Detecting Malicious Traffic through Two-phase Machine Learning

10.7125/40.6 ◽  
2015 ◽  
Vol 40 (0) ◽  
pp. 34
Author(s):  
Kazuma Shinomiya ◽  
Shigeki Goto
Keyword(s):  
Machine Learning ◽  
Two Phase

scholarly journals Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

2021 ◽  
Vol 11 (9) ◽  
pp. 4251
Author(s):  
Jinsong Zhang ◽  
Shuai Zhang ◽  
Jianhua Zhang ◽  
Zhiliang Wang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Digital Microfluidics ◽  
Flow Patterns ◽  
Machine Learning Algorithms ◽  
Dimensionless Numbers ◽  
Two Phase ◽  
The Difference ◽  
Input Variables ◽  
Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

scholarly journals Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 413
Author(s):  
Sandra Lopez-Zamora ◽  
Jeonghoon Kong ◽  
Salvador Escobedo ◽  
Hugo de Lasa
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Phase Equilibria ◽  
Aspen Plus ◽  
Phase Region ◽  
Two Phase ◽  
Technical Literature ◽  
Phase Liquid ◽  
Liquid Vapor ◽  
Vapor Liquid

The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_ Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10-4 mol fraction range, and it is larger than the 10-5 mol fraction predicted by Aspen Plus and the 10-7 mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.

Machine learning based models for pressure drop estimation of two-phase adiabatic air-water flow in micro-finned tubes: Determination of the most promising dimensionless feature set

2021 ◽  
Vol 167 ◽  
pp. 252-267
Author(s):  
Behzad Najafi ◽  
Keivan Ardam ◽  
Andrej Hanušovský ◽  
Fabio Rinaldi ◽  
Luigi Pietro Maria Colombo
Keyword(s):  
Machine Learning ◽  
Pressure Drop ◽  
Water Flow ◽  
Two Phase ◽  
Finned Tubes

Distributed numerical and machine learning computations via two-phase execution of aggregated join trees

2021 ◽  
Vol 14 (7) ◽  
pp. 1228-1240
Author(s):  
Dimitrije Jankov ◽  
Binhang Yuan ◽  
Shangyu Luo ◽  
Chris Jermaine
Keyword(s):  
Machine Learning ◽  
Query Execution ◽  
Relational System ◽  
Numerical Computations ◽  
Two Phase ◽  
Execution Strategy ◽  
Relational Joins

When numerical and machine learning (ML) computations are expressed relationally, classical query execution strategies (hash-based joins and aggregations) can do a poor job distributing the computation. In this paper, we propose a two-phase execution strategy for numerical computations that are expressed relationally, as aggregated join trees (that is, expressed as a series of relational joins followed by an aggregation). In a pilot run, lineage information is collected; this lineage is used to optimally plan the computation at the level of individual records. Then, the computation is actually executed. We show experimentally that a relational system making use of this two-phase strategy can be an excellent platform for distributed ML computations.

Implementation of Machine Learning Algorithms for Prediction of Fluidelastic Instability in Tube Arrays

2021 ◽  
Vol 143 (2) ◽  
Author(s):  
Joaquin E. Moran ◽  
Yasser Selima
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Two Phase ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Number Of Factors ◽  
Tube Arrays ◽  
Fluidelastic Instability

Abstract Fluidelastic instability (FEI) in tube arrays has been studied extensively experimentally and theoretically for the last 50 years, due to its potential to cause significant damage in short periods. Incidents similar to those observed at San Onofre Nuclear Generating Station indicate that the problem is not yet fully understood, probably due to the large number of factors affecting the phenomenon. In this study, a new approach for the analysis and interpretation of FEI data using machine learning (ML) algorithms is explored. FEI data for both single and two-phase flows have been collected from the literature and utilized for training a machine learning algorithm in order to either provide estimates of the reduced velocity (single and two-phase) or indicate if the bundle is stable or unstable under certain conditions (two-phase). The analysis included the use of logistic regression as a classification algorithm for two-phase flow problems to determine if specific conditions produce a stable or unstable response. The results of this study provide some insight into the capability and potential of logistic regression models to analyze FEI if appropriate quantities of experimental data are available.

scholarly journals A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5770 ◽  
Author(s):  
Keshav Thapa ◽  
Zubaer Md. Abdullah Al ◽  
Barsha Lamichhane ◽  
Sung-Hyun Yang
Keyword(s):  
Machine Learning ◽  
Random Field ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Human Activity Recognition ◽  
Second Phase ◽  
Complex Activity ◽  
Two Phase

Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are typical for now, but recognizing complex activities such as concurrent and interleaving activity is still a major challenging issue. In this paper, we propose a two-phase hybrid deep machine learning approach using bi-directional Long-Short Term Memory (BiLSTM) and Skip-Chain Conditional random field (SCCRF) to recognize the complex activity. BiLSTM is a sequential generative deep learning inherited from Recurrent Neural Network (RNN). SCCRFs is a distinctive feature of conditional random field (CRF) that can represent long term dependencies. In the first phase of the proposed approach, we recognized the concurrent activities using the BiLSTM technique, and in the second phase, SCCRF identifies the interleaved activity. Accuracy of the proposed framework against the counterpart state-of-art methods using the publicly available datasets in a smart home environment is analyzed. Our experiment’s result surpasses the previously proposed approaches with an average accuracy of more than 93%.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2012 ◽  
pp. 969-985
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2011 ◽  
pp. 121-138
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

Two-phase flow regime identification based on the liquid-phase velocity information and machine learning

2020 ◽  
Vol 61 (10) ◽  
Author(s):  
Yongchao Zhang ◽  
Amirah Nabilah Azman ◽  
Ke-Wei Xu ◽  
Can Kang ◽  
Hyoung-Bum Kim
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Phase Velocity ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Regime Identification ◽  
Velocity Information
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