This document discusses using capsule networks (CapsNets) to detect credit card fraud from transaction data. It first discusses how feature engineering is typically used to extract features from transaction records, such as spending patterns and time series analysis of transaction times. It then introduces CapsNets as a novel approach to obtain deeper, more discriminative features from the engineered features. CapsNets represent object properties like position, scale through activity vectors and could potentially learn better features for distinguishing fraudulent transactions than traditional models. The document reviews related work on credit card fraud detection using techniques like decision trees, random forests, neural networks and recent deep learning methods. It aims to apply CapsNets to engineered transaction features to improve fraud detection accuracy.
IRJET- Credit Card Fraud Detection using Random ForestIRJET Journal
This document discusses using random forest machine learning algorithms to detect credit card fraud. It begins with an abstract that outlines using random forest classification on transaction data to improve fraud detection accuracy. The introduction then provides background on credit card fraud and how machine learning has been used for detection. It describes random forest as an advanced decision tree algorithm that can improve efficiency and accuracy over other methods. The paper proposes building a fraud detection model using random forest classification to analyze a transaction dataset and optimize result accuracy. Key performance metrics like accuracy, sensitivity and precision are evaluated.
IRJET- Finalize Attributes and using Specific Way to Find Fraudulent TransactionIRJET Journal
This document discusses methods for detecting fraudulent transactions from online credit card transactions. It first reviews several existing algorithms for fraud detection from literature, including neural networks, rule induction, case-based reasoning and others. It then discusses selecting important attributes from a credit card transaction dataset containing 20 attributes related to transactions and cardholders. Several attribute selection techniques are applied to identify the most important attributes. Finally, various machine learning algorithms including AdaBoost, logistic regression, J48 and naive Bayes are tested on the dataset to identify the best algorithm for detecting fraudulent transactions, with the document concluding that AdaBoost performs best.
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
The document describes an AI-driven Occupational Skills Generator (AIOSG) that aims to automate the process of creating occupational skills reference documents. The AIOSG utilizes an intelligent web crawler, natural language processing, neural networks, and a blockchain to gather data on occupational skills from various sources, analyze the data, and generate standardized skills reference documents. It is intended to reduce the time and resources required to manually produce these documents while ensuring more comprehensive and up-to-date skills information. The AIOSG system architecture and its use of analytics, artificial intelligence, and blockchain technologies are explained in detail.
This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of
human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people
and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.
IRJET- Fraud Detection Algorithms for a Credit CardIRJET Journal
This document discusses algorithms for detecting credit card fraud. It compares the performance of two algorithms: random forest and K-nearest neighbors (KNN). Random forest uses decision trees to classify transactions as normal or fraudulent based on attributes of past transactions. KNN compares new transactions to historical ones based on attributes. The document tests these algorithms on a real-world credit card transaction dataset. It finds that random forest obtains good results on smaller datasets but has issues with imbalanced data. The authors' future work will focus on addressing these issues and improving the random forest algorithm.
IRJET- Prediction of Credit Risks in Lending Bank LoansIRJET Journal
This document discusses machine learning models for predicting credit risk in bank loan applications. It begins with an introduction to credit risk assessment and types of loans. Then, it describes how machine learning can be used to more accurately evaluate borrowers' ability to repay loans based on important variables like borrower characteristics, loan details, and repayment status. The document proposes using artificial neural network and support vector machine models to classify borrowers as good or bad credit risks based on these variables. It evaluates the accuracy of support vector machines and boosted decision tree models for the task of credit risk prediction.
Default Probability Prediction using Artificial Neural Networks in R ProgrammingVineet Ojha
The objective of the project is to analyze the ability of the Artificial Neural Network Model
developed to forecast the credit risk profile of retails banking loan consumers and credit card
customers.
From a theoretical point of view, this project introduces a literature review on the detailed
working and the application of Artificial Neural Networks for credit risk management.
Practically, the aim of this project is presenting a model for estimating the Probability of Default
using Artificial Neural Network to accrue benefit non-linear models.
IRJET- Credit Card Fraud Detection using Random ForestIRJET Journal
This document discusses using random forest machine learning algorithms to detect credit card fraud. It begins with an abstract that outlines using random forest classification on transaction data to improve fraud detection accuracy. The introduction then provides background on credit card fraud and how machine learning has been used for detection. It describes random forest as an advanced decision tree algorithm that can improve efficiency and accuracy over other methods. The paper proposes building a fraud detection model using random forest classification to analyze a transaction dataset and optimize result accuracy. Key performance metrics like accuracy, sensitivity and precision are evaluated.
IRJET- Finalize Attributes and using Specific Way to Find Fraudulent TransactionIRJET Journal
This document discusses methods for detecting fraudulent transactions from online credit card transactions. It first reviews several existing algorithms for fraud detection from literature, including neural networks, rule induction, case-based reasoning and others. It then discusses selecting important attributes from a credit card transaction dataset containing 20 attributes related to transactions and cardholders. Several attribute selection techniques are applied to identify the most important attributes. Finally, various machine learning algorithms including AdaBoost, logistic regression, J48 and naive Bayes are tested on the dataset to identify the best algorithm for detecting fraudulent transactions, with the document concluding that AdaBoost performs best.
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
The document describes an AI-driven Occupational Skills Generator (AIOSG) that aims to automate the process of creating occupational skills reference documents. The AIOSG utilizes an intelligent web crawler, natural language processing, neural networks, and a blockchain to gather data on occupational skills from various sources, analyze the data, and generate standardized skills reference documents. It is intended to reduce the time and resources required to manually produce these documents while ensuring more comprehensive and up-to-date skills information. The AIOSG system architecture and its use of analytics, artificial intelligence, and blockchain technologies are explained in detail.
This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of
human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people
and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.
IRJET- Fraud Detection Algorithms for a Credit CardIRJET Journal
This document discusses algorithms for detecting credit card fraud. It compares the performance of two algorithms: random forest and K-nearest neighbors (KNN). Random forest uses decision trees to classify transactions as normal or fraudulent based on attributes of past transactions. KNN compares new transactions to historical ones based on attributes. The document tests these algorithms on a real-world credit card transaction dataset. It finds that random forest obtains good results on smaller datasets but has issues with imbalanced data. The authors' future work will focus on addressing these issues and improving the random forest algorithm.
IRJET- Prediction of Credit Risks in Lending Bank LoansIRJET Journal
This document discusses machine learning models for predicting credit risk in bank loan applications. It begins with an introduction to credit risk assessment and types of loans. Then, it describes how machine learning can be used to more accurately evaluate borrowers' ability to repay loans based on important variables like borrower characteristics, loan details, and repayment status. The document proposes using artificial neural network and support vector machine models to classify borrowers as good or bad credit risks based on these variables. It evaluates the accuracy of support vector machines and boosted decision tree models for the task of credit risk prediction.
Default Probability Prediction using Artificial Neural Networks in R ProgrammingVineet Ojha
The objective of the project is to analyze the ability of the Artificial Neural Network Model
developed to forecast the credit risk profile of retails banking loan consumers and credit card
customers.
From a theoretical point of view, this project introduces a literature review on the detailed
working and the application of Artificial Neural Networks for credit risk management.
Practically, the aim of this project is presenting a model for estimating the Probability of Default
using Artificial Neural Network to accrue benefit non-linear models.
This document discusses the application of machine learning algorithms for fraud detection in the banking sector. It proposes using Classification and Regression Tree (CART), AdaBoost, LogitBoost, and Bagging algorithms to classify banking data and detect fraud. An experiment is conducted to analyze the performance of these algorithms on a banking data set. The results show that the Bagging algorithm has the lowest misclassification rate, indicating it performs better than the other algorithms at classifying banking data for fraud detection. In conclusion, the Bagging algorithm is deemed the best performing of the meta-learning algorithms analyzed for fraud detection in banking data.
Improving Credit Card Fraud Detection: Using Machine Learning to Profile and ...Melissa Moody
Researchers Navin Kasa, Andrew Dahbura, and Charishma Ravoori undertook a capstone project—part of the UVA Data Science Institute Master of Science in Data Science program—that addresses credit card fraud detection through a semi-supervised approach, in which clusters of account profiles are created and used for modeling classifiers.
A Review on Credit Card Default Modelling using Data ScienceYogeshIJTSRD
In the last few years, credit card issuers have become one of the major consumer lending products in the U.S. as well as several other developed nations of the world, representing roughly 30 of total consumer lending USD 3.6 tn in 2016 . Credit cards issued by banks hold the majority of the market share with approximately 70 of the total outstanding balance. Bank’s credit card charge offs have stabilized after the financial crisis to around 3 of the outstanding total balance. However, there are still differences in the credit card charge off levels between different competitors. Harsh Nautiyal | Ayush Jyala | Dishank Bhandari "A Review on Credit Card Default Modelling using Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42461.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42461/a-review-on-credit-card-default-modelling-using-data-science/harsh-nautiyal
Operationalize deep learning models for fraud detection with Azure Machine Le...Francesca Lazzeri, PhD
Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification.
Although, neural networks have been used for fraud detection for decades, recent advancements in computing technologies along with large volumes of data available today have dramatically improved the effectiveness of these techniques. Using a sample dataset that contains transactions made by credit cards in September 2013 by European cardholders, Francesca Lazzeri and Jaya Mathew explain how to build, deploy, and operationalize a deep learning model to identify and prevent fraud, using Azure Machine Learning Workbench to show the main steps in the operationalization process (from data ingestion to consumption) and the Keras deep learning library with Microsoft Cognitive Toolkit CNTK as the backend.
Applying Convolutional-GRU for Term Deposit Likelihood PredictionVandanaSharma356
Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.
The document discusses the development of a credit default prediction model called Def_Catch using machine learning algorithms. Def_Catch was trained on a dataset of 100,000 examples with 11 attributes related to borrowers' credit histories and demographics. Random forest achieved the highest accuracy of 93.14% at predicting which borrowers would default in the next 2 years, outperforming logistic regression, naive bayes, decision trees, and multi-layer perceptron models. The top predictors of default included credit utilization, age, number of late payments, debt ratio, and income. Def_Catch provides insights into borrower risk that are difficult to discern from raw data alone.
This document summarizes research that combines statistical and machine learning methods to predict corporate failure using financial data. The researchers empirically compare discriminant analysis, logistic regression, classification trees, rule induction, and Bayesian networks on data from 120 Spanish companies, 60 that went bankrupt and 60 that did not. They also implement voting and Bayesian techniques to combine the individual models, finding improved predictive performance over single models. The key predictor variables are financial ratios gathered from company accounts over the three years before failure or survey date.
DEVELOPING PREDICTION MODEL OF LOAN RISK IN BANKS USING DATA MINING mlaij
Nowadays, There are many risks related to bank loans, for the bank and for those who get the loans. The
analysis of risk in bank loans need understanding what is the meaning of risk. In addition, the number of
transactions in banking sector is rapidly growing and huge data volumes are available which represent
the customers behavior and the risks around loan are increased. Data Mining is one of the most motivating
and vital area of research with the aim of extracting information from tremendous amount of accumulated
data sets. In this paper a new model for classifying loan risk in banking sector by using data mining. The
model has been built using data form banking sector to predict the status of loans. Three algorithms have
been used to build the proposed model: j48, bayesNet and naiveBayes. By using Weka application, the
model has been implemented and tested. The results has been discussed and a full comparison between
algorithms was conducted. J48 was selected as best algorithm based on accuracy.
Predicting Credit Card Defaults using Machine Learning AlgorithmsSagar Tupkar
This is a project that I worked on as a Capstone for my Masters in Business Analytics program at the University of Cincinnati. In this project, I have performed an end-to-end data mining exercise including data cleaning, distribution analysis, exploratory data analysis, model building etc. to identify and predict Credit Card defaults using Customer's data on past payments and general profile. In the process for building Machine Learning models, I have fit and compared the performance of multiple models and algorithms like Logistic Regreesion, PCA, Classification tree, AdaBoost Classifier, ANN and LDA.
Project report on Share Market applicationKRISHNA PANDEY
This is the proposal document for AVS Group of Technology service offering in the website design and development and custom web application development space. The document details our understanding of the brief, the objectives of the services suite, the methodology, and deliverable and commercials.
Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Fintech & Banking Industry Research By Artivatic.aiArtivatic.ai
Fintech or BFSI is going to play important role in the economy of entire world including India. Artivatic conducted Fintech research to understand the sector specific problems, growth and opportunities and also the use of technologies.
#fintech #banking #insurance #payments #accounts #bfsi #deeptech #artivatic #startups #technology
Visit: https://artivatic.ai/ or write us at contact at artivatic dot ai
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
Application of Data Mining and Machine Learning techniques for Fraud Detectio...Christian Adom
This document provides a summary and comparison of two academic papers that apply machine learning techniques to credit card fraud detection. It discusses how one paper uses a Hidden Markov Model (HMM) to model credit card transaction sequences and detect anomalies. The other paper uses a neural network to model transaction sequences. Both papers aim to detect fraudulent transactions while keeping false positives low. The document analyzes and compares the techniques, results and performance of the two papers to evaluate their effectiveness in addressing credit card fraud.
Benchmarking supervived learning models for emotion analysisConference Papers
The document summarizes a study that benchmarked supervised learning models for emotion analysis on sentence-level text data. It evaluated one deep learning model (BiLSTM) and one machine learning model (FastText) on two datasets with six emotion categories. The results showed that while requiring significantly more training time, the BiLSTM model consistently outperformed the FastText models in terms of precision, recall, and F1-score. Text preprocessing was also found to improve the performance of both models.
This document proposes a new approach to reduce computational time for credit scoring models based on support vector machines (SVM) and stratified sampling. The approach uses SVM incorporated with feature selection using F-scores and takes a sample of the dataset instead of the whole dataset to create the credit scoring model. The new method is shown to have competitive accuracy to other methods while significantly reducing computational time by optimizing features and reducing dataset size.
Blockchain Impact in Financial Sector Research by Artivatic.aiArtivatic.ai
Blockchain is the hot & most important new age technology that is going to disrupt the decentralization among the enterprises for information exchange, transaction and storing the information in most secure and safe way. Decentralized technology is going to change the way businesses are done in the future. Its the future of technology. Do read some research insights from Artivatic.ai team for the Blockchain industry.
Evaluating the impact of removing less important terms on sentiment analysisConference Papers
This document summarizes a research paper that evaluated the impact of removing less important terms on sentiment analysis. It discusses how sentiment analysis is an important natural language processing task but is complex due to linguistic challenges. Supervised machine learning is commonly used but requires high-quality training data. The paper experiments with identifying and removing less important words like stopwords and supporting part-of-speech tags from training data to see if it improves the precision of a sentiment classification model. The results showed removal of some unimportant words improved precision on a large generic dataset but not a smaller context-specific one.
Credit Card Fraud Detection Using Unsupervised Machine Learning AlgorithmsHariteja Bodepudi
This document summarizes a research paper that uses unsupervised machine learning algorithms to detect credit card fraud. It describes how credit card fraud has increased with the rise of online shopping and payments. Unsupervised algorithms are well-suited for this task since labeled fraud data can be difficult to obtain. The paper tests Isolation Forest, Local Outlier Factor, and One Class SVM on a credit card transaction dataset to find anomalies (fraudulent transactions). Isolation Forest achieved the highest accuracy at 99.74%, slightly outperforming Local Outlier Factor, while One Class SVM had much lower accuracy. The paper concludes unsupervised algorithms are effective for anomaly detection tasks like credit card fraud detection.
An Identification and Detection of Fraudulence in Credit Card Fraud Transacti...IRJET Journal
This document summarizes a research paper that proposes a system for detecting fraudulent credit card transactions using data mining techniques. The system uses the Apriori algorithm to perform frequent item set mining on a credit card transaction dataset. It then uses the Support Vector Machine (SVM) classification method to match new transactions to either a legal transaction pattern database or a fraudulent transaction pattern database that was formed based on users' previous transactions. The results showed this proposed method achieved better fraud detection with a lower false alarm rate than existing methods like Hidden Markov Models.
Online Transaction Fraud Detection System Based on Machine LearningIRJET Journal
This document discusses machine learning methods for detecting online transaction fraud. It proposes a system that uses behavior and location analysis to detect unusual patterns in a user's payment history and geographic location. If an unexpected pattern is identified, the user is re-verified. The system detects fraud by analyzing users' previous spending habits and locations. It can ban users after three unsuccessful verification attempts. The document also reviews several machine learning techniques for fraud detection, including nearest neighbor, naive Bayes, and support vector machines. It proposes using a bidirectional graph and transition probability matrix to model users' transaction behaviors and assess transaction validity.
This document discusses the application of machine learning algorithms for fraud detection in the banking sector. It proposes using Classification and Regression Tree (CART), AdaBoost, LogitBoost, and Bagging algorithms to classify banking data and detect fraud. An experiment is conducted to analyze the performance of these algorithms on a banking data set. The results show that the Bagging algorithm has the lowest misclassification rate, indicating it performs better than the other algorithms at classifying banking data for fraud detection. In conclusion, the Bagging algorithm is deemed the best performing of the meta-learning algorithms analyzed for fraud detection in banking data.
Improving Credit Card Fraud Detection: Using Machine Learning to Profile and ...Melissa Moody
Researchers Navin Kasa, Andrew Dahbura, and Charishma Ravoori undertook a capstone project—part of the UVA Data Science Institute Master of Science in Data Science program—that addresses credit card fraud detection through a semi-supervised approach, in which clusters of account profiles are created and used for modeling classifiers.
A Review on Credit Card Default Modelling using Data ScienceYogeshIJTSRD
In the last few years, credit card issuers have become one of the major consumer lending products in the U.S. as well as several other developed nations of the world, representing roughly 30 of total consumer lending USD 3.6 tn in 2016 . Credit cards issued by banks hold the majority of the market share with approximately 70 of the total outstanding balance. Bank’s credit card charge offs have stabilized after the financial crisis to around 3 of the outstanding total balance. However, there are still differences in the credit card charge off levels between different competitors. Harsh Nautiyal | Ayush Jyala | Dishank Bhandari "A Review on Credit Card Default Modelling using Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42461.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42461/a-review-on-credit-card-default-modelling-using-data-science/harsh-nautiyal
Operationalize deep learning models for fraud detection with Azure Machine Le...Francesca Lazzeri, PhD
Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification.
Although, neural networks have been used for fraud detection for decades, recent advancements in computing technologies along with large volumes of data available today have dramatically improved the effectiveness of these techniques. Using a sample dataset that contains transactions made by credit cards in September 2013 by European cardholders, Francesca Lazzeri and Jaya Mathew explain how to build, deploy, and operationalize a deep learning model to identify and prevent fraud, using Azure Machine Learning Workbench to show the main steps in the operationalization process (from data ingestion to consumption) and the Keras deep learning library with Microsoft Cognitive Toolkit CNTK as the backend.
Applying Convolutional-GRU for Term Deposit Likelihood PredictionVandanaSharma356
Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.
The document discusses the development of a credit default prediction model called Def_Catch using machine learning algorithms. Def_Catch was trained on a dataset of 100,000 examples with 11 attributes related to borrowers' credit histories and demographics. Random forest achieved the highest accuracy of 93.14% at predicting which borrowers would default in the next 2 years, outperforming logistic regression, naive bayes, decision trees, and multi-layer perceptron models. The top predictors of default included credit utilization, age, number of late payments, debt ratio, and income. Def_Catch provides insights into borrower risk that are difficult to discern from raw data alone.
This document summarizes research that combines statistical and machine learning methods to predict corporate failure using financial data. The researchers empirically compare discriminant analysis, logistic regression, classification trees, rule induction, and Bayesian networks on data from 120 Spanish companies, 60 that went bankrupt and 60 that did not. They also implement voting and Bayesian techniques to combine the individual models, finding improved predictive performance over single models. The key predictor variables are financial ratios gathered from company accounts over the three years before failure or survey date.
DEVELOPING PREDICTION MODEL OF LOAN RISK IN BANKS USING DATA MINING mlaij
Nowadays, There are many risks related to bank loans, for the bank and for those who get the loans. The
analysis of risk in bank loans need understanding what is the meaning of risk. In addition, the number of
transactions in banking sector is rapidly growing and huge data volumes are available which represent
the customers behavior and the risks around loan are increased. Data Mining is one of the most motivating
and vital area of research with the aim of extracting information from tremendous amount of accumulated
data sets. In this paper a new model for classifying loan risk in banking sector by using data mining. The
model has been built using data form banking sector to predict the status of loans. Three algorithms have
been used to build the proposed model: j48, bayesNet and naiveBayes. By using Weka application, the
model has been implemented and tested. The results has been discussed and a full comparison between
algorithms was conducted. J48 was selected as best algorithm based on accuracy.
Predicting Credit Card Defaults using Machine Learning AlgorithmsSagar Tupkar
This is a project that I worked on as a Capstone for my Masters in Business Analytics program at the University of Cincinnati. In this project, I have performed an end-to-end data mining exercise including data cleaning, distribution analysis, exploratory data analysis, model building etc. to identify and predict Credit Card defaults using Customer's data on past payments and general profile. In the process for building Machine Learning models, I have fit and compared the performance of multiple models and algorithms like Logistic Regreesion, PCA, Classification tree, AdaBoost Classifier, ANN and LDA.
Project report on Share Market applicationKRISHNA PANDEY
This is the proposal document for AVS Group of Technology service offering in the website design and development and custom web application development space. The document details our understanding of the brief, the objectives of the services suite, the methodology, and deliverable and commercials.
Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Fintech & Banking Industry Research By Artivatic.aiArtivatic.ai
Fintech or BFSI is going to play important role in the economy of entire world including India. Artivatic conducted Fintech research to understand the sector specific problems, growth and opportunities and also the use of technologies.
#fintech #banking #insurance #payments #accounts #bfsi #deeptech #artivatic #startups #technology
Visit: https://artivatic.ai/ or write us at contact at artivatic dot ai
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
Application of Data Mining and Machine Learning techniques for Fraud Detectio...Christian Adom
This document provides a summary and comparison of two academic papers that apply machine learning techniques to credit card fraud detection. It discusses how one paper uses a Hidden Markov Model (HMM) to model credit card transaction sequences and detect anomalies. The other paper uses a neural network to model transaction sequences. Both papers aim to detect fraudulent transactions while keeping false positives low. The document analyzes and compares the techniques, results and performance of the two papers to evaluate their effectiveness in addressing credit card fraud.
Benchmarking supervived learning models for emotion analysisConference Papers
The document summarizes a study that benchmarked supervised learning models for emotion analysis on sentence-level text data. It evaluated one deep learning model (BiLSTM) and one machine learning model (FastText) on two datasets with six emotion categories. The results showed that while requiring significantly more training time, the BiLSTM model consistently outperformed the FastText models in terms of precision, recall, and F1-score. Text preprocessing was also found to improve the performance of both models.
This document proposes a new approach to reduce computational time for credit scoring models based on support vector machines (SVM) and stratified sampling. The approach uses SVM incorporated with feature selection using F-scores and takes a sample of the dataset instead of the whole dataset to create the credit scoring model. The new method is shown to have competitive accuracy to other methods while significantly reducing computational time by optimizing features and reducing dataset size.
Blockchain Impact in Financial Sector Research by Artivatic.aiArtivatic.ai
Blockchain is the hot & most important new age technology that is going to disrupt the decentralization among the enterprises for information exchange, transaction and storing the information in most secure and safe way. Decentralized technology is going to change the way businesses are done in the future. Its the future of technology. Do read some research insights from Artivatic.ai team for the Blockchain industry.
Evaluating the impact of removing less important terms on sentiment analysisConference Papers
This document summarizes a research paper that evaluated the impact of removing less important terms on sentiment analysis. It discusses how sentiment analysis is an important natural language processing task but is complex due to linguistic challenges. Supervised machine learning is commonly used but requires high-quality training data. The paper experiments with identifying and removing less important words like stopwords and supporting part-of-speech tags from training data to see if it improves the precision of a sentiment classification model. The results showed removal of some unimportant words improved precision on a large generic dataset but not a smaller context-specific one.
Credit Card Fraud Detection Using Unsupervised Machine Learning AlgorithmsHariteja Bodepudi
This document summarizes a research paper that uses unsupervised machine learning algorithms to detect credit card fraud. It describes how credit card fraud has increased with the rise of online shopping and payments. Unsupervised algorithms are well-suited for this task since labeled fraud data can be difficult to obtain. The paper tests Isolation Forest, Local Outlier Factor, and One Class SVM on a credit card transaction dataset to find anomalies (fraudulent transactions). Isolation Forest achieved the highest accuracy at 99.74%, slightly outperforming Local Outlier Factor, while One Class SVM had much lower accuracy. The paper concludes unsupervised algorithms are effective for anomaly detection tasks like credit card fraud detection.
An Identification and Detection of Fraudulence in Credit Card Fraud Transacti...IRJET Journal
This document summarizes a research paper that proposes a system for detecting fraudulent credit card transactions using data mining techniques. The system uses the Apriori algorithm to perform frequent item set mining on a credit card transaction dataset. It then uses the Support Vector Machine (SVM) classification method to match new transactions to either a legal transaction pattern database or a fraudulent transaction pattern database that was formed based on users' previous transactions. The results showed this proposed method achieved better fraud detection with a lower false alarm rate than existing methods like Hidden Markov Models.
Online Transaction Fraud Detection System Based on Machine LearningIRJET Journal
This document discusses machine learning methods for detecting online transaction fraud. It proposes a system that uses behavior and location analysis to detect unusual patterns in a user's payment history and geographic location. If an unexpected pattern is identified, the user is re-verified. The system detects fraud by analyzing users' previous spending habits and locations. It can ban users after three unsuccessful verification attempts. The document also reviews several machine learning techniques for fraud detection, including nearest neighbor, naive Bayes, and support vector machines. It proposes using a bidirectional graph and transition probability matrix to model users' transaction behaviors and assess transaction validity.
A Comparative Study on Online Transaction Fraud Detection by using Machine Le...IRJET Journal
This document summarizes a study that used machine learning and Python to detect online transaction fraud. It describes how online transactions and fraud are increasing. The study used a real credit card dataset to train models like KNN, NB, and SVM to detect fraudulent transactions based on user behavior patterns and restrict fraudulent users after three failed attempts. The goal was to develop a system that can detect fraud in real-time and prevent losses for banks and credit card users.
Credit Card Fraud Detection Using Machine LearningIRJET Journal
This document discusses machine learning methods for credit card fraud detection using imbalanced datasets. It provides a literature review summarizing several research papers on this topic. The papers evaluated algorithms like logistic regression, decision trees, random forests and support vector machines for credit card fraud prediction. Random forests generally performed best, accurately predicting defaults 77% of the time in one study. The document emphasizes that imbalanced datasets can bias models towards the majority class. Sampling techniques like adaptive synthetic sampling were found to improve classifier performance on minority default classes. No single algorithm can fully address class imbalance, and more diverse credit card data is needed to develop better predictive models.
Credit Card Fraud Detection Using Machine LearningIRJET Journal
This document summarizes a research paper that compares various machine learning algorithms for credit card fraud detection using an imbalanced credit card transaction dataset. The paper analyzes logistic regression, decision tree, random forest, and support vector machine classifiers on the basis of precision, recall, and accuracy. It finds that random forest provides the best performance with an accuracy of 77% for predicting credit card defaulters from the imbalanced data. The paper concludes that machine learning algorithms can effectively identify high-risk credit card users to help reduce financial losses for credit card issuers.
A Comparative Study on Credit Card Fraud DetectionIRJET Journal
This document summarizes a study that compares the performance of multiple classification algorithms for detecting credit card fraud. The study uses a dataset of over 284,000 credit card transactions from European consumers. Various sampling techniques are applied to address the skewed nature of the data. Classification algorithms like random forest, logistic regression, and k-NN are evaluated based on metrics like accuracy, sensitivity, specificity, and precision. The results suggest that k-NN performs best in detecting credit card fraud from this imbalanced transaction dataset. The document also briefly reviews several other related studies on credit card fraud detection techniques.
Machine Learning-Based Approaches for Fraud Detection in Credit Card Transact...IRJET Journal
This document presents a comparative study of machine learning approaches for credit card fraud detection. It explores isolation forest, local outlier factor (LOF), and one-class support vector machine (SVM) algorithms for fraud detection and compares their performance. The document first collects a dataset of credit card transactions containing both legitimate and fraudulent examples. It then implements and evaluates the machine learning models, assessing their ability to accurately identify fraud while minimizing false positives. Statistical tests and visualization techniques like ROC curves are used to analyze the models' performance. The best-performing aspects of each model are identified to inform optimal fraud detection.
A Study on Credit Card Fraud Detection using Machine Learningijtsrd
Due to the high level of growth in each number of transactions done using credit card has led to high rise in fraudulent activities. Fraud is one of the major issues related to credit card business, since each individual do more of offline or online purchase of product via internet there is need to developed a secured approach of detecting if the credit card been used is a fraudulent transaction or not. Pattern involves in the fraud detection has to be re analyze to change from reactive approach to a proactive approach. In this paper, our objectives are to detect at least 95 of fraudulent activities using machine learning to deployed anomaly detection system such as logistic regression, k nearest neighbor and support vector machine algorithm. Ajayi Kemi Patience | Dr. Lakshmi J. V. N "A Study on Credit Card Fraud Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30688.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30688/a-study-on-credit-card-fraud-detection-using-machine-learning/ajayi-kemi-patience
Concept drift and machine learning model for detecting fraudulent transaction...IJECEIAES
The document presents a machine learning model for detecting fraudulent transactions in a streaming environment that addresses concept drift. The proposed approach uses the extreme gradient boosting (XGBoost) algorithm and employs four algorithms to continuously detect concept drift in data streams. The approach is evaluated on credit card and Twitter fraud datasets and is shown to outperform traditional machine learning models in terms accuracy, precision, and recall, and is more robust to concept drift. The proposed approach can be utilized as a real-time fraud detection system across different industries.
A Review of deep learning techniques in detection of anomaly incredit card tr...IRJET Journal
This document summarizes a review of deep learning techniques for detecting anomalies in credit card transactions. It discusses how credit card fraud causes major financial losses and how machine learning can help identify fraudulent transactions. The document outlines the objectives of comparing support vector machines and random forests for credit card fraud detection and discusses challenges like class imbalance in the data. It presents the system architecture for credit card fraud detection and analyzes results on a dataset of European credit card transactions, finding random forests outperform decision trees. Future work to improve accuracy is also discussed.
A rule-based machine learning model for financial fraud detectionIJECEIAES
Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
CREDIT CARD FRAUD DETECTION USING MACHINE LEARNINGIRJET Journal
The document discusses using machine learning algorithms, specifically the Random Forest algorithm, to detect credit card fraud. It begins with an abstract that outlines how machine learning can be used to analyze large amounts of transaction data and detect fraudulent patterns. The document then provides background on the challenges of credit card fraud and how machine learning is being increasingly used to identify fraudulent transactions. It proposes using the Random Forest algorithm for credit card fraud detection as it can effectively handle large datasets, non-linear relationships between features, and provide important feature analysis. The document discusses preprocessing data, feature engineering, handling imbalanced data, training the Random Forest model, and evaluating performance based on metrics like accuracy, precision, recall and F1 score. It finds that Random Forest achieved
Credit Card Fraud Detection System Using Machine Learning AlgorithmIRJET Journal
This document discusses using machine learning algorithms to detect credit card fraud. It begins with an abstract that introduces credit card fraud as an increasing problem and machine learning as a solution. The introduction provides more background on credit card fraud and detection methods. It then discusses several machine learning algorithms that can be used for credit card fraud detection, including logistic regression, decision trees, random forests, and XGBoost. It concludes that hybrid models combining individual algorithms performed best on a publicly available credit card dataset, with the highest Matthews correlation coefficient of 0.823. References are provided on related work in credit card fraud detection techniques.
A Research Paper on Credit Card Fraud DetectionIRJET Journal
This document summarizes a research paper that aims to detect credit card fraud using machine learning algorithms. It discusses how credit card fraud is a growing problem and describes challenges in detecting fraud like class imbalance in the data. The proposed system uses different machine learning classifiers like decision trees and random forest on a credit card transaction dataset to identify the best algorithm for predicting fraudulent transactions. It performs exploratory data analysis on the dataset, trains and evaluates the models, and calculates various metrics to select the most accurate classifier for detecting credit card fraud.
IRJET- Credit Card Fraud Detection using Isolation ForestIRJET Journal
This document discusses using machine learning algorithms like Isolation Forest and Local Outlier Factor to detect credit card fraud. It begins with an introduction to the increasing problem of credit card fraud and challenges in detecting fraudulent transactions among millions occurring daily. The document then provides background on supervised and unsupervised machine learning algorithms and describes how Isolation Forest and Local Outlier Factor work. Related work discussing other fraud detection techniques and the limitations of existing approaches is also summarized. The goal of the paper is to compare Isolation Forest and Local Outlier Factor to determine the most effective algorithm for credit card fraud detection.
CREDIT CARD FRAUD DETECTION AND AUTHENTICATION SYSTEM USING MACHINE LEARNINGIRJET Journal
This document discusses developing a machine learning-based credit card fraud detection and authentication system. It aims to identify fraudulent transactions and authenticate users before transactions are completed. The system will use algorithms like decision trees, random forests, and KNN to analyze transaction data and detect suspicious activity by training on past fraud data. It will also implement authentication methods like face recognition and one-time passwords. The system is expected to increase credit card transaction security and prevent fraud by detecting and authenticating issues before they occur, saving consumers and financial institutions money.
FRAUD DETECTION IN CREDIT CARD TRANSACTIONSIRJET Journal
This document summarizes a research paper on detecting credit card fraud using machine learning algorithms. It begins by introducing the challenges of credit card fraud detection and how traditional methods are insufficient. Then it discusses how machine learning algorithms can be applied to transaction data to identify complex fraud patterns in real-time. The document outlines the methodology, including data collection, preprocessing, feature extraction, model selection and training, and model evaluation. Finally, it presents the results and performance of logistic regression, support vector machines, and random forest algorithms on the fraud detection task and concludes that machine learning is a promising approach.
This document discusses the application of meta-learning algorithms in banking sector data mining for fraud detection. It proposes using Classification and Regression Tree (CART), AdaBoost, LogitBoost, Bagging and Dagging algorithms for classification of banking transaction data. The experimental results show that Bagging algorithm has the best performance with the lowest misclassification rate, making it effective for banking fraud detection through data mining. Data mining can help banks detect patterns for applications like credit scoring, payment default prediction, fraud detection and risk management by analyzing customer transaction history and loan details.
IRJET- Credit Card Fraud Detection using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to detect credit card fraud. It begins with an introduction to machine learning and its applications. A literature review covers previous research using algorithms like AdaBoost, decision trees, random forests, SVM and logistic regression for fraud detection. The proposed system architecture has five layers of control, including security checks, transaction blocking rules, scoring rules, and human investigators. Dimensionality reduction using PCA and classification with SVM are applied to transaction data. Data visualization with heatmaps is also discussed. The document concludes machine learning proves accurate for fraud detection and future work could explore additional algorithms.
Similar to IRJET- Analysis on Credit Card Fraud Detection using Capsule Network (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.