Credit Card Fraud Detection Using Machine Learning Techniques

Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were used to detect fraudulent transactions. However, because fraud patterns are always changing, it is becoming increasingly vital to investigate new frauds and develop the model based on the new patterns. The purpose of this research is to create a machine learning classifier that not only detects fraud but also detects legitimate transactions. As a result, the model should have excellent accuracy, precision, recall, and f1-score. As a result, we began with a large dataset in this study and used four machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, and Random Forest. The random forest classifier scored 99.96% overall accuracy with the best precision, recall, f1-score, and Matthews correlation coefficient in the experiments.

Water-pipe tobacco epidemic in tobacco naive communities: Experience from Ethiopia

Water-pipe tobacco (WPT) is a public health threat of the 21st century. As a fashion, it swiftly spreads to all corners of the world in no more than two decades. It is a new phenomenon for Ethiopiain general and for Amhara National Regional Statein particular. The major objective of this study was to gain in-depth baseline information about water-pipe tobacco smoking practice in Bahir Dar City, 2012. An explorative study was conducted at Bahir Dar City—capital of Amhara National Regional State. It is home for one of the largest universities in the country with more than 40,000 students. Of the total 50 WPT lounges, six were identified based on their customer variety, and loaded by the help of police officers who had ample experience in fighting the expansion of WPT smoking in the city. A total of 115 people participated in this study. In-depth interviews and focus group discussions (FGD) were conducted, and the tape recorded data were thematically analyzed. More than 80% of the study participants were below 40 Years, and more than 30% of the total study participants were students. The profession of the remaining participants ranges from daily laborers to university instructors. Females accounted for 37.39%. The following factors were found to be pertinent for rapid increment of water-pipe tobacco smoking: geographic and economic accessibility, peer pressure, deceiving characteristics of WPT-non-irritant and aromatic, lack of knowledge, and absence of effective policy. The most outstanding findings of this study were the following: almost all of the study participants were tobacco naive and really unaware of the contents WPT;but about 94% of them had been khat chewers before they started with WPT smoking. In this region, khat had until recently been used by the Muslims only. WPT smoking is an even more recent phenomenon. “Khat stimulates, and WPT calms down,” said study participants. That means by using the later as an antidote for the former, people start ruining their health with substances containing chemicals proven to be notorious to human health. This combination of deadly addictions seems to be peculiar to Ethiopia and appears to be a serious public health threat to tobacco naive communities in the region. Therefore, their rapid progression needs to be met with appropriate interventions urgently. It also warrants further investigations.

Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation

Cancer is a complex disease associated with multiple gene mutations and malignant phenotypes,and multi-target drugs provide a promising therapy idea for the treatment of cancer.Natural products with abundant chemical structure types and rich pharmacological characteristics could be ideal sources for screening multi-target antineoplastic drugs.In this paper,50 tumor-related targets were collected by searching the Therapeutic Target Database and Thomson Reuters Integrity database,and a multi-target anti-cancer prediction system based on mt-QSAR models was constructed by using naïve Bayesian and recursive partitioning algorithm for the first time.Through the multi-target anti-cancer prediction system,some dominant fragments that act on multiple tumor-related targets were analyzed,which could be helpful in designing multi-target anti-cancer drugs.Anti-cancer traditional Chinese medicine(TCM)and its natural products were collected to form a TCM formula-based natural products library,and the potential targets of the natural products in the library were predicted by multi-target anti-cancer prediction system.As a result,alkaloids,flavonoids and terpenoids were predicted to act on multiple tumor-related targets.The predicted targets of some representative compounds were verified according to literature review and most of the selected natural compounds were found to exert certain anti-cancer activity in vitro biological experiments.In conclusion,the multi-target anti-cancer prediction system is very effective and reliable,and it could be further used for elucidating the functional mechanism of anti-cancer TCM formula and screening for multi-target anti-cancer drugs.The anti-cancer natural compounds found in this paper will lay important information for further study.