Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140737
Keerti Kulkarni
Compressive Sensing is a relatively new technique for acquiring signals and images. This technique is a part of sparse signal processing and it exploits sparsity of the signal in one or the other domain. The main objective of this work is to show that sparse signal can be reconstructed with a lesser number of samples than that dictated by the Nyquist criteria. This research work considers a synthetically generated time domain sparse signal, and sample it using a random measurement matrix. Then, a time domain signal, which is sparse in the frequency domain is sampled using a delta matrix. This signal is first converted to the frequency domain using DFT. It is shown in this work that the reconstruction is better when 64 samples are used as compared to when 32 samples are used in the measurements.
{"title":"Analysis of the Measurement Matrices for Compressive Sensing of Signals","authors":"Keerti Kulkarni","doi":"10.1109/ICAAIC56838.2023.10140737","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140737","url":null,"abstract":"Compressive Sensing is a relatively new technique for acquiring signals and images. This technique is a part of sparse signal processing and it exploits sparsity of the signal in one or the other domain. The main objective of this work is to show that sparse signal can be reconstructed with a lesser number of samples than that dictated by the Nyquist criteria. This research work considers a synthetically generated time domain sparse signal, and sample it using a random measurement matrix. Then, a time domain signal, which is sparse in the frequency domain is sampled using a delta matrix. This signal is first converted to the frequency domain using DFT. It is shown in this work that the reconstruction is better when 64 samples are used as compared to when 32 samples are used in the measurements.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134435187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140756
Nagagopiraju Vullam, S. Vellela, Venkateswara Reddy B, M. V. Rao, K. Sk, Roja D
As more sectors began to switch from conventional business models to e-commerce in response to the general trend toward mobile Internet use, the scale of e-commerce grew rapidly. There are three types of recommendation systems: hybrid, collaborative, content-based. Content based systems take into consideration the characteristics of the recommended objects. Then, titles in the database that have been classified as “romantic” are selected using a content-based recommendation method. Collaborative filtering systems utilize similarity measures to recommend items that are shared by individuals or objects with similar interests. Users are recommended items based on their preferences. In the recommendation system, collaborative filtering is the most popular and effective suggestion process. However, system performance impact as the amount of time required to locate the target user's closest neighbor across the entire user space increases with the number of users and products in the e-commerce system. The applied and designed Multi-Agent personalized recommendation system in E-commerce can be analyzed using user clustering in the Multi-Agent to E-commerce personalized recommendation system. An implementation strategy for recommendations based on user clustering is shown in this analysis. According to their scores for commodity categories, users are clustered, and only the nearest neighbours in their categories are searched, so that as many nearest neighbors as possible can be searched. The accuracy, recall, and specificity of this analysis are used to calculate its performance. In this analysis the presented method will give better results.
{"title":"Multi-Agent Personalized Recommendation System in E-Commerce based on User","authors":"Nagagopiraju Vullam, S. Vellela, Venkateswara Reddy B, M. V. Rao, K. Sk, Roja D","doi":"10.1109/ICAAIC56838.2023.10140756","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140756","url":null,"abstract":"As more sectors began to switch from conventional business models to e-commerce in response to the general trend toward mobile Internet use, the scale of e-commerce grew rapidly. There are three types of recommendation systems: hybrid, collaborative, content-based. Content based systems take into consideration the characteristics of the recommended objects. Then, titles in the database that have been classified as “romantic” are selected using a content-based recommendation method. Collaborative filtering systems utilize similarity measures to recommend items that are shared by individuals or objects with similar interests. Users are recommended items based on their preferences. In the recommendation system, collaborative filtering is the most popular and effective suggestion process. However, system performance impact as the amount of time required to locate the target user's closest neighbor across the entire user space increases with the number of users and products in the e-commerce system. The applied and designed Multi-Agent personalized recommendation system in E-commerce can be analyzed using user clustering in the Multi-Agent to E-commerce personalized recommendation system. An implementation strategy for recommendations based on user clustering is shown in this analysis. According to their scores for commodity categories, users are clustered, and only the nearest neighbours in their categories are searched, so that as many nearest neighbors as possible can be searched. The accuracy, recall, and specificity of this analysis are used to calculate its performance. In this analysis the presented method will give better results.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132100693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140635
R. Arunadevi, S. Sudha, V. Karthi, M. D. Saranya, Thurai V B Raaj, Kavin Kumar K
Atherosclerosis is a chronic degenerative disease that results in cardiovascular diseases (CVDs) and is detected either by cardiac arrest or stroke. Early diagnosis of CVDs is made possible by identifying Intima Media Thickness (IMT) and elasticity. B-mode ultrasound imaging has on no account ionizing radiation and is economical and non-invasive to assess CVDs. This paper proposes an effective automatic image segmentation method using deep learning CNN for segmenting the region containing intima media of far wall carotid artery. The proposed approach is compared with SVM classifier and RBF neural network and is proven to be robust with improved accuracy and F1 score.
{"title":"Deep Learning based ROI Segmentation using Convolution Neural Network","authors":"R. Arunadevi, S. Sudha, V. Karthi, M. D. Saranya, Thurai V B Raaj, Kavin Kumar K","doi":"10.1109/ICAAIC56838.2023.10140635","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140635","url":null,"abstract":"Atherosclerosis is a chronic degenerative disease that results in cardiovascular diseases (CVDs) and is detected either by cardiac arrest or stroke. Early diagnosis of CVDs is made possible by identifying Intima Media Thickness (IMT) and elasticity. B-mode ultrasound imaging has on no account ionizing radiation and is economical and non-invasive to assess CVDs. This paper proposes an effective automatic image segmentation method using deep learning CNN for segmenting the region containing intima media of far wall carotid artery. The proposed approach is compared with SVM classifier and RBF neural network and is proven to be robust with improved accuracy and F1 score.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131753813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140812
N. R. Babu, Kalagotla Chenchireddy, V. H. V. Reddy, D. Samhitha, P. Apparao, C. P. Kalyan
In the current world, where we depend on a variety of systems and technologies, batteries play a critical role. They are necessary for supplying portable power for cellphones, laptops, and other mobiles as well as for regenerative energy sources including solar and wind, electric cars, And home energy storage systems. Rechargeable nickel-metal hydride (NiMH) batteries have grown in significance as a result of their many advantages due to great performance, Extended life, and eco-friendly alternative to throwing away batteries, these batteries have grown in popularity for years. As a result, we examine in this research how well a Ni-MH battery performance when coupled to a boost converter for boosting and battery state of charge
{"title":"Case study on Ni-MH Battery","authors":"N. R. Babu, Kalagotla Chenchireddy, V. H. V. Reddy, D. Samhitha, P. Apparao, C. P. Kalyan","doi":"10.1109/ICAAIC56838.2023.10140812","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140812","url":null,"abstract":"In the current world, where we depend on a variety of systems and technologies, batteries play a critical role. They are necessary for supplying portable power for cellphones, laptops, and other mobiles as well as for regenerative energy sources including solar and wind, electric cars, And home energy storage systems. Rechargeable nickel-metal hydride (NiMH) batteries have grown in significance as a result of their many advantages due to great performance, Extended life, and eco-friendly alternative to throwing away batteries, these batteries have grown in popularity for years. As a result, we examine in this research how well a Ni-MH battery performance when coupled to a boost converter for boosting and battery state of charge","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115953747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140956
S. S, S. S., Rajeshkumar G, G. S, V. K, Karma Rajesh P
The purity of the water has recently been threatened by a number of contaminants. As a result, it is now crucial for the management of water pollution to model and anticipate water quality. In order to forecast the water quality index (WQI) and water quality classification (WQC), this work creates cutting-edge artificial intelligence (AI) approaches. Today, many people are afflicted with severe illnesses brought on by tainted water. This study will look at a water quality monitoring system because it provides information on water quality. It is planned to identify forecasts for water quality using a machine learning system. The depletion of natural water resources including lakes, streams, and estuaries is one of the most significant and alarming issues facing humanity. The effects of dirty water are widespread and have an impact on several people. Water resource management is therefore essential for maximizing water quality. If data are analyzed and water quality is foreseen, the effects of water contamination can be effectively addressed. Even though this subject has been covered in a large number of earlier research, more has to be done to boost the effectiveness, dependability, accuracy, and utility of the current techniques to managing water quality. The goal of this study is to develop an Artificial Neural Network (ANN) and time-series analysisbased water quality prediction model. The historical water quality data used in this study has a 6-minute time period and is from the year 2014. The National Water Information System, a website operated by the United States Geological Survey (USGS) is where the data comes from.
{"title":"Predict the Quality of Freshwater using Support Vector Machines","authors":"S. S, S. S., Rajeshkumar G, G. S, V. K, Karma Rajesh P","doi":"10.1109/ICAAIC56838.2023.10140956","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140956","url":null,"abstract":"The purity of the water has recently been threatened by a number of contaminants. As a result, it is now crucial for the management of water pollution to model and anticipate water quality. In order to forecast the water quality index (WQI) and water quality classification (WQC), this work creates cutting-edge artificial intelligence (AI) approaches. Today, many people are afflicted with severe illnesses brought on by tainted water. This study will look at a water quality monitoring system because it provides information on water quality. It is planned to identify forecasts for water quality using a machine learning system. The depletion of natural water resources including lakes, streams, and estuaries is one of the most significant and alarming issues facing humanity. The effects of dirty water are widespread and have an impact on several people. Water resource management is therefore essential for maximizing water quality. If data are analyzed and water quality is foreseen, the effects of water contamination can be effectively addressed. Even though this subject has been covered in a large number of earlier research, more has to be done to boost the effectiveness, dependability, accuracy, and utility of the current techniques to managing water quality. The goal of this study is to develop an Artificial Neural Network (ANN) and time-series analysisbased water quality prediction model. The historical water quality data used in this study has a 6-minute time period and is from the year 2014. The National Water Information System, a website operated by the United States Geological Survey (USGS) is where the data comes from.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115942220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140405
D. K, D. M, Mangaladharsini L. G, Devipriya R, V. V.
The skin, being the largest organ in the human body, plays a crucial role in protecting and covering the body while performing various functions. However, skin diseases, such as vitiligo, can result in changes to the skin's appearance, leading to white patches. Vitiligo is a prevalent skin disorder affecting millions of individuals worldwide. Despite the lack of a cure for vitiligo, early detection and treatment can prevent its dissemination to other body parts. To address this issue, an innovative system has been developed to enable users to check their skin condition for the presence of vitiligo in a user-friendly manner. This system comprises both hardware and software components. Specifically, a color sensor is utilized to gather RGB values of the user's skin surface, which are subsequently analyzed using a machine learning algorithm to ascertain the presence or absence of vitiligo. The device offers an easy-to-use tool for users to monitor their skin condition, which could significantly improve the quality of life for those affected by vitiligo comprehensive data collection and analysis.
{"title":"IoT based Vitiligo Detection","authors":"D. K, D. M, Mangaladharsini L. G, Devipriya R, V. V.","doi":"10.1109/ICAAIC56838.2023.10140405","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140405","url":null,"abstract":"The skin, being the largest organ in the human body, plays a crucial role in protecting and covering the body while performing various functions. However, skin diseases, such as vitiligo, can result in changes to the skin's appearance, leading to white patches. Vitiligo is a prevalent skin disorder affecting millions of individuals worldwide. Despite the lack of a cure for vitiligo, early detection and treatment can prevent its dissemination to other body parts. To address this issue, an innovative system has been developed to enable users to check their skin condition for the presence of vitiligo in a user-friendly manner. This system comprises both hardware and software components. Specifically, a color sensor is utilized to gather RGB values of the user's skin surface, which are subsequently analyzed using a machine learning algorithm to ascertain the presence or absence of vitiligo. The device offers an easy-to-use tool for users to monitor their skin condition, which could significantly improve the quality of life for those affected by vitiligo comprehensive data collection and analysis.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"764 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116133892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141488
Raj Kishor Bisht, Sarthak Sharma, Ashna Gusain, N. Thakur
Collocations are not merely frequently appearing word combinations (n-grams). Words in collocations have some kind of strong association among them. Collocations play an important role in various natural language processing (NLP) applications. Sentiment analysis is one of the growing areas of research in NLP because of its utilization in various business strategies. The present paper investigates collocations in positive and negative sentiments and their usefulness in sentiment analysis. We considered Amazon Products Review dataset for the purpose and analyzed positive and negative reviews separately. Different statistical techniques; Pointwise Mutual information (PMI), Chi Square test (Chi2), t-test, and likelihood ratio (LH) have been used to extract collocations from these texts and the common collocations have been extracted and analyzed. We found that collocation may be a potential feature for sentiment analysis.
{"title":"A Study of Collocations in Sentiment Analysis","authors":"Raj Kishor Bisht, Sarthak Sharma, Ashna Gusain, N. Thakur","doi":"10.1109/ICAAIC56838.2023.10141488","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141488","url":null,"abstract":"Collocations are not merely frequently appearing word combinations (n-grams). Words in collocations have some kind of strong association among them. Collocations play an important role in various natural language processing (NLP) applications. Sentiment analysis is one of the growing areas of research in NLP because of its utilization in various business strategies. The present paper investigates collocations in positive and negative sentiments and their usefulness in sentiment analysis. We considered Amazon Products Review dataset for the purpose and analyzed positive and negative reviews separately. Different statistical techniques; Pointwise Mutual information (PMI), Chi Square test (Chi2), t-test, and likelihood ratio (LH) have been used to extract collocations from these texts and the common collocations have been extracted and analyzed. We found that collocation may be a potential feature for sentiment analysis.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117023024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141080
Venkata Subba Reddy Gade, M. Sumathi
Speaker recognition depends on identifying the speaker using particular segments of the sound stream. A single speech characteristic only reveals the speaker's identity partially. Current advances in machine learning have considerably enhanced automatic voice recognition and localization systems. Nevertheless, this advantage comes at the expense of requiring complicated models and calculations. Additional microphone arrays will be used, as well as practice data. This study introduces a novel deep convolutional neural network-based end-to-end hybrid identification and localization model (HDCNN). HDCNN are employing a cutting-edge data augmentation strategy. This model can recognize both single- and multi-speaker arrangements and show which speaker is active with outstanding accuracy. HDCNN, a hybrid machine-learning algorithm. The final outcomes of proposed HDCNN model show greatest performance with an accuracy of 98.33%, which is higher than existing model's performance metrics.
{"title":"Hybrid Deep Convolutional Neural Network based Speaker Recognition for Noisy Speech Environments","authors":"Venkata Subba Reddy Gade, M. Sumathi","doi":"10.1109/ICAAIC56838.2023.10141080","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141080","url":null,"abstract":"Speaker recognition depends on identifying the speaker using particular segments of the sound stream. A single speech characteristic only reveals the speaker's identity partially. Current advances in machine learning have considerably enhanced automatic voice recognition and localization systems. Nevertheless, this advantage comes at the expense of requiring complicated models and calculations. Additional microphone arrays will be used, as well as practice data. This study introduces a novel deep convolutional neural network-based end-to-end hybrid identification and localization model (HDCNN). HDCNN are employing a cutting-edge data augmentation strategy. This model can recognize both single- and multi-speaker arrangements and show which speaker is active with outstanding accuracy. HDCNN, a hybrid machine-learning algorithm. The final outcomes of proposed HDCNN model show greatest performance with an accuracy of 98.33%, which is higher than existing model's performance metrics.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123293123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140671
Srikrishna M, G Nirmala
Near Infrared (NIR) images involves the generation of an edge-map by combining two edge-maps generated from the same eye image for pupil detection. It is accomplished by the use of Gaussian filtering, picture binarization, and Sobel edge detection techniques. Image segmentation is used to group similar pixels based on the rate of change in intensity or depth, allowing for the representation of information from the image. The Hough transformation is employed as an efficient method for detecting lines in images, with this work proposing the use of angle-radius parameters instead of slope-intercept parameters, simplifying computation and facilitating pupil detection. This approach increases the accuracy and speed of pupil recognition by reducing erroneous edges in the edge-map. This technique's hardware implementation on an FPGA platform may be utilized for recognition and iris localization applications.
{"title":"Realization of Human Eye Pupil Detection System using Canny Edge Detector and Circular Hough Transform Technique","authors":"Srikrishna M, G Nirmala","doi":"10.1109/ICAAIC56838.2023.10140671","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140671","url":null,"abstract":"Near Infrared (NIR) images involves the generation of an edge-map by combining two edge-maps generated from the same eye image for pupil detection. It is accomplished by the use of Gaussian filtering, picture binarization, and Sobel edge detection techniques. Image segmentation is used to group similar pixels based on the rate of change in intensity or depth, allowing for the representation of information from the image. The Hough transformation is employed as an efficient method for detecting lines in images, with this work proposing the use of angle-radius parameters instead of slope-intercept parameters, simplifying computation and facilitating pupil detection. This approach increases the accuracy and speed of pupil recognition by reducing erroneous edges in the edge-map. This technique's hardware implementation on an FPGA platform may be utilized for recognition and iris localization applications.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124612390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141427
Ananya Singhai, S. Aanjankumar, S. Poonkuntran
Credit cards offer a convenient and efficient option for online transactions; however, their increasing use has led to a rise in credit card fraud, resulting in significant financial losses for both cardholders and financial institutions. This research aims to identify such frauds by considering various criteria, including the availability of public data, high-class disparity statistics, changes in fraudulent processes, and high false alarm rates. With the growth of e-payments, fraudsters have resorted to various tactics such as fake emails and data breaches to steal money during online transactions. Although these methods are inaccurate, cutting-edge machine-learning algorithms must be used to reduce fraud losses. Therefore, this study's primary focus is on the recent advancements in machine learning algorithms for credit card fraud detection. The research paper aims to investigate the application of machine learning algorithms in distinguishing between genuine and fake online transactions. In the paper, KNN is compared to other machine-learning methods for detecting credit card fraud. The proposed approach achieved an accuracy of 99.95%, a precision of 97.2%, a recall of 85.71%, and an F1-score of 90.3%.
{"title":"A Novel Methodology for Credit Card Fraud Detection using KNN Dependent Machine Learning Methodology","authors":"Ananya Singhai, S. Aanjankumar, S. Poonkuntran","doi":"10.1109/ICAAIC56838.2023.10141427","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141427","url":null,"abstract":"Credit cards offer a convenient and efficient option for online transactions; however, their increasing use has led to a rise in credit card fraud, resulting in significant financial losses for both cardholders and financial institutions. This research aims to identify such frauds by considering various criteria, including the availability of public data, high-class disparity statistics, changes in fraudulent processes, and high false alarm rates. With the growth of e-payments, fraudsters have resorted to various tactics such as fake emails and data breaches to steal money during online transactions. Although these methods are inaccurate, cutting-edge machine-learning algorithms must be used to reduce fraud losses. Therefore, this study's primary focus is on the recent advancements in machine learning algorithms for credit card fraud detection. The research paper aims to investigate the application of machine learning algorithms in distinguishing between genuine and fake online transactions. In the paper, KNN is compared to other machine-learning methods for detecting credit card fraud. The proposed approach achieved an accuracy of 99.95%, a precision of 97.2%, a recall of 85.71%, and an F1-score of 90.3%.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125161402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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