Evaluating One-Hot Encoding Finite State Machines for SEU Reliability in SRAM-based FPGAs

Cassel, M.; Lima, F.

doi:10.1109/iolts.2006.32

Cited by 60 publications

(22 citation statements)

References 4 publications

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“…Certain features, such as protocol, service or flag, are not presented numerically, which is why one-hot coding was used [47]. In addition, some of the characteristics, such as duration or src bytes (sbytes), present data with widely dispersed values over a wide numerical range, so they are normalized by both the min-max function…”

Section: Data Set Understudymentioning

confidence: 99%

Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies

et al. 2020

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Artificial intelligence algorithms have a leading role in the field of cybersecurity and attack detection, being able to present better results in some scenarios than classic intrusion detection systems such as Snort or Suricata. In this sense, this research focuses on the evaluation of characteristics for different well-established Machine Leaning algorithms commonly applied to IDS scenarios. To do this, a categorization for cybersecurity data sets that groups its records into several groups is first considered. Making use of this division, this work seeks to determine which neural network model (multilayer or recurrent), activation function, and learning algorithm yield higher accuracy values, depending on the group of data. Finally, the results are used to determine which group of data from a cybersecurity data set are more relevant and representative for the intrusion detection, and the most suitable configuration of Machine Learning algorithm to decrease the computational load of the system.

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Section: Data Set Understudymentioning

confidence: 99%

Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies

et al. 2020

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“…Ideal methods would be robust to handling mixed data types as each value may contain meaningful information. Alternatively, data may be altered to a single type by using methods such as the categorization of continuous variables, and dummy or one-hot encoding[57] of continuous variables. However, the drawback of this approach is that the type of variable encoding used has been shown to affect results[11].…”

Section: Challengesmentioning

confidence: 99%

Informatics and machine learning to define the phenotype

Basile

Ritchie

2018

Expert Review of Molecular Diagnostics

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Introduction For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed. The authors address approaches for phenotype refinement that can aid in the more accurate characterization of traits. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR) derived data. Expert Commentary The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge, which may be inaccurate. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

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“…Its principle is to map words to fixed dense data vectors, namely Word Embedding [10]. Compared with traditional one-hot Encoding [11,12], bag-of-word model [13], and vector space model [14,15], etc., the distributed representation possesses relatively good semantic feature expression competence, and in the meantime data form can be read and processed efficiently by neural networks. In recent years, word vector is widely popular in the field of text semantic modeling, which can be attributed to Google's open-source Word2vec word vector tool [16].…”

Section: Introductionmentioning

confidence: 99%

DUKMSVM: A Framework of Deep Uniform Kernel Mapping Support Vector Machine for Short Text Classification

et al. 2020

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This paper mainly deals with the problem of short text classification. There are two main contributions. Firstly, we introduce a framework of deep uniform kernel mapping support vector machine (DUKMSVM). The significant merit of this framework is that by expressing the kernel mapping function explicitly with a deep neural network, it is in essence an explicit kernel mapping instead of the traditional kernel function, and it allows better flexibility in dealing with various applications by applying different neural network structures. Secondly, to validate the effectiveness of this framework and to improve the performance of short text classification, we explicitly express the kernel mapping using bidirectional recurrent neural network (BRNN), and propose a deep bidirectional recurrent kernel mapping support vector machine (DRKMSVM) for short text classification. Experimental results on five public short text classification datasets indicate that in terms of classification accuracy, precision, recall rate and F1-score, the DRKMSVM achieves the best performance with the average values of accuracy, precision, recall rate, and F1-score of 87.23%, 86.99%, 86.13% and 86.51% respectively compared to traditional SVM, convolutional neural network (CNN), Naive Bayes (NB), and Deep Neural Mapping Support Vector Machine (DNMSVM) which applies multi-layer perceptron for kernel mapping.

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Evaluating One-Hot Encoding Finite State Machines for SEU Reliability in SRAM-based FPGAs

Cited by 60 publications

References 4 publications

Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies

Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies

Informatics and machine learning to define the phenotype

DUKMSVM: A Framework of Deep Uniform Kernel Mapping Support Vector Machine for Short Text Classification

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