Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve

Enhancing credit card fraud detection: an ensemble machine learning approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve

“The Way She Makes Me Feel”: Examining The Effects of Celebrity Instagram Images on Body Satisfaction and self-esteem in Young Nigerian Women

Using an experimental method, we examined the effects of local female celebrity Instagram images on women’s body satisfaction and self-esteem among groups of young Nigerian women. Result showed that women reported the highest self-esteem and body satisfaction when viewing thin-ideal images, but self-esteem and body satisfaction decreased after viewing plump ideal images, followed by neutral images. Additionally, while state appearance comparison was found as a partial mediator of the effects of image type on body satisfaction and self-esteem, extent of celebrity worship fully mediated these effects. Nonetheless, the intensity of Instagram and other SNSs use did not mediate the effects observed in the study. These findings highlight the significance of how celebrity images and diverse body ideals to the ‘thin’ one can have negative effects in a non-Western sample. Study results and future research directions are discussed within the context of social comparison and agenda setting theories

Computed Tomography Scanner Distribution and Downtimes in Southeast Nigeria

Background: It is clearly known and documented that the first computed tomography (CT) scanner was installed in 1987 at the University College Hospital (UCH) Ibadan, Southwest-Nigeria. Ironically, it is neither clearly documented how many more scanners have been installed after then, nor about their functionality. Objective: To establish the actual number and functionality of CT scanners in the Southeast geopolitical zone of Nigeria.Methodology: The survey was undertaken from March – June, 2016. Radiographers across different tertiary hospitals in southeast (SE) Nigeria, were requested through phone calls to ascertain the number of CT scanners in their respective states of residence. Their feedback was crosschecked using entirely different Radiographers. Internet searches were conducted to authenticate some information obtained. For Anambra State where all but one of the authors worked or schooled, physical visits were made to all centres. Data elicited covered scanner specifications, installation details, ownership, and functionality, amongst others. These were recorded in a pro forma sheet and later collated and presented in tables. Result: A total of 23 CT centres with 28 CT scanners were confirmed. These were distributed across the zone as follows: Anambra; 10 (35.8 %), Imo; 6 (21.4 %), Enugu; 6 (21.4 %), Abia; 4 (14.3 %) and Ebonyi; 2 (7.1 %). Private ownership accounted for 19 (68.0 %) of the scanners while the remaining 9 (32.0 %) were distributed between the Federal Government (n = 5; 18.0 %), public-private partnership (n = 2; 7.0 %), and state governments (n = 2; 7.0 %), respectively. Appropriate personnel were engaged in the facilities. Majority of the scanners were installed in the current decade (2006 – 2016). At least 12 (43.0 %) of the scanners experienced downtime within the period of the survey with 7 scanners having downtime ≥ 1 year. Conclusion: There are 23 radiodiagnostic facilities with 28 CT scanners in the Southeast zone of Nigeria. Five facilities each own two scanners. There appears to be a good distribution of CT scanners with appropriate personnel. A high downtime rate was observed, suggesting the need for the employment of centre-based CT engineers, to ensure that CT patients have as prompt an access as can be achieved

“The Way She Makes Me Feel”: Examining The Effects of Celebrity Instagram Images on Body Satisfaction and self-esteem in Young Nigerian Women

Using an experimental method, we examined the effects of local female celebrity Instagram images on women’s body satisfaction and self-esteem among groups of young Nigerian women. Result showed that women reported the highest self-esteem and body satisfaction when viewing thin-ideal images, but self-esteem and body satisfaction decreased after viewing plump ideal images, followed by neutral images. Additionally, while state appearance comparison was found as a partial mediator of the effects of image type on body satisfaction and self-esteem, extent of celebrity worship fully mediated these effects. Nonetheless, the intensity of Instagram and other SNSs use did not mediate the effects observed in the study. These findings highlight the significance of how celebrity images and diverse body ideals to the ‘thin’ one can have negative effects in a non-Western sample. Study results and future research directions are discussed within the context of social comparison and agenda setting theories

Predictors and consequences of early sexual debut among students in tertiary institutions in Lagos metropolis, Nigeria

This study investigated the factors associated with early sexual debut, consensual sexual debut and multiple sexual partners in tertiary institutions in Lagos Metropolis, Nigeria. The study adopted a cross-sectional survey design with a proportional sampling method. Structured questionnaire was used to elicit information from respondents. Four hundred and thirty-three questionnaires were deemed eligible for data analysis. Chi-square, t-test and binary logistic regression were utilised to analyse the data. It was found that respondents who attended private secondary schools were more likely to have early sexual debut (X2= 3.076; p<0.05). There was no significant difference in the age at sexual debut for respondents from nuclear and extended families (M.D = -0.377). Females were less likely to experience consensual sexual debut than their male counterparts (OR=0.469; p<0.01). Also, early sexual debut influenced exposure to multiple sexual partners- those who delayed sex till age 22 were the least likely to be exposed (OR= 0.056; p<0.001). Adequate sex education of young people-beginning at early years- before their sexual debut is important for improved sexual health. Keywords: Sexual debut, multiple sexual partners, consensual sex, undergraduates; family type; Nigeria   Cette étude a examiné les facteurs associés aux débuts sexuels précoces, aux débuts sexuels consensuels et aux partenaires sexuels multiples dans des établissements tertiaires de Lagos Metropolis, au Nigéria. L'étude a adopté un plan d'enquête transversal avec une méthode d'échantillonnage proportionnel. Un questionnaire structuré a été utilisé pour obtenir des informations auprès des répondants. Quatre cent trente-trois questionnaires ont été jugés éligibles pour l'analyse des données. Le chi carré, le test t et la régression logistique binaire ont été utilisés pour analyser les données. Il a été constaté que les répondants qui fréquentaient des écoles secondaires privées étaient plus susceptibles d'avoir des débuts sexuels précoces (X2 = 3,076; p <0,05). Il n'y avait pas de différence significative d'âge au début des rapports sexuels pour les répondants issus de familles nucléaires et élargies (M.D = -0,377). Les femmes étaient moins susceptibles d'avoir des débuts sexuels consensuels que leurs homologues masculins (OR = 0,469; p <0,01). En outre, les débuts sexuels précoces ont influencé l'exposition à plusieurs partenaires sexuels - ceux qui ont retardé les rapports sexuels jusqu'à l'âge de 22 ans étaient les moins susceptibles d'être exposés (OR = 0,056; p <0,001). Une éducation sexuelle adéquate des jeunes - dès les premières années - avant leurs débuts sexuels est importante pour une meilleure santé sexuelle. Mots-clés: Débuts sexuels, partenaires sexuels multiples, rapports sexuels consensuels, étudiants de premier cycle; type de famille; Nigeri

Survey of Hypertension Diabetes and Obesity in Three Nigerian Urban Slums

Background: Non-communicable diseases (NCDs) exist in slums as the inhabitants adopt an urbanized lifestyle which places them at a higher risk for. Lack of knowledge about the morbidity, complications and the method of control contributes to a large percentage of undetected and untreated cases.Methods: This cross-sectional survey polled 2,434 respondents from Ijora Oloye, Ajegunle and Makoko, three urban slums in Lagos metropolis, southwestern Nigeria between June 2010 and October 2012. We investigated the prevalence of hypertension, diabetes and obesity. Respondents signed consent forms and their health conditions were documented based on self-reported history of diabetes, hypertension and family history using a semi-structured questionnaire. Diagnostic tests; weight and height for body mass index, blood glucose, and blood pressure were performed.Results: More than one quarter of the participants were suffering from hypertension and only half of this were diagnosed earlier, while a further few were already on treatment. Therefore on screening, it had been possible to diagnose over three hundred more respondents, who were not previously aware of their health status. The respondents’ BMI showed that more than half of them were either overweight or obese and are at risk for diabetes, while 3.3% were confirmed as being diabetic, with their sugar levels greater than the normal range.Conclusion: This study therefore revealed the near absence of screening programs for chronic diseases such as hypertension, diabetes and obesity in these urban slums. This was further confirmed by the detection of new and undiagnosed cases of hypertension in about one quarter of the respondents