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

A big data study of language use and impact in radio broadcasting in China

Broadcasting more educating and language-reviving contents are ways radio stations can help revitalize the use of the English language in the Hunan province of China. The challenges faced in communicating in English in Chinese radio stations are majorly caused by the lack of language professionals and linguists in the broadcast stations. The absence of these professionals is a major constraint to the development of the community. The broadcast media can help manage multilingualism through the introduction of new words which would give little or no room for lexicon dearth but would expand the language lexicon. Using the English language during broadcast reduces language dearth, and helps reach a much larger audience, even those not in China. Programmes anchored in English in places where the language is barely spoken enhances the vocabulary, comprehension and language vitality of the listeners. This study examined the impact of the English language used in radio broadcasting using a descriptive Big Data survey research design. The study’s population comprises of the inhabitants of the Hunan province in China, from which a sample of 50 broadcast staff and 150 regular inhabitants was drawn using a stratified random sampling technique. The instrument of data collection was a structured questionnaire with closed questions and a self-structured interview. The sample employed frequency distribution tables, percentages, and charts in the presentation and analysis of data. The results revealed that majority of the respondents in Hunan listened to radio broadcast indicating that the use of English language can have massive impact on the people. The study also found that majority of the respondents use their indigenous languages in their day-to-day activities as well as their schools with English being used majorly only in schools with only English-speaking students. The study recommends, amongst others, that the Broadcasting Corporation of China (BCC) review their policy on the allocated time of broadcast in English languages, and that more English language experts and linguists should be incorporated into the broadcast system

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

Understanding magnetic enhancement in hydrocarbon reservoirs

A detailed magnetic study has been undertaken for over 115 samples of 12 wells in the Brazeau area of the Devonian Nisku formation, Alberta Canada. I report a range of magnetic results including room temperature hysteresis, first-order reverse curve (FORCs), low-temperature magnetometry, high-temperature magnetic susceptibility results, Scanning Electron Microscopy (SEM) results of samples from sour gas pools, sweet gas and oil pools, and dry well. The sour gas pools had the strongest magnetization, followed by the sweet oil and gas pools, while the dry well had the weakest magnetization from the room temperature results. There was observable strong magnetic enhancement at the hydrocarbon fluid contact for some wells particularly the sour gas wells, while some did not have anomalies exactly at the contacts but few meters away from the contact. These anomalies are suspected to be paleo-contacts. This observable magnetic enhancement was caused by inorganic precipitation of interacting single domain hematite (0.03-0.08 μm), multidomain magnetite with a vortex (3 μm above), and SP (˂0.03 μm) greigite and pyrrhotite nanoparticle magnetic minerals at the hydrocarbon contacts. Iron oxide and sulphide minerals were revealed to be present in all the wells both from low and high-temperature measurements and also with the FORC analysis showing high coercivity ~100-350mT. From the data, iron sulphide greigite and pyrrhotite appears to be in dominant proportion alongside other minerals. I had the strong presence of siderite, greigite, and pyrite in the sour gas pools, and monoclinic pyrrhotite and maghemite in the sweet oil and gas pool and background presence of detrital magnetite, hematite, and siderite in the dry well. Authigenic magnetite and hematite were equally present in all the hydrocarbon wells. However, iron sulphide greigite and pyrrhotite appears to be in dominant proportion alongside other mineralsOpen Acces

Twitter sentiment analysis and emotion detection using NLTK and TextBlob

On a worldwide level, every second around 6000 tweets are sent, which counts to around 200 billion tweets in a year. People share their ideas and views publicly on Twitter, thus it serves as a good platform for analyzing public trends and behaviour towards any person, product or news. Customers frequently utilize social media platforms to share their thoughts and experiences regarding goods and services. Businesses can find areas for improvement and better understand the attitudes of their customers towards their goods and services by using sentiment analysis. In order to perform sentiment analysis on twitter, text classification using Natural Language Processing(NLP) has been proved to be very helpful. Using NLP word tokenizer, we can divide the sentences into different sets of words, thereafter we remove the stop words. Manually tokenizing long tweets and categorizing them into separate groups is challenging. The primary goal of this model is to analyze the tweets related to a given keyword entered by the user, classify the tweets as positive, negative or neutral using (VADER sentiment analysis or alternately). TextBlob library, which will help consumers as well as manufacturers to understand people's overall opinion regarding the product. This study makes an attempt to suggest a text sentiment analysis on twitter data using the NLTK and TextBlob libraries