Sensor-based localization of epidemic sources on human mobility networks

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.

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Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model

Applied Sciences ◽

10.3390/app11115213 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5213

Author(s):

Chin-Shiuh Shieh ◽

Wan-Wei Lin ◽

Thanh-Tuan Nguyen ◽

Chi-Hong Chen ◽

Mong-Fong Horng ◽

...

Keyword(s):

Deep Learning ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Denial Of Service ◽

Gaussian Mixture ◽

Mitigation Measures ◽

Distribution Model ◽

Ddos Attacks ◽

Open Set ◽

The Impact

DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.

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Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

BioMed Research International ◽

10.1155/2016/6727290 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10

Author(s):

Yunjie Chen ◽

Tianming Zhan ◽

Ji Zhang ◽

Hongyuan Wang

Keyword(s):

Magnetic Resonance ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Magnetic Resonance Images ◽

Intensity Inhomogeneity ◽

Mr Images ◽

Proposed Model ◽

Brain Mr Images ◽

The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

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A Method to Calculate Carbon Footprint of Processing Sectors Based on Gaussian Mixture Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.415.692 ◽

2013 ◽

Vol 415 ◽

pp. 692-696 ◽

Cited By ~ 1

Author(s):

Xue Ping Liu ◽

Zhi Shan Liu ◽

Dong Xiang ◽

Lang Gao ◽

Yang Cui ◽

...

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Carbon Footprint ◽

Gaussian Mixture ◽

Statistical Character ◽

The Impact

Carbon footprint is used to measure the impact of products or services on environment in recently years. The main technology in evaluating carbon footprint comes from LCA and PAS2050. However, the detail in calculating carbon footprint is not well studied. In this paper, the focus was drawn on the processing sectors. Due to the statistical character of data collected in processing sectors, the GMM (Gaussian Mixture Model) is introduced to calculate carbon footprint. With this method, the statistical meaning of carbon footprint data is well understood.

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Syrian Crisis and Migration

MIGRATION LETTERS ◽

10.33182/ml.v12i3.273 ◽

2015 ◽

Vol 12 (3) ◽

pp. 181-192 ◽

Cited By ~ 22

Author(s):

Pinar Yazgan ◽

Deniz Eroglu Utku ◽

Ibrahim Sirkeci

Keyword(s):

Human Mobility ◽

Special Issue ◽

Host Countries ◽

Syrian Crisis ◽

Mass Migration ◽

Large Numbers ◽

And Migration ◽

Europe Today ◽

The Impact ◽

Near Future

With the growing insurrections in Syria in 2011, an exodus in large numbers have emerged. The turmoil and violence have caused mass migration to destinations both within the region and beyond. The current "refugee crisis" has escalated sharply and its impact is widening from neighbouring countries toward Europe. Today, the Syrian crisis is the major cause for an increase in displacement and the resultant dire humanitarian situation in the region. Since the conflict shows no signs of abating in the near future, there is a constant increase in the number of Syrians fleeing their homes. However, questions on the future impact of the Syrian crisis on the scope and scale of this human mobility are still to be answered. As the impact of the Syrian crisis on host countries increases, so does the demand for the analyses of the needs for development and protection in these countries. In this special issue, we aim to bring together a number of studies examining and discussing human mobility in relation to the Syrian crisis.

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