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India will have a billion mobile phone users by 2020: GSMA

Mashable

Nearly every Indian will be a mobile subscriber by 2020. SEE ALSO: India will have 730 million internet users by 2020 and is already in a'Post-PC' era India is already one of the largest markets for mobile phones, internet users, and mobile subscribers, and experts believe it's just the beginning of even bigger things to come. The country will have one billion unique mobile subscribers by 2020, up from 616 million unique users as of June 2016, according to projections by mobile trade association GSMA. Once considered a luxury, Indian telecom operators now offer new subscription at no charge (or at nominal cost). The network coverage has also improved significantly over the past two decades with many of the remote places offering LTE connectivity now.


Towards Experienced Anomaly Detector Through Reinforcement Learning

AAAI Conferences

This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under specific scenarios, and 3) keeps evolving with the growth of anomaly detection experience. Essentially, the anomaly detector is powered by the Recurrent Neural Network (RNN) and adopts the Reinforcement Learning (RL) method to achieve the self-learning process. Our initial experiments demonstrate promising results of using the detector in network time series anomaly detection problems.


A note about finding anomalies – Towards Data Science

#artificialintelligence

This article is inspired by the research done during studies in the university. Goal of this article is to act as a note and reminder that finding anomalies is not a trivial task (currently). Anomaly detection refers to the task of finding observations that do not conform to the normal, expected behaviour. These observations can be named as anomalies, outliers, novelty, exceptions, surprises in different application domains. The most popular terms that occur most often in literature are anomalies and outliers.


Active Anomaly Detection for time-domain discoveries

arXiv.org Machine Learning

We present the first application of adaptive machine learning to the identification of anomalies in a data set of non-periodic astronomical light curves. The method follows an active learning strategy where highly informative objects are selected to be labelled. This new information is subsequently used to improve the machine learning model, allowing its accuracy to evolve with the addition of every new classification. For the case of anomaly detection, the algorithm aims to maximize the number of real anomalies presented to the expert by slightly modifying the decision boundary of a traditional isolation forest in each iteration. As a proof of concept, we apply the Active Anomaly Discovery (AAD) algorithm to light curves from the Open Supernova Catalog and compare its results to those of a static Isolation Forest (IF). For both methods, we visually inspected objects within 2% highest anomaly scores. We show that AAD was able to identify 80% more true anomalies than IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.


Group Anomaly Detection using Flexible Genre Models

Neural Information Processing Systems

An important task in exploring and analyzing real-world data sets is to detect unusual and interesting phenomena. In this paper, we study the group anomaly detection problem. Unlike traditional anomaly detection research that focuses on data points, our goal is to discover anomalous aggregated behaviors of groups of points. For this purpose, we propose the Flexible Genre Model (FGM). FGM is designed to characterize data groups at both the point level and the group level so as to detect various types of group anomalies. We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data, and show that it is superior to existing approaches in detecting group anomalies.