Relevant bibliographies by topics / Weighted Visibility Network

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Academic literature on the topic 'Weighted Visibility Network'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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Journal articles on the topic "Weighted Visibility Network"

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Ma, Zhi-Yi, Xiao-Dong Yang, Ai-Jun He, Lu Ma, and Jun Wang. "Complex network recognition of electrocardiograph signals in health and myocardial infarction patients based on multiplex visibility graph." Acta Physica Sinica 71, no. 5 (2022): 050501. http://dx.doi.org/10.7498/aps.71.20211656.

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The visibility graph algorithm proves to be a simple and efficient method to transform time series into complex network and has been widely used in time series analysis because it can inherit the dynamic characteristics of original time series in topological structure. Now, visibility graph analysis of univariate time series has become mature gradually. However, most of complex systems in real world are multi-dimensional, so the univariate analysis is difficult to describe the global characteristics when applied to multi-dimensional series. In this paper, a novel method of analyzing the multivariate time series is proposed. For patients with myocardial infarction and healthy subjects, the 12-lead electrocardiogram signals of each individual are considered as a multivariate time series, which is transformed into a multiplex visibility graph through visibility graph algorithm and then mapped to fully connected complex network. Each node of the network corresponds to a lead, and the inter-layer mutual information between visibility graphs of two leads represents the weight of edges. Owing to the fully connected network of different groups showing an identical topological structure, the dynamic characteristics of different individuals cannot be uniquely represented. Therefore, we reconstruct the fully connected network according to inter-layer mutual information, and when the value of inter-layer mutual information is less than the threshold we set, the edge corresponding to the inter-layer mutual information is deleted. We extract average weighted degree and average weighted clustering coefficient of reconstructed networks for recognizing the 12-lead ECG signals of healthy subjects and myocardial infarction patients. Moreover, multiscale weighted distribution entropy is also introduced to analyze the relation between the length of original time series and final recognition result. Owing to higher average weighted degree and average weighted clustering coefficient of healthy subjects, their reconstructed networks show a more regular structure, higher complexity and connectivity, and the healthy subjects can be distinguished from patients with myocardial infarction, whose reconstructed networks are sparser. Experimental results show that the identification accuracy of both parameters, average weighted degree and average weighted clustering coefficient, reaches 93.3%, which can distinguish between the 12-lead electrocardiograph signals of healthy people and patients with myocardial infarction, and realize the automatic detection of myocardial infarction.
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Kong, Tianjiao, Jie Shao, Jiuyuan Hu, Xin Yang, Shiyiling Yang, and Reza Malekian. "EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph." Sensors 21, no. 5 (March 7, 2021): 1870. http://dx.doi.org/10.3390/s21051870.

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Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.
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Supriya, Supriya, Siuly Siuly, Hua Wang, Jinli Cao, and Yanchun Zhang. "Weighted Visibility Graph With Complex Network Features in the Detection of Epilepsy." IEEE Access 4 (2016): 6554–66. http://dx.doi.org/10.1109/access.2016.2612242.

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Wang, Hongping, Hongming Mo, Rehan Sadiq, Yong Hu, and Yong Deng. "Ordered visibility graph weighted averaging aggregation operator: A methodology based on network analysis." Computers & Industrial Engineering 88 (October 2015): 181–90. http://dx.doi.org/10.1016/j.cie.2015.06.021.

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Yoshimura, Takaaki, Kentaro Nishioka, Takayuki Hashimoto, Takashi Mori, Shoki Kogame, Kazuya Seki, Hiroyuki Sugimori, et al. "Prostatic urinary tract visualization with super-resolution deep learning models." PLOS ONE 18, no. 1 (January 6, 2023): e0280076. http://dx.doi.org/10.1371/journal.pone.0280076.

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In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen’s weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract.
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Tian, Feng, Dan Wang, Qin Wu, and Daijun Wei. "An empirical study on network conversion of stock time series based on STL method." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 10 (October 2022): 103111. http://dx.doi.org/10.1063/5.0089059.

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A complex network has been widely used to reveal the rule of a complex system. How to convert the stock data into a network is an open issue since the stock data are so large and their random volatility is strong. In this paper, a seasonal trend decomposition procedure based on the loess ([Formula: see text]) method is applied to convert the stock time series into a directed and weighted symbolic network. Three empirical stock datasets, including the closing price of Shanghai Securities Composite Index, S&P 500 Index, and Nikkei 225 Index, are considered. The properties of these stock time series are revealed from the topological characteristics of corresponding symbolic networks. The results show that: (1) both the weighted indegree and outdegree distributions obey the power-law distribution well; (2) fluctuations of stock closing price are revealed by related network topological properties, such as weighting degree, betweenness, pageranks, and clustering coefficient; and (3) stock closing price, in particular, periods such as financial crises, can be identified by modularity class of the symbolic networks. Moreover, the comparison between the [Formula: see text] method and the visibility graph further highlights the advantages of the [Formula: see text] method in terms of the time complexity of the algorithm. Our method offers a new idea to study the network conversion of stock time series.
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Chen, Luyuan, Yong Deng, and Kang Hao Cheong. "Probability transformation of mass function: A weighted network method based on the ordered visibility graph." Engineering Applications of Artificial Intelligence 105 (October 2021): 104438. http://dx.doi.org/10.1016/j.engappai.2021.104438.

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Karimimoshaver, Mehrdad, Hatameh Hajivaliei, Manouchehr Shokri, Shakila Khalesro, Farshid Aram, and Shahab Shamshirband. "A Model for Locating Tall Buildings through a Visual Analysis Approach." Applied Sciences 10, no. 17 (September 2, 2020): 6072. http://dx.doi.org/10.3390/app10176072.

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Tall buildings have become an integral part of cities despite all their pros and cons. Some current tall buildings have several problems because of their unsuitable location; the problems include increasing density, imposing traffic on urban thoroughfares, blocking view corridors, etc. Some of these buildings have destroyed desirable views of the city. In this research, different criteria have been chosen, such as environment, access, social-economic, land-use, and physical context. These criteria and sub-criteria are prioritized and weighted by the analytic network process (ANP) based on experts’ opinions, using Super Decisions V2.8 software. On the other hand, layers corresponding to sub-criteria were made in ArcGIS 10.3 simultaneously, then via a weighted overlay (map algebra), a locating plan was created. In the next step seven hypothetical tall buildings (20 stories), in the best part of the locating plan, were considered to evaluate how much of theses hypothetical buildings would be visible (fuzzy visibility) from the street and open spaces throughout the city. These processes have been modeled by MATLAB software, and the final fuzzy visibility plan was created by ArcGIS. Fuzzy visibility results can help city managers and planners to choose which location is suitable for a tall building and how much visibility may be appropriate. The proposed model can locate tall buildings based on technical and visual criteria in the future development of the city and it can be widely used in any city as long as the criteria and weights are localized.
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Craciunescu, Teddy, Andrea Murari, and Michela Gelfusa. "Improving Entropy Estimates of Complex Network Topology for the Characterization of Coupling in Dynamical Systems." Entropy 20, no. 11 (November 20, 2018): 891. http://dx.doi.org/10.3390/e20110891.

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A new measure for the characterization of interconnected dynamical systems coupling is proposed. The method is based on the representation of time series as weighted cross-visibility networks. The weights are introduced as the metric distance between connected nodes. The structure of the networks, depending on the coupling strength, is quantified via the entropy of the weighted adjacency matrix. The method has been tested on several coupled model systems with different individual properties. The results show that the proposed measure is able to distinguish the degree of coupling of the studied dynamical systems. The original use of the geodesic distance on Gaussian manifolds as a metric distance, which is able to take into account the noise inherently superimposed on the experimental data, provides significantly better results in the calculation of the entropy, improving the reliability of the coupling estimates. The application to the interaction between the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole and to the influence of ENSO on influenza pandemic occurrence illustrates the potential of the method for real-life problems.
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Im, Chan-Gi, Dong-Min Son, Hyuk-Ju Kwon, and Sung-Hak Lee. "Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion." Entropy 24, no. 10 (October 9, 2022): 1435. http://dx.doi.org/10.3390/e24101435.

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In this paper, to improve the slow processing speed of the rule-based visible and NIR (near-infrared) image synthesis method, we present a fast image fusion method using DenseFuse, one of the CNN (convolutional neural network)-based image synthesis methods. The proposed method applies a raster scan algorithm to secure visible and NIR datasets for effective learning and presents a dataset classification method using luminance and variance. Additionally, in this paper, a method for synthesizing a feature map in a fusion layer is presented and compared with the method for synthesizing a feature map in other fusion layers. The proposed method learns the superior image quality of the rule-based image synthesis method and shows a clear synthesized image with better visibility than other existing learning-based image synthesis methods. Compared with the rule-based image synthesis method used as the target image, the proposed method has an advantage in processing speed by reducing the processing time to three times or more.
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Dissertations / Theses on the topic "Weighted Visibility Network"

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Supriya, Supriya. "Brain Signal Analysis and Classification by Developing New Complex Network Techniques." Thesis, 2020. https://vuir.vu.edu.au/40551/.

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Brain signal analysis has a crucial role in the investigation of the neuronal activity for diagnosis of brain diseases and disorders. The electroencephalogram (EEG) is the most efficient biomarker for the analysis of brain signal that assists in the diagnosis of brain disorder medication and also plays an essential role in all the neurosurgery related to the brain. EEG findings illustrate the meticulous condition, and clinical content of the brain dysfunctions, and has an undisputed importance role in the detection of epilepsy condition and sleep disorders and dysfunctions allied to alcohol. The clinicians visually study the EEG recording to determine the manifestation of abnormalities in the brain. The visual EEG assessment is tiresome, fallible, and also high-priced. In this dissertation, a number of frameworks have been developed for the analysis and classification of EEG signals by addressing three different domains named: Epilepsy, Sleep staging, and Alcohol Use Disorder. Epilepsy is a non-contagious chronic disease of the brain that affects around 65 million people worldwide. The sudden onset tendency of the epileptic attacks vulnerable their sufferers to injuries. It is also challenging for the clinical staff to detect the epileptic-seizure activity early enough for determining the semiology associated with the seizure onset. For that reason, automated techniques that can accurately detect the epilepsy from EEG are of great importance to epileptic patients and especially to those patients who are resistive to therapies and medications. In this dissertation, four different techniques (named Weighted Visibility Network, Weighted Horizontal Visibility Network, Weighted Complex Network, and New Weighted Complex Network) have been developed for the automated identification of epileptic activity from the EEG signals. Most of the developed schemes attained 100% classification outcomes in their experimental evaluation for the identification of seizure activity from non-seizure activity. A sleep disorder can increase the menace of seizure incidence or severity, cognitive tasks impairments, mood deviation, diminution in the functionality of the immune system and other brain anomalies such as insomnia, sleep apnoea, etc. Hence, sleep staging is essential to discriminate among distinct sleep stages for the diagnosis of sleep and its disorders. EEG provides vital and inimitable information regarding the sleeping brain. The study of EEG has documented deformities in sleep patterns. This research has developed an innovative graph- theory based framework named weighted visibility network for sleep staging from EEG signals. The developed framework in this thesis, outperforms with 97.93% overall classification accuracy for categorizing distinct sleep states Alcoholism causes memory issues as well as motor skill defects by affecting the different portions of the brain. Excessive use of alcohol can cause sudden cardiac death and cardiomyopathy. Also, alcohol use disorder leads to respiratory infections, Vision impairment, liver damage, and cancer, etc. Research study demonstrates the use of EEG for diagnosis the patient with a high menace of developmental impediments with alcohol. In this current Ph.D. project, I developed a weighted graph-based technique that analyses EEG to distinguish between alcoholic subject and non-alcoholic person. The promising classification outcome demonstrates the effectiveness of the proposed technique.
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Book chapters on the topic "Weighted Visibility Network"

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Supriya, Siuly, Hua Wang, Guangping Zhuo, and Yanchun Zhang. "Analyzing EEG Signal Data for Detection of Epileptic Seizure: Introducing Weight on Visibility Graph with Complex Network Feature." In Lecture Notes in Computer Science, 56–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46922-5_5.

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William, P., Siddhartha Choubey, Abha Choubey, and Apurv Verma. "Darknet Traffic Analysis and Network Management for Malicious Intent Detection by Neural Network Frameworks." In Advances in Digital Crime, Forensics, and Cyber Terrorism, 1–19. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6444-1.ch001.

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Security breaches may be difficult to detect because attackers are continually tweaking methods to evade detection and utilize legitimate credentials that have already been deployed in network environments. Many firms have a way to resist the evolving sophistication of attacks in network traffic analysis technology. As cloud computing, DevOps, and the internet of things (IoT) become common, it has become more difficult to maintain network visibility. Automated detection of malicious intent using a weight-agnostic neural network architecture is possible with the authors' unique darknet traffic analysis and network management technology. Intelligent forensics tool for network traffic analysis and real-time identification of encrypted information is powerful. Automated neural network search techniques based on a weight-agnostic neural network (WANNs) approach may be used to discover zero-day threats. Many firms struggle to protect their important assets because of the effort required to identify malicious intent on the darknet manually. The advanced solution proposed here overcomes such obstacles.
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Shruthi J., Sumathi M. S., Bharathi R., and Vidya R. Pai. "Neural Net Architecture Strategy Identifying Zero-Day Attacks in the Dark Web." In Advances in Digital Crime, Forensics, and Cyber Terrorism, 86–102. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3942-5.ch007.

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Companies must foresee most critical security threats to keep one step ahead of attackers. Because attackers always refine their techniques to avoid detection and because attackers are persistently imaginative, network traffic analysis solutions have evolved providing organizations with a feasible path forward. Maintaining network visibility has gotten more challenging and time demanding as DevOps, cloud computing, and IoT (internet of things) gain popularity. Network traffic analysis can incorporate its core functionalities to detect malicious intent. The authors developed a unique darknet traffic analysis and network management solution to automate the malicious intent detection process. This strong computational intelligence forensics tool decodes network traffic, viral traffic, and encrypted communication. WANNs, a weight-independent neural network design, can detect zero-day threats. With a sophisticated solution, many businesses can protect their most valuable assets from malicious intent detection on the dark web.
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Conference papers on the topic "Weighted Visibility Network"

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Zeng, Ming, Wenkang Xu, Chunyu Zhao, Qi Li, and Jingjing Han. "Weighted Complex Network Based on Visibility Angle Measurement." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9189168.

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Artameeyanant, Patcharin, Sivarit Sultornsanee, and Kosin Chamnongthai. "Classification of electromyogram using weight visibility algorithm with multilayer perceptron neural network." In 2015 7th International Conference on Knowledge and Smart Technology (KST). IEEE, 2015. http://dx.doi.org/10.1109/kst.2015.7051485.

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Şentürk, Berkant, and Hüsnügül Yılmaz Atay. "Production of Radar Absorbing Composite Materials Using Carbon Nanotubes." In 6th International Students Science Congress. Izmir International Guest Student Association, 2022. http://dx.doi.org/10.52460/issc.2022.046.

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In order to increase the combat effectiveness of any platform or long-range munition in use today, it is necessary to reduce its visibility to radar. In this sense, important development in radar systems started after World War II. The interaction between electromagnetic waves at radar frequencies and different materials was investigated, electromagnetic radiation absorption mechanism; it has been observed that the materials consist of electrical, magnetic and dielectric properties. In line with this information, radar absorbing material design studies gained momentum. A significant development in radar systems of stealth technology made radar absorbing materials RAMs gaining a long-standing interest as a possible way to disguise aircrafts and submarines from radar systems. Carbon nanotubes and magnetic materials such as Fe, Ni, and Co have attracted researchers' significant interest as radar absorbers. In recent years, numerous studies have been made using carbon nanotubes due to their unique properties. However, few studies have considered the influence of both particle size and weight fraction. This work aims to produce material with unique properties such as solid absorption, low weight/thickness, and cost-effective, minimizing the reflection of electromagnetic waves using a polymeric composite structure reinforced with carbon nanotubes. Carbon nanotubes with different particles sizes of 8 nm,18 nm, and 78 nm were mixed with polyester in different weight fractions of 1%, 2%, and 3%. Three different composites structures were prepared in single, double, and three layers. Composites were characterized using XRD, SEM, and network analyzer in the frequency range of 8 to12 GHz. According to the results, it was observed that radar absorption increased with the increase in grain size, but the number of layers had no linear effect on the results. Network analyzer results show that the minimum reflection loss value at 9.9 GHz with a thickness of 1.5 mm is −33.1 dB, and the effective bandwidth is 9.9 GHz. Multi-layer carbon nanotubes composites might be a potential radar absorber because of their flexibility to adjust their absorption band to fit different applications in different frequency bands by modifying their particle sizes and weight.
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