A Brief Review of Machine Learning and Its Application

Wang, Hua; Ma, Cheng; Zhou, Lijuan

doi:10.1109/iciecs.2009.5362936

Cited by 107 publications

(68 citation statements)

References 6 publications

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“…It is to improve their survival rates and details of offspring of biological organisms. [3] III. Cryptography: Cryptography modifies over data into a design that is indiscernible for an unapproved customer, empowering it to be transmitted without unapproved components translating it by and by into a noticeable course of action, in this manner exchanging off the data.…”

Section: Evolutionary Learningmentioning

confidence: 99%

Intrusion Detection using Machine Learning

2019

IJRTE

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System savage technicians work to keep administrations accessible every time by dealing with gatecrasher assaults. Interruption Recognition System (IRS) is one of the possible components that is used to detect and order any anomalous activities. In this manner, the IRS must be dependably fully informed regarding the most recent gatecrasher assaults marks to save privacy, trustworthiness, and accessibility of administrations. The fast of IRS is an imperative problem. This examination work represents how the Knowledge Disclosure and Data Mining (or Knowledge Discovery in Databases) The CART and RBFN have been picked for this investigation. It has been demonstrated that the CART classifier has accomplished the most elevated exactness rate for distinguishing and arranging all KDD dataset assaults, which are of sort DOS, R2C, C2R, and Test

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Section: Evolutionary Learningmentioning

confidence: 99%

Intrusion Detection using Machine Learning

2019

IJRTE

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“…In recent years, machine learning techniques have emerged as powerful tools for the clustering, regression, and classification of discriminative features by drawing complex decision boundaries in the hyperplanes [40][41][42][43]. However, in light of the preceding discussion, it is not possible to define a superset of discriminative features that could be used to distinguish between all kinds of damage modes (delamination, matrix crack, fiber fracture, voids, etc.)…”

Section: Introductionmentioning

confidence: 99%

Damage assessment of smart composite structures via machine learning: a review

et al. 2019

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Composite materials are heterogeneous in nature and suffer from complex non-linear modes of failure, such as delamination, matrix crack, fiber-breakage, and voids, among others. The early detection of damage in composite structures, such as airplanes, is imperative to avoid catastrophic failure and tragic consequences. This paper reports on the use of machine learning techniques for the damage assessment (i.e., detection, quantification, and localization) of smart composite structures. The success of the machine learning paradigm for damage assessment depends on the representational capability of the discriminative features for the problems of interest. However, from a practical standpoint, it is not possible to define a global or superset of discriminative features that could discriminate between damaged and undamaged states of the structures, and simultaneously make a distinction between various modes of failures. In addition, one machine learning algorithm may show optimum performance for the discriminative features of a particular problem but fails for others. This article focuses on a review of discriminative features and the corresponding machine learning algorithms (both supervised and unsupervised), for various types of damage in smart composite structures.

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“…ML has been defined a number of different ways in the past, [26][27][28] but for the purpose of this work, it is taken to mean a computer "learning" how to best perform some task under a given performance metric via optimization of a set of vector operations. For example, common ML applications are regression problems and classification problems, where the performance metric is usually obvious -e.g.…”

Section: Introductionmentioning

confidence: 99%

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow

Barrett¹,

Chakraborty²,

Gandhi³

et al. 2019

Preprint

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We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, Hoomd-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. Tensor computation graphs allow for designation of robust, flexible, and easily replicated computational models for a variety of tasks. Our plugin leverages the generality and speed of computational tensor graphs in TensorFlow to enable four previously challenging tasks in molecular dynamics: (1) the calculation of arbitrary force-fields including neural-network-based, stochastic, and/or automatically-generated force-fields which are differentiated from potential functions; (2) the efficient computation of arbitrary collective variables; (3) the biasing of simulations via automatic differentiation of collective variables and consequently the implementation of many free energy biasing methods; (4) ML on any of the above tasks, including coarse grain force fields, on-the-fly learned biases, and collective variable calculations. The TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present examples of the four major tasks this method can accomplish, benchmark data, and describe the architecture of our implementation. This method should lead to both the design of new models in computational chemistry research and reproducible model specification without requiring recompiling or writing low-level code. <br>

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A Brief Review of Machine Learning and Its Application

Cited by 107 publications

References 6 publications

Intrusion Detection using Machine Learning

Intrusion Detection using Machine Learning

Damage assessment of smart composite structures via machine learning: a review

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow

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