scholarly journals Detection of Fraud Transactions Using Recurrent Neural Network during COVID-19

2020 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
SHAWNI DUTTA
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Credit Card ◽  
Usage Pattern ◽  
Financial Loss ◽  
Online Transactions ◽  
Machine Learning Methods ◽  
Payment Schemes ◽  
Money Transfer ◽  
Clustering Approach

Online transactions are becoming more popular in present situation where the globe is facing an unknown disease COVID-19. Now authorities of Countries requested peoples to use cashless transaction as far as possible. Practically it is not always possible to use it in all transactions. Since number of such cashless transactions have been increasing during lockdown period due to COVID-19, fraudulent transactions are also increasing in a rapid way. Fraud can be analysed by viewing a series of customer transactions data that was done in his/her previous transactions. Normally banks or other transaction authorities warned their customers about the transaction If any deviation is noticed by them from available patterns. These authorities think that it is possibly of fraudulent transaction. For detection of fraud during COVID-19, banks and credit card companies are applying various methods such as data mining , decision tree, rule based mining, neural network, fuzzy clustering approach and machine learning methods. These approaches is try to find out normal usage pattern of customers based on their past activities. The objective of this paper is to find out such fraud transactions during such unmanageable situation.Digital payment schemes are often threatened by fraudulent activities. Detecting fraud transaction in during money transfer may save customers from financial loss. Mobile based money transactions are focused in this paper for fraud detection. A Deep Learning (DL) framework is suggested in this paper that monitors and detects fraudulent activities. Implementing and applying recurrent neural network on PaySim generated synthetic financial dataset, deceptive transactions are identified. The proposed method is capable to detect deceptive transactions with an accuracy of 99.87%, F1-Score of 0.99 and MSE of 0.01.

scholarly journals Detection of Fraud Transactions Using Recurrent Neural Network during COVID-19

2020 ◽  
Vol 07 (03) ◽  
pp. 16-21
Author(s):  
Samir Kuma Bandyopadhyay ◽  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Credit Card ◽  
Usage Pattern ◽  
Financial Loss ◽  
Online Transactions ◽  
Machine Learning Methods ◽  
Payment Schemes ◽  
Money Transfer ◽  
Clustering Approach

Online transactions are becoming more popular in present situation where the globe is facing an unknown disease COVID-19. Now authorities of several countries have requested people to use cashless transaction as far as possible. Practically, it is not always possible to use it in all transactions. Since number of such cashless transactions has been increasing during lockdown period due to COVID-19, fraudulent transactions are also increasing in a rapid way. Fraud can be analysed by viewing a series of customer transactions data that was done in his/ her previous transactions. Normally banks or other transaction authorities warn their customers about the transaction, if they notice any deviation from available patterns; the authorities consider it as a possibly fraudulent transaction. For detection of fraud during COVID-19, banks and credit card companies are applying various methods such as data mining, decision tree, rule based mining, neural network, fuzzy clustering approach and machine learning methods. The approach tries to find out normal usage pattern of customers based on their former activities. The objective of this paper is to propose a method to detect such fraud transactions during such unmanageable situation of the pandemic. Digital payment schemes are often threatened by fraudulent activities. Detecting fraud transactions during money transfer may save customers from financial loss. Mobile-based money transactions are focused in this paper for fraud detection. A Deep Learning (DL) framework is suggested in the paper that monitors and detects fraudulent activities. Implementing and applying Recurrent Neural Network on PaySim generated synthetic financial dataset, deceptive transactions are identified. The proposed method is capable to detect deceptive transactions with an accuracy of 99.87%, F1-Score of 0.99 and MSE of 0.01.

scholarly journals Credit Card Fraud Detection using Deep Learning based on Neural Network and Auto-encoder

2020 ◽  
Vol 9 (5) ◽  
pp. 1140-1143
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Credit Card ◽  
Online Media ◽  
Credit Card Fraud ◽  
Online Transactions ◽  
The Past ◽  
Past Behavior ◽  
The Neural Network ◽  
The Comparative Study

Credit card fraud is an event problem and fraud detecting techniques getting more sophisticated each day. Mainly internet is becoming more common in almost every domain. Online transactions, shopping, and e-commerce are expanding step by step. Due to which in the online payment system, fraudulent activities have also increased. It has cost banks and their customers a loss of billions of rupees. The techniques used now a day detects the anomaly only after the fraud transaction takes place. The intruders have found ways to crack the system loopholes and defeat the security. These frauds are not consistent in their actions, they constantly alter. Thus, Artificial Intelligent (AI) algorithms are used to detect the behavior of such activity by learning the past behavior of the transaction of the users. An unsupervised algorithm is used to detect online transactions, as fraudsters commit fraud once by online media and then move on to other techniques. This paper discusses the performance analysis and the comparative study of the two Deep Learning algorithms which include auto-encoder and the neural network. In this paper accuracy, precision, recall, and AUC curve are considered as a model evaluation factor.

Forecasting emergency department admissions

2021 ◽  
Vol 25 (6) ◽  
pp. 1579-1601
Author(s):  
Carlos Narciso Rocha ◽  
Fátima Rodrigues
Keyword(s):  
Neural Network ◽  
Emergency Department ◽  
Recurrent Neural Network ◽  
Patient Flow ◽  
Clinical Staff ◽  
Quality Service ◽  
Emergency Admissions ◽  
Machine Learning Methods ◽  
Recurrent Neuronal Network ◽  
High Quality Service

The emergency department of a hospital plays an extremely important role in the healthcare of patients. To maintain a high quality service, clinical professionals need information on how patient flow will evolve in the immediate future. With accurate emergency department forecasts it is possible to better manage available human resources by allocating clinical staff before peak periods, thus preventing service congestion, or releasing clinical staff at less busy times. This paper describes a solution developed for the presentation of hourly, four-hour, eight-hour and daily number of admissions to a hospital’s emergency department. A 10-year history (2009–2018) of the number of emergency admissions in a Portuguese hospital was used. To create the models several methods were tested, including exponential smoothing, SARIMA, autoregressive and recurrent neural network, XGBoost and ensemble learning. The models that generated the most accurate hourly time predictions were the recurrent neural network with one-layer (sMAPE = 23.26%) and with three layers (sMAPE = 23.12%) and XGBoost (sMAPE = 23.70%). In terms of efficiency, the XGBoost method has by far outperformed all others. The success of the recurrent neuronal network and XGBoost machine learning methods applied to the prediction of the number of emergency department admissions has been demonstrated here, with an accuracy that surpasses the models found in the literature.

Transfer Learning Based Recurrent Neural Network Algorithm for Linguistic Analysis

2021 ◽  
Vol 20 (3) ◽  
pp. 1-16
Author(s):  
Peipei Jiang ◽  
Liailun Chen ◽  
Min-Feng Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
The Core ◽  
Machine Learning Methods ◽  
Neural Network Algorithm ◽  
Comparative Results

Each language is a system of understanding and skills that allows language users to interact, express thoughts, hypotheses, feelings, wishes, and all that needs to be expressed. Linguistics is the research of these structures in all respects: the composition, usage, and sociology of language, in particular, are the core of linguistics. Machine Learning is the research area that allows machines to learn without being specifically scheduled. In linguistics, the design of writing is understood to be a foundation for many distinct company apps and probably the most useful if incorporated with machine learning methods. Research shows that besides text tagging and algorithm training, there are major problems in the field of Big Data. This article provides a collaborative effort (transfer learning integrated into Recurrent Neural Network) to analyze the distinct kinds of writing between the language's linear and non-computational sides, and to enhance granularity. The outcome demonstrates stronger incorporation of granularity into the language from both sides. Comparative results of machine learning algorithms are used to determine the best way to analyze and interpret the structure of the language.

scholarly journals Online Urban Waterlogging Monitoring Based on Recurrent Neural Network for Classification of Microblogging Text

2020 ◽  
Author(s):  
Hui Liu ◽  
Ya Hao ◽  
Wenhao Zhang ◽  
Hanyue Zhang ◽  
Fei Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Global Climate ◽  
Flood Disaster ◽  
Rapid Urbanization ◽  
Urban Flood ◽  
Sina Weibo ◽  
Machine Learning Methods ◽  
Online Monitoring System

Abstract. With the global climate change and rapid urbanization, urban flood disaster spreads and becomes increasingly serious in China. The urban rainstorm and waterlogging have become an urgent challenge that needs to be real-time monitored and further predicted for the improvement of urbanization construction. In this paper, we trained a recurrent neural network (RNN) model to classify microblogging posts related to urban waterlogging, and establish an online monitoring system of urban waterlogging caused by flood disaster. We manually curated more than 4,400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely find out urban waterlogging. The RNN model has been thoroughly evaluated, and our experimental results showed that it achieved higher accuracy than traditional machine learning methods, such as SVM and GBDT. Furthermore, we build a nationwide map of urban waterlogging based on recent two-year microblogging data.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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