A New Approach for Deception Detection in Open Domain Text

2021 ◽  
Vol 8 (3) ◽  
pp. 1-13
Author(s):  
Jamil R. Alzghoul ◽  
Muath Alzghool ◽  
Emad E. Abdallah
Keyword(s):  
Deception Detection ◽  
Support Vector ◽  
Open Domain ◽  
Production Rules ◽  
New Approach ◽  
Part Of Speech ◽  
Or Groups ◽  
Learning Technique ◽  
Fake Reviews ◽  
Combined Feature

The gigantic growth of platforms that give individuals the ability to write a review that is visible to everyone and the huge number of documents shared on the internet have triggered the researchers to try to detect if these platforms are trying to mislead and deceive people. There is a crucial need to find ways to automatically identify fake reviews and detect deceptive people or groups. The main aim of this research is to detect deception in open domain text by using a machine learning technique. Several sets of features are used to analyse the text including unigram, part of speech, and production rules. The experimental results showed that combined feature sets of (part of speech and production rules) using the support vector machine classifier achieve the best accuracy, and it clearly improves on the accuracy of the results reported in a previous study.

Persian Handwritten Number Recognition Using Adapted Framing Feature and Support Vector Machines

2016 ◽  
Vol 15 (01) ◽  
pp. 1650004 ◽  
Author(s):  
Hedieh Sajedi ◽  
Mehran Bahador
Keyword(s):  
Support Vector Machines ◽  
Recognition Rate ◽  
Nearest Neighbors ◽  
Polynomial Kernel ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
New Approach ◽  
Number Recognition ◽  
Vector Machines

In this paper, a new approach for segmentation and recognition of Persian handwritten numbers is presented. This method utilizes the framing feature technique in combination with outer profile feature that we named this the adapted framing feature. In our proposed approach, segmentation of the numbers into digits has been carried out automatically. In the classification stage of the proposed method, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) are used. Experimentations are conducted on the IFHCDB database consisting 17,740 numeral images and HODA database consisting 102,352 numeral images. In isolated digit level on IFHCDB, the recognition rate of 99.27%, is achieved by using SVM with polynomial kernel. Furthermore, in isolated digit level on HODA, the recognition rate of 99.07% is achieved by using SVM with polynomial kernel. The experiments illustrate that applying our proposed method resulted higher accuracy compared to previous researches.

scholarly journals A glucan from active dry baker’s yeast (Saccharomyces cerevisiae): A chemical and enzymatic investigation of the structure

2003 ◽  
Vol 68 (11) ◽  
pp. 805-809 ◽  
Author(s):  
Dragan Zlatkovic ◽  
Dragica Jakovljevic ◽  
Djordje Zekovic ◽  
Miroslav Vrvic
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Optical Rotation ◽  
Linear Chain ◽  
Periodate Oxidation ◽  
Methylation Analysis ◽  
Yeast Saccharomyces Cerevisiae ◽  
New Approach ◽  
Main Structural Feature ◽  
Or Groups

The structure of a polysaccharide consisting of D-glucose isolated from the cell-wall of active dry baker?s yeast (Saccharomyces cerevisiae) was investigated by using methylation analysis, periodate oxidation, mass spectrometry, NMR spectroscopy, and enzymic hydrolysis, as a new approach in determination of structures. The main structural feature of the polysaccharide deduced on the basis of the obtained results is a linear chain of (1?3)-linked ?-D-glucopyranoses, a part of which is substituted through the positions O-6. The side units or groups are either a single D-glucopyranose or (1?3)-?-oligoglucosides, linked to the main chaing through (1?6)-glucosidic linkages. The low optical rotation as well as the 13C-NMR and FTIR spectra suggest that the glycosidic linkages are in the ?-D-configuration.

scholarly journals Experiments in Open Domain Deception Detection

2015 ◽  
Author(s):  
Verónica Pérez-Rosas ◽  
Rada Mihalcea
Keyword(s):  
Deception Detection ◽  
Open Domain

scholarly journals Introducing the Viewpoint in the Resource Description using Machine Learning

2021 ◽  
Author(s):  
Ouahiba Djama
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Search Engines ◽  
Machine Learning Technique ◽  
New Approach ◽  
Learning Technique ◽  
Resource Description

Search engines allow providing the user with data and information according to their interests and specialty. Thus, it is necessary to exploit descriptions of the resources, which take into consideration viewpoints. Generally, the resource descriptions are available in RDF (e.g., DBPedia of Wikipedia content). However, these descriptions do not take into consideration viewpoints. In this paper, we propose a new approach, which allows converting a classic RDF resource description to a resource description that takes into consideration viewpoints. To detect viewpoints in the document, a machine learning technique will be exploited on an instanced ontology. This latter allows representing the viewpoint in a given domain. An experimental study shows that the conversion of the classic RDF resource description to a resource description that takes into consideration viewpoints, allows giving very relevant responses to the user’s requests.

scholarly journals A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6319
Author(s):  
Esam Mahdi ◽  
Víctor Leiva ◽  
Saed Mara’Beh ◽  
Carlos Martin-Barreiro
Keyword(s):  
Investment Decisions ◽  
Trading Strategies ◽  
Support Vector ◽  
Uncertain Environments ◽  
New Approach ◽  
Adverse Conditions ◽  
Related Data ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Real World Situation

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.

scholarly journals Quantitative Methods for Analyzing Intimate Partner Violence in Microblogs: Observational Study

10.2196/15347 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e15347
Author(s):  
Christopher Michael Homan ◽  
J Nicolas Schrading ◽  
Raymond W Ptucha ◽  
Catherine Cerulli ◽  
Cecilia Ovesdotter Alm
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Intimate Partner Violence ◽  
Language Processing ◽  
Partner Violence ◽  
Quantitative Methods ◽  
Intimate Partner ◽  
Support Vector ◽  
Data Set ◽  
Part Of Speech

Background Social media is a rich, virtually untapped source of data on the dynamics of intimate partner violence, one that is both global in scale and intimate in detail. Objective The aim of this study is to use machine learning and other computational methods to analyze social media data for the reasons victims give for staying in or leaving abusive relationships. Methods Human annotation, part-of-speech tagging, and machine learning predictive models, including support vector machines, were used on a Twitter data set of 8767 #WhyIStayed and #WhyILeft tweets each. Results Our methods explored whether we can analyze micronarratives that include details about victims, abusers, and other stakeholders, the actions that constitute abuse, and how the stakeholders respond. Conclusions Our findings are consistent across various machine learning methods, which correspond to observations in the clinical literature, and affirm the relevance of natural language processing and machine learning for exploring issues of societal importance in social media.

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