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Classifying online corporate reputation with machine learning: a study in the banking domain

Anette Rantanen, Joni Salminen, Filip Ginter, Bernard J. Jansen
2019 Internet Research  
Practical implications For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains  ...  The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations.  ...  domain of online corporate reputation.  ... 
doi:10.1108/intr-07-2018-0318 fatcat:q2hrhcrxaffwzjafqdadyzo5ve

Learning to Detect Phishing Webpages

Ram B. Basnet, Andrew H. Sung
2014 Journal of Internet Services and Information Security  
Along with the features from URLs, we propose many novel content based features and apply cutting-edge machine learning techniques to demonstrate that our approach can detect phishing webpages with error  ...  Phishing has become a lucrative business for cyber criminals whose victims range from end users to large corporations and government organizations.  ...  The authors would like to acknowledge the generous support received from ICASA (the Institute for Complex Additive Systems Analysis), a division of New Mexico Tech.  ... 
doi:10.22667/jisis.2014.08.31.021 dblp:journals/jisis/BasnetS14 fatcat:vpfjzvsvjngepfwrcqkgyyrdia

Machine learning for targeted display advertising: transfer learning in action

C. Perlich, B. Dalessandro, T. Raeder, O. Stitelman, F. Provost
2013 Machine Learning  
We close the paper with a collection of lessons learned from over half a decade of research and development on this complex, deployed, and intensely used machine learning system.  ...  Notably, the machine learning system itself is deployed and has been in continual use for years for thousands of advertising campaigns-in contrast to the more common case where predictive models are built  ...  Prior papers on the use of machine learning have focused on challenges associated with the applying machine learning in online advertising, such as the modeling of very rare outcomes using high-dimensional  ... 
doi:10.1007/s10994-013-5375-2 fatcat:bfdelqob7vgu3nkhef67t4r7my

Machine Learning in Banking Risk Management: A Literature Review

Martin Leo, Suneel Sharma, K. Maddulety
2019 Risks  
A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems.  ...  This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas  ...  Also, in the areas of operational risk, there are a few papers on fraud risk detection in credit cards and online banking.  ... 
doi:10.3390/risks7010029 fatcat:laddvv3hxbaxzau5zgjrv5pkhe

Phishing Scam Detection using Machine Learning

2019 International Journal of Engineering and Advanced Technology  
We measure this by grouping them with diverse parameters and features, thereby assisting the machine learning algorithm to edify.  ...  As a product discovery plot, two primary methodologies are generally utilized: blacklists/whitelists and machine learning approaches.  ...  ACKNOWLEDGMENT The Scholars would like to extend its gratitude to Mrs.R.Elakya for assisting and guiding us through the project. The concepts presented are thought out while implementing the project.  ... 
doi:10.35940/ijeat.a1023.1091s19 fatcat:u4eynp5jonad7ao2jrqayqyxka

A Literature Review on Application of Sentiment Analysis Using Machine Learning Techniques

Anvar Shathik J, Krishna Prasad K
2020 Zenodo  
Machine Learning (ML) is a multidisciplinary field, a mixture of statistics and computer science algorithms that are commonly used in predictive and classification analyses.  ...  This paper presents the common techniques of analyzing sentiment from a machine learning perspective.  ...  The pattern vagueness can be done with fuzzy reasoning. Domain information can be given by CRF. Big data are used for RBFNN and for online learning algorithms.  ... 
doi:10.5281/zenodo.3977576 fatcat:djsvzgiypnfibcvj6swo3pw75u

Towards Deep Learning Prospects: Insights for Social Media Analytics

Malik Khizar Hayat, Ali Daud, Abdulrahman A. Alshdadi, Ameen Banjar, Rabeeh Ayaz Abbasi, Yukun Bao, Hussain Dawood
2019 IEEE Access  
INDEX TERMS Social media data, dynamic network, deep learning, feature learning. 36958 He has published about 70 papers in reputed international Impact Factor journals and conferences.  ...  In this paper, we keenly discuss the practiced DL architectures by presenting a taxonomy-oriented summary, following the major efforts made toward the SM analytics (SMA).  ...  a: DEEP LEARNING DL deals with a collection of machine learning methods that train several levels of data representations in deep architectures.  ... 
doi:10.1109/access.2019.2905101 fatcat:65mxyey3frdrfngvbfnfss3gpa

Development of a Machine Learning Model for Knowledge Acquisition, Relationship Extraction and Discovery in Domain Ontology Engineering using Jaccord Relationship Extraction and Neural Network

2019 International journal of recent technology and engineering  
This manuscript presents a machine learning model based on heterogeneous data from multiple domains including agriculture, health care, food and banking, etc.  ...  The ontology instances are classified based on the domain.  ...  Paper Presentations  Machine Learning Methods in Ontology Engineering: A Literature Review -ICRMR 2109, Goa, India  Design of a Machine Learning Model for Automatic Generation of Domain-Specific Ontologies  ... 
doi:10.35940/ijrte.c6362.098319 fatcat:whgnce4aa5aofalvusaypt6pje

Machine Learning in Application Security [chapter]

Nilaykumar Kiran Sangani, Haroot Zarger
2017 Advances in Security in Computing and Communications  
In this chapter, the authors have explained on the technical aspects of integrating machine learning within applications in detecting malicious user behavioural pattern.  ...  The phenomenal transformation has led the attackers to have a new strategy born in their attack vector methodology making it more targeted-a direct aim towards the weakest link in the security chain aka  ...  Thus, the need of the hour is to implement which is a dynamic and signature-less thus evolved machine learning (ML). Machine learning is not a new domain or technology.  ... 
doi:10.5772/intechopen.68796 fatcat:gn5zjbwq7vfxje4x3smesdeclm

Identifying Corporate Sustainability Issues by Analyzing Shareholder Resolutions: A Machine-Learning Text Analytics Approach

Viju Raghupathi, Jie Ren, Wullianallur Raghupathi
2020 Sustainability  
This exploratory study attempts to extract useful insight from shareholder sustainability resolutions using machine learning-based text analytics.  ...  The primary source for this study is the Ceres sustainability shareholder resolution database, with 1737 records spanning 2009–2019.  ...  Author Contributions: All the authors contributed equally to all parts of the manuscript. All authors have read and agreed to the published version of the manuscript.  ... 
doi:10.3390/su12114753 fatcat:en2f5gl3urakhduamjtvhq6tgy

Federated learning for privacy-preserving data access

Małgorzata Śmietanka, Hirsh Pithadia, Philip Treleaven
2021 International Journal of Data Science and Big Data Analytics  
Federated learning pioneered by Google is the emerging privacy-preserving data technology and also a new class of distributed machine learning models.  ...  Federated learning is a pioneering privacy-preserving data technology and also a new machine learning model trained on distributed data sets.  ...  in healthcare Study Table 2 : 2 Federated learning in retail Study Table 3 : 3 Federated learning in finance Study Black Swan -A massive unpredictable event that has potentially severe  ... 
doi:10.51483/ijdsbda.1.2.2021.1-13 fatcat:b4rbxexaerburf5xrscizs27ae

Overview of RepLab 2014: Author Profiling and Reputation Dimensions for Online Reputation Management [chapter]

Enrique Amigó, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, Damiano Spina
2014 Lecture Notes in Computer Science  
This year the focus lied on two new tasks: reputation dimensions classification and author profiling, which complement the aspects of reputation analysis studied in the previous campaigns.  ...  The participants were asked (1) to classify tweets applying a standard typology of reputation dimensions and (2) categorise Twitter profiles by type of author as well as rank them according to their influence  ...  This research was partially supported by the European  ... 
doi:10.1007/978-3-319-11382-1_24 fatcat:ywwjzb6j5rb2xcgen4pxzt5dqi

InfoGram and Admissible Machine Learning [article]

Subhadeep Mukhopadhyay
2021 arXiv   pre-print
We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints  ...  The purpose of this article is to introduce a new information-theoretic learning framework (admissible machine learning) and algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that  ...  The author was benefited from many useful discussions with Michael Guerzhoy, Hany Farid, Julia Dressel, Beau Coker, and Hanchen Wang on demystifying some aspects of COMPASS data; Daniel Osei on the data  ... 
arXiv:2108.07380v2 fatcat:rqnbtnnrurdqvecpltplnzsj5q

Deep learning and explainable artificial intelligence techniques applied for detecting money laundering – a critical review

Dattatray V. Kute, Biswajeet Pradhan, Nagesh Shukla, Abdullah Alamri
2021 IEEE Access  
III Machine learning methods used in AML domain No.  ...  A. MACHINE LEARNING There are several comprehensive and systematic review papers published over the decade that describes data mining and machine learning methods applied in AML domain.  ...  NAGESH SHUKLA is currently a Senior Lecturer in business analytics with the University of Technology Sydney, Ultimo, NSW, Australia.  ... 
doi:10.1109/access.2021.3086230 fatcat:n4wwkfoiaff5rjpelnddtcruwu

A Survey on Deep Learning in Big Data and its Applications [article]

Zair Bouzidi, Mourad Amad, Abdelmalek Boudries
2021 figshare.com  
This active participation with the corporate data, as emails, documents, databases, business processor history, etc and content published on the Web, as age and contact details, reviews, comments, photos  ...  Data recovery from different sources can be a difficult task ...  ...  Table V shows the Comparison of Machine Learning Technics. Restricted Boltzmann computer is a classifier, regression, subject modeling, collaborative filtering, and feature learning algorithm.  ... 
doi:10.6084/m9.figshare.14737953.v2 fatcat:l4fzcr4fpfevxpcpwbnwvevvpa
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