Exploring Aphasia Using Community Detection Analysis And Machine Learning

2021 ◽  
Author(s):  
Jon-Frederick Landrigan
Keyword(s):  
Machine Learning ◽  
Community Detection ◽  
Detection Analysis

Adaptive Ensemble Multi-Agent Based Intrusion Detection Model

2010 ◽  
pp. 36-48 ◽  
Author(s):  
Tarek Helmy
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Agent Based ◽  
Detection Model ◽  
Detection Analysis ◽  
Proposed Model ◽  
Multi Agent

The system that monitors the events occurring in a computer system or a network and analyzes the events for sign of intrusions is known as intrusion detection system. The performance of the intrusion detection system can be improved by combing anomaly and misuse analysis. This chapter proposes an ensemble multi-agent-based intrusion detection model. The proposed model combines anomaly, misuse, and host-based detection analysis. The agents in the proposed model use rules to check for intrusions, and adopt machine learning algorithms to recognize unknown actions, to update or create new rules automatically. Each agent in the proposed model encapsulates a specific classification technique, and gives its belief about any packet event in the network. These agents collaborate to determine the decision about any event, have the ability to generalize, and to detect novel attacks. Empirical results indicate that the proposed model is efficient, and outperforms other intrusion detection models.

scholarly journals Machine Learning Algorithms for Visualization and Prediction Modeling of Boston Crime Data

2020 ◽  
Author(s):  
Jiarui Yin ◽  
Inikuro Afa Michael ◽  
Iduabo John Afa
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Crime Data ◽  
Detection Analysis ◽  
Supervised Learning Algorithms ◽  
Supervised Methods

Machine learning plays a key role in present day crime detection, analysis and prediction. The goal of this work is to propose methods for predicting crimes classified into different categories of severity. We implemented visualization and analysis of crime data statistics in recent years in the city of Boston. We then carried out a comparative study between two supervised learning algorithms, which are decision tree and random forest based on the accuracy and processing time of the models to make predictions using geographical and temporal information provided by splitting the data into training and test sets. The result shows that random forest as expected gives a better result by 1.54% more accuracy in comparison to decision tree, although this comes at a cost of at least 4.37 times the time consumed in processing. The study opens doors to application of similar supervised methods in crime data analytics and other fields of data science

scholarly journals Exploring inclusiveness towards immigrants as related to basic values: A network approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260624
Author(s):  
Hadi Sam Nariman ◽  
Lan Anh Nguyen Luu ◽  
Márton Hadarics
Keyword(s):  
Ethnic Groups ◽  
Community Detection ◽  
Latent Class ◽  
European Social Survey ◽  
Response Patterns ◽  
Social Survey ◽  
Detection Analysis ◽  
Network Properties ◽  
Global Connectivity ◽  
The Relationship

Using the 9th round of European Social Survey (ESS), we explored the relationship between Europeans’ basic values and their attitudes towards immigrants. Employing a latent class analysis (LCA), we classified the respondents based on three items capturing the extent to which participants would support allowing three groups of immigrants to enter and live in their countries: immigrants of same ethnic groups, immigrants of different ethnic groups, and immigrants from poorer countries outside Europe. Four classes of Europeans with mutually exclusive response patterns with respect to their inclusive attitudes towards immigrants were found. The classes were named Inclusive (highly inclusive), Some (selective), Few (highly selective), and Exclusive (highly exclusive). Next, using a network technique, a partial correlation network of 10 basic human values was estimated for each class of participants. The four networks were compared to each other based on three network properties namely: global connectivity, community detection, and assortativity coefficient. The global connectivity (the overall level of interconnections) between the 10 basic values was found to be mostly invariant across the four networks. However, results of the community detection analysis revealed a more complex value structure among the most inclusive class of Europeans. Further, according to the assortativity analysis, as expected, for the most inclusive Europeans, values with similar motivational backgrounds were found to be interconnected most strongly to one another. We further discussed the theoretical and practical implications of our findings.

An Anatomy of Bengaluru's ICT Cluster: A Community Detection Approach

2019 ◽  
Vol 15 (03) ◽  
pp. 533-561
Author(s):  
Ekaterina Turkina ◽  
Ari Van Assche
Keyword(s):  
Community Detection ◽  
Empirical Support ◽  
Community Formation ◽  
Community Discovery ◽  
Organizational Network ◽  
Detection Analysis ◽  
Detection Approach ◽  
Cluster A ◽  
Knowledge Seeking ◽  
Ict Cluster

ABSTRACTWe use community detection analysis to investigate the structure of Bengaluru's ICT cluster's inter-organizational network during the period 2015–2017. Building on the knowledge sourcing literature, we conjecture that cluster firms primarily build knowledge-seeking horizontal linkages with technologically similar companies, and that this splits the network into multiple technological communities within which firms are tightly connected, but between which linkages are scarce. We further propose that community-spanning firms which build horizontal linkages that bridge technological communities are more likely to conduct radical innovation than their peers. We finally argue that no relation exists between technological proximity and community formation in the network of vertical buyer-supplier relations. Using a voltage-based algorithm for community discovery, we draw empirical support for these predictions. We discuss the implications of our findings for Bengaluru's upgrading potential.

scholarly journals MEGA: Machine Learning-Enhanced Graph Analytics for COVID-19 Infodemic Control

2020 ◽  
Author(s):  
Ching Nam Hang ◽  
Pei-Duo Yu ◽  
Lin Ling ◽  
Chee Wei Tan
Keyword(s):  
Machine Learning ◽  
Parallel Computing ◽  
Hierarchical Clustering ◽  
Community Detection ◽  
Geodesic Distance ◽  
Critical Role ◽  
Network Motif ◽  
Source Detection ◽  
Computing Technique ◽  
Special Cases

AbstractStatistical network analysis plays a critical role in managing the coronavirus disease (COVID-19) infodemic such as addressing community detection and rumor source detection problems in social networks. As the data underlying infodemiology are fundamentally huge graphs and statistical in nature, there are computational challenges to the design of graph algorithms and algorithmic speedup. A framework that leverages cloud computing is key to designing scalable data analytics for infodemic control. This paper proposes the MEGA framework, which is a novel joint hierarchical clustering and parallel computing technique that can be used to process a variety of computational tasks in large graphs. Its unique feature lies in using statistical machine learning to exploit the inherent statistics of data to accelerate computation. Our MEGA framework consists of first pruning, followed by hierarchical clustering based on geodesic distance and then parallel computing, lending itself readily to parallel computing software, e.g., MapReduce or Hadoop. In particular, we illustrate how our MEGA framework computes two representative graph problems for infodemic control, namely network motif counting for community detection and network centrality computation for rumor source detection. Interesting special cases of optimal tuning in the MEGA framework are identified based on geodesic distance characterization and random graph model analysis. Finally, we evaluate its performance using cloud software implementation and real-world graph datasets to demonstrate its computational efficiency over existing state of the art.

scholarly journals Semisupervised Community Detection by Voltage Drops

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Min Ji ◽  
Dawei Zhang ◽  
Fuding Xie ◽  
Ying Zhang ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potential Theory ◽  
Community Detection ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Machine Learning Methods ◽  
Voltage Drops ◽  
Discrete Potential Theory ◽  
Community Detection Algorithm ◽  
The Relationship

Many applications show that semisupervised community detection is one of the important topics and has attracted considerable attention in the study of complex network. In this paper, based on notion of voltage drops and discrete potential theory, a simple and fast semisupervised community detection algorithm is proposed. The label propagation through discrete potential transmission is accomplished by using voltage drops. The complexity of the proposal isOV+Efor the sparse network withVvertices andEedges. The obtained voltage value of a vertex can be reflected clearly in the relationship between the vertex and community. The experimental results on four real networks and three benchmarks indicate that the proposed algorithm is effective and flexible. Furthermore, this algorithm is easily applied to graph-based machine learning methods.

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