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Journal articles on the topic 'Heterogenous information network'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Xie, Jiang, Jing Zhang, Xiangcheng He, Shaolei Chen, Tai Zhang, and Jing Zhao. "Deep Reinforcement Learning-Based Automated Network Selection in Heterogenous CRNs." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–16. http://dx.doi.org/10.4018/ijsir.352858.

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The development of technology that enables network convergence and the rising acceptance of heterogeneous network architectures have made it possible for many of the most important cognitive radio networks to communicate with a broad variety of authorized networks. Traditional algorithms for network selection make use of selection approaches that are dependent on prior information about the network under consideration. We use the proposed algorithm to choose different networks and integrate them into computers. This paper offers a network selection technique that is based on deep reinforcement

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DURRESI, ARJAN, VAMSI PARUCHURI, and RAJ JAIN. "GEOMETRIC BROADCAST PROTOCOL FOR HETEROGENEOUS SENSOR NETWORKS." Journal of Interconnection Networks 06, no. 03 (2005): 193–207. http://dx.doi.org/10.1142/s0219265905001381.

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We present Geometric Broadcast for Heterogeneous Sensor Networks (GBS), a novel broadcasting protocol for heterogeneous wireless sensor and actor networks. While broadcasting is a very energy expensive protocol, it is also widely used as a building block for a variety of other network layer protocols. Therefore, reducing the energy consumption by optimizing broadcasting is a major improvement in heterogenous sensor networking. GBS is a distributed algorithm where nodes make local decisions on whether to transmit based on a geometric approach. GBS does not need any neighborhood information and

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Geerts, Robbe, Frédéric Vandermoere, and Stijn Oosterlynck. "The Functionality of Dissimilarity: Pro-Environmental Behavior through Heterogenous Networks." Social Sciences 9, no. 12 (2020): 221. http://dx.doi.org/10.3390/socsci9120221.

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This study explores whether social interaction with dissimilar others can lead to pro-environmental behavior. Dissimilar others are people who differ from the person in question (e.g., in terms of lifestyle or culture). While most research focuses on homogenous social networks (e.g., spatial communities), we explore the potential of network heterophily. Specifically, using data (n = 1370) from the Flemish Survey on Sociocultural Shifts, we examine the relationship between network heterophily and pro-environmental behavior (i.e., shopping decisions and curtailment behavior). Building on Granove

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Alawi, Mahmoud, Raed Alsaqour, Maha Abdelhaq, et al. "Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network." Systems 10, no. 5 (2022): 142. http://dx.doi.org/10.3390/systems10050142.

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A heterogeneous vehicular network (HetVNET) is a promising network architecture that combines multiple network technologies such as IEEE 802.11p, dedicated short-range communication (DSRC), and third/fourth generation cellular networks (3G/4G). In this network area, vehicle users can use wireless fidelity access points (Wi-Fi APs) to offload 4G long-term evolution (4G-LTE) networks. However, when using Wi-Fi APs, the vehicles must organize themselves and select an appropriate mobile gateway (MGW) to communicate to the cellular infrastructure. Researchers are facing the problem of selecting the

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Jankovics, Vince, Michael Garcia Ortiz, and Eduardo Alonso. "HetSAGE: Heterogenous Graph Neural Network for Relational Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15803–4. http://dx.doi.org/10.1609/aaai.v35i18.17898.

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This paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (GNN) approaches and provide a comparative study. We argue that the natural evolution of NSL leads to GNNs, while the logic programming foundations of NSL can bring powerful tools to improve the way information is represented and pre-processed for the GNN. In order to make this comparison, we propose HetSAGE, a GNN architecture that can efficiently deal with the resulting heterogeneous graphs that represent typical NSL learning problems. We show that our approach outperforms the state-of-the-art

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Chen, Xiaonan, and Suxia Zhang. "An SEIR model for information propagation with a hot search effect in complex networks." Mathematical Biosciences and Engineering 20, no. 1 (2022): 1251–73. http://dx.doi.org/10.3934/mbe.2023057.

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<abstract><p>We formulate an SEIR model for information propagation with the effect of a hot search in complex networks. Mathematical analysis is conducted in both a homogeneous network and heterogenous network. The results reveal that the dynamics are completely determined by the basic propagation number if the effect of a hot search is absent. On the other hand, when the effect of a hot search is taken into account, there exists no information-free equilibrium, and the information-propagating equilibrium is stable if the threshold is greater than 1. Numerical simulations were per

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A. P, Kavya, and D. J. Ravi. "A Distributed Clustering Based Energy Management Scheme for Heterogenous Wireless Sensor Network." Journal of Communication Engineering and Its Innovations 9, no. 1 (2023): 1–13. http://dx.doi.org/10.46610/jocei.2023.v09i01.001.

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The Internet of Things (IoT) is an emerging paradigm that offers a wide array of benefits for real-world applications. An IoT system is supported by a network of heterogeneous sensor nodes that collect information about the environment over time. The non-rechargeable batteries and energy constraints limit the network's lifespan. For this reason, controlling energy dissipation is one of the most important considerations when designing communication protocols for sensor networks. The clustering scheme is one of the most efficient ways to help networks be more energy-efficient. However, the major

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Shao, Ruizhe, Chun Du, Hao Chen, and Jun Li. "SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network." Remote Sensing 13, no. 18 (2021): 3750. http://dx.doi.org/10.3390/rs13183750.

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Change Detection in heterogeneous remote sensing images plays an increasingly essential role in many real-world applications, e.g., urban growth tracking, land use monitoring, disaster evaluation and damage assessment. The objective of change detection is to identify changes of geo-graphical entities or phenomena through two or more bitemporal images. Researchers have invested a lot in the homologous change detection and yielded fruitful results. However, change detection between heterogenous remote sensing images is still a great challenge, especially for change detection of heterogenous remo

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Singh, Harmeet, Manju Bala, and Sukhvinder Singh Bamber. "Augmenting network lifetime for heterogenous WSN assisted IoT using mobile agent." Wireless Networks 26, no. 8 (2020): 5965–79. http://dx.doi.org/10.1007/s11276-020-02422-z.

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Avirmed, Altankhuyag, Uranchimeg Erdenedalai, Selenge Erdenechimeg, Yansen Su, and Tseren-Onolt Ishdorj. "Biomolecular Network-based Study of a Parasitic Disease and Therapeutic Drugs." ICT Focus 1, no. 1 (2022): 22–34. http://dx.doi.org/10.58873/sict.v1i1.31.

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Computational drug repurposing methods, particularly biomolecular network-based disease-drug-target interaction models, are essential tools for integrating large-scale heterogenous molecular information and revealing functional mechanisms, as well as for main regulatory modules of interactants which can be useful in developing new drugs. In the present study, a drug-centric network for a parasitic disease (Echinococcosis) and therapeutic drugs have been considered. A complex network with more than 12,000 vertices and more than 33,000 edges representing interactions of 84 echinococcosis-related

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Li, Xuan, Renhe Shi, Yingjie Jiao, and Nannan Gao. "A systematic modeling and effectiveness evaluation framework for heterogeneous missile swarm systems." Journal of Physics: Conference Series 2891, no. 12 (2024): 122004. https://doi.org/10.1088/1742-6596/2891/12/122004.

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Abstract Heterogeneous missile swarm employs different kinds of missiles to work cooperatively for systematic warfare, which attract much attention in recent years. In order to effectively calculate and evaluate the systematic fighting capacity of heterogenous missile swarm in different scenarios, a systematic modeling and effectiveness evaluation framework is proposed in this paper. Firstly, the combat loop is defined, where the task assignment between missiles and targets is carried out using the contract net auction algorithm. Then the systematic model of heterogeneous missile swarm is cons

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Du, Dehui, Tong Guo, and Yao Wang. "DSML4CS." International Journal of Web Services Research 17, no. 2 (2020): 59–75. http://dx.doi.org/10.4018/ijwsr.2020040104.

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Cyber physical systems (CPS's) are a kind of complex system with highly integrated interaction between computing resources and physical environment in a network environment. There are some challenges in modeling and simulation of heterogeneous CPS due to its hybrid and heterogenous characteristics. To address the issue, we propose an executable domain specific modeling language for co-simulation (DSML4CS) to model the co-simulation of CPS. According to the construction method of domain modeling language, we present the abstract syntax, concrete syntax and operational semantics of DSML4CS. We a

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Cwalina, Krzysztof K., Piotr Rajchowski, Alicja Olejniczak, Olga Błaszkiewicz, and Robert Burczyk. "Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning." Sensors 21, no. 22 (2021): 7716. http://dx.doi.org/10.3390/s21227716.

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Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part

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Yang, Yihao, Howard Gritton, Martin Sarter, Sara J. Aton, Victoria Booth, and Michal Zochowski. "Theta-gamma coupling emerges from spatially heterogeneous cholinergic neuromodulation." PLOS Computational Biology 17, no. 7 (2021): e1009235. http://dx.doi.org/10.1371/journal.pcbi.1009235.

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Theta and gamma rhythms and their cross-frequency coupling play critical roles in perception, attention, learning, and memory. Available data suggest that forebrain acetylcholine (ACh) signaling promotes theta-gamma coupling, although the mechanism has not been identified. Recent evidence suggests that cholinergic signaling is both temporally and spatially constrained, in contrast to the traditional notion of slow, spatially homogeneous, and diffuse neuromodulation. Here, we find that spatially constrained cholinergic stimulation can generate theta-modulated gamma rhythms. Using biophysically-

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Wang, Yiding, Zhenyi Wang, Chenghao Li, Yilin Zhang, and Haizhou Wang. "Online social network individual depression detection using a multitask heterogenous modality fusion approach." Information Sciences 609 (September 2022): 727–49. http://dx.doi.org/10.1016/j.ins.2022.07.109.

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Chen, Lin, and Wan-Yu Deng. "Instance-Wise Denoising Autoencoder for High Dimensional Data." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4365372.

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Denoising Autoencoder (DAE) is one of the most popular fashions that has reported significant success in recent neural network research. To be specific, DAE randomly corrupts some features of the data to zero as to utilize the cooccurrence information while avoiding overfitting. However, existing DAE approaches do not fare well on sparse and high dimensional data. In this paper, we present a Denoising Autoencoder labeled here as Instance-Wise Denoising Autoencoder (IDA), which is designed to work with high dimensional and sparse data by utilizing the instance-wise cooccurrence relation instead

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Chicchi, Lorenzo, Gloria Cecchini, Ihusan Adam, et al. "Reconstruction scheme for excitatory and inhibitory dynamics with quenched disorder: application to zebrafish imaging." Journal of Computational Neuroscience 49, no. 2 (2021): 159–74. http://dx.doi.org/10.1007/s10827-020-00774-1.

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AbstractAn inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution

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Zhou, Lu, Si-Xin Chen, Yi-Qing Ni, and Liu Jiang. "Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation." Smart Materials and Structures 31, no. 1 (2021): 015005. http://dx.doi.org/10.1088/1361-665x/ac36b0.

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Abstract Ultrasonic guided waves (UGWs) have been extensively utilized in nondestructive testing and structural health monitoring (SHM) for detection and real-time monitoring of structural defects. By implementing multiple piezoelectric sensors onto a plane of the target structure to form a sensor network, damages within the sensing range can be detected or even visualized through a pitch-catch configuration. On the other hand, deep learning (DL) techniques have recently been widely used to aid UGW-based SHM when the waveform is over complicated to extract a specific mode of interest due to ir

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Barrera-Cruz, María, Oreto García-Puchol, Joaquín Jiménez-Puerto, Alfredo Cortell-Nicolau, and Joan Bernabeu-Aubán. "Weaving social networks from cultural similarities on the neolithisation process in the Western Mediterranean: Evolutionary trajectories using projectile tools." PLOS ONE 19, no. 7 (2024): e0306027. http://dx.doi.org/10.1371/journal.pone.0306027.

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In this paper, we concentrate on the neolithisation process in Mediterranean Iberia through a diachronic view (from 8600–6800 cal. BP), focusing on social interaction as a factor in articulating new cultural ties. To do this, we apply techniques centred on similarities in material culture by applying Social Network Analysis (SNA). For the first time, we point to the geometric projectiles, taking into account their recurrence in both Mesolithic and Neolithic groups as part of their characteristic hunting equipment. We hypothesise that patterns of cultural variability would express the changing

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Fan, Xiying, Chuanhe Huang, Junyu Zhu, and Bin Fu. "Replication-Based Data Dissemination in Connected Internet of Vehicles." Wireless Communications and Mobile Computing 2019 (April 4, 2019): 1–16. http://dx.doi.org/10.1155/2019/2150524.

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Due to the dynamically changing topology of Internet of Vehicles (IoV), it is a challenging issue to achieve efficient data dissemination in IoV. This paper considers strongly connected IoV with a number of heterogenous vehicular nodes to disseminate information and studies distributed replication-based data dissemination algorithms to improve the performance of data dissemination. Accordingly, two data replication algorithms, a deterministic algorithm and a distributed randomised algorithm, are proposed. In the proposed algorithms, the number of message copies spread in the network is limited

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Zhang, Qi, Huibin Yu, Xiaofeng Li, Tiesheng Liu, and Junfeng Hu. "A New Upscaling Method for Fluid Flow Simulation in Highly Heterogeneous Unconventional Reservoirs." Geofluids 2020 (August 25, 2020): 1–11. http://dx.doi.org/10.1155/2020/6213183.

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High heterogeneity and nonuniformly distributed multiscale pore systems are two characteristics of the unconventional reservoirs, which lead to very complex transport mechanisms. Limited by inadequate computational capability and imaging field of view, flow simulation cannot be directly performed on complex pore structures. The traditional methods usually coarsen the grid to reduce the computational load but will lead to the missing microstructure information and inaccurate simulation results. To develop a better understanding of flow properties in unconventional reservoirs, this study propose

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Al-omari, Motea Saleh, Mohammad Ahmed Alomari, Abdul Rahman Ramli, Aduwati Sali, Raja Syamsul Azmir, and M. Hafiz Yusoff. "Effects of Femtocell Ultradense Deployment on Downlink Performance in LTE Heterogeneous Networks." Wireless Communications and Mobile Computing 2021 (September 9, 2021): 1–21. http://dx.doi.org/10.1155/2021/2735935.

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With huge number of smart gadgets and wireless devices being interconnected to each other, the demand for very high data bandwidth is becoming critically challenging. With such density of nodes inside wireless networks, providing high-quality service as well as wide coverage in indoor environment is a real challenge, which is due to the limited radio frequency and intense interference between nodes. As a one way to solve such problem and improve indoor service quality, femtocells have been introduced as an extension to the existing macrocell stations. Although femtocell is a promising technolo

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Fan, Joline, Kiwamu Kudo, Kamalini Ranasinghe, et al. "073 Whole-brain network analysis of neural oscillations during light sleep." Sleep 44, Supplement_2 (2021): A30—A31. http://dx.doi.org/10.1093/sleep/zsab072.072.

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Abstract Introduction Sleep is a highly stereotyped phenomenon that is ubiquitous across species. Although behaviorally appearing as a homogeneous process, sleep has been recognized as cortically heterogenous and locally dynamic. PET/fMRI studies have provided key insights into regional activation and deactivation with sleep onset, but they lack the high temporal resolution and electrophysiology for understanding neural interactions. Using simultaneous electrocorticography (EEG) and magnetoencephalography (MEG) imaging, we systematically characterize whole-brain neural oscillations and identif

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Ward, Gerard, and Lech Janczewski. "Investigating Data Risk Considerations in Emergent Cyber Physical Production Systems." Journal of Systemics, Cybernetics and Informatics 20, no. 2 (2022): 51–62. http://dx.doi.org/10.54808/jsci.20.02.51.

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The Industrial Internet of Things (IIoT) describes a computing model where ubiquitous networks of heterogenous devices equipped with embedded sensors and actuators support innovative data-centric business models. Emergent IIoT use cases include Cyber Physical Production Systems (CPPS) to support asset optimization through self-organization of modular machines within production systems. In CPPS, raw materials, machines, and operations are interconnected to form a tightly integrated network. To ensure manufacturing continuity as CPPS networks evolve, asset managers will need to evaluate risk acr

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Gautam, Shuva, Luc LeBel, and Baburam Rijal. "Integrating Analytical Hierarchical Process and Network Optimization Model to Support Decision-Making on Biomass Terminal Selection." Forests 13, no. 11 (2022): 1898. http://dx.doi.org/10.3390/f13111898.

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Forest biomass is an appealing bioenergy feedstock due its renewability, availability and potential to stimulate local economies. It is, however, voluminous, with heterogenous fuel characteristics and uncertainties in its supply. The feasibility of a bioenergy facility is contingent on a secure supply of uniform feedstock; a terminal in the supply chain can be useful in this regard. Biomass can be treated in the terminal to meet quality specifications and stored to overcome seasonality and supply disruptions. Nonetheless, such terminals require a significant capital investment; thus, the decis

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Popel, A. S., A. Liu, B. Dawant, A. Koller, and P. C. Johnson. "Distribution of vascular resistance in terminal arteriolar networks of cat sartorius muscle." American Journal of Physiology-Heart and Circulatory Physiology 254, no. 6 (1988): H1149—H1156. http://dx.doi.org/10.1152/ajpheart.1988.254.6.h1149.

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Morphometric information on the terminal arteriolar networks (n = 10) in cat sartorius muscle [Koller et al., Am. J. Physiol. 253 (Heart Circ. Physiol. 22): H154-H164, 1987] is utilized in the calculations of distribution of vascular hindrance throughout the networks. These networks have tree-type geometry, i.e., they do not contain closed loops. The results are discussed in terms of simulated flow distribution. The flow calculations are based on the exact geometry of the arteriolar networks (the control and dilated diameter and the length of each vascular segment) and on assumed values of pos

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Deo, Ravin N., Nikhil Singh, Kaushal Kishore, and Jayantha Kodikara. "Numerical Study on Urban Infrastructure Diagnosis in Laterally Heterogenous Soils Using Resistivity and Ground Penetrating Radar Techniques." Journal of Environmental and Engineering Geophysics 27, no. 4 (2022): 233–40. http://dx.doi.org/10.32389/jeeg22-022.

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Urban environment can be considered a complex system consisting of the engineered pavement physical structure over the buried utilities (water, gas, sewer) network embedded in the background soil environment. Assessment of buried pipeline civil infrastructures using proximal geophysical methods in such instances has to consider possible interferences, difficulties, and incorrect inferences. In this study, we have conducted a numerical modelling investigation to understand and evaluate how electrical resistivity profiling (ERP) and ground penetrating radar (GPR) can be utilised to provide subsu

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Sigamani, Anita, and Prema Selvaraj. "JOINT FUSION CROSS SPECTRAL ASSOCIATIVE DEEP LEARNING MODEL FOR FACE RECOGNITION." ICTACT Journal on Image and Video Processing 15, no. 4 (2025): 3620–29. https://doi.org/10.21917/ijivp.2025.0512.

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Several models have been previously developed for learning correlated representations between source and target modalities. In this paper, we present a novel Joint Fusion model for learning cross spectral image representation for heterogenous face recognition. The coupled receptive face recognition model is built using ResNet architecture as backbone, a fully connected neural network and triple auto encoders for learning perceptible feature points invariant to changes in Spectrum. The performance of this model is tested using CelebA and LFW datasets. Moreover, the empirical results show that t

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Ojeda-Linares, César, Gonzalo D. Álvarez-Ríos, Carmen Julia Figueredo-Urbina, et al. "Traditional Fermented Beverages of Mexico: A Biocultural Unseen Foodscape." Foods 10, no. 10 (2021): 2390. http://dx.doi.org/10.3390/foods10102390.

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Mexico is one of the main regions of the world where the domestication of numerous edible plant species originated. Its cuisine is considered an Intangible Cultural Heritage of Humanity and ferments are important components but have been poorly studied. Traditional fermented foods are still diverse, but some are endangered, requiring actions to promote their preservation. Our study aimed to (1) systematize information on the diversity and cultural history of traditional Mexican fermented beverages (TMFB), (2) document their spatial distribution, and (3) identify the main research trends and to

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Sun, Yizhou, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. "Heterogeneous information networks." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3807–11. http://dx.doi.org/10.14778/3554821.3554901.

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In 2011, we proposed PathSim to systematically define and compute similarity between nodes in a heterogeneous information network (HIN), where nodes and links are from different types. In the PathSim paper, we for the first time introduced HIN with general network schema and proposed the concept of meta-paths to systematically define new relation types between nodes. In this paper, we summarize the impact of PathSim paper in both academia and industry. We start from the algorithms that are based on meta-path-based feature engineering, then move on to the recent development in heterogeneous net

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Liu, Shi, Kaiyang Li, Yaoying Wang, Tianyou Zhu, Jiwei Li, and Zhenyu Chen. "Knowledge graph embedding by fusing multimodal content via cross-modal learning." Mathematical Biosciences and Engineering 20, no. 8 (2023): 14180–200. http://dx.doi.org/10.3934/mbe.2023634.

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<abstract><p>Knowledge graph embedding aims to learn representation vectors for the entities and relations. Most of the existing approaches learn the representation from the structural information in the triples, which neglects the content related to the entity and relation. Though there are some approaches proposed to exploit the related multimodal content to improve knowledge graph embedding, such as the text description and images associated with the entities, they are not effective to address the heterogeneity and cross-modal correlation constraint of different types of content

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Ji, Fujiao, Zhongying Zhao, Hui Zhou, Heng Chi, and Chao Li. "A comparative study on heterogeneous information network embeddings." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 3463–73. http://dx.doi.org/10.3233/jifs-191796.

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Heterogeneous information networks are widely used to represent real world applications in forms of social networks, word co-occurrence networks, and communication networks, etc. However, It is difficult for traditional machine learning methods to analyze these networks effectively. Heterogeneous information network embedding aims to convert the network into low dimensional vectors, which facilitates the following tasks. Thus it is receiving tremendous attention from the research community due to its effectiveness and efficiency. Although numerous methods have been present and applied successf

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Salihović, Nazif, and Džubur Adisa Hasković. "Information networks – concept, classification and application." Science, Engineering and Technology 1, no. 2 (2021): 59–67. https://doi.org/10.54327/set2021/v1.i2.11.

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An information network is a structure used for transmitting various forms and types of information. In basic structure, it consists of branches that connect certain nodes. Many scientists and researchers have dealt with the problem of defining an information network, depending on the functional organization and data transmission, and also with the classification of information networks.  Information networks have a very wide application in almost all scientific disciplines. A large number of researches are carried out on the application of information networks (e.g. bisociative, deep info

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Kunchok, Tenzin, and Prof Kirubanand V. B. "A lightweight hybrid encryption technique to secure IoT data transmission." International Journal of Engineering & Technology 7, no. 2.6 (2018): 236. http://dx.doi.org/10.14419/ijet.v7i2.6.10776.

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Internet of Things(IOT) is the rising innovation without bounds is required to associate billions of devices. IoT is the future where many low power resources and constrained devices are connected by means of the internet for communication, compute process and take actions in the communication network. The increased number of communication is relied upon to produce heaps of information and the security of information can be a threat resulting a secure solution for communication is necessitates among heterogenous devices. Focus of the work is to provide confidentiality, authentication and integ

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Liang, Zhaohui, Jimmy Xiangji Huang, and Sameer Antani. "Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN." Sensors 22, no. 24 (2022): 9628. http://dx.doi.org/10.3390/s22249628.

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We propose a new generative model named adaptive cycle-consistent generative adversarial network, or Ad CycleGAN to perform image translation between normal and COVID-19 positive chest X-ray images. An independent pre-trained criterion is added to the conventional Cycle GAN architecture to exert adaptive control on image translation. The performance of Ad CycleGAN is compared with the Cycle GAN without the external criterion. The quality of the synthetic images is evaluated by quantitative metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (P

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Rabia, Rahla, and Sathish Kumar. "BIM and GIS integrated utility supply station location optimization and possibilities." Journal of Applied Engineering Science 20, no. 4 (2022): 1384–94. http://dx.doi.org/10.5937/jaes0-40600.

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Optimal planning of utility supply station location is an integral part of infrastructural projects. In general, this is a multi-objective optimization process by considering engineering, financial and geographical constraints. A shift from conventional 2D-CAD, manual quantification and design application-based approach to Building Information Modelling (BIM)-Geographic Information System (GIS) integrated approach is found to be suitable for minimizing optimal planning time, cost and increasing automation. In this paper, an Autodesk Revit add-in tool is proposed aimed at integrating BIM and GI

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Wu, Zhizhong. "MPGAAN: Effective and Efficient Heterogeneous Information Network Classification." Journal of Computer Science and Technology Studies 6, no. 4 (2024): 08–16. http://dx.doi.org/10.32996/jcsts.2024.6.4.2.

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In this paper, we propose a novel Graph Neural Network (GNN) model named "Meta-Path Guided Attention Aggregation Network" (MPAAGN), which is specifically designed for graph neural network classification algorithms based on attribute information aggregation. MPAAGN combines the advantages of Meta-Paths, GraphSAGE, and GAT (Graph Attention Networks) to deal with the node classification problem in heterogeneous information networks. The core idea of MPAAGN is to use meta-paths to define higher-order relationships between nodes in heterogeneous information networks to guide neighbor selection, and

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Wu, Jibing, Zhifei Wang, Yahui Wu, Lihua Liu, Su Deng, and Hongbin Huang. "A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms." Scientific Programming 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2803091.

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Clustering analysis is a basic and essential method for mining heterogeneous information networks, which consist of multiple types of objects and rich semantic relations among different object types. Heterogeneous information networks are ubiquitous in the real-world applications, such as bibliographic networks and social media networks. Unfortunately, most existing approaches, such as spectral clustering, are designed to analyze homogeneous information networks, which are composed of only one type of objects and links. Some recent studies focused on heterogeneous information networks and yiel

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Wu, Jibing, Lianfei Yu, Qun Zhang, et al. "Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition." Complexity 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/9653404.

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The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to

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Chatterjee, Ayan, Andreas Prinz, Martin Gerdes, and Santiago Martinez. "An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study." Journal of Medical Internet Research 23, no. 4 (2021): e24656. http://dx.doi.org/10.2196/24656.

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Background Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between dif

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Yuan, Peisen, Yi Sun, and Hengliang Wang. "Heterogeneous Information Network-Based Recommendation with Metapath Search and Memory Network Architecture Search." Mathematics 10, no. 16 (2022): 2895. http://dx.doi.org/10.3390/math10162895.

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Recommendation systems are now widely used on the Internet. In recommendation systems, user preferences are predicted by the interaction of users with products, such as clicks or purchases. Usually, the heterogeneous information network is used to capture heterogeneous semantic information in data, which can be used to solve the sparsity problem and the cold-start problem. In a more complex heterogeneous information network, the types of nodes and edges are very large, so there are lots of types of metagraphs in a complex heterogeneous information network. At the same time, machine learning ta

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Lu, Yuanfu, Chuan Shi, Linmei Hu, and Zhiyuan Liu. "Relation Structure-Aware Heterogeneous Information Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4456–63. http://dx.doi.org/10.1609/aaai.v33i01.33014456.

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Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough

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Zhao, Yan, Weifeng Rao, Zihui Hu, and Qi Zheng. "Research on Heterogeneous Information Network Link Prediction Based on Representation Learning." Journal of Electronic Research and Application 8, no. 5 (2024): 32–37. http://dx.doi.org/10.26689/jera.v8i5.8486.

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A heterogeneous information network, which is composed of various types of nodes and edges, has a complex structure and rich information content, and is widely used in social networks, academic networks, e-commerce, and other fields. Link prediction, as a key task to reveal the unobserved relationships in the network, is of great significance in heterogeneous information networks. This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks. This paper introduces the basic concepts of heterogeneous information networks, and

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Wnęk, Karol, and Piotr Boryło. "A Data Processing and Distribution System Based on Apache Nifi." Photonics 10, no. 2 (2023): 210. http://dx.doi.org/10.3390/photonics10020210.

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The monitoring of physical and logical networks is essential for the high availability of 5G/6G networks. This could become a challenge in 5G/6G deployments due to the heterogeneity of the optical layer. It uses equipment from multiple vendors, and, as a result, the protocols and methods for gathering monitoring data usually differ. Simultaneously, to effectively support 5G/6G networks, the optical infrastructure should also be dense and ensure high throughput. Thus, vast numbers of photonic transceivers operating at up to 400 Gbps are needed to interconnect network components. In demanding op

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Wang, Chen, Chengyi Zeng, Hongfu Liu, and Jing Chen. "Adversarial Hiding Deception Strategy and Network Optimization Method for Heterogeneous Network Defense." Electronics 10, no. 21 (2021): 2614. http://dx.doi.org/10.3390/electronics10212614.

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Heterogeneous networks are powerful tools for describing different types of entities and relationships and are more relevant models of complex networks. The study of heterogeneous network defense is of great practical significance for protecting useful networks such as military combat networks and critical infrastructure networks. However, a large amount of current research on complex network defense focuses on homogeneous networks under complete information conditions, which often ignore the real conditions such as incomplete information and heterogeneous networks. In this paper, we propose f

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Liu, Zhenguo, Chao Ma, Jimiao Zhao, Haiyang Hu, and Xiongyi Yin. "Graph Transformation Based on Heterogeneous Information Network for Graph Algorithms." Journal of Physics: Conference Series 2575, no. 1 (2023): 012006. http://dx.doi.org/10.1088/1742-6596/2575/1/012006.

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Abstract In the finical risk control, the graph structure data increasingly show its unique charm, especially the heterogeneous information network (HIN). And, graph computing algorithms for the data mining based on network is the most popular way at the moment. But, most of them require the graph network must have homogeneity and could not be applied to heterogeneous networks. Although the neural network models have some work in the HIN, which like heterogeneous graph neural networks, but these ways cannot provide enough interpretability due to its operations in black-box. In this paper, we s

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Wang, Ranran, Xiao Ma, Chi Jiang, Yi Ye, and Yin Zhang. "Heterogeneous information network-based music recommendation system in mobile networks." Computer Communications 150 (January 2020): 429–37. http://dx.doi.org/10.1016/j.comcom.2019.12.002.

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Xie, Fenfang, Yangjun Xu, Angyu Zheng, Liang Chen, and Zibin Zheng. "Service recommendation through graph attention network in heterogeneous information networks." International Journal of Computational Science and Engineering 25, no. 6 (2022): 643. http://dx.doi.org/10.1504/ijcse.2022.127186.

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Zheng, Zibin, Angyu Zheng, Liang Chen, Yangjun Xu, and Fenfang Xie. "Service recommendation through graph attention network in heterogeneous information networks." International Journal of Computational Science and Engineering 25, no. 6 (2022): 643. http://dx.doi.org/10.1504/ijcse.2022.10052326.

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Ross, Mindy K., Henry Zheng, Bing Zhu, et al. "Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution." Methods of Information in Medicine 59, no. 06 (2020): 219–26. http://dx.doi.org/10.1055/s-0041-1729951.

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Abstract Objectives Asthma is a heterogenous condition with significant diagnostic complexity, including variations in symptoms and temporal criteria. The disease can be difficult for clinicians to diagnose accurately. Properly identifying asthma patients from the electronic health record is consequently challenging as current algorithms (computable phenotypes) rely on diagnostic codes (e.g., International Classification of Disease, ICD) in addition to other criteria (e.g., inhaler medications)—but presume an accurate diagnosis. As such, there is no universally accepted or rigorously tested co

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