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Objective. Mild cognitive impairment (MCI) is a precursor stage of dementia characterized by mild cognitive decline in one or more cognitive domains, without meeting the criteria for dementia. MCI is considered a prodromal form of Alzheimer's disease (AD). Early identification of MCI is crucial for both intervention and prevention of AD. To accurately identify MCI, a novel multimodal 3D imaging data integration graph convolutional network (GCN) model is designed in this paper. Approach. The proposed model utilizes 3D-VGGNet to extract three-dimensional features from multimodal imaging data (such as structural magnetic resonance imaging and fluorodeoxyglucose positron emission tomography), which are then fused into feature vectors as the node features of a population graph. Non-imaging features of participants are combined with the multimodal imaging data to construct a population sparse graph. Additionally, in order to optimize the connectivity of the graph, we employed the pairwise attribute estimation （PAE） method to compute the edge weights based on non-imaging data, thereby enhancing the effectiveness of the graph structure. Subsequently, a population-based GCN integrates the structural and functional features of different modal images into the features of each participant for MCI classification. Main results. Experiments on the AD Neuroimaging Initiative demonstrated accuracies of 98.57%, 96.03%, and 96.83% for the normal controls (NC)-early MCI (EMCI), NC-late MCI (LMCI), and EMCI-LMCI classification tasks, respectively. The AUC, specificity, sensitivity, and F1-score are also superior to state-of-the-art models, demonstrating the effectiveness of the proposed model. Furthermore, the proposed model is applied to the ABIDE dataset for autism diagnosis, achieving an accuracy of 91.43% and outperforming the state-of-the-art models, indicating excellent generalization capabilities of the proposed model. Significance. This study demonstrate s the proposed model's ability to integrate multimodal imaging data and its excellent ability to recognize MCI. This will help achieve early warning for AD and intelligent diagnosis of other brain neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Plant diseases present substantial challenges to global agriculture, significantly affecting crop yields and jeopardizing food security. Accurate and timely detection of these diseases is paramount for mitigating their adverse effects. This paper proposes a novel approach for plant disease classification by integrating convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The model aims to enhance classification accuracy by leveraging both visual features extracted by CNNs and relational information captured by GCNs. Using a Kaggle dataset containing images of diseased and healthy plant leaves from 31 classes, including apple, corn, grape, peach, pepper bell, potato, strawberry, and tomato. Standalone CNN models were trained on image data from each plant type, while standalone GCN models utilized graph-structured data representing plant relationships within each subset. The proposed integrated CNN-GCN model capitalizes on the complementary strengths of CNNs and GCNs to achieve improved classification performance. Through rigorous experimentation and comparative analysis, the effectiveness of the integrated CNN-GCN approach was evaluated alongside standalone CNN and GCN models across all plant types. Results demonstrated the superiority of the integrated model, highlighting its potential for enhancing plant disease classification accuracy. [ABSTRACT FROM AUTHOR]

To fulfill the explosion of multi-modal data, multi-modal sentiment analysis (MSA) emerged and attracted widespread attention. Unfortunately, conventional multi-modal research relies on large-scale datasets. On the one hand, collecting and annotating large-scale datasets is challenging and resource-intensive. On the other hand, the training on large-scale datasets also increases the research cost. However, the few-shot MSA (FMSA), which is proposed recently, requires only few samples for training. Therefore, in comparison, it is more practical and realistic. There have been approaches to investigating the prompt-based method in the field of FMSA, but they have not sufficiently considered or leveraged the information specificity of visual modality. Thus, we propose a vision-enhanced prompt-based model based on graph structure to better utilize vision information for fusion and collaboration in encoding and optimizing prompt representations. Specifically, we first design an aggregation-based multi-modal attention module. Then, based on this module and the biaffine attention, we construct a syntax–semantic dual-channel graph convolutional network to optimize the encoding of learnable prompts by understanding the vision-enhanced information in semantic and syntactic knowledge. Finally, we propose a collaboration-based optimization module based on the collaborative attention mechanism, which employs visual information to collaboratively optimize prompt representations. Extensive experiments conducted on both coarse-grained and fine-grained MSA datasets have demonstrated that our model significantly outperforms the baseline models. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) is an affective disorder that can lead to persistent sadness and a decline in the quality of life, increasing the risk of suicide. Utilizing multimodal data such as electroencephalograms and patient interview audios can facilitate the timely detection of MDD. However, existing depression detection methods either consider only a single modality or do not fully account for the differences and similarities between modalities in multimodal approaches, potentially overlooking the latent information inherent in various modal data. To address these challenges, we propose EMO-GCN, a multimodal depression detection method based on an adaptive multi-graph neural network. By employing graph-based methods to model data from various modalities and extracting features from them, the potential correlations between modalities are uncovered. The model's performance on the MODMA dataset is outstanding, achieving an accuracy (ACC) of 96.30%. Ablation studies further confirm the effectiveness of the model's individual components.The experimental results of EMO-GCN demonstrate the application prospects of graph-based multimodal analysis in the field of mental health, offering new perspectives for future research. [ABSTRACT FROM AUTHOR]

AbstractEffective building pattern recognition, a complex task that requires the simultaneous consideration of individual building features and spatial relations, is essential for successfully generalizing maps. However, existing deep learning approaches must still be adequately comprehensive in jointly quantifying the individual features and spatial relationships of buildings, suggesting further improvement in the quantitative representation of building spaces. This study presents a novel edge-attention multi-head graph convolutional network (GCN) that concurrently considers the quantitative modeling and representation of individual features and spatial relations, enhancing building pattern recognition. The proposed method captures individual building features and spatial relations, including proximity and arrangement similarity, by using spatial relationship descriptors and attention mechanisms to generate spatial relevance coefficients. These coefficients are then integrated into a weighted multi-head GCN to participate in the quantitative expression of individual features, facilitating the quantitative analysis and modeling of building features, and thus, improving recognition performance. Our experimental analysis confirms the method’s superior capability in recognizing complex spatial features. The method also demonstrates strong generalization across different scales and areas, underscoring its efficacy and potential for enhancing geospatial analyses. [ABSTRACT FROM AUTHOR]

Background: The prediction of protein–protein interaction sites plays a crucial role in biochemical processes. Investigating the interaction between viruses and receptor proteins through biological techniques aids in understanding disease mechanisms and guides the development of corresponding drugs. While various methods have been proposed in the past, they often suffer from drawbacks such as long processing times, high costs, and low accuracy. Results: Addressing these challenges, we propose a novel protein–protein interaction site prediction network based on multi-information fusion. In our approach, the initial amino acid features are depicted by the position-specific scoring matrix, hidden Markov model, dictionary of protein secondary structure, and one-hot encoding. Simultaneously, we adopt a multi-channel approach to extract deep-level amino acids features from different perspectives. The graph convolutional network channel effectively extracts spatial structural information. The bidirectional long short-term memory channel treats the amino acid sequence as natural language, capturing the protein's primary structure information. The ProtT5 protein large language model channel outputs a more comprehensive amino acid embedding representation, providing a robust complement to the two aforementioned channels. Finally, the obtained amino acid features are fed into the prediction layer for the final prediction. Conclusion: Compared with six protein structure-based methods and six protein sequence-based methods, our model achieves optimal performance across evaluation metrics, including accuracy, precision, F1, Matthews correlation coefficient, and area under the precision recall curve, which demonstrates the superiority of our model. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) applications has heightened the vulnerability of information security, making it susceptible to attacks that may lead to the compromise of sensitive data. Intrusion Detection System (IDS) is deployed in IoT networks for the detection of attacks and to ensure the security of information. In previous works, the IDS datasets suffer from an imbalanced distribution of data about attacks and, the flow of packets in IDS which hinders the ability of deep learning models for potent and coherent classification. With the emergence of graph convolution neural network (GCN), a new sub-field of deep learning models, the structure of graphs can be leveraged to represent the data effectively. IDS datasets typically consist of flow records of data which can naturally be represented as graph structures capturing both edge features and network topology information for classification of attacks. Hence, in this paper, a novel GCN-Ensemble fusion model is proposed for enhanced IoT IDS. There are three stages in this proposed model: (1) Data processing and attribute graph generation, (2) Feature engineering and (3) Classification. The flow attributes of data packets in IDS datasets are represented as the edges and the corresponding varying attacks as nodes of the attribute graph. Here the GCN model is leveraged for feature engineering of the IDS dataset. Further, a novel Ensemble of Convolution Neural Networks is proposed for the classification task. The evaluation of the proposed model encompasses the utilization of four distinct datasets, namely BoT-IoT, ToN-IoT, CIC-IDS2018, and NF UQ NIDS. In the BoT-IoT dataset, the proposed model demonstrates superior performance compared to state-of-art models like Deep learning and Graph neural network (GNN), achieving accuracy improvements of 3.16 and 0.91%, respectively. The observed superior performance of the model in comparison to the baseline models serves to emphasize its potential to augment IoT network security. [ABSTRACT FROM AUTHOR]

In the face of complex user behavior patterns and massive data, improving the performance of recommender system models is an urgent challenge. Traditional methods often struggle to effectively handle feature interactions and complex user-item relationships. Combining the advantages of graph neural networks and the Deep Crossing network, this paper proposes a recommendation method based on hybrid neural networks with Deep Crossing (Deep Crossing with Graph Convolution and GRU, DCGCN-GRU). First, by constructing the graph structure of users and items, higher-order feature representations are extracted, and node features are updated using a multilayer graph convolution operation. Then, the higher-order features learned by the graph convolution network are spliced and weighted with the original features to form new feature inputs. Next, a Gated Recurrent Unit (GRU) is introduced to capture the inter-feature temporal dynamic relationships and sequence information. Finally, the Deep Crossing model is utilized to learn the interactions between the fused features at multiple levels and enhance the interactions between the features. Comparative experiments on three public datasets, MovieLens-ml-25m, Book-Crossings, and Amazon Reviews'23, show that the model achieves significant improvements in accuracy, mean square error (MSE), and mean absolute error (MAE). [ABSTRACT FROM AUTHOR]

To address the challenges of low accuracy in indoor positioning caused by factors such as signal interference and visual distortions, this paper proposes a novel method that integrates ultra-wideband (UWB) technology with visual positioning. In the UWB positioning module, the powerful feature-extraction ability of the graph convolutional network (GCN) is used to integrate the features of adjacent positioning points and improve positioning accuracy. In the visual positioning module, the residual results learned from the bidirectional gate recurrent unit (Bi-GRU) network are compensated into the mathematical visual positioning model's solution results to improve the positioning results' continuity. Finally, the two positioning coordinates are fused based on particle filter (PF) to obtain the final positioning results and improve the accuracy. The experimental results show that the positioning accuracy of the proposed UWB positioning method based on a GCN is less than 0.72 m in a single UWB positioning, and the positioning accuracy is improved by 55% compared with the Chan–Taylor algorithm. The proposed visual positioning method based on Bi-GRU and residual fitting has a positioning accuracy of 0.42 m, 71% higher than the Zhang Zhengyou visual positioning algorithm. In the fusion experiment, 80% of the positioning accuracy is within 0.24 m, and the maximum error is 0.66 m. Compared with the single UWB and visual positioning, the positioning accuracy is improved by 56% and 52%, respectively, effectively enhancing indoor pedestrian positioning accuracy. [ABSTRACT FROM AUTHOR]

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model's superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles. [ABSTRACT FROM AUTHOR]

The present work describes the synthesis of graphitic carbon nitride (GCN) via simple two‐step thermal decomposition of urea at a moderate temperature of 550 °C. The as‐synthesized GCN is further doped with transition metals like nickel, and both the pure and doped GCN are characterized by X‐ray diffraction (XRD), field emission scanning electron microscope (FESEM), X‐ray photoelectron spectroscopy (XPS), and Fourier transformed infrared (FTIR) spectroscopy. XRD shows the perfect phase formation in the pure GCN, which also remains in the doped sample but with much lesser crystallinity. FESEM shows that after doping, the small chips‐like structure of GCN gets transformed to an elongated one. XPS confirms the successful doping by keeping the signature of both nickel 2P1/2 and 2P3/2 oxidation states in the spectra, whereas FTIR gave an idea about different bonding present in the sample. The pure sample, when irradiated with an excitation wavelength of 350 nm, gives an intense peak at 457 nm, which gets considerably quenched in the case of the doped sample. However, a new peak appears in the photoluminescence (PL) spectra of the doped sample at 624 nm. The quenching of PL intensity in the doped sample is assumed to be due to the fact that the dopant‐induced state traps the electron, hindering them from immediate recombination. This quenching of PL intensity generates the possibilities of sensing the presence of different metals and thus taking measurable steps for removing them. The CIE chromaticity diagrams for the doped and undoped samples confirm that the emission color changes from blue to cyan region after the doping. [ABSTRACT FROM AUTHOR]

To address the challenge of constructing spatial and temporal dependencies in existing traffic flow prediction methods, this paper proposed a new method called spatial temporal position attention graph neural network (ST-PAGNN), which utilized spatiotemporal location attention. Firstly, the graph neural network contained a location attention mechanism, which could better capture the spatial dependence of traffic nodes in the urban road network. Then, it used a gated recurrent neural network with trend adaptive transformer (Trendformer) to capture the local and global information of the traffic flow sequence in the time dimension. Finally, it used the improved grid search optimization method to optimize the introduced para-meters of the model, obtaining the global optimal solution with high time efficiency. The experimental results show that in the dataset PEMS-BAY, the evaluation indexes RMSE, MAE and MAPE of the ST-PAGNN method are 1.37, 2.57, 2.67%, 1.55, 3.64, 3.37%, 1.97, 4. 37 and 4.43%, respectively, when the prediction step size is 15 min, 30 min and 60 min, respectively. In the dataset METR-LA, when the prediction step size is 15 min, 30 min and 60 min, the evaluation indexes RMSE, MAE and MAPE of the ST-PAGNN method are 2. 73, 5. 16, 7.13%, 2.99, 5.97, 7.86%, 3.53, 7. 16 and 9.96%, respectively. The results show that the proposed ST-PAGNN method is higher than the existing models in the evaluation indexes under different granularities, which illustrates the effectiveness and superiority of ST-PAGNN in solving traffic prediction problems. [ABSTRACT FROM AUTHOR]

Long-tail distribution is a difficult challenge for knowledge graph embedding. We expect to solve the problem by complementing the information through the neighbor aggregation mechanism of GCN. However, the GCN method and its derivations are unable to learn the representation of edges. To address this problem, we propose RCGCN-TE, Relation Correlations-aware Graph Convolutional Network with Text-Enhanced for knowledge graph embedding, which is the first effort to enable GCN to learn the representation of relations directly. First, the pre-trained language model is used to extract semantic information. Then, the relation correlation graph is constructed by defining the relation relevance function based on the co-occurrence pattern and semantic similarity of relations. Finally, two GCNS are designed to learn entities and relations respectively. Experimental results on tasks such as triple classification and link prediction are better than the baseline. For example, Hits@10, Hits@3, and Hits@1 improved by 8.23 % , 37.49 % , and 46.94 % , respectively, on the entity prediction task. [ABSTRACT FROM AUTHOR]

Abstract Major Depressive Disorder (MDD) is an affective disorder that can lead to persistent sadness and a decline in the quality of life, increasing the risk of suicide. Utilizing multimodal data such as electroencephalograms and patient interview audios can facilitate the timely detection of MDD. However, existing depression detection methods either consider only a single modality or do not fully account for the differences and similarities between modalities in multimodal approaches, potentially overlooking the latent information inherent in various modal data. To address these challenges, we propose EMO-GCN, a multimodal depression detection method based on an adaptive multi-graph neural network. By employing graph-based methods to model data from various modalities and extracting features from them, the potential correlations between modalities are uncovered. The model’s performance on the MODMA dataset is outstanding, achieving an accuracy (ACC) of 96.30%. Ablation studies further confirm the effectiveness of the model’s individual components.The experimental results of EMO-GCN demonstrate the application prospects of graph-based multimodal analysis in the field of mental health, offering new perspectives for future research.

Abstract Background The prediction of protein–protein interaction sites plays a crucial role in biochemical processes. Investigating the interaction between viruses and receptor proteins through biological techniques aids in understanding disease mechanisms and guides the development of corresponding drugs. While various methods have been proposed in the past, they often suffer from drawbacks such as long processing times, high costs, and low accuracy. Results Addressing these challenges, we propose a novel protein–protein interaction site prediction network based on multi-information fusion. In our approach, the initial amino acid features are depicted by the position-specific scoring matrix, hidden Markov model, dictionary of protein secondary structure, and one-hot encoding. Simultaneously, we adopt a multi-channel approach to extract deep-level amino acids features from different perspectives. The graph convolutional network channel effectively extracts spatial structural information. The bidirectional long short-term memory channel treats the amino acid sequence as natural language, capturing the protein’s primary structure information. The ProtT5 protein large language model channel outputs a more comprehensive amino acid embedding representation, providing a robust complement to the two aforementioned channels. Finally, the obtained amino acid features are fed into the prediction layer for the final prediction. Conclusion Compared with six protein structure-based methods and six protein sequence-based methods, our model achieves optimal performance across evaluation metrics, including accuracy, precision, F1, Matthews correlation coefficient, and area under the precision recall curve, which demonstrates the superiority of our model.

Abstract Nowadays, the malicious MS-Office document has already become one of the most effective attacking vectors in APT attacks. Though many protection mechanisms are provided, they have been proved easy to bypass, and the existed detection methods show poor performance when facing malicious documents with unknown vulnerabilities or with few malicious behaviors. In this paper, we first introduce the definition of im-documents, to describe those vulnerable documents which show implicitly malicious behaviors and escape most of public antivirus engines. Then we present GLDOC—a GCN based framework that is aimed at effectively detecting im-documents with dynamic analysis, and improving the possible blind spots of past detection methods. Besides the system call which is the only focus in most researches, we capture all dynamic behaviors in sandbox, take the process tree into consideration and reconstruct both of them into graphs. Using each line to learn each graph, GLDOC trains a 2-channel network as well as a classifier to formulate the malicious document detection problem into a graph learning and classification problem. Experiments show that GLDOC has a comprehensive balance of accuracy rate and false alarm rate − 95.33% and 4.33% respectively, outperforming other detection methods. When further testing in a simulated 5-day attacking scenario, our proposed framework still maintains a stable and high detection accuracy on the unknown vulnerabilities.

With the widespread adoption of social media, the rapid dissemination of rumors poses a severe threat to public perception and social stability, emerging as a major challenge confronting society. Hence, the development of efficient and accurate rumor detection models has become an urgent need. Given the challenges of rumor detection tasks, including data scarcity, feature complexity, and difficulties in cross-task knowledge transfer, this paper proposes a BERT–GCN–Transfer Learning model, an integrated rumor detection model that combines BERT (Bidirectional Encoder Representations from Transformers), Graph Convolutional Networks (GCNs), and transfer learning techniques. By harnessing BERT's robust text representation capabilities, the GCN's feature extraction prowess on graph-structured data, and the advantage of transfer learning in cross-task knowledge sharing, the model achieves effective rumor detection on social media platforms. Experimental results indicate that this model achieves accuracies of 0.878 and 0.892 on the Twitter15 and Twitter16 datasets, respectively, significantly enhancing the accuracy of rumor detection compared to baseline models. Moreover, it greatly improves the efficiency of model training. [ABSTRACT FROM AUTHOR]

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human key point detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of key points. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model's robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to reweight the importance of features is proposed in this study, significantly enhancing the model's robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded datasets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores. [ABSTRACT FROM AUTHOR]

Introduction: The automatic and precise classification of epilepsy types using electroencephalogram (EEG) data promises significant advancements in diagnosing patients with epilepsy. However, the intricate interplay among multiple electrode signals in EEG data poses challenges. Recently, Graph Convolutional Neural Networks (GCN) have shown strength in analyzing EEG data due to their capability to describe complex relationships among different EEG regions. Nevertheless, several challenges remain: (1) GCN typically rely on predefined or prior graph topologies, which may not accurately reflect the complex correlations between brain regions. (2) GCN struggle to capture the long-temporal dependencies inherent in EEG signals, limiting their ability to effectively extract temporal features. Methods: To address these challenges, we propose an innovative epileptic seizure classification model based on an Iterative Gated Graph Convolutional Network (IGGCN). For the epileptic seizure classification task, the original EEG graph structure is iteratively optimized using a multi-head attention mechanism during training, rather than relying on a static, predefined prior graph. We introduce Gated Graph Neural Networks (GGNN) to enhance the model's capacity to capture long-term dependencies in EEG series between brain regions. Additionally, Focal Loss is employed to alleviate the imbalance caused by the scarcity of epileptic EEG data. Results: Our model was evaluated on the Temple University Hospital EEG Seizure Corpus (TUSZ) for classifying four types of epileptic seizures. The results are outstanding, achieving an average F1 score of 91.5% and an average Recall of 91.8%, showing a substantial improvement over current state-of-the-art models. Discussion: Ablation experiments verified the efficacy of iterative graph optimization and gated graph convolution. The optimized graph structure significantly differs from the predefined EEG topology. Gated graph convolutions demonstrate superior performance in capturing the long-term dependencies in EEG series. Additionally, Focal Loss outperforms other commonly used loss functions in the TUSZ classification task. [ABSTRACT FROM AUTHOR]

Regional visibility forecasting encounters challenges due to data imbalance, temporal non‐linearity and the consideration of multi‐scale spatial factors. To tackle these challenges, this study introduces a novel approach for collaborative multi‐site visibility forecasting based on spatiotemporal convolutional networks. Firstly, we preprocess the ERA5 reanalysis dataset and ground observation dataset, standardizing the spatiotemporal dimensions. We employ correlation coefficient analysis to select relevant meteorological factors. Subsequently, we create a spatiotemporal convolutional network model (TCN_GCN), which combines the power of temporal convolutional network (TCN) and graph convolutional network (GCN). Additionally, a weighted loss function is incorporated, accounting for the distribution of visibility values. The model is trained with multi‐site data, enabling it to learn spatiotemporal visibility patterns across various sites. This empowers the model to generate multi‐site visibility forecasts, thereby significantly improving regional visibility forecasting accuracy. Using 50 meteorological stations in Fujian Province, China, as a case study, we assess the model's predictions using key metrics such as mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). The experimental results demonstrate that the inclusion of both temporal and spatial features leads to a substantial enhancement in model prediction performance. The TCN_GCN model outperforms other deep learning methods in multi‐site visibility forecasting, highlighting its effectiveness and superiority in improving regional visibility forecasting accuracy. [ABSTRACT FROM AUTHOR]

In this study, iron oxide nanofins (Fe2O3 NFs) were synthesized using Elaeocarpus hygrophilus leaves extract and decorated on graphitic carbon nitride (gCN) substrate to form the Fe2O3/gCN composite, as a photocatalytic candidate to degrade Rhodamine B (RhB) and produce hydrogen peroxide (H2O2). The morphological, structural, electrochemical, and optical properties of Fe2O3/gCN were determined via analytical methods, including scanning electron microscopy, energy dispersive X-ray, scanning electron microscopy field emission, transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, photoluminescence spectroscopy, ultraviolet–visible diffuse reflectance spectroscopy (UV-DRS), electrochemical impedance spectroscopy, and photocurrent repones. As a result, the band gap of Fe2O3/gCN was determined to be 2.79 eV through UV-DRS and Kubelka–Munk function, which is lower than that of pure gCN (2.82 eV). Such phenomenon provides an RhB photodegradation efficiency of 99.23% within 120 min at pH 4, as well as an H2O2 concentration of 4237.03 μM/g h under visible light radiation, over the 1.0Fe2O3/gCN sample. Further insights elucidate that ⋅O2– plays an important part in the photocatalysis, contributing to light-driven RhB degradation and H2O2 production. The catalytic performance of 1.0Fe2O3/gCN was also maintained after 4 consecutive cycles, which indicates a high potential for environmental remediation and cleaner production processes using light as the driving force. [ABSTRACT FROM AUTHOR]

Although there are a lot of methods for multi-label classification in the past research, there are still many problems. For example, in the real world, labels are not necessarily independent of each other, and there may be some connection between labels. Therefore, exploring and utilizing the interdependence between labels is a key issue in current research. For example, in the photo category, a picture that contains blue sky often also contains white clouds, and in the text category, a political story is less likely to be entertainment news. Therefore, the key to improve the accuracy of multi-label classification is to effectively learn the possible correlations between each label. Therefore, we propose a novel multi-label educational emotion prediction based on graph convolutional network and long and short term memory network fusion label correlation in online social networks. This model uses Word2Vec method to train word vectors and combines graph convolutional neural network (GCN) with long and short term memory network (LSTM). The GCN is used to dig deeper word features of text, the LSTM layer is used to learn the longterm dependence relationship between words, and the multi-granularity attention mechanism is used to assign higher weight to the affective word features. At the same time, label correlation matrix is used to complete the label feature vector and text features as the input of the classifier, and the correlation between labels is investigated. The experimental results on the open data set show that the proposed model has a good classification effect compared with other advanced methods. The research results promote the combination of deep learning and affective computing, and can promote the research of network user behavior analysis and prediction, which can be used in personalized recommendation, targeted advertising and other fields, and has wide academic significance and application prospects. [ABSTRACT FROM AUTHOR]

The combination of memory forensics and deep learning for malware detection has achieved certain progress, but most existing methods convert process dump to images for classification, which is still based on process byte feature classification. After the malware is loaded into memory, the original byte features will change. Compared with byte features, function call features can represent the behaviors of malware more robustly. Therefore, this article proposes the ProcGCN model, a deep learning model based on DGCNN (Deep Graph Convolutional Neural Network), to detect malicious processes in memory images. First, the process dump is extracted from the whole system memory image; then, the Function Call Graph (FCG) of the process is extracted, and feature vectors for the function node in the FCG are generated based on the word bag model; finally, the FCG is input to the ProcGCN model for classification and detection. Using a public dataset for experiments, the ProcGCN model achieved an accuracy of 98.44% and an F1 score of 0.9828. It shows a better result than the existing deep learning methods based on static features, and its detection speed is faster, which demonstrates the effectiveness of the method based on function call features and graph representation learning in memory forensics. [ABSTRACT FROM AUTHOR]

Nowadays, the malicious MS-Office document has already become one of the most effective attacking vectors in APT attacks. Though many protection mechanisms are provided, they have been proved easy to bypass, and the existed detection methods show poor performance when facing malicious documents with unknown vulnerabilities or with few malicious behaviors. In this paper, we first introduce the definition of im-documents, to describe those vulnerable documents which show implicitly malicious behaviors and escape most of public antivirus engines. Then we present GLDOC—a GCN based framework that is aimed at effectively detecting im-documents with dynamic analysis, and improving the possible blind spots of past detection methods. Besides the system call which is the only focus in most researches, we capture all dynamic behaviors in sandbox, take the process tree into consideration and reconstruct both of them into graphs. Using each line to learn each graph, GLDOC trains a 2-channel network as well as a classifier to formulate the malicious document detection problem into a graph learning and classification problem. Experiments show that GLDOC has a comprehensive balance of accuracy rate and false alarm rate − 95.33% and 4.33% respectively, outperforming other detection methods. When further testing in a simulated 5-day attacking scenario, our proposed framework still maintains a stable and high detection accuracy on the unknown vulnerabilities. [ABSTRACT FROM AUTHOR]

Cicadas, known for their distinctive acoustic signals, have been subjects of classification research for years. Recent researches elaborated the species composition as effect of climate change, further raising the need of effective classification system. Tra- ditional methods rely on manual classification by domain experts, while recent trends favor Artificial Intelligence (AI)-assisted approaches due to their efficiency. However, image-based recognition faces challenges due to cicadas' varied appearances and environmental factors. Deep learning approaches, particularly utilizing Mel-frequency cepstral coefficients (MFCC) spectrograms, have been effective but are limited by dataset size. Graph Neural Networks (GNN) have surfaced as a promising alternative, lever- aging graph represen- tations to provide additional information like data relationships. In this study, we address the challenge of efficient classification with a small dataset while maximizing feature representation. We explore the effectiveness of MFCC and Chromagram features in a noisy environment, constructing unique graphs for each. Dynamic Time Warping (DTW) is employed to establish connec- tions between nodes. Our experiments on the cicada audio dataset demonstrate the superiority of Chroma- gram over MFCC, with graph-based approaches outperforming graph-less methods such as Recurrent Neural Networks (RNN). Our findings suggest the potential of graph neural networks in audio classification tasks and contribute to advancing the field's methodologies. [ABSTRACT FROM AUTHOR]

Due to the vigorous development of big data, news topic text classification has received extensive attention, and the accuracy of news topic text classification and the semantic analysis of text are worth us to explore. The semantic information contained in news topic text has an important impact on the classification results. Traditional text classification methods tend to default the text structure to the sequential linear structure, then classify by giving weight to words or according to the frequency value of words, while ignoring the semantic information in the text, which eventually leads to poor classification results. In order to solve the above problems, this paper proposes a BiLSTM-GCN (Bidirectional Long Short-Term Memory and Graph Convolutional Network) hybrid neural network text classification model based on dependency parsing. Firstly, we use BiLSTM to complete the extraction of feature vectors in the text; Then, we employ dependency parsing to strengthen the influence of words with semantic relationship, and obtain the global information of the text through GCN; Finally, aim to prevent the overfitting problem of the hybrid neural network which may be caused by too many network layers, we add a global average pooling layer. Our experimental results show that this method has a good performance on the THUCNews and SogouCS datasets, and the F-score reaches 91.37% and 91.76% respectively. [ABSTRACT FROM AUTHOR]

Non-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes. The core ideology of the Graph Convolutional Network is to aggregate node information by using edge information, thereby generating a new node feature. In updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node; the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the adaptive attention mechanism (AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution (MHGC). Finally, we adopt the cross-entropy (CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on the AAM, MHGC, and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). The experiments show that classification accuracy achieves outstanding performances on Cora, Citeseer, and Pubmed datasets. [ABSTRACT FROM AUTHOR]

Multisensor data fusion method can improve the accuracy of bearing fault diagnosis, in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis, a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network (MCMI-GCFN) is proposed in this paper. Firstly, a Convolutional Autoencoder (CAE) and Squeeze-and-Excitation Block (SE block) are used to extract features of raw current and vibration signals. Secondly, the model introduces source domain classifiers and domain discriminators to capture modal invariance between different modal data based on domain adversarial training, making use of the redundancy and complementarity between multimodal data. Then, the spatial aggregation property of Graph Convolutional Neural Networks (GCN) is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information. Finally, the validation is conducted on the public bearing damage current and vibration dataset from Paderborn University. The experimental results showed that the delivered fusion method achieved a bearing fault diagnosis accuracy of 99.6 %, which was about 9 %–11.4 % better than that with nonfusion methods.

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures.

Summary: Real‐time estimation of crowd counting in underground metro systems, constrained by limited space, is crucial for managing heightened pedestrian volumes and responding promptly to emergencies. To address this challenge, we propose a passenger state transition‐based model, called STRmt, designed for the seamless and continuous monitoring of real‐time crowd movement within service areas of stations and trains, leveraging auto fare collection systems (AFC) as a comprehensive sensor network. Our innovation lies in modeling the dynamic movement of passengers within a metro system over time as a state transition process aligned with the train schedule. To achieve this, we introduce a spatio‐temporal deep learning framework, denoted as STnet, designed to dynamically predict these state transitions. The performance of our method is rigorously assessed through extensive experiments conducted spanning 2 years in Shenzhen, China, utilizing AFC data, train schedule data, and weather data. The results demonstrate that the proposed method surpasses baseline methods, achieving an estimation precision of 0.92. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction. [ABSTRACT FROM AUTHOR]

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson's correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures. [ABSTRACT FROM AUTHOR]