Hierarchical Network Model Research Articles

Hierarchical convolution neural network models for classifying the segmented OCT and OCTA images using U-Net model

Hierarchical convolution neural network models for classifying the segmented OCT and OCTA images using U-Net model

Music source feature extraction based on improved attention mechanism and phase feature

Music source feature extraction is an important research direction in music information retrieval and music recommendation system. To extract the features of music sources more effectively, the study introduces the jump attention mechanism and combines it with the convolutional attention module. Also, a feature extraction module based on Unet + + and spatial attention module is proposed. In addition, the phase feature information of the mixed music signals is utilized to improve the network performance. Results showed that this model was studied to perform well in music source separation experiments of vocals and accompaniment. For vocal separation on the MIR-1K dataset, the model achieves 11.25 dB, 17.34 dB, and 13.83 dB for each metric, respectively. Meanwhile, for drum separation on the DSD100 dataset, the model achieves a median signal-to-source distortion ratio of 4.36 dB, which is 2.91 dB better than that of the Spectral Hierarchical Network model. For the separation of the bass sound and the human voice, the model's in the separation of bass and human voice, the median distortion ratio of the model is as high as 4.87 dB and 6.09 dB, which is better than that of the Spectral Hierarchical Network model. This indicates the significant performance advantages in feature extraction and separation of music sources, and it has important application values in music production and speech recognition.

Weighted-Neighborhood-Information-Network-Enabled Anomaly Detection Method for Electronic Sensors and Sensor Networks

As electronic sensors and sensor networks advance, perception data are increasingly characterized by mixed attributes. Traditional anomaly detection methods predominantly focus on numerical attributes. In this paper, we introduce a weighted neighborhood information network (WNIN)-enabled anomaly detection method tailored for mixed-attribute data from electronic sensors and sensor networks. Firstly, we employ the analytic hierarchy process (AHP) to analyze the security of sensor networks, leveraging a hierarchical electronic sensor network model to construct a hierarchical perception security architecture for anomaly detection. Subsequently, a neighborhood information system is established to ascertain the relationships between data objects with mixed attributes. We then develop the WNIN to encapsulate the relationships, and a state-transferring probability matrix based on data object similarity is derived. Ultimately, a random wandering process within the WNIN is executed, and the importance of data objects is evaluated using the steady-state distribution vector, thereby determining the anomaly data. Simulation outcomes reveal that our proposed method attains superior anomaly detection rates compared with existing methods.

Energy-aware proof-of-authority: Blockchain consensus for clustered wireless sensor network

This study addresses integrating blockchain technology into lightweight devices, specifically clustered Wireless Sensor Networks (WSNs). Integrating blockchain into the WSNs solves the problems of heterogeneity, data integrity, and data confidentiality. However, no blockchain integration considers network lifetime in WSNs. This research focuses on developing a permissioned blockchain system that incorporates a consensus mechanism known as Proof-of-Authority (PoA) within clustered WSNs with two main features. The first feature is to enhance the network lifetime by introducing a rotational selection of block proposers using an Energy-Aware PoA (EA-PoA) weighting mechanism. Known as the Multi-Level Blockchain Model (MLBM), the subsequent feature is to create a hierarchical network model within a blockchain network. The MLBM network comprises both local and master blockchains. Each cluster inside a WSN possesses its local blockchain network. In the MLBM, the local blockchain creates a block on the main blockchain by proposing the headers of every 10 blocks to improve data integrity. Each local blockchain has its leader, which can increase block production. The results show that the proposed solution can overcome traditional PoA performance and is suitable for clustered WSNs. In terms of lifetime, the EA-PoA selection method can extend the network lifetime by up to 10%. In addition, the MLBM can increase block production by up to twice each additional cluster compared to a single blockchain network used in traditional PoA.

In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study.

We proposed a deep learning approach to classify various error types in daily VMAT treatment of head and neck cancer patients based on EPID dosimetry, which could provide additional information to support clinical decisions for adaptive planning. 146 arcs from 42 head and neck patients were analyzed. Anatomical changes and setup errors were simulated in 17,820 EPID images of 99 arcs obtained from 30 patients using in-house software for model training, validation, and testing. Subsequently, 141 clinical EPID images from 47 arcs belonging to the remaining 12 patients were utilized for clinical testing. The hierarchical convolutional neural network (HCNN) model was trained to classify error types and magnitudes using EPID dose difference maps. Gamma analysis with 3%/2mm (dose difference/distance to agreement) criteria was also performed. The F1 score, a combination of precision and recall, was utilized to evaluate the performance of the HCNN model and gamma analysis. The adaptive fractioned doses were calculated to verify the HCNN classification results. For error type identification, the overall F1 score of the HCNN model was 0.99 and 0.91 for primary type and subtype identification, respectively. For error magnitude identification, the overall F1 score in the simulation dataset was 0.96 and 0.70 for the HCNN model and gamma analysis, respectively; while the overall F1 score in the clinical dataset was 0.79 and 0.20 for the HCNN model and gamma analysis, respectively. The HCNN model-based EPID dosimetry can identify changes in patient transmission doses and distinguish the treatment error category, which could potentially provide information for head and neck cancer treatment adaption.

Application of one-dimensional hierarchical network assisted screening for cervical cancer based on Raman spectroscopy combined with attention mechanism

Cervical cancer is one of the most common malignant tumors among women, and its pathological change is a relatively slow process. If it can be detected in time and treated properly, it can effectively reduce the incidence rate and mortality rate of cervical cancer, so the early screening of cervical cancer is particularly critical and significant. In this paper, we used Raman spectroscopy technology to collect the tissue sample data of patients with cervicitis, Low-grade Squamous Intraepithelial Lesion, High-grade Squamous Intraepithelial Lesion, Well differentiated squamous cell carcinoma, Moderately differentiated squamous cell carcinoma, Poorly differentiated squamous cell carcinoma and cervical adenocarcinoma. A one-dimensional hierarchical convolutional neural network based on attention mechanism was constructed to classify and identify seven types of tissue samples. The attention mechanism Efficient Channel Attention Networks module and Squeeze-and-Excitation Networks module were combined with the established one-dimensional convolutional hierarchical network model, and the results showed that the combined model had better diagnostic performance. The average accuracy, F1, and AUC of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model after 5-fold cross validations could reach 96.49%±2.12%, 0.97±0.03, and 0.98±0.02, respectively, which were 1.58%, 0.0140, and 0.008 higher than those of hierarchical network. The recall rate of the Principal Component Analysis-Efficient Channel Attention-hierarchical network model was as high as 96.78%±2.85%, which is 1.47% higher than hierarchical network. Compared with the classification results of traditional CNN and ResNet for seven types of cervical cancer staging, the accuracy of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model is 3.33% and 11.05% higher, respectively. The experimental results indicate that the model established in this study is easy to operate and has high accuracy. It has good reference value for rapid screening of cervical cancer, laying a foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer.

Equity and spatial accessibility of healthcare resources in online health community network

Introduction: This study investigates the geographical distribution and fractal characteristics of the medical service network in China, using the “Good Doctor website” as a case study.Methods: Data for this study were extracted from the Good Doctor website Health Community. A two-tiered hierarchical network model was developed to analyze the geographical distribution and fractal characteristics of the medical service network in China.Results: Results unveil the hierarchical nature of hospital distribution and the interconnectivity among healthcare institutions. Shandong Province as a central node within the national hospital network, and networks of secondary hospitals show significant self-similarity and scale-free properties.Discussion: The small world and fractal characteristics shed light on the rapid dissemination of medical information and the robustness of the healthcare network. The results offer a novel perspective for understanding and optimizing the distribution of medical resources, and help improve the efficiency of healthcare services supply.

An Effective Hierarchical Graph Attention Network Modeling Approach for Pronunciation Assessment

An Effective Hierarchical Graph Attention Network Modeling Approach for Pronunciation Assessment

A Multi-Section Hierarchical Deep Neural Network Model for Time Series Classification: Applied to Wearable Sensor-Based Human Activity Recognition

A Multi-Section Hierarchical Deep Neural Network Model for Time Series Classification: Applied to Wearable Sensor-Based Human Activity Recognition

Understanding English Rhetorical Strategies Based on Neurosemantic Analysis

Abstract This paper designs and implements a model for analyzing rhetorical strategies of English texts to address practical needs. This paper combines the true word confusion set and the N meta-model to correct word errors. In addition, a discourse parser is constructed based on the theory of rhetorical structure, which analyzes the semantics of the English text from the intra-sentence and inter-sentence perspectives to extract the key information of the discourse. The information is inputted into a memory network that resembles a tree, and the memory units and gating mechanism are employed to obtain semantic structural features. Finally, a hierarchical network model based on the attention mechanism is designed, using a bidirectional long short-term memory network to extract the global dependency information of the text and adding the attention mechanism to make it adaptively select important features related to English rhetorical strategies. Combining learning of a tree-based long short-term memory network and a hierarchical network based on an attention mechanism is used to generate a model for semantic analysis of English text. This paper’s model has the highest accuracy, recall, and F1 value among the mentioned models in the 20newsgroups dataset, with 93.77%, 91.23%, and 90.15%, respectively. The performance on the three rhetorical strategies of explicit metaphor, personification, and analogy is excellent, with classification accuracy above 90%, which will accomplish the task of analyzing and understanding English modification strategies.

RETRACTED: Yang et al. An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network. Entropy 2022, 24, 702.

This journal retracts the article, 'An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network' [...].

Targeted V1 comodulation supports task-adaptive sensory decisions

Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and flexible, task-specific readout. The former has been studied extensively, but the latter remains poorly understood. We introduce a theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We show that responses of neurons in area V1 of monkeys performing a visual discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in task-informative neurons and can be used to decode from neural activity after few training trials, consistent with observed behavior. In a simulated hierarchical neural network model, such labels are learned quickly and can be used to adapt downstream readout, even after several intervening processing stages. Consistently, we find the modulatory signal estimated in V1 is also present in the activity of simultaneously recorded MT units, and is again strongest in task-informative neurons. These results support the idea that co-modulation facilitates task-adaptive hierarchical information routing.

Comprehensive factor analysis and risk quantification study of fall from height accidents

Working at heights poses frequent and significant risks, demanding scientific approaches for investigating fall-from-height (FFH) incidents and proposing preventive measures to enhance building safety. Nevertheless, ongoing research on analyzing the causal factors behind fall-from-height accidents lacks a comprehensive qualitative and quantitative assessment of the interplay between these factors. To bridge this gap, this study introduces an integrated risk analysis model. Utilizing incident reports and leveraging the multi-case rootedness theory, the model initially identifies influential elements. Subsequently, employing the Grey Decision Making Laboratory (Grey-DEMATEL) and Interpretive Structural Modeling (ISM) techniques, a hierarchical network is constructed, followed by the transformation of this hierarchical network model into a Bayesian Network (BN) model using GeNie2.0 software. Ultimately, the study was based on data from 420 accident cases and analyzed the causes and diagnosis of the accidents. The findings indicate that A5 (Low-security awareness) is the most significant factor contributing to falls from great heights and that the connection between the components is dynamic and non-linear rather than simply independent and linear. Furthermore, the study established a likelihood of occurrence of such incidents of up to 57 % and ranked the probability of occurrence of each contributing component in the case of a fall from height. This study presents a scientifically valid method for analyzing fall-from-height accidents. Experimental results confirm the model's applicability, empowering contractors to improve safety management by accessing precise risk information and prioritizing preventive measures against interrelated accidents. The model facilitates informed decision-making for contractors to effectively mitigate fall-from-height risks and establish a safer working environment.

MSHGN: Multi-scenario adaptive hierarchical spatial graph convolution network for GPU utilization prediction in heterogeneous GPU clusters

Accurately predicting GPU utilization is crucial for effectively managing heterogeneous GPU clusters, yet existing prediction methods are tailored to homogeneous clusters or ignore the unique characteristics of heterogeneous ones. To address this problem, we propose the Multi-Scenarios Adaptive Hierarchical Spatial Graph Convolution Network (MSHGN) model. This model leverages the hierarchical relationships among users, tasks, and machines to construct multiple scenarios' undirected graphs and uses Graph Convolution Neural (GCN) to capture the spatial dependency relationships between the computational resources consumed during execution. The MSHGN model accurately predicts the future GPU utilization rates of each machine in a heterogeneous GPU cluster, achieving an RMSE of only 0.0101, an MAE of 0.0072, and an MAPE of 0.0641. We evaluate the MSHGN model on a real-world Alibaba dataset collected from a heterogeneous GPU cluster and find that it achieves superior accuracy and robustness in predicting resource utilization, outperforming other baseline models.

Deep learning based classification of COVID-19 severity using hierarchical deep maxout model

COVID-19 is a huge pandemic disease and is gradually growing into our daily lives. It has affected us all in different ways, which the World Health Organization classified as a pandemic in 2020. A number of deep learning methods have been developed to identify COVID-19 in chest X-rays, but very few techniques are used to estimate the severity of COVID-19. Additionally, it is very complex to effectively train the data, resulting in slower convergence and time complexities. Therefore, to overcome these drawbacks, this study proposes a unique model to classify the severity of Covid-19 using a deep learning method. The pre-processing step includes image enhancement and noise reduction using the Amended Bilateral Filter (ABF). The relevant features are then extracted using an ensemble-based deep learning model, which includes the DenseNet-201, ResNet-152 and VGG-19 models, which are integrated via an ensemble average approach. The most optimal features are selected using the Modified Archer Fish Optimization Algorithm (Mod-AFO). The proposed optimization algorithm exhibits high convergence and reduced model complexity. COVID-19 severity classification can be effectively categorized using a Multihead attention-assisted hierarchical deep-maxout network model (Multi-HDM). The proposed classification model is comparatively faster in severity estimation and high in performance. Using Python, the proposed model performances are analyzed and compared with the existing models to demonstrate the superiority of the proposed model. The proposed model achieved 98.2% accuracy, 98.5% sensitivity, 96.4% specificity and 96.4% precision in COVID-19 severity estimation using the COVID-19 Chest X-ray images and Lung masks dataset.

Hierarchical Relational Graph Learning for Autonomous Multirobot Cooperative Navigation in Dynamic Environments

As a specific kind of Cyber-Physical Systems (CPSs), autonomous robot clusters play an important role in various intelligent manufacturing fields. However, due to the increasing design complexity of robot clusters, it is becoming more and more challenging to guarantee the safety and efficiency for multi-robot cooperative navigation in dynamic and complex environments. Although Deep Reinforcement Learning (DRL) shows great potential in learning multi-robot cooperative navigation policies, existing DRL-based approaches suffer from scalability issues and rarely consider the transferability of trained policies to new tasks. To address these problems, this paper presents a novel DRL-based multi-robot cooperative navigation approach named HRMR-Navi that equips each robot with both a two-layered hierarchical graph network model and an attention-based communication model. In our approach, the hierarchical graph network model can efficiently figure out hierarchical relations among all agents that either cooperate for efficiency or avoid obstacles for safety to derive more advanced strategies, and the communication model can accurately form a global view of the environment for a specific robot, thus the multi-robot cooperation efficiency can be further strengthened. Meanwhile, we propose an improved Proximal Policy Optimization (PPO) algorithm based on the Maximum Entropy reinforcement learning, named MEPPO, to enhance the robot exploration ability. Comprehensive experimental results demonstrate that, compared with state-of-the-art approaches, HRMR-Navi can achieve more efficient cooperative navigation with less time cost, lower collision rate, higher scalability, and better knowledge transferability.

Unveiling hierarchical relationships for social image representation learning

Amidst the rapid propagation of images across social media platforms, the representation learning of social images has emerged as a prime research pursuit. A salient characteristic of social images lies in their multimodal properties, encompassing the visual content and textual descriptions present within individual images and the social relationships among different images. Notably, the content information within individual images and the structural information among different images exhibit a hierarchical nature. However, existing endeavors generally employ a flat framework, leading to suboptimal utilization of hierarchical relationships. Furthermore, the heterogeneity inherent in data content and structural information presents additional challenges in social image representation. In light of these challenges, we propose a novel Hierarchical Heterogeneous Graph Neural Network model for Social Image Representation learning, dubbed HHGSI. Our motivation lies in exploring and exploiting the hierarchical relationship between diverse modalities through designing the hierarchical heterogeneous network framework. HHGSI consists of an Intra-node Multimodal Graph Encoder and an Inter-node Heterogeneous Graph Neural Network to simultaneously capture fine-grained correlation within the image and heterogeneous relationships among the images. Moreover, a task-independent optimization objective is designed to make the model suitable for numerous network-oriented and multimodal tasks. Our proposal is extensively evaluated over four real-world datasets, and experimental results demonstrate the superiority of our proposal. Our code is publicly available in https://github.com/multimodal-code/HHGSI.

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices.

A hierarchical neural network model for Japanese toward detecting mild cognitive impairment

AbstractWe found that some signs of mild cognitive impairment (MCI) might be presented in a structure of a sentence and a relation between sentences talked by a man, and develop a neural network model which has an analogy with the hierarchical structure of speakers, topics, sentences and words in Japanese. We build our model based on 2‐layered bi‐directional LSTM, corresponding to words‐sentences and sentences‐topics hierarchy. As a layer corresponding to speakers, we use a linear classifier with self‐attention. The test result shows a largely improved AUC, compared with another test by using the normal 2‐layered bi‐directional LSTM with TBPTT. The result also indicates that there are some characteristic patterns in a talk by an elderly person with MCI. We classify the character vectors of topics generated from our model through learning into clusters whose number is 1/10 of the number of persons in our data. Since these clusters have almost less than 10% or more than 90% rate of positive share, we conclude that we can develop a screening method based on a talk in Japanese by an elderly person in the near future.

A hierarchical constraint-based graph neural network for imputing urban area data

Urban area data are strategically important for public safety, urban management, and planning. Previous research has attempted to estimate the values of unsampled regular areas, while minimal attention has been paid to the values of irregular areas. To address this problem, this study proposes a hierarchical geospatial graph neural network model based on the spatial hierarchical constraints of areas. The model first characterizes spatial relationships between irregular areas at different spatial scales. Then, it aggregates information from neighboring areas with graph neural networks, and finally, it imputes missing values in fine-grained areas under hierarchical relationship constraints. To investigate the performance of the proposed model, we constructed a new dataset consisting of the urban statistical values of irregular areas in New York City. Experiments on the dataset show that the proposed model outperforms state-of-the-art baselines and exhibits robustness. The model is adaptable to numerous geographic applications, including traffic management, public safety, and public resource allocation.