Secure Framework for E-Commerce Applications in Cloud Environment

2018 ◽  
pp. 82-109
Author(s):  
Sonika Shrivastava ◽  
R. K. Pateriya
Keyword(s):  
Credit Card ◽  
Big Data Analytics ◽  
Personal Data ◽  
Financial Service ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Cloud Environment ◽  
Financial Loss ◽  
Learning Techniques ◽  
Use Of The Internet

There has been a massive increase in the use of the internet for shopping and payment. The ease and availability of the internet has accelerated the growth of online applications. E-commerce applications handle many sensitive pieces of data like financial or personal data, which are used for critical tasks like banking, socializing, shopping, and tax filling. Online financial service and shopping sites are an attractive target for fraudsters because money transactions are done through these sites. Credit card fraud, identity theft, and account hijack are key concerns for these organizations. These frauds cause financial loss and hurt the reputation of e-commerce sites. The use of the cloud platform has made these sites more productive, but at the same time, it has opened them to a variety of threats. This chapter describes a framework for credit card and identity fraud detection using big data analytics and machine learning techniques to make e-commerce sites more secure and efficient.

scholarly journals Electronic Credit Card Fraud Detection System by Collaboration of Machine Learning Models

2019 ◽  
Vol 8 (12S) ◽  
pp. 92-94 ◽  
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Detection System ◽  
Industrial Sector ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Identification System ◽  
Financial Loss ◽  
Learning Techniques ◽  
Free Data

In the financial industrial sector the lightning growth and participation of internet-based transactional events give rise to malicious activities like a fraud that result in financial loss. The malicious activities have no continuous pattern their pattern, behavior, working always keep on changing with the increasing growth in technology. Every time a new technology comes in the market the hoaxer study about that technology and implement malicious activity through the learned technology and internet-based activities. The hoaxer analyzes the behavior patterns of consumers to execute the plan of fraud to cause loss to the consumer. So to overcome this problem of fraud, hoax, cheat in the financial sector a fraud identification system is needed to identify the cheating, fraud and alike activities in internet-based money transactions by employing machine learning techniques. This presented paper focuses on fraud activities that cannot be detected manually by carrying out research and examine the results of logistic regression, decision tree and support vector machine. A dataset of electronic payment card is taken from European electronic cardholders, the machine learning techniques are applied on the unstructured and process-free data.

scholarly journals Predictors of outpatients’ no-show: big data analytics using apache spark

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

scholarly journals IDENTIFICATION DATA MANAGEMENT: LEGAL REGULATION AND CLASSIFICATION

10.23856/4325 ◽  
2021 ◽  
Vol 43 (6) ◽  
pp. 198-203
Author(s):  
Oleksii Kostenko
Keyword(s):  
Information And Communication Technologies ◽  
Social Relations ◽  
Driving Forces ◽  
Personal Data ◽  
Legal Regulation ◽  
The Internet ◽  
Personal Data Protection ◽  
Protection Information ◽  
Use Of The Internet ◽  
Almost All

The scale, speed and multi-vector development of science and technology are extremely effective in influencing legal, economic, political, spiritual, professional and other social relations. The development of information and communication technologies, the use of the Internet, the creation, storage, transmission, processing and management of information became the driving forces of the new scientific and technological revolution. This facilitates the introduction of technologies for the transmission and use of information in digital form in almost all spheres of public life, namely text data, photo, audio, video images, which are transmitted in various ways via the Internet and other systems and means of communication. One of the key elements of data transmission technologies and systems is the availability of information by which it is possible to identify their subjects and objects by their inherent identification attributes. In Ukrainian legislation, in particular in the Law of Ukraine «On Personal Data Protection», information or a set of information about an individual who is or can be identified specifically is defined as personal data. However, despite its modernity, this law still contains a number of shortcomings and uncertainties, both in terminology and in the legal mechanisms for working with data by which a person can be identified, i.e. identification data.

Big Data Analytics for Tourism Destinations

2018 ◽  
pp. 349-363 ◽  
Author(s):  
Wolfram Höpken ◽  
Matthias Fuchs ◽  
Maria Lexhagen
Keyword(s):  
Data Analysis ◽  
Big Data Analytics ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
Implicit Information ◽  
Heterogeneous Data Sources ◽  
Learning Techniques ◽  
Exploratory Data ◽  
Tourism Destinations ◽  
User Ratings

The objective of this chapter is to address the above deficiencies in tourism by presenting the concept of the tourism knowledge destination – a specific knowledge management architecture that supports value creation through enhanced supplier interaction and decision making. Information from heterogeneous data sources categorized into explicit feedback (e.g. tourist surveys, user ratings) and implicit information traces (navigation, transaction and tracking data) is extracted by applying semantic mapping, wrappers or text mining (Lau et al., 2005). Extracted data are stored in a central data warehouse enabling a destination-wide and all-stakeholder-encompassing data analysis approach. By using machine learning techniques interesting patterns are detected and knowledge is generated in the form of validated models (e.g. decision trees, neural networks, association rules, clustering models). These models, together with the underlying data (in the case of exploratory data analysis) are interactively visualized and made accessible to destination stakeholders.

Automated Soil Residue Levels Detecting Device With IoT Interface

2021 ◽  
pp. 123-135
Author(s):  
Adiraju Prasanth Rao ◽  
K. Sudheer Reddy ◽  
Sathiyamoorthi V.
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
The Novel ◽  
Cloud Environment ◽  
Application System ◽  
Learning Techniques ◽  
Social And Economic Development ◽  
Residue Levels ◽  
Soil Residue ◽  
Agricultural Farm

Cloud computing and internet of things (IoT) are playing a crucial role in the present era of technological, social, and economic development. The novel models where cloud and IoT are integrated together are foreseen as disruptive and enable a number of application scenarios. The e-smart is an application system designed by leveraging cloud, IoT, and several other technology frameworks that are deployed on the agricultural farm to collect the data from the farm fields. The application extracts and collects the information about the residue levels of soil and crop details and the same data will be hosted in the cloud environment. The proposed e-smart application system is to analyze, integrate, and correlate datasets and produce decision-oriented reports to the farmer by using several machine learning techniques.

Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

2022 ◽  
pp. 123-145
Author(s):  
Pelin Yildirim Taser ◽  
Vahid Khalilpour Akram
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Learning Algorithms ◽  
Distance Estimation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Traditional Methods ◽  
Learning Techniques ◽  
Localization Systems

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

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