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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="government credit card"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4453</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: government credit card</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4453</span> Government Credit Card in State Financial Management: Public Sector Innovation in Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paramita%20Nur%20Kurniati">Paramita Nur Kurniati</a>, <a href="https://publications.waset.org/abstracts/search?q=Stanislaus%20Riyanta"> Stanislaus Riyanta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the midst of the heightened usage of electronic money (e-money), Indonesian government expenditure is yet governed through cash-basis transactions. This conventional system brings about a number of potential risks and obstacles to operational conduct, including state financial liquidity issue. Consequently, Ministry of Finance is currently establishing the cashless payment methods for State Budget (APBN). Included in those advance methods is credit card facility as a government expenditure payment scheme. This policy is one of the innovations within the public sector learned from other countries’ best practices. Moreover, this particular method is already prominent within the private-sector realm. Qualitative descriptive analysis technique is implemented to evaluate the contemporary innovation of using government credit card in the path towards cashless society. This approach is expected to generate several benefits for the government, particularly in minimizing corruption within the state financial management. Effective coordination among policy makers and policy implementers is essential for the success of this policy’s exercise, without neglecting prudence and public transparency aspects. Government credit card usage shall be the potent resolution for enhancing the government’s overall public service performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cashless%20basis" title="cashless basis">cashless basis</a>, <a href="https://publications.waset.org/abstracts/search?q=cashless%20society" title=" cashless society"> cashless society</a>, <a href="https://publications.waset.org/abstracts/search?q=government%20credit%20card" title=" government credit card"> government credit card</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20sector%20innovation" title=" public sector innovation"> public sector innovation</a> </p> <a href="https://publications.waset.org/abstracts/104227/government-credit-card-in-state-financial-management-public-sector-innovation-in-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104227.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4452</span> Advanced Machine Learning Algorithm for Credit Card Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manpreet%20Kaur">Manpreet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20fraud%20detection" title="automated fraud detection">automated fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=isolation%20forest%20method" title=" isolation forest method"> isolation forest method</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20outlier%20factor" title=" local outlier factor"> local outlier factor</a>, <a href="https://publications.waset.org/abstracts/search?q=ML%20algorithm" title=" ML algorithm"> ML algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20card" title=" credit card"> credit card</a> </p> <a href="https://publications.waset.org/abstracts/167417/advanced-machine-learning-algorithm-for-credit-card-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167417.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">113</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4451</span> Efficient Credit Card Fraud Detection Based on Multiple ML Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neha%20Ahirwar">Neha Ahirwar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=efficient%20credit%20card%20fraud%20detection" title="efficient credit card fraud detection">efficient credit card fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=XGBoost" title=" XGBoost"> XGBoost</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/179778/efficient-credit-card-fraud-detection-based-on-multiple-ml-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179778.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">67</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4450</span> A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niloofar%20Yousefi">Niloofar Yousefi</a>, <a href="https://publications.waset.org/abstracts/search?q=Marie%20Alaghband"> Marie Alaghband</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivan%20Garibay"> Ivan Garibay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increase of credit card usage, the volume of credit card misuse also has significantly increased, which may cause appreciable financial losses for both credit card holders and financial organizations issuing credit cards. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part, we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Credit%20Card%20Fraud%20Detection" title="Credit Card Fraud Detection">Credit Card Fraud Detection</a>, <a href="https://publications.waset.org/abstracts/search?q=User%20Authentication" title=" User Authentication"> User Authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=Behavioral%20Biometrics" title=" Behavioral Biometrics"> Behavioral Biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=Machine%20Learning" title=" Machine Learning"> Machine Learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Literature%20Survey" title=" Literature Survey"> Literature Survey</a> </p> <a href="https://publications.waset.org/abstracts/135569/a-comprehensive-survey-on-machine-learning-techniques-and-user-authentication-approaches-for-credit-card-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135569.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4449</span> Fraud Detection in Credit Cards with Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anjali%20Chouksey">Anjali Chouksey</a>, <a href="https://publications.waset.org/abstracts/search?q=Riya%20Nimje"> Riya Nimje</a>, <a href="https://publications.waset.org/abstracts/search?q=Jahanvi%20Saraf"> Jahanvi Saraf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fraud%20detection" title=" fraud detection"> fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=k%20nearest%20neighbour" title=" k nearest neighbour"> k nearest neighbour</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=XGBOOST" title=" XGBOOST"> XGBOOST</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/136504/fraud-detection-in-credit-cards-with-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4448</span> The Determinants of Customer’s Purchase Intention of Islamic Credit Card: Evidence from Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nasir%20Mehmood">Nasir Mehmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Yar%20Khan"> Muhammad Yar Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Anam%20Javeed"> Anam Javeed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to scrutinize the dynamics which tend to impact customer’s purchasing intention of Islamic credit card and nexus of product’s knowledge and religiosity with the attitude of potential Islamic credit card’s customer. The theory of reasoned action strengthened the idea that intentions due to its proven predictive power are most likely to instigate intended consumer behavior. Particularly, the study examines the relationships of perceived financial cost (PFC), subjective norms (SN), and attitude (ATT) with the intention to purchase Islamic credit cards. Using a convenience sampling approach, data have been collected from 450 customers of banks located in Rawalpindi and Islamabad. A five-point Likert scale self-administered questionnaire was used to collect the data. The data were analyzed using the Statistical Package of Social Sciences (SPSS) through the procedures of principal component and multiple regression analysis. The results suggested that customer’s religiosity and product knowledge are strong indicators of attitude towards buying Islamic credit cards. Likewise, subjective norms, attitude, and perceived financial cost have a significant positive impact on customers’ purchase intent of Islamic bank’s credit cards. This study models a useful path for future researchers to further investigate the underlined phenomenon along with a variety of psychodynamic factors which are still in its infancy, at least in the Pakistani banking sector. The study also provides an insight to the practitioners and Islamic bank managers for directing their efforts toward educating customers regarding the use of Islamic credit cards and other financial products. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attitude" title="attitude">attitude</a>, <a href="https://publications.waset.org/abstracts/search?q=Islamic%20credit%20card" title=" Islamic credit card"> Islamic credit card</a>, <a href="https://publications.waset.org/abstracts/search?q=religiosity" title=" religiosity"> religiosity</a>, <a href="https://publications.waset.org/abstracts/search?q=subjective%20norms" title=" subjective norms"> subjective norms</a> </p> <a href="https://publications.waset.org/abstracts/118122/the-determinants-of-customers-purchase-intention-of-islamic-credit-card-evidence-from-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118122.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4447</span> Modulation of the Europay, MasterCard, and VisaCard Authentications by Using Avispa Tool</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ossama%20Al-Maliki">Ossama Al-Maliki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Europay, MasterCard, and Visa (EMV) is the transaction protocol for most of the world and especially in Europe and the UK. EMV protocol consists of three main stages which are: card authentication, cardholder verification methods, and transaction authorization. This paper details in full the EMV card authentications. We have used AVISPA and SPAN tools to do our modulization for the EMV card authentications. The code for each type of the card authentication was written by using CAS+ language. The results showed that our modulations were successfully addressed all the steps of the EMV card authentications and the entire process of the EMV card authentication are secured. Also, our modulations were successfully addressed all the main goals behind the EMV card authentications according to the EMV specifications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EMV" title="EMV">EMV</a>, <a href="https://publications.waset.org/abstracts/search?q=card%20authentication" title=" card authentication"> card authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=contactless%20card" title=" contactless card"> contactless card</a>, <a href="https://publications.waset.org/abstracts/search?q=SDA" title=" SDA"> SDA</a>, <a href="https://publications.waset.org/abstracts/search?q=DDA" title=" DDA"> DDA</a>, <a href="https://publications.waset.org/abstracts/search?q=CDA%20AVISPA" title=" CDA AVISPA"> CDA AVISPA</a> </p> <a href="https://publications.waset.org/abstracts/89595/modulation-of-the-europay-mastercard-and-visacard-authentications-by-using-avispa-tool" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89595.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">178</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4446</span> Self-Organizing Maps for Credit Card Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=ChunYi%20Peng">ChunYi Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Hsuan%20CHeng"> Wei Hsuan CHeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyh%20Kuang%20Ueng"> Shyh Kuang Ueng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map%20technology" title="self-organizing map technology">self-organizing map technology</a>, <a href="https://publications.waset.org/abstracts/search?q=fraud%20detection" title=" fraud detection"> fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20visualization" title=" information visualization"> information visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20intelligent%20system%20technologies" title=" composite intelligent system technologies"> composite intelligent system technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20technologies" title=" decision support technologies"> decision support technologies</a> </p> <a href="https://publications.waset.org/abstracts/183639/self-organizing-maps-for-credit-card-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183639.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">57</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4445</span> Self-Organizing Maps for Credit Card Fraud Detection and Visualization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Chun-Yi">Peng Chun-Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen%20Wei-Hsuan"> Chen Wei-Hsuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ueng%20Shyh-Kuang"> Ueng Shyh-Kuang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map%20technology" title="self-organizing map technology">self-organizing map technology</a>, <a href="https://publications.waset.org/abstracts/search?q=fraud%20detection" title=" fraud detection"> fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20visualization" title=" information visualization"> information visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20intelligent%20system%20technologies" title=" composite intelligent system technologies"> composite intelligent system technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20technologies" title=" decision support technologies"> decision support technologies</a> </p> <a href="https://publications.waset.org/abstracts/183172/self-organizing-maps-for-credit-card-fraud-detection-and-visualization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183172.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">59</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4444</span> Socio-Economic Effects of Micro-Credit on Small-Scale Poultry Farmers’ Livelihood in Ado Odo-Ota Local Government Area of Ogun State, Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20O.%20Fakoya">E. O. Fakoya</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20G.%20Abiona"> B. G. Abiona</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20O.%20Oyediran"> W. O. Oyediran</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20M.%20Omoare"> A. M. Omoare</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examined the socio-economic effects of micro-credit on small scale poultry farmers’ livelihood in Ado Odo-Ota Local Government area of Ogun State. Purposive sampling method was used to select eighty (80) small scale poultry farmers that benefited in micro credit. Interview guide was used to obtain information on the respondents’ socio-economic characteristic, sources of micro-credit and the effects of micro-credit on their livelihood. The results revealed that most of the respondents (77.50 %) were males while half (40.00%) of the respondents were between the ages of 31-40 years. A high proportion (72.50%) of the respondents had formal education. The major sources of micro credit to small scale poultry farmers were cooperative society (47.50%) and personal savings (20.00%). The findings also revealed that micro-credit had positive effect on the assets and livelihoods of small scale poultry farmers’ livelihood. Results of t-test analysis showed a significant difference between the effects before and after micro-credit on small-scale poultry farmers’ Livelihood at p < 0.05. The study recommends that formal lending institution should be given necessary support by government to enable poultry farmers have access to credit facilities in the study area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=micro-credit" title="micro-credit">micro-credit</a>, <a href="https://publications.waset.org/abstracts/search?q=effects" title=" effects"> effects</a>, <a href="https://publications.waset.org/abstracts/search?q=livelihood" title=" livelihood"> livelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=poultry%20farmers" title=" poultry farmers"> poultry farmers</a>, <a href="https://publications.waset.org/abstracts/search?q=socio-economic" title=" socio-economic"> socio-economic</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20scale" title=" small scale"> small scale</a> </p> <a href="https://publications.waset.org/abstracts/8447/socio-economic-effects-of-micro-credit-on-small-scale-poultry-farmers-livelihood-in-ado-odo-ota-local-government-area-of-ogun-state-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8447.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">442</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4443</span> The Underground Ecosystem of Credit Card Frauds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abhinav%20Singh">Abhinav Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Point Of Sale (POS) malwares have been stealing the limelight this year. They have been the elemental factor in some of the biggest breaches uncovered in past couple of years. Some of them include • Target: A Retail Giant reported close to 40 million credit card data being stolen • Home Depot : A home product Retailer reported breach of close to 50 million credit records • Kmart: A US retailer recently announced breach of 800 thousand credit card details. Alone in 2014, there have been reports of over 15 major breaches of payment systems around the globe. Memory scrapping malwares infecting the point of sale devices have been the lethal weapon used in these attacks. These malwares are capable of reading the payment information from the payment device memory before they are being encrypted. Later on these malwares send the stolen details to its parent server. These malwares are capable of recording all the critical payment information like the card number, security number, owner etc. All these information are delivered in raw format. This Talk will cover the aspects of what happens after these details have been sent to the malware authors. The entire ecosystem of credit card frauds can be broadly classified into these three steps: • Purchase of raw details and dumps • Converting them to plastic cash/cards • Shop! Shop! Shop! The focus of this talk will be on the above mentioned points and how they form an organized network of cyber-crime. The first step involves buying and selling of the stolen details. The key point to emphasize are : • How is this raw information been sold in the underground market • The buyer and seller anatomy • Building your shopping cart and preferences • The importance of reputation and vouches • Customer support and replace/refunds These are some of the key points that will be discussed. But the story doesn’t end here. As of now the buyer only has the raw card information. How will this raw information be converted to plastic cash? Now comes in picture the second part of this underground economy where-in these raw details are converted into actual cards. There are well organized services running underground that can help you in converting these details into plastic cards. We will discuss about this technique in detail. At last, the final step involves shopping with the stolen cards. The cards generated with the stolen details can be easily used to swipe-and-pay for purchased goods at different retail shops. Usually these purchases are of expensive items that have good resale value. Apart from using the cards at stores, there are underground services that lets you deliver online orders to their dummy addresses. Once the package is received it will be delivered to the original buyer. These services charge based on the value of item that is being delivered. The overall underground ecosystem of credit card fraud works in a bulletproof way and it involves people working in close groups and making heavy profits. This is a brief summary of what I plan to present at the talk. I have done an extensive research and have collected good deal of material to present as samples. Some of them include: • List of underground forums • Credit card dumps • IRC chats among these groups • Personal chat with big card sellers • Inside view of these forum owners. The talk will be concluded by throwing light on how these breaches are being tracked during investigation. How are credit card breaches tracked down and what steps can financial institutions can build an incidence response over it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=POS%20mawalre" title="POS mawalre">POS mawalre</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20card%20frauds" title=" credit card frauds"> credit card frauds</a>, <a href="https://publications.waset.org/abstracts/search?q=enterprise%20security" title=" enterprise security"> enterprise security</a>, <a href="https://publications.waset.org/abstracts/search?q=underground%20ecosystem" title=" underground ecosystem"> underground ecosystem</a> </p> <a href="https://publications.waset.org/abstracts/19566/the-underground-ecosystem-of-credit-card-frauds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19566.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">439</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4442</span> A Product-Specific/Unobservable Approach to Segmentation for a Value Expressive Credit Card Service</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manfred%20F.%20Maute">Manfred F. Maute</a>, <a href="https://publications.waset.org/abstracts/search?q=Olga%20Naumenko"> Olga Naumenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymond%20T.%20Kong"> Raymond T. Kong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Using data from a nationally representative financial panel of Canadian households, this study develops a psychographic segmentation of the customers of a value-expressive credit card service and tests for effects on relational response differences. The variety of segments elicited by agglomerative and k means clustering and the familiar profiles of individual clusters suggest that the face validity of the psychographic segmentation was quite high. Segmentation had a significant effect on customer satisfaction and relationship depth. However, when socio-demographic characteristics like household size and income were accounted for in the psychographic segmentation, the effect on relational response differences was magnified threefold. Implications for the segmentation of financial services markets are considered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title="customer satisfaction">customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20services" title=" financial services"> financial services</a>, <a href="https://publications.waset.org/abstracts/search?q=psychographics" title=" psychographics"> psychographics</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20differences" title=" response differences"> response differences</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/39282/a-product-specificunobservable-approach-to-segmentation-for-a-value-expressive-credit-card-service" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39282.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4441</span> Use of Multistage Transition Regression Models for Credit Card Income Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Denys%20Osipenko">Denys Osipenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Crook"> Jonathan Crook</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multinomial%20regression" title="multinomial regression">multinomial regression</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20logistic%20regression" title=" conditional logistic regression"> conditional logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20account%20state" title=" credit account state"> credit account state</a>, <a href="https://publications.waset.org/abstracts/search?q=transition%20probability" title=" transition probability"> transition probability</a> </p> <a href="https://publications.waset.org/abstracts/19488/use-of-multistage-transition-regression-models-for-credit-card-income-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19488.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4440</span> An Attentional Bi-Stream Sequence Learner (AttBiSeL) for Credit Card Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Shahab%20Shahabi">Amir Shahab Shahabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Hasirian"> Mohsen Hasirian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modern societies, marked by expansive Internet connectivity and the rise of e-commerce, are now integrated with digital platforms at an unprecedented level. The efficiency, speed, and accessibility of e-commerce have garnered a substantial consumer base. Against this backdrop, electronic banking has undergone rapid proliferation within the realm of online activities. However, this growth has inadvertently given rise to an environment conducive to illicit activities, notably electronic payment fraud, posing a formidable challenge to the domain of electronic banking. A pivotal role in upholding the integrity of electronic commerce and business transactions is played by electronic fraud detection, particularly in the context of credit cards which underscores the imperative of comprehensive research in this field. To this end, our study introduces an Attentional Bi-Stream Sequence Learner (AttBiSeL) framework that leverages attention mechanisms and recurrent networks. By incorporating bidirectional recurrent layers, specifically bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, the proposed model adeptly extracts past and future transaction sequences while accounting for the temporal flow of information in both directions. Moreover, the integration of an attention mechanism accentuates specific transactions to varying degrees, as manifested in the output of the recurrent networks. The effectiveness of the proposed approach in automatic credit card fraud classification is evaluated on the European Cardholders' Fraud Dataset. Empirical results validate that the hybrid architectural paradigm presented in this study yields enhanced accuracy compared to previous studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credit%20card%20fraud" title="credit card fraud">credit card fraud</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=attention%20mechanism" title=" attention mechanism"> attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20networks" title=" recurrent neural networks"> recurrent neural networks</a> </p> <a href="https://publications.waset.org/abstracts/194143/an-attentional-bi-stream-sequence-learner-attbisel-for-credit-card-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194143.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">14</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4439</span> Analysis of Access to Credit among Rural Farmers in Giwa Local Government Area of Kaduna State, Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ibrahim">S. Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Bashir%20Umar"> Bashir Umar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Agricultural credit is very important for sustainable agricultural development to be achieved in any country of the world. Rural credit has proven to be a powerful instrument against poverty reduction and development in rural area. Agricultural credit enhances productivity and promotes standard of living by breaking vicious cycle of poverty of small scale farmers. This study examined access to credit among rural farmers in Giwa local government area of Kaduna state. Two stages sampling procedure was employed to select forty-two (42) respondents for the study. Primary data were collected using structured questionnaire with the help of well-trained enumerators. Data were analyzed using simple descriptive statistics. The results revealed that farmers were predominantly male (57.1%) and most (54.7%), were married with one level of education or another (66.5.%). Majority of the households’ head were between the ages of 31 to 50. majority of the farmers (68.2%) had more than 2ha of farmlands with at least 5 years of farming experience and an annual farm income of N 61,000 to 100,000 (61.9%). The Various sources of credit by the farmers in the study area were commercial banks (38.1%), Co-operative banks (47.6%), Development banks (14.2%) (formal) and Relatives (26.1%), Personal Savings (Adashi scheme) (52.3%), Moneylenders (21.4%) (informal). As regard to the amount of credit obtained by the farmers 38.1% received N 50,000-100,000, 50 % obtained N 100,001-500,000 while 11.9% obtained N 500,001-1,000,000. High interest Inadequate collateral, Complicated Procedures, lack of guarantor were the major constrains encountered by the farmers in accessing loans. The study therefore recommends that Rural farmers should be encouraged to form credit and thrift cooperative societies from which they can access much cheaper credits, Moreover, to ensure that any credit obtained may be manageable for the farmers, financial institutions should provide loans with low interest rates and government and non-governmental organizations should simplify procedures associated with accessing loans. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis" title="analysis">analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=access" title=" access"> access</a>, <a href="https://publications.waset.org/abstracts/search?q=credit" title=" credit"> credit</a>, <a href="https://publications.waset.org/abstracts/search?q=farmers" title=" farmers"> farmers</a> </p> <a href="https://publications.waset.org/abstracts/179771/analysis-of-access-to-credit-among-rural-farmers-in-giwa-local-government-area-of-kaduna-state-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179771.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">62</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4438</span> Credit Card Fraud Detection with Ensemble Model: A Meta-Heuristic Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gong%20Zhilin">Gong Zhilin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jing%20Yang"> Jing Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Yin"> Jian Yin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credit%20card" title="credit card">credit card</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=fraud%20detection" title=" fraud detection"> fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=money%20transactions" title=" money transactions"> money transactions</a> </p> <a href="https://publications.waset.org/abstracts/147387/credit-card-fraud-detection-with-ensemble-model-a-meta-heuristic-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147387.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4437</span> E-Hailing Taxi Industry Management Mode Innovation Based on the Credit Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuan-lin%20Liu">Yuan-lin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ye%20Li"> Ye Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Tian%20Xia"> Tian Xia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are some shortcomings in Chinese existing taxi management modes. This paper suggests to establish the third-party comprehensive information management platform and put forward an evaluation model based on credit. Four indicators are used to evaluate the drivers’ credit, they are passengers’ evaluation score, driving behavior evaluation, drivers’ average bad record number, and personal credit score. A weighted clustering method is used to achieve credit level evaluation for taxi drivers. The management of taxi industry is based on the credit level, while the grade of the drivers is accorded to their credit rating. Credit rating determines the cost, income levels, the market access, useful period of license and the level of wage and bonus, as well as violation fine. These methods can make the credit evaluation effective. In conclusion, more credit data will help to set up a more accurate and detailed classification standard library. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credit" title="credit">credit</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20internet" title=" mobile internet"> mobile internet</a>, <a href="https://publications.waset.org/abstracts/search?q=e-hailing%20taxi" title=" e-hailing taxi"> e-hailing taxi</a>, <a href="https://publications.waset.org/abstracts/search?q=management%20mode" title=" management mode"> management mode</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20cluster" title=" weighted cluster"> weighted cluster</a> </p> <a href="https://publications.waset.org/abstracts/60869/e-hailing-taxi-industry-management-mode-innovation-based-on-the-credit-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60869.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4436</span> Profit-Based Artificial Neural Network (ANN) Trained by Migrating Birds Optimization: A Case Study in Credit Card Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashkan%20Zakaryazad">Ashkan Zakaryazad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ekrem%20Duman"> Ekrem Duman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=profit-based%20neural%20network" title=" profit-based neural network"> profit-based neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20of%20squared%20errors%20%28SSE%29" title=" sum of squared errors (SSE)"> sum of squared errors (SSE)</a>, <a href="https://publications.waset.org/abstracts/search?q=MBO" title=" MBO"> MBO</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20descent" title=" gradient descent"> gradient descent</a> </p> <a href="https://publications.waset.org/abstracts/31637/profit-based-artificial-neural-network-ann-trained-by-migrating-birds-optimization-a-case-study-in-credit-card-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31637.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">475</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4435</span> Security Analysis of SIMSec Protocol</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kerem%20Ok">Kerem Ok</a>, <a href="https://publications.waset.org/abstracts/search?q=Cem%20Cevikbas"> Cem Cevikbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Vedat%20Coskun"> Vedat Coskun</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Alsadi"> Mohammed Alsadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Busra%20Ozdenizci"> Busra Ozdenizci</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Un-keyed SIM cards do not contain the required security infrastructure to provide end-to-end encryption with Service Providers. Hence, new, emerging, or smart services those require end-to-end encryption between SIM card and a Service Provider is impossible. SIMSec key exchange protocol creates symmetric keys between SIM card and Service Provider. After a successful protocol execution, SIM card and Service Provider creates the symmetric keys and can perform end-to-end data encryption when required. In this paper, our aim is to analyze the SIMSec protocol’s security. According to the results, SIM card and Service Provider can generate keys securely using SIMSec protocol. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=End-to-end%20encryption" title="End-to-end encryption">End-to-end encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20exchange" title=" key exchange"> key exchange</a>, <a href="https://publications.waset.org/abstracts/search?q=SIM%20card" title=" SIM card"> SIM card</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20card" title=" smart card"> smart card</a> </p> <a href="https://publications.waset.org/abstracts/46503/security-analysis-of-simsec-protocol" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46503.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4434</span> Implementation of Smart Card Automatic Fare Collection Technology in Small Transit Agencies for Standards Development</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walter%20E.%20Allen">Walter E. Allen</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20D.%20Murray"> Robert D. Murray</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many large transit agencies have adopted RFID technology and electronic automatic fare collection (AFC) or smart card systems, but small and rural agencies remain tied to obsolete manual, cash-based fare collection. Small countries or transit agencies can benefit from the implementation of smart card AFC technology with the promise of increased passenger convenience, added passenger satisfaction and improved agency efficiency. For transit agencies, it reduces revenue loss, improves passenger flow and bus stop data. For countries, further implementation into security, distribution of social services or currency transactions can provide greater benefits. However, small countries or transit agencies cannot afford expensive proprietary smart card solutions typically offered by the major system suppliers. Deployment of Contactless Fare Media System (CFMS) Standard eliminates the proprietary solution, ultimately lowering the cost of implementation. Acumen Building Enterprise, Inc. chose the Yuma County Intergovernmental Public Transportation Authority (YCIPTA) existing proprietary YCAT smart card system to implement CFMS. The revised system enables the purchase of fare product online with prepaid debit or credit cards using the Payment Gateway Processor. Open and interoperable smart card standards for transit have been developed. During the 90-day Pilot Operation conducted, the transit agency gathered the data from the bus AcuFare 200 Card Reader, loads (copies) the data to a USB Thumb Drive and uploads the data to the Acumen Host Processing Center for consolidation of the data into the transit agency master data file. The transition from the existing proprietary smart card data format to the new CFMS smart card data format was transparent to the transit agency cardholders. It was proven that open standards and interoperability design can work and reduce both implementation and operational costs for small transit agencies or countries looking to expand smart card technology. Acumen was able to avoid the implementation of the Payment Card Industry (PCI) Data Security Standards (DSS) which is expensive to develop and costly to operate on a continuing basis. Due to the substantial additional complexities of implementation and the variety of options presented to the transit agency cardholder, Acumen chose to implement only the Directed Autoload. To improve the implementation efficiency and the results for a similar undertaking, it should be considered that some passengers lack credit cards and are averse to technology. There are more than 1,300 small and rural agencies in the United States. This grows by 10 fold when considering small countries or rural locations throughout Latin American and the world. Acumen is evaluating additional countries, sites or transit agency that can benefit from the smart card systems. Frequently, payment card systems require extensive security procedures for implementation. The Project demonstrated the ability to purchase fare value, rides and passes with credit cards on the internet at a reasonable cost without highly complex security requirements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20fare%20collection" title="automatic fare collection">automatic fare collection</a>, <a href="https://publications.waset.org/abstracts/search?q=near%20field%20communication" title=" near field communication"> near field communication</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20transit%20agencies" title=" small transit agencies"> small transit agencies</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20cards" title=" smart cards"> smart cards</a> </p> <a href="https://publications.waset.org/abstracts/63545/implementation-of-smart-card-automatic-fare-collection-technology-in-small-transit-agencies-for-standards-development" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63545.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">283</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4433</span> Influence of Telkom Membership Card Customer Perceived Value on Retaining PT. Telkom Indonesia's Customer in 2013-2014</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eka%20Yuliana">Eka Yuliana</a>, <a href="https://publications.waset.org/abstracts/search?q=Siska%20Shabrina%20Julyan"> Siska Shabrina Julyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The competitive environment and high customer’s churn rate in telecommunication industries lead Indonesian telecommunication companies become strive to offer products with more value. Offering product with more value can encourage customers to keep using the companies product. One of way to retain customer is give a membership card to the customers as practiced by PT. Telkom by giving Telkom Membership Card to PT. Telkom loyal customer. This study aims to determine the influence of Telkom Membership Card customer perceived value on retaining PT. Telkom Indonesia’s customer in 2013-2014 by using quantitative method with causal study. Analythical technique used in this study is Structural Equation Modelling (SEM) to test the causal relationship with 216 owner of Telkom Membership Card in Indonesia. This study conclude that: (i) Customer perceived value on Telkom Membership Card is located in fair value zone, (ii) PT. Telkom efforts in order to retain the customers is classified as good, (iii) Customer perceived value is influencing the effort to retain the customer with the probability value less than 0.05 and level of influence 69%. Based on result of this study, PT. Telkom should (i) Improve Telkom Membership Card’s promotion because not all customer of PT. Telkom have the membership card. (iia) Adding Telkom Membership Card’s benefit such as discount at various merchant (iib) Making call center for member of Telkom Membership Card (iii) PT. Telkom should be ensure availability of their service. (iv) PT. Telkom should make a priority to customer who have telkom membership card and offers a better service.For future research should be use different variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20perceived%20value" title="customer perceived value">customer perceived value</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20retention" title=" customer retention"> customer retention</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing" title=" marketing"> marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=relationship%20marketing" title=" relationship marketing"> relationship marketing</a> </p> <a href="https://publications.waset.org/abstracts/31461/influence-of-telkom-membership-card-customer-perceived-value-on-retaining-pt-telkom-indonesias-customer-in-2013-2014" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31461.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">321</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4432</span> Theoretical and ML-Driven Identification of a Mispriced Credit Risk</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuri%20Katz">Yuri Katz</a>, <a href="https://publications.waset.org/abstracts/search?q=Kun%20Liu"> Kun Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Arunram%20Atmacharan"> Arunram Atmacharan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credit%20risk" title="credit risk">credit risk</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20ratings" title=" credit ratings"> credit ratings</a>, <a href="https://publications.waset.org/abstracts/search?q=bond%20pricing" title=" bond pricing"> bond pricing</a>, <a href="https://publications.waset.org/abstracts/search?q=spread-to-maturity" title=" spread-to-maturity"> spread-to-maturity</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/171152/theoretical-and-ml-driven-identification-of-a-mispriced-credit-risk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171152.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">80</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4431</span> Estimating Destinations of Bus Passengers Using Smart Card Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hasik%20Lee">Hasik Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Seung-Young%20Kho"> Seung-Young Kho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=destination%20estimation" title="destination estimation">destination estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20density%20estimation" title=" Kernel density estimation"> Kernel density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20card%20data" title=" smart card data"> smart card data</a>, <a href="https://publications.waset.org/abstracts/search?q=validation" title=" validation"> validation</a> </p> <a href="https://publications.waset.org/abstracts/80452/estimating-destinations-of-bus-passengers-using-smart-card-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80452.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4430</span> Consumer Protection: An Exploration of the Role of the State in Protecting Consumers Before and During Inflation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatimah%20Opebiyi">Fatimah Opebiyi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Economic growth promotion, inflation reduction and consumer protection are among the core public interest aims of governments. Nevertheless, higher rates of default by consumers in relation to credit card loans and mortgages in recent times illustrate that government’s performance in balancing the protection of the economy and consumer is subpar. This thereby raises an important question on the role of government in protecting consumers during prolonged spells of inflation, particularly when such inflationary trends may be traceable to the acts of the government. Adopting a doctrinal research methodology, this article investigates the evolution of the concept of consumer protection in the United Kingdom and also brings to the fore the tensions and conflicts of interests in the aims and practices of the main regulators within the financial services industry. Relying on public interest theories of regulation and responsive regulatory theory, the article explores the limitations in the state’s ability to strike the right balance in meeting regulatory aims of the regulatory agencies at the opposite ends of the spectrum. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20regulation" title="financial regulation">financial regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20protection" title=" consumer protection"> consumer protection</a>, <a href="https://publications.waset.org/abstracts/search?q=prudential%20regulation" title=" prudential regulation"> prudential regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20interest%20theories%20of%20regulation" title=" public interest theories of regulation"> public interest theories of regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=central%20bank" title=" central bank"> central bank</a> </p> <a href="https://publications.waset.org/abstracts/171357/consumer-protection-an-exploration-of-the-role-of-the-state-in-protecting-consumers-before-and-during-inflation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171357.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">77</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4429</span> Unauthorized License Verifier and Secure Access to Vehicle </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Prakash">G. Prakash</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Mohamed%20Aasiq"> L. Mohamed Aasiq</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Dhivya"> N. Dhivya</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Jothi%20Mani"> M. Jothi Mani</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Mounika"> R. Mounika</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Gomathi"> B. Gomathi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our day to day life, many people met with an accident due to various reasons like over speed, overload in the vehicle, violation of the traffic rules, etc. Driving license system is difficult task for the government to monitor. To prevent non-licensees from driving who are causing most of the accidents, a new system is proposed. The proposed system consists of a smart card capable of storing the license details of a particular person. Vehicles such as cars, bikes etc., should have a card reader capable of reading the particular license. A person, who wishes to drive the vehicle, should insert the card (license) in the vehicle and then enter the password in the keypad. If the license data stored in the card and database about the entire license holders in the microcontroller matches, he/she can proceed for ignition after the automated opening of the fuel tank valve, otherwise the user is restricted to use the vehicle. Moreover, overload detector in our proposed system verifies and then prompts the user to avoid overload before driving. This increases the security of vehicles and also ensures safe driving by preventing accidents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=license" title="license">license</a>, <a href="https://publications.waset.org/abstracts/search?q=verifier" title=" verifier"> verifier</a>, <a href="https://publications.waset.org/abstracts/search?q=EEPROM" title=" EEPROM"> EEPROM</a>, <a href="https://publications.waset.org/abstracts/search?q=secure" title=" secure"> secure</a>, <a href="https://publications.waset.org/abstracts/search?q=overload%20detection" title=" overload detection"> overload detection</a> </p> <a href="https://publications.waset.org/abstracts/3963/unauthorized-license-verifier-and-secure-access-to-vehicle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3963.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">242</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4428</span> Assessment of Mortgage Applications Using Fuzzy Logic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Swathi%20Sampath">Swathi Sampath</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Kalaichelvi"> V. Kalaichelvi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The assessment of the risk posed by a borrower to a lender is one of the common problems that financial institutions have to deal with. Consumers vying for a mortgage are generally compared to each other by the use of a number called the Credit Score, which is generated by applying a mathematical algorithm to information in the applicant’s credit report. The higher the credit score, the lower the risk posed by the candidate, and the better he is to be taken on by the lender. The objective of the present work is to use fuzzy logic and linguistic rules to create a model that generates Credit Scores. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credit%20scoring" title="credit scoring">credit scoring</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=mortgage" title=" mortgage"> mortgage</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20assessment" title=" risk assessment"> risk assessment</a> </p> <a href="https://publications.waset.org/abstracts/16553/assessment-of-mortgage-applications-using-fuzzy-logic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16553.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">405</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4427</span> A Study on the Reliability Evaluation of a Timer Card for Air Dryer of the Railway Vehicle </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chul%20Su%20Kim">Chul Su Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Ku%20Lee"> Jun Ku Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Won%20Jun%20Lee"> Won Jun Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The EMU (electric multiple unit) vehicle timer card is a PCB (printed circuit board) for controlling the air-dryer to remove the moisture of the generated air from the air compressor of the braking device. This card is exposed to the lower part of the railway vehicle, so it is greatly affected by the external environment such as temperature and humidity. The main cause of the failure of this timer card is deterioration of soldering area of the PCB surface due to temperature and humidity. Therefore, in the viewpoint of preventive maintenance, it is important to evaluate the reliability of the timer card and predict the replacement cycle to secure the safety of the air braking device is one of the main devices for driving. In this study, the existing and the improved products were evaluated on the reliability through ALT (accelerated life test). In addition, the acceleration factor by the 'Coffin-Manson' equation was obtained, and the remaining lifetime was compared and examined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability%20evaluation" title="reliability evaluation">reliability evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=timer%20card" title=" timer card"> timer card</a>, <a href="https://publications.waset.org/abstracts/search?q=Printed%20Circuit%20Board" title=" Printed Circuit Board"> Printed Circuit Board</a>, <a href="https://publications.waset.org/abstracts/search?q=Accelerated%20Life%20Test" title=" Accelerated Life Test"> Accelerated Life Test</a> </p> <a href="https://publications.waset.org/abstracts/69077/a-study-on-the-reliability-evaluation-of-a-timer-card-for-air-dryer-of-the-railway-vehicle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69077.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4426</span> Acceptance of Health Information Application in Smart National Identity Card (SNIC) Using a New I-P Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ismail%20Bile%20Hassan">Ismail Bile Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Masrah%20Azrifah%20Azmi%20Murad"> Masrah Azrifah Azmi Murad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study discovers a novel framework of individual level technology adoption known as I-P (Individual- Privacy) towards Smart National Identity Card health information application. Many countries introduced smart national identity card (SNIC) with various applications such as health information application embedded inside it. However, the degree to which citizens accept and use some of the embedded applications in smart national identity remains unknown to many governments and application providers as well. Moreover, the previous studies revealed that the factors of trust, perceived risk, privacy concern and perceived credibility need to be incorporated into more comprehensive models such as extended Unified Theory of Acceptance and Use of Technology known as UTAUT2. UTAUT2 is a mainly widespread and leading theory existing in the information system literature up to now. This research identifies factors affecting the citizens’ behavioural intention to use health information application embedded in SNIC and extends better understanding on the relevant factors that the government and the application providers would need to consider in predicting citizens’ new technology acceptance in the future. We propose a conceptual framework by combining the UTAUT2 and Privacy Calculus Model constructs and also adding perceived credibility as a new variable. The proposed framework may provide assistance to any government planning, decision, and policy makers involving e-government projects. The empirical study may be conducted in the future to provide proof and empirically validate this I-P framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unified%20theory%20of%20acceptance%20and%20use%20of%20technology%20%28UTAUT%29%20model" title="unified theory of acceptance and use of technology (UTAUT) model">unified theory of acceptance and use of technology (UTAUT) model</a>, <a href="https://publications.waset.org/abstracts/search?q=UTAUT2%20model" title=" UTAUT2 model"> UTAUT2 model</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20national%20identity%20card%20%28SNIC%29" title=" smart national identity card (SNIC)"> smart national identity card (SNIC)</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20information%20application" title=" health information application"> health information application</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20calculus%20model%20%28PCM%29" title=" privacy calculus model (PCM)"> privacy calculus model (PCM)</a> </p> <a href="https://publications.waset.org/abstracts/10398/acceptance-of-health-information-application-in-smart-national-identity-card-snic-using-a-new-i-p-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10398.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">468</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4425</span> Board of Directors Characteristics and Credit Union Financial Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luisa%20Unda">Luisa Unda</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamran%20Ahmed"> Kamran Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Mather"> Paul Mather</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We examine the effect of board characteristics on the performance and asset quality of credit unions in Australia, using a large sample covering the period 2004-2012. Credit unions are unique in that they are customer-owned financial institutions and directors are democratically elected by members, which is distinctly different from other financial institutions, such as commercial banks. We find that board remuneration, board expertise, and attendance at board meetings have significantly positive impacts on credit union performance and asset quality, while board members who hold multiple directorships (busy directors), have a significant negative impact on credit union performance. Financial performance also improves with larger boards and long-tenured directors in credit unions. All of these relations hold after we control for alternative measures of performance, credit union characteristics and endogeneity problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credit%20unions" title="credit unions">credit unions</a>, <a href="https://publications.waset.org/abstracts/search?q=corporate%20governance" title=" corporate governance"> corporate governance</a>, <a href="https://publications.waset.org/abstracts/search?q=board%20of%20directors" title=" board of directors"> board of directors</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20performance" title=" financial performance"> financial performance</a>, <a href="https://publications.waset.org/abstracts/search?q=Australia" title=" Australia"> Australia</a>, <a href="https://publications.waset.org/abstracts/search?q=asset%20quality" title=" asset quality"> asset quality</a> </p> <a href="https://publications.waset.org/abstracts/26547/board-of-directors-characteristics-and-credit-union-financial-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26547.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">518</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4424</span> Economic Analysis of the Impact of Commercial Agricultural Credit Scheme (CACS) on Farmers Income in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Titus%20Wuyah%20Yunana">Titus Wuyah Yunana </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study analyzed the impact of commercial agricultural credit scheme on income of beneficiary farmers in Kaduna State using the Net farm income and double difference method. A questionnaire was used to source the data from 306 farmers comprising of 153 beneficiaries and 153 non-beneficiaries. The results indicated that the net farm income of the commercial agricultural credit scheme beneficiaries increases from N15,006,352.00 before scheme to N24,862,585.00 after the first and the second phases of the scheme. There was also an increase in the net farm income of the non-beneficiaries from N9, 670,385.40 to N14, 391,469.00 during the scheme. The double difference method analysis indicated a positive mean income difference value between beneficiaries and nonbeneficiaries after the first and the second phases of the scheme. The study recommends expansion in the number of beneficiaries and efficient allocation and utilization of the resources. The government should also introduce more programs that will assist the farmers to increase their productivity, income and the economy as a whole. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agriculture" title="agriculture">agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20scheme" title=" credit scheme"> credit scheme</a>, <a href="https://publications.waset.org/abstracts/search?q=farmers" title=" farmers"> farmers</a>, <a href="https://publications.waset.org/abstracts/search?q=income" title=" income"> income</a>, <a href="https://publications.waset.org/abstracts/search?q=beneficiary" title=" beneficiary"> beneficiary</a> </p> <a href="https://publications.waset.org/abstracts/49104/economic-analysis-of-the-impact-of-commercial-agricultural-credit-scheme-cacs-on-farmers-income-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49104.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">339</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=government%20credit%20card&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=government%20credit%20card&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=government%20credit%20card&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=government%20credit%20card&page=5">5</a></li> 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