A machine-learning-based post filtering method utilizing block boundary information in HEVC

Currently a spam filtering method is extracting attributes from e-mail header and using machine learning methods to classify the sample sets. But as time goes on, spammers transform different ways to send spam, which result in a great change of spam's header. So the attributes defined in the past could not deal with this change sufficiently. This paper extracted attributes from all possible forged header fields to expand the feature sets, then used the rough set theory to classify the sample sets. Experiment validated more attributes including in feature sets may lead to greater performance, in terms of higher recall and precision, lower fake recognition than other algorithms.

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Machine Learning as a method of adapting offers to the clients

Journal of Computer Sciences Institute ◽

10.35784/jcsi.1293 ◽

2019 ◽

Vol 13 ◽

pp. 267-271

Author(s):

Jacek Bielecki ◽

Oskar Ceglarski ◽

Maria Skublewska-Paszkowska

Keyword(s):

Machine Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Recommendation Systems ◽

Filtering Method ◽

Advantages And Disadvantages ◽

Information Filter ◽

User Ratings ◽

Research Show

Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.

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Information Filtering Method for Twitter Streaming Data Using Human-in-the-Loop Machine Learning

Lecture Notes in Computer Science - Database and Expert Systems Applications ◽

10.1007/978-3-319-98812-2_13 ◽

2018 ◽

pp. 167-175 ◽

Cited By ~ 1

Author(s):

Yu Suzuki ◽

Satoshi Nakamura

Keyword(s):

Machine Learning ◽

Information Filtering ◽

Streaming Data ◽

Filtering Method ◽

Human In The Loop

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An Effective Pre-Processing Phase for Gene Expression Classification

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v11.i3.pp1223-1227 ◽

2018 ◽

Vol 11 (3) ◽

pp. 1223

Author(s):

Choon Sen Seah ◽

Shahreen Kasim ◽

Mohd Farhan Md Fudzee ◽

Mohd Saberi Mohamad ◽

Rd Rohmat Saedudin ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Random Walk ◽

Bioconductor Package ◽

Processing Stage ◽

Missing Value ◽

Filtering Method ◽

Initial Stage ◽

Expression Classification ◽

Learning Data

A raw dataset prepared by researchers comes with a lot of information. Whether the information is usefull or not, completely depends on the requirement and purposes. In machine learning, data pre-processing is the very initial stage. It is a must to make sure the dataset is totally suitable for the requirement. In significant directed random walk (sDRW), there are three steps in data pre-processing stage. First, we remove unwanted attributes, missing value and proper arrangement, followed by normalization of the expression value and lastly, filtering method is applied. The first two steps are completed by Bioconductor package while the last step is works in sDRW.

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MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2021.109125 ◽

2021 ◽

pp. 109125

Author(s):

Xiaomin Zhang ◽

Alois Schlögl ◽

David Vandael ◽

Peter Jonas

Keyword(s):

Machine Learning ◽

Optimal Filtering ◽

Filtering Method ◽

Efficient Detection

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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