Search results for "machine learning."

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

2021

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO(2), O(3), and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO(2) and particulate matter with aerodynamic diameters <2.5 μm by –30.1% and –17.5%, respectively, bu…

A method to optimize a typology-based classification system

2014

This study sought to provide guidelines for implementing typology-based qualitative analysis of human movement patterns.Fifteen participant-analysts were instructed how to classify treading water behaviours into eight different categories using a training set of videos. They were later provided with two additional sets of videos called validation, and test sets. Results first identified reliable (n=9), and not reliable (n=6) analysts. A decision study outlined that one analyst was sufficient to reliably categorize the behaviours in the ‘reliable’ analyst group, whereas up to four were necessary in the ‘unreliable’ group. These data provided new insights into more objective qualitative analy…

A metabolomic study of yeast/bacteria interactions

2015

As a complex microbial ecosystem, wine is a particularly interesting model for studying interactions between microorganisms. Contact-independent interactions (indirect interactions) between the yeast Saccharomyces cerevisae and the lactic acid bacterium Oenococcus oeni have a direct effect on malolactic fermentation (MLF), induction and completion, which is an important factor in wine quality. Yeast strains could be classified as MLF+ phenotype if it usually stimulates the bacterial growth or MLF- in the opposite case. The known metabolites that stimulate or inhibit the MLF cannot always explain the phenotypic distinction. In this work, a multidisciplinary workflow combining non-targeted me…

Learning Automaton Based On-Line Discovery and Tracking of Spatio-temporal Event Patterns

2010

Published version of an article from the book: Lecture Notes in Computer Science, 2010, Volume 6230/2010, 327-338. The original publication is available at Springerlink. http://dx.doi.org/10.1007/978-3-642-15246-7_31 Discovering and tracking of spatio-temporal patterns in noisy sequences of events is a difficult task that has become increasingly pertinent due to recent advances in ubiquitous computing, such as community-based social networking applications. The core activities for applications of this class include the sharing and notification of events, and the importance and usefulness of these functionalites increases as event-sharing expands into larger areas of one’s life. Ironically, …

Intruder Pattern Identification

2008

This paper considers the problem of intrusion detection in information systems as a classification problem. In particular the case of masquerader is treated. This kind of intrusion is one of the more difficult to discover because it may attack already open user sessions. Moreover, this problem is complex because of the large variability of user models and the lack of available data for the learning purpose. Here, flexible and robust similarity measures, suitable also for non-numeric data, are defined, they will be incorporated on a one-class training K N N and compared with several classification methods proposed in the literature using the Masquerading User Data set (www.schonlau.net) repr…

Bot recognition in a Web store: An approach based on unsupervised learning

2020

Abstract Web traffic on e-business sites is increasingly dominated by artificial agents (Web bots) which pose a threat to the website security, privacy, and performance. To develop efficient bot detection methods and discover reliable e-customer behavioural patterns, the accurate separation of traffic generated by legitimate users and Web bots is necessary. This paper proposes a machine learning solution to the problem of bot and human session classification, with a specific application to e-commerce. The approach studied in this work explores the use of unsupervised learning (k-means and Graded Possibilistic c-Means), followed by supervised labelling of clusters, a generative learning stra…

2021

In COVID-19 related infodemic, social media becomes a medium for wrongdoers to spread rumors, fake news, hoaxes, conspiracies, astroturf memes, clickbait, satire, smear campaigns, and other forms of deception. It puts a tremendous strain on society by damaging reputation, public trust, freedom of expression, journalism, justice, truth, and democracy. Therefore, it is of paramount importance to detect and contain unreliable information. Multiple techniques have been proposed to detect fake news propagation in tweets based on tweets content, propagation on the network of users, and the profile of the news generators. Generating human-like content allows deceiving content-based methods. Networ…

Extracting Features from Social Media Networks Using Semantics

2016

This paper focuses on the analysis of social media content generated by social networks (e.g. Twitter) in order to extract semantic features. By using text categorization to sort text feeds into categories of similar feeds, it has been proved to reduce the overhead that is required to retrieve these feeds and at the same time, it provides smaller pools in which further investigations can be made easier. The aim of this survey is to draw a user profile, by analysing his or her tweets. In this early stage of research, being a pre-processing phase, a dictionary based approach is considered. Moreover, the paper describes an algorithm used in analysing the text and its preliminary results. This …

On the analysis of a new Markov chain which has applications in AI and machine learning

2011

Accepted version of an article from the conference: 2011 24th Canadian Conference on Electrical and Computer Engineering. Published version available from IEEE: http://dx.doi.org/10.1109/CCECE.2011.6030727 In this paper, we consider the analysis of a fascinating Random Walk (RW) that contains interleaving random steps and random "jumps". The characterizing aspect of such a chain is that every step is paired with its counterpart random jump. RWs of this sort have applications in testing of entities, where the entity is never allowed to make more than a pre-specified number of consecutive failures. This paper contains the analysis of the chain, some fascinating limiting properties, and some i…

Tracking the Preferences of Users Using Weak Estimators

2011

Published version of am article from the book:AI 2011: Advances in Artificial Intelligence. Also available from the publisher on SpringerLink:http://dx.doi.org/10.1007/978-3-642-25832-9_81 Since a social network, by definition, is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary, estimating a user’s interests, typically, involves non-stationary distributions. The consequent time varying nature of the distribution to be trac…