A noise reduction method for semi-supervised community detection based on harmonic function

2018 ◽  
Vol 32 (14) ◽  
pp. 1850166 ◽  
Author(s):  
Lilin Fan ◽  
Kaiyuan Song ◽  
Dong Liu
Keyword(s):  
Harmonic Function ◽  
Prior Knowledge ◽  
Community Detection ◽  
Detection Methods ◽  
Detection Process ◽  
Knowledge Based ◽  
Detection Approach ◽  
Series Of Experiments ◽  
Novel Strategy ◽  
The Impact

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved.

scholarly journals Hierarchical Hidden Community Detection for Protein Complex Prediction

2020 ◽  
Author(s):  
Chao Li ◽  
Kun He ◽  
Guang shuai Liu ◽  
John E. Hopcroft
Keyword(s):  
Community Detection ◽  
Hierarchical Structure ◽  
Biological Networks ◽  
Protein Complexes ◽  
Detection Methods ◽  
Biological Interactions ◽  
Protein Protein Interaction ◽  
Protein Complex Prediction ◽  
Detection Approach ◽  
New Perspective

Abstract BackgroundDiscovering functional modules in protein-protein interaction networks through optimization remains a longstanding challenge in Biology. Traditional algorithms simply consider strong protein complexes found in the original network by optimizing some metric, which may cause obstacles for discovering weak and hidden complexes that are overshadowed by strong complexes. Additionally, protein complexes have not only different densities but also various ranges of scales, making them extremely difficult to be detected. We address these issues and propose a hierarchical hidden community detection approach to predict protein complexes of various strengths and scales accurately. ResultsWe propose a meta-method called HirHide (Hierarchical Hidden Community Detection). It is the first combination of hierarchical structure with hidden structure, which provides a new perspective for finding protein complexes of various strengths and scales. We compare the performance of several standard community detection methods with their HirHide versions. Experimental results show that the HirHide versions achieve better performance and sometimes even significantly outperform the baselines. ConclusionsHirHide can adopt any standard community detection method as the base algorithm and enable it to discover hidden hierarchical communities as well as boosting the detection of strong hierarchical communities. Some biological networks are too complex for standard community detection algorithms to produce a positive performance. Most of the time, a better choice is to choose a corresponding algorithm based on the characteristics of a specific biological network. Under these circumstances, HirHide has clear advantages because of its flexibility. At the same time, according to the natural hierarchy of cells, organelle, intracellular compound etc., hierarchical structure with hidden structure is in line with the characteristics of the data itself, thus helping researchers to study biological interactions more deeply.

Use of Nutrition Information and Understanding of “Percent Daily Value” on Nutrition Facts Tables: Evaluating the Impact of a National Public Education Campaign among Youth and Young Adults in Canada

2019 ◽  
Vol 80 (4) ◽  
pp. 200-204 ◽  
Author(s):  
Brittany Cormier ◽  
Lana Vanderlee ◽  
David Hammond
Keyword(s):  
Population Level ◽  
Correct Recall ◽  
Nutrition Information ◽  
Education Campaign ◽  
Knowledge Based ◽  
Public Education Campaign ◽  
Youth And Young Adults ◽  
Campaign Message ◽  
Health Canada ◽  
The Impact

Purpose: In 2010, Health Canada implemented a national campaign to improve understanding of “percent daily value” (%DV) in Nutrition Facts Tables (NFTs). This study examined sources of nutrition information and knowledge of %DV information communicated in the campaign. Methods: Respondents aged 16–30 years completed the Canada Food Study in 2016 (n = 2665). Measures included sources of nutrition information, NFT use, and %DV knowledge based on the campaign message (“5% DV or less is a little; 15% DV or more is a lot”). A logistic regression examined correlates of providing “correct” responses to %DV questions related to the campaign messaging. Results: Overall, 7.2% (n = 191) respondents correctly indicated that 5% is “a little”, and 4.3% (n = 115) correctly indicated 15% DV was “a lot”. Only 4.0% (n = 107) correctly answered both. Correct recall of %DV amounts was not associated with number of information sources reported, but was greater among those who were female, were younger, and reported greater NFT understanding and serving size information use (P < 0.05 for all). Conclusions: Results show low awareness of messaging from the Nutrition Facts Education Campaign among young Canadians. Such a mass media campaign may be insufficient on its own to enhance population-level understanding of %DV.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

scholarly journals Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

scholarly journals A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 997
Author(s):  
Jun Zhong ◽  
Xin Gou ◽  
Qin Shu ◽  
Xing Liu ◽  
Qi Zeng
Keyword(s):  
Millimeter Wave ◽  
Detection Methods ◽  
Economic Losses ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Range Resolution ◽  
Radar Sensors ◽  
Detection Approach ◽  
Wave Radar ◽  
Whale Optimization

Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

scholarly journals Screening for Melanoma and Other Skin Cancer Shows a Higher Early Melanoma Incidence: Social Educational Program “Life Fear-Free”

2021 ◽  
Vol 8 (1) ◽  
pp. 54-68
Author(s):  
Lev Demidov ◽  
Igor Samoylenko ◽  
Nina Vand ◽  
Igor Utyashev ◽  
Irina Shubina ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Screening Program ◽  
Excisional Biopsy ◽  
Skin Lesions ◽  
Detection Methods ◽  
Non Melanoma Skin Cancer ◽  
The Social ◽  
Educational Campaign ◽  
Skin Cancer Detection ◽  
The Impact

Background: The screening program Life Fear-Free (LFF) aimed at early diagnosis of cutaneous melanoma (CM) was introduced in Samara, Chelyabinsk, Yekaterinburg, and Krasnodar (Russia) in 2019. Objectives: To analyze the impact of the program on early CM and non-melanoma skin cancer (NMSC) detection. Methods: According to the social educational campaign, people were informed about CM risk factors and symptoms and were invited for skin examination. The program planned to involve 3200 participants in total. Participants with suspicious lesions were invited for excisional biopsy. Results: 3143 participants, including 75.4% women, were examined for skin lesions. The average age of the participants was 43.7 years. Mostly skin phototypes II and III were registered (48.2% and 41.0%, respectively); 3 patients had CM, 15 had basal cell carcinoma, and 1 had Bowen’s disease, which were confirmed histologically. All detected melanomas had Breslow’s thickness of 1 mm. Conclusion: The participants showed high interest in early skin cancer detection programs. The incidence rate of CM and NMSCs among the program participants was higher than in general public. The early disease grade was proven for the detected CMs and NMSCs. The study has shown that it is important to continue such programs.

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