scholarly journals Extracting Backbones from Weighted Complex Networks with Incomplete Information

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liqiang Qian ◽  
Zhan Bu ◽  
Mei Lu ◽  
Jie Cao ◽  
Zhiang Wu
Keyword(s):  
Local Search ◽  
Complex Networks ◽  
Incomplete Information ◽  
Real Life ◽  
Extraction Methods ◽  
Filter Method ◽  
Search Model ◽  
Or Efficiency ◽  
Incomplete Knowledge ◽  
Global View

The backbone is the natural abstraction of a complex network, which can help people understand a networked system in a more simplified form. Traditional backbone extraction methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency—the exhaustive search of all nodes or edges is often prohibitively expensive. In this paper, we propose a backbone extraction heuristic with incomplete information (BEHwII) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way, which only relies on the local/incomplete knowledge rather than the global view of the network. Experimental results on four real-life networks demonstrate the advantage of BEHwII over the classic disparity filter method by either effectiveness or efficiency validity.

A Review of Different Techniques of Age Estimation from Human Face

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Khemchandra Patel ◽  
Dr. Kamlesh Namdev
Keyword(s):  
Age Estimation ◽  
Real Life ◽  
Extraction Methods ◽  
Human Face ◽  
Age Changes ◽  
Missing Children ◽  
Different Types ◽  
Age Variation ◽  
Human Faces ◽  
Facial Age

Age changes cause major variations in the appearance of human faces. Due to many lifestyle factors, it is difficult to precisely predict how individuals may look with advancing years or how they looked with "retreating" years. This paper is a review of age variation methods and techniques, which is useful to capture wanted fugitives, finding missing children, updating employee databases, enhance powerful visual effect in film, television, gaming field. Currently there are many different methods available for age variation. Each has their own advantages and purpose. Because of its real life applications, researchers have shown great interest in automatic facial age estimation. In this paper, different age variation methods with their prospects are reviewed. This paper highlights latest methodologies and feature extraction methods used by researchers to estimate age. Different types of classifiers used in this domain have also been discussed.

scholarly journals Graph fractal dimension and the structure of fractal networks

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Pavel Skums ◽  
Leonid Bunimovich
Keyword(s):  
Complex Networks ◽  
Evolutionary Biology ◽  
Real Life ◽  
Fractal Dimensions ◽  
Network Models ◽  
Self Similarity ◽  
Descriptive Complexity ◽  
Network Communities ◽  
Fractal Properties ◽  
Continuous Objects

Abstract Fractals are geometric objects that are self-similar at different scales and whose geometric dimensions differ from so-called fractal dimensions. Fractals describe complex continuous structures in nature. Although indications of self-similarity and fractality of complex networks has been previously observed, it is challenging to adapt the machinery from the theory of fractality of continuous objects to discrete objects such as networks. In this article, we identify and study fractal networks using the innate methods of graph theory and combinatorics. We establish analogues of topological (Lebesgue) and fractal (Hausdorff) dimensions for graphs and demonstrate that they are naturally related to known graph-theoretical characteristics: rank dimension and product dimension. Our approach reveals how self-similarity and fractality of a network are defined by a pattern of overlaps between densely connected network communities. It allows us to identify fractal graphs, explore the relations between graph fractality, graph colourings and graph descriptive complexity, and analyse the fractality of several classes of graphs and network models, as well as of a number of real-life networks. We demonstrate the application of our framework in evolutionary biology and virology by analysing networks of viral strains sampled at different stages of evolution inside their hosts. Our methodology revealed gradual self-organization of intra-host viral populations over the course of infection and their adaptation to the host environment. The obtained results lay a foundation for studying fractal properties of complex networks using combinatorial methods and algorithms.

Using Texture Feature in Fruit Classification

2021 ◽  
Vol 39 (1B) ◽  
pp. 67-79
Author(s):  
Mauj H. Abd al kreem ◽  
Abd allameer A. Karim
Keyword(s):  
Fruits And Vegetables ◽  
Real Life ◽  
Texture Feature ◽  
Extraction Methods ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Tree Classifier ◽  
Fresh Products ◽  
Occurrence Matrix

Recent advances in computer vision have allowed wide-ranging applications in every area of ​​life. One such area of ​​application is the classification of fresh products, but the classification of fruits and vegetables has proven to be a complex problem and needs further development. In recent years, various machine learning techniques have been exploited with many methods of describing the different features of fruit and vegetable classification in many real-life applications. Classification of fruits and vegetables presents significant challenges due to similarities between layers and irregular characteristics within the class.Hence , in this work, three feature extractor/ descriptor which are local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and, histogram of oriented gradient(HoG) has been proposed to extract fruite features , the  extracted  features have been saved in three feature vectors , then desicion tree classifier has been proposed to classify the fruit types. fruits 360 datasets  is  used  in this work,   where 70% of the dataset were used  in the training phase while 30% of it used in the testing phase. The three proposed feature extruction methods plus the tree  classifier have been used to  classifying  fruits 360 images, results show that the the three feature extraction methods  give a promising results , while the HoG method yielded a poerfull results in which  the accuracy obtained is 96%.

Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach

2016 ◽  
pp. 321-346
Author(s):  
Bhuvaneswari Chandran ◽  
P. Aruna ◽  
D. Loganathan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Lung Diseases ◽  
Hybrid Genetic Algorithm ◽  
Extraction Methods ◽  
Disease Classification ◽  
Filter Method ◽  
Svm Classifier ◽  
Genetic Approach ◽  
Feature Extraction Method

The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.

scholarly journals Community Detection Based on a Preferential Decision Model

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Jinfang Sheng ◽  
Ben Lu ◽  
Bin Wang ◽  
Jie Hu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Decision Model ◽  
Structural Characteristics ◽  
Real Life ◽  
Stable State ◽  
Ground Truth ◽  
Label Propagation ◽  
Comparison Algorithms

The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

Business Students as End-User Developers

2011 ◽  
pp. 2886-2894
Author(s):  
Sandra Barker
Keyword(s):  
Incomplete Information ◽  
Case Studies ◽  
Business Students ◽  
Good Practice ◽  
Real Life ◽  
End User ◽  
Design Procedures ◽  
Design Issue ◽  
Undergraduate Business Students ◽  
Insight Into

In this chapter, the introduction of “real-life” scenarios to undergraduate business students to enhance their understanding of end-user development of databases is investigated. The problems experienced with end-user development due to incomplete information, incorrect design procedures, and inadequate software knowledge are identified. It is the hope of the author that by identifying the design issue relevant to good database production and using “real-life” case studies as insight into how businesses use and store data, the students will be more aware of good practice for their future employment.

Business Students as End-User Developers

2003 ◽  
pp. 304-312 ◽  
Author(s):  
Sandra Barker
Keyword(s):  
Incomplete Information ◽  
Case Studies ◽  
Business Students ◽  
Good Practice ◽  
Real Life ◽  
End User ◽  
Design Procedures ◽  
Design Issue ◽  
Undergraduate Business Students ◽  
Insight Into

In this chapter, the introduction of “real-life” scenarios to undergraduate business students to enhance their understanding of end-user development of databases is investigated. The problems experienced with end-user development due to incomplete information, incorrect design procedures, and inadequate software knowledge are identified. It is the hope of the author that by identifying the design issue relevant to good database production and using “real-life” case studies as insight into how businesses use and store data, the students will be more aware of good practice for their future employment.

scholarly journals Py3plex toolkit for visualization and analysis of multilayer networks

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Blaž Škrlj ◽  
Jan Kralj ◽  
Nada Lavrač
Keyword(s):  
Complex Networks ◽  
Temporal Change ◽  
Real Life ◽  
Analysis Software ◽  
Multilayer Networks ◽  
Speed Up ◽  
Node Embeddings ◽  
Synthetic Networks

Abstract Complex networks are used as means for representing multimodal, real-life systems. With increasing amounts of data that lead to large multilayer networks consisting of different node and edge types, that can also be subject to temporal change, there is an increasing need for versatile visualization and analysis software. This work presents a lightweight Python library, Py3plex, which focuses on the visualization and analysis of multilayer networks. The library implements a set of simple graphical primitives supporting intra- as well as inter-layer visualization. It also supports many common operations on multilayer networks, such as aggregation, slicing, indexing, traversal, and more. The paper also focuses on how node embeddings can be used to speed up contemporary (multilayer) layout computation. The library’s functionality is showcased on both real and synthetic networks.

scholarly journals Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm

2020 ◽  
Vol 10 (9) ◽  
pp. 3126
Author(s):  
Desheng Lyu ◽  
Bei Wang ◽  
Weizhe Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Community Structure ◽  
Local Search ◽  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Particle Swarm ◽  
Search Performance ◽  
Swarm Optimization

With the development of network technology and the continuous advancement of society, the combination of various industries and the Internet has produced many large-scale complex networks. A common feature of complex networks is the community structure, which divides the network into clusters with tight internal connections and loose external connections. The community structure reveals the important structure and topological characteristics of the network. The detection of the community structure plays an important role in social network analysis and information recommendation. Therefore, based on the relevant theory of complex networks, this paper introduces several common community detection algorithms, analyzes the principles of particle swarm optimization (PSO) and genetic algorithm and proposes a particle swarm-genetic algorithm based on the hybrid algorithm strategy. According to the test function, the single and the proposed algorithm are tested, respectively. The results show that the algorithm can maintain the good local search performance of the particle swarm optimization algorithm and also utilizes the good global search ability of the genetic algorithm (GA) and has good algorithm performance. Experiments on each community detection algorithm on real network and artificially generated network data sets show that the particle swarm-genetic algorithm has better efficiency in large-scale complex real networks or artificially generated networks.

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