptic neurons is connected to the left-hand side of the post-synaptic neurons. The synchronization phenomenon is observed for neurons in level 3, even though the system is initiated with different functions. As far as we know, it is the first time that evidence of the synchronization phenomenon is provided for spatially extended Hodgkin–Huxley equations, which are periodically forced at three different sites and embedded in such a hierarchical network with space-dependent coupling interactions.

A Novel Probabilistic Approach for Driving the Sustainable Energy Circular Economy: Innovating Efficiency in Renewable Resource Markets

Our research introduces a novel hybrid model aimed at driving the sustainable economy by innovating efficiency in forecasting renewable resource market trends for recovery. The fusion of Hierarchical Recurrent Neural Networks (RNNs) and LeNet-5 Convolutional Neural Networks (CNNs) capitalizes on the distinct advantages of both architectures. RNNs capture temporal dependencies, while CNNs identify spatial patterns, providing a holistic understanding of market dynamics. This symbiosis significantly enhances predictive accuracy, empowering stakeholders with more reliable insights into the trajectory of renewable resource markets. Efficient feature selection is achieved through the incorporation of the Modified Hill Climbing (MHC) algorithm. As an evolutionary feature selection technique, MHC dynamically refines input features during the learning process, adapting to the evolving nature of renewable resource markets. This adaptive feature selection not only improves computational efficiency and interpretability but also ensures a focus on the most relevant information, aligning with the goal of driving sustainable circular economic recovery. The fortified hybrid model, harmonizing RNNs, CNNs, and MHC, showcases heightened robustness and generalization across diverse market conditions. By effectively merging deep learning architectures with an evolutionary feature selection process, the model adeptly captures intricate patterns while mitigating overfitting. This contribution underscores the applicability of the proposed approach to real-world scenarios, positioning it as a catalyst for innovative efficiency in renewable resource markets—a pivotal driver for the recovery of the sustainable economy. Our research marks a crucial advancement in the realm of reliable and adaptable forecasting models, essential for informed decision-making in the dynamic landscape of renewable energy markets.

Intelligent Hierarchical NOMA-Based Network Slicing in Cell-Free RAN for 6G Systems

Intelligent Hierarchical NOMA-Based Network Slicing in Cell-Free RAN for 6G Systems

Learning spatiotemporal dependencies using adaptive hierarchical graph convolutional neural network for air quality prediction

Air quality prediction has garnered significant attention from both governmental bodies and the general public due to its close connection with people’s daily lives. Learning spatio-temporal dependencies within air quality data are crucial for accurate air quality forecasting. Graph Convolutional Network (GCN) has been widely utilized in spatiotemporal data prediction due to its remarkable ability of capturing non-Euclidean spatial dependencies among nodes. However, previous research on Spatiotemporal Graph Neural Networks (ST-GNNs) has either utilized graph adjacency matrices predefined by heuristic rules or learned through trainable parameters. These approaches fail to accurately represent the true spatial relationships between stations and adaptively capture the required multi-level spatial dependencies for prediction. To address these limitations,this paper proposes an Adaptive Hierarchical Graph Convolutional Neural Network (AHGCNN) for PM2.5 prediction. More specially, an adaptive hierarchical graph convolutional structure is initially developed for dynamically extracting multi-level spatial dependencies. Then, a hierarchical aggregation scheme is designed to consolidate the unique topological structures of graph neural networks across different levels. Finally, a unified Adaptive Hierarchical Graph Convolution Gated Recurrent Unit (AHGC-GRU) model is proposed by integrating Gated Recurrent Unit (GRU) with hierarchical graph convolution in an effort to capture spatiotemporal dependencies of air quality data. Extensive experiments are conducted on real-world datasets, and the results demonstrate that the proposed model outperforms existing models.