Image registration based on SOFM neural network clustering

Ge, Linlin; Wu, Yunhua; Hua, Bing; Chen, Zhiming; Chen, Lin

doi:10.23919/chicc.2017.8028313

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Examples include real-time image registration using Self-Orienting Feature Maps (SOFMS) [32], and the combination of TSK-DBN fuzzy learning (i.e., Takagi-Sugeno-Kang(TSK) system with Deep Belief Network (DBN)) [33].…”

Section: ) Unsupervised Learningmentioning

confidence: 99%

Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly

et al. 2019

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The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. Predictably, these technologies have seen rapid adoption, which has left many implementations vulnerable to threats unforeseen or undefended against. Moreover, such technologies can be used by malicious actors, and the potential for cyber threats, attacks, intrusions, and obfuscation that are only just being considered, applied, and countered. In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT. In detail, we consider the numerous benefits (good use) that machine learning has brought, both in general, and specifically for security and CPS/IoT, such as the improvement of intrusion detection mechanisms and decision accuracy in CPS/IoT. More pressing, we consider the vulnerabilities of machine learning (bad use) from the perspectives of security and CPS/IoT, including the ways in which machine learning systems can be compromised, misled, and subverted at all stages of the machine learning life-cycle (data collection, pre-processing, training, validation, implementation, etc.). Finally, the most concerning, a growing trend has been the utilization of machine learning in the execution of cyberattacks and intrusions (ugly use). Thus, we consider existing mechanisms with the potential to improve target acquisition and existing threat patterns, as well as those that can enable novel attacks yet to be seen.INDEX TERMS Security, machine learning, cyber physical systems, Internet of Things, applications, distributed environments.

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Section: ) Unsupervised Learningmentioning

confidence: 99%

Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly

et al. 2019

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“…SOFM Neural Network applied in clustering analysis [20] had better adaptability for image registration tasks. The Dynamic SOFM classification process is used as a character classification before the conversion of the handwritten image into machine readable format [21].…”

Section: Related Workmentioning

confidence: 99%

An Appropriate Feature Classification Model Using Kohonen Network

Sridevi¹,

Dinadayalan²,

Britto³

2019

IJCET

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Self-Organizing Maps are widely used unsupervised neural network architecture to discover group of structures in a dataset. Feature Selection plays a major role in Machine Learning. "An Appropriate Feature Classification Model using Kohonen Network (AFCM)" is based on Recurrent Neural Network approach for feature selection which clusters relevant and irrelevant features from the dataset present in cloud environment. The proposed model not only clusters relevant and irrelevant features but also refine the clustering process by minimizing the errors and irrelevant features. The AFCM consists of Feature Selection Organizer and Convergence SOM. In the Feature Selection Organizer, features are clusters into Relevant and Irrelevant Feature classes. The Convergence SOM helps to improve the prediction accuracy in the Relevant Feature set and to reduce the irrelevant features. The efficiency of the proposed model is extensively tested upon real world medical datasets. The experimental result on standard medical dataset shows that the AFCM is better than the Traditional models.

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