Card Information Research Articles

Analysis attackers’ methods with hashing secure password using CSPRNG and PBKDF2

Using the Internet, nothing is secure and as we are in need of means of protecting our data, the use of passwords has become important in the electronic world. To ensure that there is no hacking and to protect the database that contains important information such as the ID card and banking information, the proposed system stores the username after hashing it using the 256 hash algorithm and strong passwords are saved to repel attackers using one of two methods: -The first method is to add a random salt to the password using the CSPRNG algorithm, then hash it using hash 256 and store it on the website. -The second method is to use the PBKDF2 algorithm, which salts the passwords and extends them (deriving the password) before being hashed and stored on the website. The results of the two methods are compared in terms of speed and resistance to attackers, then password attacks are analysed in addition to calculating the strength of the password, the time it takes for the user to log in, and the time required for the attacker to guess the passwords per second. The Wolfram Alpha is used to calculate the mathematic operations and system implementation was done by using IntelliJ IDEA with java FX.

Strategic Assessment of Intricacies in Healthcare Cyber Security: Analyzing Distinctive Challenges, Evaluating Their Ramifications on Healthcare Delivery, and Proposing Advanced Mitigation Strategies

Healthcare firms have access to highly sensitive and valuable data, such as patient health records and payment card information. They are also becoming more reliant on Internet of Medical Things (IoMT) devices to provide treatment, and assaults on this networked equipment can result in data breaches or disruptions to crucial care of patients. Hence there is need to critically assess the intricacies in healthcare cyber security by analyzing the unique challenges facing healthcare sector, their impact on healthcare delivery and suggest some effective cyber security measures to mitigate the identified Cyber security challenges facing health care sector. The research employs a quantitative method by using the descriptive and survey approach through the use of questionnaires to elicit and gather relevant information regarding the Unique Cyber security Challenges in the Healthcare Industry. The descriptive method was used to achieve the objectives of the study while the survey technique was used to get qualitative information from respondents about effect of those cyber security challenges in the health care industry. The target sample size used for this study was 1300. However, only 980 responses were recovered and used for the analysis. Future research work suggested that strong encryption procedures can be developed and implemented for healthcare security, sophisticated anomaly detection tools can be incorporated, and cooperative frameworks for information exchange across the healthcare ecosystem could be established. Also the suggested effective measures to mitigate cyber security challenges in healthcare sector can be implemented in order to secure the patient sensitive and valuable data.

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

Consumer perception and acceptance of edible packaging for various food products.

Excessive use of single-use plastic packaging presents an imminent threat to the environment. One of the emerging solutions is using edible food packaging. However, there is lack of consumer information toward edible packaging. This study evaluated consumer attitude, acceptability, and purchase intent of three types of edible food packaging: muffin liner, cranberry pomace fruit leather wrap, and powdered drink sachet. One hundred consumers who frequently consumed muffins, strawberry fruit leather, and powdered lemonade were selected from metropolitan area of Portland, Oregon to participate in the study. The panelists were presented with the edible films and the food products with the edible packaging, information card highlighting the environmental-friendly edible package, and were prompted with describing the sensory attributes, purchase intent, and qualities regarding the edible packaging with and without food. Overall, panelists liked the three foods with the edible packaging giving overall liking scores of 7.48, 8.06, and 7.48 for the muffin liners, edible fruit leather wraps, and powdered drink sachets, respectively, based on a 9-point hedonic scale where 1=dislike extremely and 9=like extremely. When asked about hypothetical purchase intent, 64%-68% of panelists positively reacted to purchase intent and would buy all three types of edible packaging products. Based on the positive reaction from panelists, edible packaging maybe a possible solution to reducing single-use plastic packaging in the food industry. This study can be the catalyst for further investigation of the efficacy of different applications of edible food packaging as well as consumer perceptions of eating their packaging. PRACTICAL APPLICATION: Edible food packaging is an emerging solution for reducing single-use plastic waste. This study investigated consumer attitude, acceptability, and purchase intent of edible food packaging for three food packaging applications, including edible muffin liner, fruit leather wrap, and powdered drink sachet. This study demonstrated that consumers strongly agree that edible packaging would serve as an environmentally sustainable solution to single-use plastics, and are willing to spend more to purchase these sustainable alternatives. This study provides new information toward the future development of edible packaging and consumer perceptions of eating their packaging.

Credit Card Fraud Detection using ANN

Abstract: Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.

Research of artificial intelligence in imperfect information card games

Artificial intelligence (AI) in games has advanced significantly, notably in perfect information games such as Go and Chess. Imperfect information games, in which participants do not have complete information about the game state, create more difficulties. They incorporate both public and private observations, where strategies must be improved to achieve a Nash equilibrium. This study investigates artificial intelligence and reinforcement learning approaches, in which agents learn to maximize future rewards through interactions with their surroundings. The paper then focuses on card game research platforms such as RLCard and OpenAI Gym. It gives a comprehensive summary of research in No Limit Texas Hold'em, a difficult two-player poker game with a large decision space. DeepStack and Libratus are successful systems that have attained expert-level and superhuman play, respectively. Pluribus, a superhuman artificial intelligence for six-player poker, and DouZero, a pure reinforcement learning technique for the multiplayer card game, DouDiZhu, are both investigated. Overall, this paper provides background information on reinforcement learning and imperfect information games, analyzes commonly used research platforms, evaluates the effectiveness of AI algorithms in various card games, and offers future research areas and directions.

Analyzing credit card fraud cases with supervised machine learning methods: logistic regression and Naive Bayes

Frauds involving credit cards are simple and effortless to target. With the rise of online payment credit cards have had a huge role in our daily life and economy for the past two decades and it is an important task for companies to identify fraud and non-fraud transactions. As the number of credit cards grows every day and the volume of transactions increases quickly in tandem, fraudsters who wish to exploit this market for illegitimate gains have come to light. Nowadays, it’s quite easy to access anyone’s credit card information, which makes it simpler for card fraudsters to do their crimes. Thanks to advances in technology, it is now possible to determine whether information gained with malicious intent has been used by looking at the costs and time involved in altering account transactions. The Credit Card Fraud analysis data set, which is obtained from the Kaggle database, is used in the modeling process together with The Logistic regression method and Naive Bayes algorithms. Using the Knime platform, we are going to apply machine learning techniques to practical data in this study. The goal of this study is to identify who performed the transaction by examining the periods when people use their credit cards. The Logistic regression approach and the Naive Bayes method both had success rates of 99.83%, which is the highest. The two methods’ results are based on Cohen’s kappa, accuracy, precision, recall, and other metrics.

Abstract B085: Increasing utilization of breast cancer risk assessment tools: Identifying and adapting implementation strategies using multiple stakeholder groups

Abstract Background: Each year ~12,000 female breast cancers are diagnosed in US women ages 18-45 with overrepresentation of aggressive cancers, advanced stage disease at diagnosis, and inferior outcomes. The American College of Radiology and American Society of Breast Surgeons recommend that breast cancer risk assessment (RA) start no later than age 30 to identify high-risk individuals in whom to initiate screening earlier. However, RA tools are currently utilized in as few as 3-26% of eligible women. Methods: Our study aims are to assess feasibility, reach, acceptability, and appropriateness of customized strategies to increase uptake of breast cancer RA. We identified barriers to increasing utilization of RA tools through discussions with long-term community partners in the Tennessee Breast and Cervical Cancer Screening Program (TBCSP), a three-phase adapted Feasibility of Intervention Measure (FIM) in five health clinics, and one-on-one interviews guided by the Consolidated Framework for Implementation Research with 24 providers in public health settings and 37 interviews guided by the Health Belief Model with Black women age 30-50 years with no personal history of breast cancer. Interview data contributed to the development of a conceptual framework to depict influences of provider behavior in the utilization of breast cancer risk assessment. Using the CFIR-ERIC Matching Tool and working with the study’s Community Advisory Board (CAB), the research team identified three potential strategies to increase the use of breast cancer RA in public health settings. Findings: Selected strategies included having a site champion for each clinic, offering a short series of education modules, and distributing print educational materials including a provider education sheet and patient-facing information cards. These strategies were further revised in collaboration with the CAB and patients and providers in Community Engagement Studios. Implications for D&I Research: This project demonstrates the contributions of a long-standing relationship with a state-wide public health agency, implementation science tools, and collaboration with community advisors for identifying and addressing barriers to breast cancer RA. The study has generated replicable scripts for educating providers and community-informed reproducible patient education materials to increase breast cancer RA. Evaluation of the impact of these strategies on provider behavior in six clinical settings is currently underway. Citation Format: Rebecca Selove, Lucy Spalluto, Maureen Sanderson, Laura Ernst, Carolyn Audet. Increasing utilization of breast cancer risk assessment tools: Identifying and adapting implementation strategies using multiple stakeholder groups [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B085.

Molecular docking technology and network pharmacology based on Rhapontici Radix-Cremastrae Pseudobulbus drug pair in treating breast cancer.

Network pharmacology is a bioinformatics-based research strategy for identifying the mechanisms of drugs and promoting drug development. This study used network pharmacology to investigate the mechanism of the Loulu-Cremastrae Pseudobulbus drug pair treating breast cancer (BC). The ingredients and potential targets of the drug pair were searched with Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCSMP). National Center for Biotechnology Information (NCBI) and gene cards were used to search the targets of BC. Networks of "drugs-components-targets" and protein-protein interaction were constructed through Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were carried out through common targets. Using AutoDock tool, molecular docking was performed to verify the binding between key targets and compounds. Finally, we selected 6 active compounds from the drug pair. A total of 61 targets were associated with the drug pair, and 15,295 targets were related to BC. 55 common targets were obtained after the intersection. The key targets included Transcription factor Jun (JUN), Heat shock protein HSP 90-alpha (HSP90AA1), and Caspase-3(CASP3). 327 terms were obtained by GO analysis. 78 pathways (p < 0.05) were identified through KEGG analysis. Molecular docking indicated that important compounds combined well with key targets. Various active compounds, including beta-sitosterol, 2-methoxy-9,10-dihydrophenanthrene-4,5-diol, and stigmasterol, can regulate multiple signaling pathways related to BC, such as the estrogen and prolactin signaling pathways, playing therapeutic roles in BC.

Silencing the Scammers: Effective Strategies in Credit Card Fraud Detection

The amount of fraud has recently grown. This fraud includes your personal information or sensitive data, which someone can steal. Financial fraud is increasing significantly with the development of modern technology. Companies and financial institutions lose large amounts due to fraud. Fraud analytics combines big data analysis techniques with human interactions to help detect fraud. Credit card fraud is a pervasive and costly problem for individuals, businesses, and financial institutions worldwide. It involves the unauthorized use of someone's credit card information to make fraudulent transactions, leading to financial losses and potential harm to the cardholder's credit score. Detecting and preventing credit card fraud is a critical concern in the modern financial ecosystem, and it relies on advanced technologies and data analysis techniques.

Enhancing Card Transaction’s Security through Cyber Security

This paper highlights the security issues associated with credit cards and underscores the crucial role of encryption in mitigating the risk of credit or debit card data theft. Credit card encryption encompasses safeguarding the card itself, securing the terminal used for card scanning, and ensuring the protection of card information during transmission between the terminal and a backend computer system. The encryption mechanism is specifically engineered to validate and limit access to card security features. In our project, we developed a web application using VS Code, employing HTML for the frontend and PHP for the backend, and implemented AES encryption as a robust security measure.

320 AKI-ng for better adherence: a quality improvement initiative on the medication ‘sick day rule’ in patients over 65 with acute kidney injury (AKI)

Abstract Background The successful mitigation of Acute Kidney Injury (AKI) among older patients is contingent on meticulous medication management, specifically adherence to the medication ‘sick day rule’. These rules, recommended by the National Institute for Health and Care Excellence (NICE) in their guidelines ‘Acute kidney injury: prevention, detection and management’, involves the temporary cessation of certain medications during episodes of acute illness. However, suboptimal adherence to this rule presents a considerable challenge in clinical practice. This quality improvement project aimed to address this issue, seeking to enhance adherence to the sick day rule among patients over the age of 65 admitted with AKI. Methods Initially we performed a medication reconciliation on all patients admitted to the medical take over a five-day period. Our intervention involved the distribution of information cards that clearly detailed the NICE guidelines. The cards were displayed prominently in key medical admission areas and could attach to doctors’ identification badges. We also provided education to Non-Consultant Hospital Doctors at Grand Rounds. Results Data collected revealed that of the 92 patients over 65 admitted, 27 met the AKI criteria. 23 of these 27 AKI patients were on medications that could exacerbate their AKI, while only 6 had all appropriate medications held. This trend extended to specific medication categories: only 8/19 patients on ACE inhibitors/ARBs, 1/5 on SGLT2 inhibitors, and 4/9 on metformin had their medication held. However, both patients on NSAIDs had their medication held, indicating a better adherence rate. Conclusion The goal of this quality improvement initiative is to bolster adherence to the medication sick day rule, thereby potentially reducing the incidence and severity of AKI in our older patient population. We plan to reaudit in the coming weeks to assess the impact of our intervention, affirming our ongoing commitment to improving patient outcomes through evidence-based quality improvement strategies.

Rental Space

The purpose of “Rental Space” is to transport the goods from source to destination in an effective and efficient manner. Rental Space is a one stop rental portal. It provides services Such as Hiring cars, Service Apartments, Hotels, Guest houses, Meeting &amp; Conference halls, Audio visuals, Party rentals and Computers. It provides the facility to make online orders and get everything done before you reach the destination. It is going to implement the web site for different cities. Instead of providing products from only one rental show room the application is acting as interface between user and the rental showroom owners. There is no restriction of rental showrooms for the website. Means any rental showroom owners want to display their products in the site then they simply register in the site by providing personal information and credit card number

Enhancing Card Transaction Security through Cyber Security

This paper highlights the security issues associated with credit cards and underscores the crucial role of encryption in mitigating the risk of credit or debit card data theft. Credit card encryption encompasses safeguarding the card itself, securing the terminal used for card scanning, and ensuring the protection of card information during transmission between the terminal and a backend computer system. The encryption mechanism is specifically engineered to validate and limit access to card security features. In our project, we developed a web application using VS Code, employing HTML for the frontend and PHP for the backend, and implemented AES encryption as a robust security measure.

Smart - Contract Privacy Preservation Mechanism

Sensitive records stored in the cloud such as healthcare records, private conversation and credit card information are targets of hackers and privacy abuse. Current information and record management systems have difficulties achieving privacy protection of such sensitive records in a secure, transparent, decentralized and trustless environment. The Blockchain technology is a nascent and a promising technology that facilitates data sharing and access in a secure, decentralized and trustless environment. The technology enables the use of smart contracts that can be leveraged to complement existing traditional systems to achieve security objectives that were never possible before. In this paper, we propose a framework based on Blockchain technology to enable privacy-preservation in a secured, decentralized, transparent and trustless environment. We name our framework SmartCoAuth. It is based on Ethereum Smart Contract functions as the secure, decentralized, transparent authentication and authorization mechanism in the framework. It also enables tamper-proof auditing of access to the protected records. We analysed how SmartCoAuth could be integrated into a cloud application to provide reliable privacy-preservation among stakeholders of healthcare records stored in the cloud. The proposed framework provides a satisfactory level of data utility and privacy preservation.

E-service quality and customer loyalty in the e-commerce market, South West, Nigeria: Post-COVID-19

In today’s fiercely competitive e-commerce arena, online service providers are compelled to focus on improving quality service delivery to remain competitive. The e-commerce market in Nigeria has experienced low patronage due to factors such as establishing cost, accessibility, credit card threat, information privacy, data security, and network reliability. Thus, the study explored the impact of quality service delivery on customer loyalty. A survey research design was used to seek information about the phenomenon of interest from the sampled respondents through an online platform, mobile device, and mail. The study used copies of the questionnaire as an instrument to gather data from 385 customers of Jumia and Konga who are engaged in e-business service delivery. The data were analyzed using SPSS version 25. The results indicated that the computed t values and the associated significant probabilities of responsiveness, privacy, fulfillment, compensation, insistence action by customers, switching restraint by customers, repeat purchase by customers, customer satisfaction and customers loyalty were 16.08 (P &amp;amp;lt; 0.001), 26.33 (P &amp;amp;lt; 0.001), 12.97 (P &amp;amp;lt; 0.001), 6.75 (P &amp;amp;lt; 0.001), 10.60 (P &amp;amp;lt; 0.001), 7.35 (P &amp;amp;lt; 0.001), 15.75 (P &amp;amp;lt; 0.001), 13.74 (P &amp;amp;lt; 0.001), and 11.92 (P &amp;amp;lt; 0.001), respectively. Given the foregoing, it is evident that respondents perceive a firm’s responsiveness, privacy, and compensation to be significant at the ninety-nine percent confidence level. Furthermore, insistence action by customers, switching restraint by customers, repeat purchase by customers, customer satisfaction, and customer loyalty are perceived to be significant at the ninety-nine percent confidence level.

Encryption and Tokenization of Card Security using Cryptographic Algorithms

There are several alternative strategies that can be used when utilising cryptography to encrypt card information. Encryption's main objective in this situation is to prevent sensitive card information from being intercepted and used fraudulently, including the card number, expiration date, and security code. Using SSL (Secure Socket Layer) or TLS (Transport Layer Security), which establishes a safe, encrypted connection between a website or app and the server where the card information is stored, is one popular method of encrypting card information. This is frequently used for online transactions, such as when paying with a mobile app or buying something from an e-commerce website. Using end-to-end encryption is an alternative strategy that guarantees that card information is encrypted from the time it is entered into a device, website, or app until it reaches its destination. This is frequently used with tokenization, which exchanges the sensitive card information for a singular token that can be safely kept and used for upcoming transactions without disclosing the actual card data. Additionally, a number of cryptographic protocols and algorithms, including AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and ECC (Elliptic Curve Cryptography), can be used to encrypt card information. These algorithms and protocols jumble the data using intricate mathematical computations so that it cannot be decoded without the right decryption key.

Workshop (Clinical/Best Practice Implementation) ID 1984795

Background/Objectives Following an initial summit in March 2019, various Canadian stakeholders have collaborated within a Community of Practice (CoP) with a vision to increase the access to, and quality of activity-based therapies (ABT) for persons with spinal cord injury/disease (SCI/D). This workshop will describe key knowledge products and study findings emanating from working groups of the ABT CoP as well as a study initiated through the Canadian SCI Standing and Walking Module Group. Design/Methods Priority areas were identified by consensus across the ABT CoP, leading to several studies and development of specific knowledge products. Key challenges included defining and standardizing ABT, gaps in knowledge/training and practice variation with a focus on achieving optimal timing, dosage and intensity of ABT. Various knowledge products and study findings related to these challenges will be presented, with audience interaction encouraged through polls and open discussion. Results Presentation highlights include a framework for structured reporting of ABT and a series of videos, pod-casts and information cards promoting ABT. In addition, a mixed methods study of Canadian physiotherapists demonstrated that ABTs were a much desired, yet not fully available treatment option. Therapists identified needs for more fulsome research partnerships, improved and more creative staffing models, enhanced training methods and the importance of a strong therapeutic alliance. Conclusion The work of the ABT CoP will continue to focus on these and other priorities, with the ultimate goal of universal access and identification of approaches that will enhance outcomes for persons with SCI/D.

Comparative Study of Machine Learning Algorithms for Credit Card Fraud Detection

Abstract: In the present world, advancement in technologies like e-commerce and financial technology (FinTech) has led to a surge in the daily volume of online card transactions and there are a lot of issues with credit cards. Identifying CC scams is currently among the most typical problems worldwide. An individual's credit card information may be fraudulently obtained and used by criminals for fraudulent purposes. Due to this significant increase in credit card fraud that impacts banks, merchants, and card issuers, it is essential to develop mechanisms that ensure the security and integrity of credit card transactions. This research examines divergent approaches to detecting credit card fraud using machine learning (ML) Models. The algorithms used are the Random Forest algorithm, Decision Trees, and the AdaBoost algorithm. The outcomes of these algorithms are based on accuracy, precision, recall, and F1-score and AUC-ROC score. Different models are compared and the algorithm that has good evaluation metrics is considered the best algorithm that is used to detect fraud

Automated conversion of numeral to words in Hausa language

Working with cardinal numbers is undoubtedly an integral part of our everyday life, which tells us about quantity. They are used for instance, in numeracy lessons, on receipts, slips, tellers in banks, to represent figures on documents, to tell time and are often represented inconsistently. However, the words form representations are often inconsistent with the standard Hausa. Standard Hausa as compared to other languages such as Arabic and British English, the Numeric System is characterized by a multitude of concatenations as well as dialect variations which causes misinterpretations and confusion.  Also, initial findings show that no such automation published work has been done specifically in the numerical system of Standard Hausa language. In order to fill the gap, this study proposed an algorithm for converting numerals to words for the numeric system of Hausa language automatically in standard practice. The aim of the study is to examine the effect of iteration and mapping structures algorithm for converting numeral to cardinal form in standard Hausa language. The algorithm has been tested on approximately one hundred million sequences of numbers produced accurate results. The results showed that our method is successful in automating conversion of numerals to words with a high precision.