Occupational diseases present a significant global challenge, affecting a vast number of workers. Accurate prediction of occupational disease incidence is crucial for effective prevention and control measures. Althoug...
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Occupational diseases present a significant global challenge, affecting a vast number of workers. Accurate prediction of occupational disease incidence is crucial for effective prevention and control measures. Although deep learning methods have recently emerged as promising tools for disease forecasting, existing research often focuses solely on patient body parameters and disease symptoms, potentially overlooking vital diagnostic information. Addressing this gap, our study introduces a deep graph convolutional neural network (DGCNN) designed to detect occupational diseases by utilizing demographic information, work environment data, and the intricate relationships between these data points. Experimental results demonstrate that our DGCNN method surpasses other state-of-the-art methods, achieving high performance with an Area Under the Curve (AUC) of 96.2%, an accuracy of 98.7%, and an F1-score of 75.2% on the testing set. This study not only highlights the effectiveness of DGCNNs in occupational disease prediction but also underscores the value of integrating diverse data types for comprehensive disease diagnosis.
Some architects struggle to choose the best form of how the building meets the ground and may benefit from a suggestion based on precedents. This paper presents a novel proof of concept workflow that enables machine l...
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Some architects struggle to choose the best form of how the building meets the ground and may benefit from a suggestion based on precedents. This paper presents a novel proof of concept workflow that enables machine learning (ML) to automatically classify three-dimensional (3D) prototypes with respect to formulating the most appropriate building/ground relationship. Here, ML, a branch of artificial intelligence (AI), can ascertain the most appropriate relationship from a set of examples provided by trained architects. Moreover, the system classifies 3D prototypes of architectural precedent models based on a topological graph instead of 2D images. The system takes advantage of two primary technologies. The first is a software library that enhances the representation of 3D models through non-manifold topology (Topologic). The second is an end-to-end deep graph convolutional neural network (DGCNN). The experimental workflow in this paper consists of two stages. First, a generative simulation system for a 3D prototype of architectural precedents created a large synthetic database of building/ground relationships with numerous topological variations. This geometrical model then underwent conversion into semantically rich topological dual graphs. Second, the prototype architectural graphs were imported to the DGCNN model for graph classification. While using a unique data set prevents direct comparison, our experiments have shown that the proposed workflow achieves highly accurate results that align with DGCNN's performance on benchmark graphs. This research demonstrates the potential of AI to help designers identify the topology of architectural solutions and place them within the most relevant architectural canons.
The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deep g...
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The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deepgraphconvolutionalnetwork. First, the acquired raw vibration signals are pre-processed and divided into sub-samples. Secondly, a number of sub-samples in different health states are constructed as graph-structured data, divided into a training set and a test set. Finally, the training set is used as input to a deep graph convolutional neural network (DGCN) model, which is trained to determine the optimal structure and parameters of the network. A test set verifies the feasibility and effectiveness of the network. The experimental result shows that the DGCN can effectively identify compound faults in rolling bearings, which provides a new approach for the identification of compound faults in bearings.
The rise in cyber attacks on cyber-physical critical infrastructures, like water treatment networks, is evidenced by the growing frequency of breaches and the evolving sophistication of attack methods. Attack detectio...
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ISBN:
(纸本)9798400704369
The rise in cyber attacks on cyber-physical critical infrastructures, like water treatment networks, is evidenced by the growing frequency of breaches and the evolving sophistication of attack methods. Attack detection in such vulnerable critical infrastructures can be generalized into a task of anomaly detection with multivariate stream data. There are two essential challenges of this task: 1) Evolving and Shifting data streams;and 2) Robust Attack Pattern representation. Existing anomaly detection approaches, including statistical, distance, density, neuralnetwork, and graph-based methods, are not specialized in solving the spurious statistical relationships of evolving distribution shifts in sensing data streams. To address the two challenges, we propose a multi-view causal graph perspective, where 1) We build causal graphs to capture invariant anomaly patterns in varying streams;and 2) Introduce multi-view fusion for robust attack pattern representation. To implement this technical perspective, we develop a fused multi-view causal graph-aware anomaly detection framework. This framework includes two phases: 1) Multi-view Causal graphs and Spectral Fusion, where we learn the dense view and sparse view causal graphs from sensory data streams and fuse the two causal graphs into a single weighted Laplacian matrix representation. 2) graph Anomaly Detection, where we train a deepconvolutionalgraphneuralnetwork (DGCNN) on the Laplacian representation of the "Attack" and "Normal" status graphs to detect attack statuses on sensory data streams per time interval. Our framework achieves a ROC-Score of 82.4% and 93.2% on the SWaT andWADIWater Treatment network Datasets with an improvement of 9.03% and 16.5% on the f1-score respectively when compared with the best-performing baseline methods on both the datasets. The proposed method, related code, and the tested datasets are available in the following git repository https://***/arunvignesh28/SMV- CGAD.
Inspired by the success of graphneuralnetwork in graph data classification, graphneuralnetworks have been widely used in malware classification and they have been proven to be the state-of-the-art malware classifi...
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ISBN:
(纸本)9781538674628
Inspired by the success of graphneuralnetwork in graph data classification, graphneuralnetworks have been widely used in malware classification and they have been proven to be the state-of-the-art malware classification models. However, most of existing adversarial samples generation techniques against machine learning-based malware classification models modify malware samples by inserting dead codes or modifying binaries directly, which is less effective against graphneuralnetwork-based malware classification models. In this paper, we propose an adversarial attack framework powered by reinforcement learning to spoof the deep graph convolutional neural network (DGCNN)-based malware classifiers called Intelligent Malware Evader (IMaler). We construct functionality-preserved manipulations based on traditional obfuscation techniques that can modify both node features and structural features of malware. The reinforcement learning agent can make optimal decisions on how to obfuscate malware with functionality-preserved manipulations. We use a large dataset with more than 10,000 samples to evaluate the performance of IMaler and use a random agent attack as a baseline attack. The experiment results show that IMaler can achieve a significantly higher evasion rate (88.26%) than the random agent attack with fewer query times.
The problem of human activity recognition from mobile sensor data applies to multiple domains, such as health monitoring, personal fitness, daily life logging, and senior care. A critical challenge for training human ...
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ISBN:
(纸本)9781665416474
The problem of human activity recognition from mobile sensor data applies to multiple domains, such as health monitoring, personal fitness, daily life logging, and senior care. A critical challenge for training human activity recognition models is data quality. Acquiring balanced datasets containing accurate activity labels requires humans to correctly annotate and potentially interfere with the subjects' normal activities in real-time. Despite the likelihood of incorrect annotation or lack thereof, there is often an inherent chronology to human behavior. For example, we take a shower after we exercise. This implicit chronology can be used to learn unknown labels and classify future activities. In this work, we propose HARGCCN, a deepgraph CNN model that leverages the correlation between chronologically adjacent sensor measurements to predict the correct labels for unclassified activities that have at least one activity label. We propose a new training strategy enforcing that the model predicts the missing activity labels by leveraging the known ones. HAR-GCCN shows superior performance relative to previously used baseline methods, improving classification accuracy by about 25% and up to 68% on different datasets.
Detecting source code vulnerabilities is an essential issue today. In this paper, to improve the efficiency of detecting vulnerabilities in software written in C/C++, we propose to use a combination of deepgraph Conv...
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Detecting source code vulnerabilities is an essential issue today. In this paper, to improve the efficiency of detecting vulnerabilities in software written in C/C++, we propose to use a combination of deep graph convolutional neural network (DGCNN) and code property graph (CPG). Specifically, 3 main proposed phases in the research method include: phase 1: building feature profiles of source code. At this step, we suggest using analysis techniques such as Word2vec, one hot encoding to standardize and analyze the source code;phase 2: extracting features of source code based on feature profiles. Accordingly, at this phase, we propose to use deep graph convolutional neural network (DGCNN) model to analyze and extract features of the source code;phase 3: classifying source code based on the features extracted in phase 2 to find normal source code and source code containing security vulnerabilities. Some scenarios for comparing and evaluating the proposed method in this study compared with other approaches we have taken show the superior effectiveness of our approach. Besides, this result proves that our method in this paper is not only correct and reasonable, but it also opens up a new approach to the task of detecting source code vulnerabilities.
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