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A Novel Method for Enhancing Accuracy in Mining Twitter Data Using Naive Bayes over Logistic Regression 一种基于朴素贝叶斯逻辑回归提高Twitter数据挖掘精度的新方法
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759006
V. S. Reddy, T. Poovizhi
{"title":"A Novel Method for Enhancing Accuracy in Mining Twitter Data Using Naive Bayes over Logistic Regression","authors":"V. S. Reddy, T. Poovizhi","doi":"10.1109/ICBATS54253.2022.9759006","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759006","url":null,"abstract":"To Enhance the accuracy performance in mining twitter data movie reviews. Naive Bayes with sample size of (N=5) and Logistic Regression with sample size of (N=5) was iterated at different times for prediction accuracy performance of movie reviews. The sigmoid function used in Naive Bayes Prediction to probability which helps to improve the prediction of accuracy. There was a statistical significance between Naive Bayes and Logistic Regression (p=0.00). Result proved that the Naive Bayes got significant result with 91% accuracy compared to Logistic Regression with 63% accuracy. Naive Bayes is a simple and most effective algorithm to build fast machine learning models. Naive Bayes helps predicting with more accuracy percentage of movie review.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121581502","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}
引用次数: 0
Novel Detection of Accurate Spam Content using Logistic Regression Algorithm Compared with Gaussian Algorithm 基于逻辑回归算法的垃圾邮件内容精确检测与高斯算法的比较
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759003
K. V. Bhavitha, S. Thangaraj
{"title":"Novel Detection of Accurate Spam Content using Logistic Regression Algorithm Compared with Gaussian Algorithm","authors":"K. V. Bhavitha, S. Thangaraj","doi":"10.1109/ICBATS54253.2022.9759003","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759003","url":null,"abstract":"Aim: To detect the spam content over the internet and social media using Logistic Regression algorithm over Gaussian algorithm. Methods and Materials: Detection of spam content messages are performed using Logistic Regression algorithm and Gaussian algorithm (sample size=20) Where values are taken randomly. G-power was maintained to be 80%. Results and Discussion: This article is an attempt to improve the accuracy of spam content detection using the Logistic Regression algorithm, a machine learning algorithm. The AI based Application avoids overfitting. The proposed model has improved accuracy of 95% with p value which is less than 0.03(p<0.05) in spam detection than Gaussian algorithm having accuracy of 93%. Conclusion: The outcomes of the proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed algorithm seems to have higher accuracy than the Gaussian algorithm.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124380538","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}
引用次数: 1
Credit Card Fraud Detection using AdaBoost Algorithm in Comparison with Various Machine Learning Algorithms to Measure Accuracy, Sensitivity, Specificity, Precision and F-score 使用AdaBoost算法进行信用卡欺诈检测，与各种机器学习算法进行比较，以测量准确性，灵敏度，特异性，精度和f分数
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759022
Bhargavi Gedela, P. Karthikeyan
{"title":"Credit Card Fraud Detection using AdaBoost Algorithm in Comparison with Various Machine Learning Algorithms to Measure Accuracy, Sensitivity, Specificity, Precision and F-score","authors":"Bhargavi Gedela, P. Karthikeyan","doi":"10.1109/ICBATS54253.2022.9759022","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759022","url":null,"abstract":"Credit card fraud detection is a critical problem for any credit card issuing banks. The AdaBoost classifier is used in this study to identify fraudulent transactions. By comparing the proposed algorithm with Naive Bayes, logistic regression, ANN and decision tree algorithms the efficiency of the algorithm is evaluated. A total of 2,84,807 transactions are divided into two subsets: a training dataset [n=2,27,845 (80%)] and a test dataset [n=56,962 (20%)] (0.8 g power). Out of 2,84,S07 transactions in the dataset, 492 transactions are fraud transactions. To detect the credit card frauds Adaboost algorithm is used and various machine learning algorithms are compared with it for performance evaluation. To determine the performance of algorithms, metrics such as accuracy, sensitivity, specificity, precision, and f-score are estimated. The detection accuracies of AdaBoost, Naive Bayes, logistic regression, ANN and decision tree algorithms are 99.43%, 90.93%, 95.35%, 94.81% and 94.81% respectively. The AdaBoost algorithm obtained an f-score of 99.48% with significance value p<0.05. From the qualitative analysis, it is observed that the proposed AdaBoost algorithm performed significantly better than the Naive Bayes, logistic regression, ANN and decision tree algorithms in detecting credit card frauds.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124072700","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}
引用次数: 4
Comparative Analysis to Improve the Image Accuracy In Face Recognition System Using Hybrid LDA Compared With PCA 混合LDA与PCA在人脸识别系统中提高图像精度的比较分析
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759074
V.R. Thushitha, M. Priya
{"title":"Comparative Analysis to Improve the Image Accuracy In Face Recognition System Using Hybrid LDA Compared With PCA","authors":"V.R. Thushitha, M. Priya","doi":"10.1109/ICBATS54253.2022.9759074","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759074","url":null,"abstract":"Aim-This study depicts the improvisation and comparison of accuracy of two different face recognition algorithms to improvise the novel face detection rate from the stored database under various lighting conditions. Materials and methods-From Kaggle, a total of 13000 face samples are being collected from LFW people face recognition dataset. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) algorithms are improvised and compared to recognize the faces and to increase the accuracy rate. Results-Based on the MATLAB simulation and verification, LDA shows the recognition rate of 85.2% and PCA shows 73%. From the statistical analysis, the significant accuracy ratio is <0.05. Conclusion-It is concluded that the LDA algorithm shows enhanced features in edge detection, image equalization and image normalization than the PCA algorithm in the face recognition system for the dataset considered.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126364733","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}
引用次数: 0
Rule Based Method to Detect Fire and Compare the Accuracy and Precision with Vibe Method 基于规则的火灾探测方法及其与感应法的准确度和精密度比较
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759061
D. Niharika, J. Mohana
{"title":"Rule Based Method to Detect Fire and Compare the Accuracy and Precision with Vibe Method","authors":"D. Niharika, J. Mohana","doi":"10.1109/ICBATS54253.2022.9759061","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759061","url":null,"abstract":"The purpose of this article is to compare a novel rule-based technique for detecting fire to the vibe method. This method is based on image processing and computational techniques that simplify the computational process. This procedure was carried out on a sample size of twenty. The same samples were used for both the control and experimental groups, with a G power of 80%. The proposed strategy resulted in increased detection accuracy. Accuracy and precision for the rule-based technique were determined to be 99.19 and 70.62, respectively. For conventional approaches, accuracy and precision values of 86.24 and 60.19 were obtained. Additionally, it indicates a significance of 0.000 for precision and 0.015 for accuracy, both of which are less than 0.05. It may be stated that when compared to the vibe approach, the rule-based method produces greater accuracy and precision. It is useful for lexical and data mining analysis.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133397801","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}
引用次数: 1
A Deep Learning Based Approach for Detection of Face Mask Wearing using YOLO V3-tiny Over CNN with Improved Accuracy 基于深度学习的YOLO V3-tiny Over CNN口罩佩戴检测方法
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9758925
N. A, K. Jaisharma
{"title":"A Deep Learning Based Approach for Detection of Face Mask Wearing using YOLO V3-tiny Over CNN with Improved Accuracy","authors":"N. A, K. Jaisharma","doi":"10.1109/ICBATS54253.2022.9758925","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9758925","url":null,"abstract":"Aim: The objective is to build an efficient face mask detector using YOLO V3-tiny. Materials and Methods: The algorithm used to detect face masks is novel YOLO V3-tiny in comparison with Convolutional Neural Network (CNN), the dataset used was (“Facemask Detection Dataset”) the sample size was 136. Results: Novel YOLO V3-tiny gets accuracy of 95% and for CNN it was 84%. On the basis of the network’s original two-scale prediction target, a scale is added to create a three-scale prediction, which can improve the accuracy of detecting small targets such as masks. The YOLO V3-tiny and CNN have a statistically significant independent sample t-test value (p0.001) with a 95 percent confidence level. Conclusion: face mask detection in YOLO V3-tiny has a significantly better accuracy than CNN.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127058617","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}
引用次数: 2
Evaluation of Smart City Healthcare Features (SCHF) through Machine Learning 通过机器学习评估智慧城市医疗保健特征(SCHF
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759060
Muhammad Waqas, T. Alyas, Muhammad Masood Ajmal, Faheem Khan, T. Whangbo, Nasir Mahmood
{"title":"Evaluation of Smart City Healthcare Features (SCHF) through Machine Learning","authors":"Muhammad Waqas, T. Alyas, Muhammad Masood Ajmal, Faheem Khan, T. Whangbo, Nasir Mahmood","doi":"10.1109/ICBATS54253.2022.9759060","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759060","url":null,"abstract":"Internet of Things (IoT) approaches are allowing new creativities all over the world in smart cities. There is not any specific tool or criteria for calculate of worth for enable smart city for Healthcare area. There are many key emphasis as facts are on dealing with issues faced by urban cummunities sustainable but my work is moving around only the healthcare sector to prediction of it implementation valid criteria. My work is move around the Evaluation of Smart City Healthcare Features (SCHF) that is a Machine Learning(ML) methodology is core concept to successful implementation of the IoT-based wireless devices networks for this tenacity since there is huge amount of dataset to be handled & implemented. All over this paper, I have been take 17 city of Pakistan for evaluate its healthcare features as results that how AI-based IoT and ML devices with applications are applied in the healthcare sector. This work will be a model study for empathetic the role of the IoT in Health sector in smart cities.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"505 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115550533","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}
引用次数: 0
Education, Employment and Women Empowerment in an Agrarian Economy: Acase Study Note: Sub-titles are not captured in Xplore and should not be used 农业经济中的教育、就业和妇女赋权:案例研究说明:在explore中没有捕获副标题，不应该使用
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759025
S. Mubeen, M. H. Shahid, Nizar Sahawneh, A. Al-kassem, Aiesha Ahmad, Iftikhar Naseer
{"title":"Education, Employment and Women Empowerment in an Agrarian Economy: Acase Study Note: Sub-titles are not captured in Xplore and should not be used","authors":"S. Mubeen, M. H. Shahid, Nizar Sahawneh, A. Al-kassem, Aiesha Ahmad, Iftikhar Naseer","doi":"10.1109/ICBATS54253.2022.9759025","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759025","url":null,"abstract":"The less-developed Countries (LDC’s) depend mainly upon the economy’s agriculture. It is a vital and dynamic element of employment, growth, and source of empowerment. Pakistan is not an exception in this case, and being an agrarian economy, it also depends on agriculture for its national income and employment generation. The findings reveal that the women who participate in the agricultural sector of rural Punjab and Sindh are more empowered than other Pakistan regions. The empirical analysis also supports that women in the agricultural sector are more independent in family planning. However, the property’s ownership negatively impacts empowerment.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129499966","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}
引用次数: 0
Machine Learning Approach for Cybersecurity Implementation 网络安全实现的机器学习方法
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759091
H. Idris
{"title":"Machine Learning Approach for Cybersecurity Implementation","authors":"H. Idris","doi":"10.1109/ICBATS54253.2022.9759091","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759091","url":null,"abstract":"Cybersecurity has become a necessary component among all aspects of modern life. The internet is becoming increasingly important in everyday life around the world. As people become more reliant on the internet, malicious attacks have become more likely. The increasing in cybersecurity is increasingly vulnerable to the ever-increasing risk of being attacked by many cyber dangers. This paper deals with the design and development of a malicious software removal application using deep learning (DL) approaches along with Artificial intelligence (AI) that will be able to identify and eliminate unwanted malicious software and understanding all of the recent advancements in detecting methods for potential cybersecurity threats.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129509589","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}
引用次数: 0
Machine Learning-Based Methods in Source Camera Identification: A Systematic Review 基于机器学习的源相机识别方法:系统综述
2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759089
O. Gouda, A. Bouridane, M. A. Talib, Q. Nasir
{"title":"Machine Learning-Based Methods in Source Camera Identification: A Systematic Review","authors":"O. Gouda, A. Bouridane, M. A. Talib, Q. Nasir","doi":"10.1109/ICBATS54253.2022.9759089","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759089","url":null,"abstract":"Source identification is one of the most critical problems in the field of multimedia forensics. In the last decade, researchers have been studying and improving in this field. Photo Response Non-Uniformity is one of the unique noise patterns that is being used to match a media to its originating device. Utilizing the noise patterns with machine learning algorithms has been the focus of research in recent years. Therefore, a systematic review is needed to present the latest contributions in this field. This systematic review focuses on the published work from 2015 to 2021 in source identification using noise patterns in machine learning-based systems. The results of the review indicate that a benchmark should be proposed and used to fairly compare past and future methods. Moreover, a minimum number of devices used for evaluating a model should be set by the research community in order to accurately evaluate the accuracy of the model in real-life situations.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129811728","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}
引用次数: 0
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