Card Holders Research Articles

Assessing the diagnostic utility of the Cannabis Use Disorder Identification Test – Revised (CUDIT-R) among veterans with medical and non-medical cannabis use

BackgroundFew studies examine the utility of the Cannabis Use Disorder Identification Test – Revised (CUDIT-R) in relation to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, (DSM-5) criteria for cannabis use disorder (CUD). This study assesses the performance of the CUDIT-R among a sample of Veterans with and without medical cannabis use. MethodsWe approached and consented primary care patients presenting to one of three Department of Veterans Affairs (VA) Medical Centers. Veterans with at least monthly cannabis use and complete CUD data at baseline were included in this analysis (n=234). CUDIT-R scores were compared against Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (DSM-5) CUD as the standard to calculate measures of validity (sensitivity, specificity), identify optimal CUDIT-R cutoff values, and assess the diagnostic proficiency of the CUDIT-R using receiver operating characteristic (ROC) curves. We further stratified analyses by active medical cannabis card holder status and DSM-5 CUD severity (any, moderate, and severe). ResultsAmong the entire sample, 38.9% qualified for any DSM-5 CUD, with 10.7% and 3.0% meeting criteria for moderate and severe CUD, respectively. We identified optimal CUDIT-R scores at 10 for any DSM-5 CUD (sensitivity=0.58; specificity=0.80), at 12 for moderate CUD (sensitivity=0.72; specificity=0.82), and at 14 for severe CUD (sensitivity=0.71; specificity=0.87). ROC curves showed higher CUDIT-R validity among non-card holders compared with medical cannabis card holders. ConclusionThe present study identified optimal CUDIT-R cutoff scores for Veterans who use cannabis. Varying DSM-5 validity measures inform the need for population-specific CUDIT-R cutoff values.

Effect of Credit Card Holders’ Age on the Credit Limit

Credit Limit of the credit cards is one of the affecting factors influencing people’s credit scores, playing a great role in determining their ability of paying debts and could be involved in banks evaluation in mortgage. In order to help people better assess their credit scores quantitatively and be aware of the influencing factors that determine their specific credit limits, this paper analyzed 10127 bank customers’ data. Based on the estimation, the evaluation is presented according to the information given to obtain the formula used to evaluate people’s Credit Limit and its relationship with some influencing factors, e.g., age, gender, education and marital status, and education level. According to the analysis from STATA, it is shown that per unit age increase would increase the credit limit by 1.76 percentage. In addition, impacts of other variables (e.g., gender, education level, marital status, income level, card type, card utilization ratio) are also found. The model presented could be used to help evaluating a persons’ Credit Limit according to the data provided. Overall, these results shed light on guiding further exploration of credit limit designs.

Skin cancer-related conditions managed in general practice in Australia, 2000–2016: a nationally representative, cross-sectional survey

ObjectiveSkin cancer is Australia’s most common and costly cancer. We examined the frequency of Australian general practice consultations for skin cancer-related conditions, by patient and general practitioner (GP) characteristics and...

Credit Card Fraud Detection Using LSTM

As credit card becomes the most popular payment mode particularly in the online sector, the fraudu- lent activities using credit card payment technologies are rapidly increasing as a result. The purpose of this work is to develop a novel system for credit card fraud detection based on sequential modeling of data, using attention mechanism Long Short Term Mem- ory(LSTM) deep Recurrent Neural Networks(RNN) and Synthetic Minority Oversampling Technique Edited Nearest Neighbour(SMOTE-ENN). The pro- posed approach aims to capture the historic purchase behavior of credit card holders with the goal of im- proving fraud detection accuracy on new incoming transactions. Experiments show that our proposed model gives strong results and its accuracy is quite high. keywords: Credit Card, SMOTE-ENN, FraudDetection, Machine learning,LSTM.

Analysis of Implementation the National Health Insurance Program Related to Health Services Based on Resources Aspects in Konawe District

Introduction: The Konawe Regency Government has unilaterally terminated the cooperation program with the Social Security Administrator which has been covering health insurance for the poor in Konawe. The Konawe local government has stopped paying premiums to the Health Insurance Agency for the Konawe community with Family Card holders and the health insurance status of family card holders is unclear until now and Family Card participants are no longer able to use family cards to get health insurance either at the health center or other health facilities. Based on this, the researcher wants to analyze the implementation of the National Health Insurance Program related to health services based on the aspect of resources in Konawe Regency.
 Method: Types of research is qualitative research using a case study approach. In this study, the researcher acts as the main research instrument. Key informants and regular informants as supporting instruments totaling 12 people.
 Result: In the interview, it was stated that based on our workload analysis, it was sufficient, including the existing health workers, namely 2 (two) general practitioners, 1 (one) dentist, 40 (forty) nurses, 30 (thirty) midwives, 2 (two) analysts people and 15 (fifteen) public health workers.
 Conclusion: The availability of health workers in 5 (five) public health center is not sufficient. Of the five health centers that became the object of research, there is still one health center that does not yet have a general practitioner.

ARTIFICIAL BANKING PAYMENT SECURITY SECURE ATM CARD PAYMENT SYSTEM

-Although new methods are being used in the banking sector today, scams are happening on a large scale these days, the main one being OTP share. OTP is used when making money with ATM card. This OTP banking sector sends to the card holder's mobile number and payments are made using it. With all the options available, such scams require a different approach to the banking sector or an important method, namely, when the bank sends OTP when making payments, a system such as face detection or face identification should be created, in which the bank sends OTP. Face detection or face identification of the person paying the money through a system will be done by taking a photo of the face and putting it on the bank and the bill, so that the card holder will know who is paying the money.

Real ID campaign raises questions about privacy concerns

This study used an advertising campaign focused on the Real ID card to explore the relationship between privacy concerns and information sharing through adoption of the new form of identification. Online surveys were conducted to assess the impact of campaign ads promoting the Real ID, and to determine whether privacy concerns were a barrier to its adoption. At post-test, an ordinal regression analysis of data from registered voters in Montana (n = 302) showed a positive association between the time spent viewing the ads on social media and intent to obtain the Real ID, but no relationship between privacy concerns and Real ID intentions. To date, the states and territories have issued approximately 110 million Real ID-compliant driver’s licenses and identification cards, representing 40 percent of all driver’s licenses and identification card holders. Montana, one of the states originally most resistant to the 2005 security act, lags only slightly behind with 20 percent of drivers and ID card-holders having Real IDs. This research shows that privacy concerns about a new technology or identification system, such as the Real ID, can erode over time. Whether these changes are due to mounting privacy apathy triggered by the public’s perceived lack of control, or a willingness to sacrifice some privacy for national security in the face of rising terrorist threats needs to be researched further.

A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions

Fraudsters are now more active in their attacks on credit card transactions than ever before. With the advancement in data science and machine learning, various algorithms have been developed to determine whether a transaction is fraudulent. We study the performance of three different machine learning models: logistic regression, random forest, and decision trees to classify, predict, and detect fraudulent credit card transactions. We compare these models’ performance and show that random forest produces a maximum accuracy of 96% (with an area under the curve value of 98.9%) in predicting and detecting fraudulent credit card transactions. Thus, we recommend random forest as the most appropriate machine learning algorithm for predicting and detecting fraud in credit card transactions. Credit Card holders above 60 years were found to be mostly victims of these fraudulent transactions, with a greater proportion of fraudulent transactions occurring between the hours of 22:00GMT and 4:00GMT.

Macao’s Split Labour Market

Abstract Edna Bonacich’s split labour market states that socio-political factors generate differential outcomes for workers in a region, generally as the result of ethnic antagonism. This ethnic antagonism, Bonacich argues, does not require open violence or even verbal confrontation but can operate through exclusion movements and “caste” systems. In this paper, we use Bonacich’s framework to analyse the production of a split labour market in the Macao, Special Administrative Region of China. Macao depends on an abundant supply of low-skilled migrant workers to remunerate the workforce. While many migrant workers are foreigners, most are ‘internal’ migrants from Mainland China, meaning that a conventional explanation of ethnic differences is insufficient. Bonacich had observed that “exclusion attempts and caste-like arrangements are found among national groupings within a racial category” giving the example of ‘whites’ in the United States excluding other ‘whites’ from different parts of Europe. However, Macao as a part of China constitutes a unique example in that an exclusion attempt and caste-like arrangement is to be found within the same national grouping of the same racial category in the same country. As this research considers how ethnic and quasi-ethnic differences are produced and sustained in Macao through government policy; social attitudes and the social practices of workers and businesses, we find that permanent Macao id card holders, which gives out numerous benefits and rights, is as a form of exclusion movement. Moreover, local workers act as a labour aristocracy: they extract concessions from businesses and suppress migrant workers economically, politically and socially. Edna Bonacich’s split labour market helps explain how a labour aristocracy is maintained subtly at the interest of local workers through concessions from the businesses.

Public opinions regarding infertility treatment insurance coverage among marginalized patient populations.

To assess public support for insurance coverage of infertility treatment among marginalized patient groups. A cross-sectional, web survey-based study using a national sample of 1226 US adults. Participants responded to questions measuring their beliefs and attitudes towards support for infertility treatment insurance coverage among specific patient populations. We then evaluated the opinions of only the participants who supported infertility treatment insurance coverage for patients meeting the standard definition of infertility. Associations between demographic data of participants and support for infertility treatment insurance coverage among these marginalized groups were queried. Of the total responses, 61.9% of the respondents generally supported insurance coverage for infertility. Of the total responses, 54.5% did not support any insurance coverage for lesbian, gay, or transgender patients. Of those who generally supported the insurance coverage for infertility, 53.0% supported coverage for gay patients requiring infertility services, 54.6% supported coverage for lesbian patients, and 42.5% supported coverage for transgender patients. Of the total responses, 47.6% did not support insurance for green card holders, undocumented immigrants, or refugees. Of those who supported the insurance coverage for infertility in general, 63.6% supported insurance coverage for patients with green cards, 29.8% for refugees, and 20.7% for undocumented patients. For disability and genetic conditions, 39.5% did not support coverage for any groups. Of those who support the insurance coverage for infertility in general, there was most support for patients with physical disabilities (60.2%) followed by genetic disease (47.9%), then mental disabilities (31.4%). Even among those who support insurance coverage for infertility in general, approximately less than half of them supported these same treatments for marginalized groups, including the diverse sexuality and gender (DSG), immigrant, and disabled populations. Increased education and awareness of infertility is needed among the general population to garner acceptance of infertility as a disease and support insurance coverage of infertility treatment for all persons.

Web Based Ration Provisioning System in Public Distribution Shop

The Government gives different offices to the general population that are underneath the destitution line, yet such offices don't reach to needy individuals because of debasement present in the chain. Among every one of the offices given by Government Of India, The proportion conveyance is most well known in the public arena. For the Ration Material Distribution System (RMDS), the legislature has given diverse sorts of cards to clients as indicated by their destitution lines. In such frameworks clients can purchase proportion materials, for example, Sugar, Rice, Oil, Kerosene, and so on from the apportion shop according to the rate chosen by the administration of India at one time physically. Whenever dispensed apportion isn't bought by the card holders at that point there is a plausibility of abuse of Ration Material by the businessperson. The business person can move the apportion material illicitly in the market with surprising expense and acquire benefit which leads towards defilement.

Factors determining the Motives for Using a Credit Card: An Empirical Study

In today's financial transactions, credit cards are a necessary component. Due to their convenience and flexibility, credit cards have been increasingly popular in India in recent years. With the help of a credit card, people can acquire money from a bank to spend on purchases or bill payments. However, depending on a variety of conditions, people's reasons for using credit cards can differ greatly. The person's financial circumstances are one of the key elements that define their reasons for utilizing a credit card. Individuals who have a reliable source of income and a clean credit history can use credit cards to accrue rewards points, cashback, and other incentives. On the other side, those with inconsistent income may resort to using credit cards to pay for unplanned purchases or fill up financial gaps. Spending patterns are another aspect that affects a person's reasons for using a credit card. Others may use credit cards for regular expenses to accrue incentives or establish credit history, while some people use them to make significant purchases that they cannot afford to pay for in full upfront. For consumers and financial institutions to use credit cards responsibly, it is essential that they have a thorough understanding of the variables that affect why people use them. This study makes a comparison of the single and multiple card holders’ motives for using the credit card with 168 credit card holders (84 from each category).

Farmers’ Challenges in Adopting Soil Health Card Recommendations in Saharsa, Bihar

The study was aimed to identify the constraints associated with the adoption of soil health cards recommended by Mandan Bharti Agriculture College (MBAC), Agwanpur in Sattarkataiya block, Saharsa district, Bihar during 2017-2021. Two villages named Barahsher and Puroshottampur Purikh were selected based on the maximum card holders. 100 respondents from each village with Soil Health Card (SHC) were selected. A pretested and well-structured interview schedule was used to collect data, tabulate, and analyze the results. Major constraints faced by farmers were inadequate follow-up by extension agency (71%); soil samples not taken from each farmer’s field (59%), not getting SHC reports in time (57%), and not using of the recommended dose of fertilizers as per SHC recommendation (49%). While major suggestions that came out to tackle the mentioned constraints were that SHC should be issued prior to crop season (70%), Soil sampling laboratory should be established at Block level with highly qualified staff (61%), farmers should be trained to take soil samples (63%). and soil sampling should be based on hands on training of farmers (59%).

The gray clouds within: Depression and loneliness among the elderly population in urban Bengaluru – A cross-sectional study

ABSTRACT Background: Aging is a global phenomenon. Depression and loneliness among the elderly are significant mental health concerns, often overlooked and underestimated in society. Globally, depression amounts to a loss of 2% disability-adjusted life years. India is 2nd most populous in the world by elderly population, depression and loneliness are often overlooked due to various reasons. Therefore, this study aims to explore the proportion of depression and loneliness and to determine their association with loneliness and other sociodemographic factors. Methodology: A community-based cross-sectional study was conducted among 132 elders (age ≥60 years) in three wards of the urban area of Bengaluru. Geriatric Depression Scale 15 and Three-Item Loneliness Scale were used to assess depression and loneliness, respectively. The association between depression and loneliness was analyzed with other demographic variables using Chi-square and Bivariate logistic regression. Results: The magnitude of depression and loneliness among the elderly population was found to be 75.7% and 91%, respectively. Depression was significantly associated with elderly 60–69 years, females, with minimal education (illiterate), homemakers, number of family members >4, with no income, and dependent on others. Loneliness is significantly associated with the female gender, with illiterates, those who are not working and homemakers, below poverty line card holders, those with chronic diseases, and those dependent on others compared to their counterparts. There is a significant positive correlation between depression and loneliness scores among the elderly. Conclusion: The higher proportion of depression and loneliness among the elderly in our study emphasizes the necessary supportive measures. It needs to be addressed adequately through a coordinated primary care approach with community and social support.

Assessing the Coverage of the Pradhan Mantri Jan Arogya Yojana Scheme and Out-of-Pocket Expenditure in Mysore Urban Slum Dwellers

Introduction: In 2018, the Government of India launched the Pradhan Mantri Jan Arogya Yojana (PM-JAY), a part of the Ayushman Bharat scheme as a progressive step toward universal health coverage. The scheme was intended to provide health insurance for the socioeconomically deprived population. This study was undertaken to assess the coverage of the scheme and to assess the reduction in medical out-of-pocket expenditure (OOPE). Methods: This was a cross-sectional study conducted among the urban slum dwellers in Mysuru. A semi-structured questionnaire was used for the study. Oral consent was taken from the participants before administering the questionnaire. Data were collected by face-to-face interviews. Statistical tests such as percentages and mean were calculated using SPSS software (Version 26, Licensed by JSSAHER). Results: The study was conducted among 104 individuals, of which 73% were females. Only 38 (33%) of the participants had PM-JAY cards while 59 (56.73%) did not have any health insurance. Thirty percentage (78.95%) of the card holders had utilized the insurance in the past 1 year and incurred an OOPE of Rs. 25,873/-. For all the cardholders, this amount was spent from their savings. Conclusion: The coverage of the scheme and OOPE reduction toward health care is still lacking. Greater measures need to be taken by medical officers, medicosocial workers, and other field workers to identify eligible households and help them avail the benefits of this scheme.

Crisis Behaviour Strategy Recognition Using Transactional Data

For ages analytics were dealing with the data which represent the financial processes of large groups of agents and the whole population. The crisis behavioural strategies were in the area of interests of psychology and social science which first put forward the idea to distinguish some groups of the customers with certain types of consumption behaviour during the unstable periods. Nowadays technologies that help to handle big data allow us to come closer to every customer and explore their personal behaviour, so we possess a tool for detailed investigation of these matters. In our research we try to recognize some behavioural patterns in sequences of transactional data of bank card holders in the pandemic situation of 2020 and apply them to the forecast of unsteadiness of February-March 2022 caused by customers’ anxious expectations. It may be useful for more precise analysis of consumers’ activity, and moreover, if we could recognize some definite strategies for the periods of critical transitions, we could cardinally improve the forecasting quality for the time of instability.

LEGAL ASPECTS OF USE OF CREDIT CARD IN TRADE AT BANDA ACEH CITY

Credit cards are one of the new instruments in the trading world that have a monetary value, so they can be used as a tool for transactions. However, credit cards cannot be called securities because they cannot be transferred. As a means of payment, the development of its use is increasingly widespread so that this has legal consequences for the parties involved in the use of the credit card, considering that the legal regulations are still relatively weak. The research method used is qualitative research with a normative juridical approach with descriptive analysis research specifications. This research uses secondary data as the main research material, which is obtained through documents. From the data obtained will be analyzed qualitatively normative. The legal aspect of using credit cards in trade in Banda Aceh City is that every agreement that occurs is incidental (a certain time) agreement, which arises at the time of buying and selling transactions or services. The mechanism of each credit card issuing bank is always different in giving approval, but the terms and procedures are relatively the same. Legal protection for the parties in using credit cards occurs at the time of signing the credit card approval agreement between the issuer and the credit card user, because legally there is no law that explicitly regulates it. It is recommended that there be wider legal protection efforts from the government, not only consumers in the sense of credit card holders, but in certain cases the sellers of goods/services and card issuers are also parties that need to receive balanced legal protection, including the affirmation of the rights of consumers. each party to sue the other party.

Study on utilization of healthcare and social security schemes among street vendors in an urban area of Bengaluru

Background: Access to healthcare may vary across countries, groups, and individuals, largely influenced by social and economic conditions as well as the health policies in place. In the Indian scenario, most health-care expenses are paid out-of-pocket by patients and their families, rather than through insurance or social security schemes. Health-seeking behavior denotes visiting the health-care facility for need of care during diseases, injury and health related events which includes public hospitals, clinics or privately owned hospitals. There is a lack of inadequate information on health-care utilization and social security benefits availed by street vendors in India. Hence, the study was planned to look into issues of health-care utilization and related issues. Aims and Objectives: To assess the utilization of health care and social security schemes among the street vendors of urban area of Bengaluru and to find the determinants of health-care utilization among them. Materials and Methods: A community-based, cross-sectional, descriptive, and explorative study was undertaken for 2 months from May to June 2021 among 160 street vendors in urban area of Bengaluru. Data on socio-demographic factors, vending type and nature, social security benefits, health insurance, and health-care utilization were collected from each vendor. The prevalence of utilization of healthcare and social security schemes among street vendors were estimated. Results: Majority of the vendors (77% males and 82.4% females) visit health-care center only when the illness was severe and only 18.8% of the vendors seek healthcare for all types of illnesses. Over-the-counter medicines were preferred than visiting a health-care facilities by 71.3% of the street vendors and majority of them were males (76.3%). The part time vendors (38.5%) used more than one health facility, i.e., government, polyclinic, and medical college facilities, whereas (27.2%) of full time vendors were dependent on government health-care facilities. The fixed place vendors (23.8%) and mobile cart vendors (32.7%) used government health-care facilities. Majority of the fruit vendors used government facilities. There were 20% of the street vendors who were aware of only Indira Gandhi National Old Age Pension Scheme (IGNOAPS), 18.8% aware of only Janani Suraksha Yojana (JSY) Scheme, 28% aware of Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), 8% were aware of Pradhan Mantra Shram Yogi Maandhan Yojana (PM-SYM), and 9.4% aware of all the above schemes as applicable. Almost two third of the study population (69% of males and 61.8% of females) were not availing any of the social security schemes. 71.4% of males above 60 years of age were availing IGNOAPS, 31% of females in the reproductive age group availed JSY as applicable, 12% availed AB-PMJAY, 67.6% of the fixed place vendors, and 67.3% of the mobile cart vendors did not avail any social security scheme. None of the vendors had any type of medical health insurance and only 59.4% of the vendors were below poverty line card holders. Conclusion: Most of the vendors and their family members used government facilities (26.9%) and only 44.4% were availing the social security schemes. Timing of vending (part/full time), time of visit of different health-care facilities, and financial status were the important determinants of utilizing healthcare. Therefore, government run health-care centers should be made more accessible and available to the unorganized working class.

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.

A Study of Credit Card Holders’ Repayment Pattern with Reference to Chennai City

In today’s world, everyone would have limited access to credit. But this access is available to all through one of the most valuable financial instruments i.e., Credit Cards. Credit Card is a boon as well as a bane. It can make or destroy the person who is using it. The Important factor of a Credit Card is the repayment of dues on a stipulated period. The study focuses on various aspects of repayment - how much amount will they pay towards credit card dues, how will they arrange funds to repay the debt, etc. The period of study is 2 months with a sample size of 100 through a questionnaire. The study uses ANOVA and Chi-Square Tests to analyze the factors that influence repayment patterns. The study reveals that there is no significant relationship between spending money and repayment of dues on time.