1,887 research outputs found
Equity Forecast: Predicting Long Term Stock Price Movement using Machine Learning
Abstract. Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial indicators and evaluate them in a right manner. So far, little help in predicting the direction of the company value over the longer period of time has been provided from the machines. In this paper we present a machine learning aided approach to evaluate the equity’s future price over the long time. Our method is able to correctly predict whether some company’s value will be 10% higher or not over the period of one year in 76.5% of cases.Keywords. Machine learning, Long term investment, Equity, Stock price prediction.JEL. H54, D92, E20
Marvin: Semantic annotation using multiple knowledge sources
People are producing more written material then anytime in the history. The
increase is so high that professionals from the various fields are no more able
to cope with this amount of publications. Text mining tools can offer tools to
help them and one of the tools that can aid information retrieval and
information extraction is semantic text annotation. In this report we present
Marvin, a text annotator written in Java, which can be used as a command line
tool and as a Java library. Marvin is able to annotate text using multiple
sources, including WordNet, MetaMap, DBPedia and thesauri represented as SKOS.Comment: 9 pages, 4 figures, keywords: Semantic annotation, text
normalization, semantic web, linked data, information management, text
mining, information extraction, data curatio
Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
Adverse drug reactions (ADRs) are unwanted or harmful effects experienced
after the administration of a certain drug or a combination of drugs,
presenting a challenge for drug development and drug administration. In this
paper, we present a set of taggers for extracting adverse drug reactions and
related entities, including factors, severity, negations, drug class and
animal. The systems used a mix of rule-based, machine learning (CRF) and deep
learning (BLSTM with word2vec embeddings) methodologies in order to annotate
the data. The systems were submitted to adverse drug reaction shared task,
organised during Text Analytics Conference in 2017 by National Institute for
Standards and Technology, archiving F1-scores of 76.00 and 75.61 respectively.Comment: Paper describing submission for TAC ADR shared tas
Deep learning guided Android malware and anomaly detection
In the past decade, the cyber-crime related to mobile devices has increased.
Mobile devices, especially the ones running on Android operating system are
particularly interesting to malware creators, as the users often keep the
biggest amount of personal information on their mobile devices, such as their
contacts, social media profiles, emails, and bank accounts. Both dynamic and
static malware analysis is necessary to prevent and detect malware, as both
techniques have their benefits and shortcomings. In this paper, we propose a
deep learning technique that relies on LSTM and encoder-decoder neural network
architectures for dynamic malware analysis based on CPU, memory and battery
usage. The proposed system is able to detect and notify users about anomalies
in system that is likely consequence of malware behaviour. The method was
implemented as a part of OWASP Seraphimdroids anti-malware mechanism and
notifies users about anomalies on their devices. The method proved to perform
with an F1-score of 79.2%.Comment: First (draft) version of the pape
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