EduNexus: A Secure and Scalable MERN-Based Educational Eco- system with Integrated Payment Authentication

Working in the software development industry in the modern world cannot be imagined without integration with some code versioning platform. In addition to allowing copies of the code to be kept, these platforms provide the ability to control changes that occur in the code itself, so at any time it can be determined which member of the development team made the change. The aim of this paper is to determine the possibilities of using code versioning platforms in education, i.e. conducting teaching in the field of software development at faculties, through the analysis of data generated on one of these platforms. In order to gain insight into the dynamics of software solution development, data analysis from the GitHub platform was performed using the GitHub REST API on the example of a public GitHub organization. The results of the analysis indicate that the data generated on these platforms can be used to better understand the learning process and monitor the dynamics of project work. In this way, lecturers have a better insight into the dynamics of the development of a given software solution. It is possible to follow iteratively how much and in what way each team member contributed to the solution through the analysis of different groups of data and thus better understand the state of individual activities, but also the state of the entire project.

The signature recognition (SR) and verification technique is used to appreciate and confirm character’s handwritten signature. Nowadays handwritten signature is among the most commonly permitted personal attributes for identification verification. Signature verification provides authorization in financial and business transaction. Signature verification finds its application within the area of internet banking, passport verification approach, provides authentication to candidates in public examination from their signatures, credit cards, bank cheques, etc. Due to the growing amount of handwritten signatures, it is vitally fundamental that a person offline handwritten signature to be identified uniquely. A signature is a behavioral biometric characterized by way of behavioral trait that a creator learns and acquires over a period of time and turns into his specific identification. Keywords: Image, signature recognition, template matching, neural networks, support vector machine Cite this Article Priyanka Chauhan, Nirupma Tiwari. A Survey Paper on Offline Signature Recognition with Various Techniques. Journal of Artificial Intelligence Research & Advances . 2017; 4(2): 19–29p.

Modern web and mobile applications require efficient and flexible data retrieval to optimize performance and improve user experience. Clients using traditional REST APIs experience inefficiencies through receiving excessive data beyond their requirements or needing multiple calls to obtain all necessary information. Such limitations produce greater network latency and data transfer charges while making client-side logic more complex as it manages unnecessary or absent data.Facebook developed GraphQL as a query language solution, which enables clients to request precise data sets through one single request. REST APIs offer static endpoints that deliver fixed data structures, while GraphQL presents a dynamic schema that enables clients to request multiple resources in one query. This method decreases API request numbers while simultaneously enhancing efficiency through payload size reduction and response time shortening. This paper analyzes GraphQL's design and implementation while evaluating how its performance and scalability advantages stack up against those of RESTful APIs. This study analyzes practical applications that demonstrate GraphQL's ability to improve data fetching efficiency while outlining best practices for building scalable GraphQL APIs. This paper provides a detailed exploration of schema design and query optimization along with caching strategies and security considerations while examining how GraphQL integrates with current backend services. We also identify problems like complex queries, performance bottlenecks, and schema changes while providing ways to resolve these problems.

Abstract: The Tax Wizard project is a comprehensive web-based application aimed at modernizing and simplifying the delivery of professional tax education and services. The platform is designed with two core interfaces: the Admin Panel, which facilitates backend management of content and users, and the Client Panel, which provides an interactive environment for learners and general users. This dual-structured architecture enhances both administrative control and user engagement. On the Admin Side, authorized users can manage essential platform components, including courses, student records, customer data, blog posts, advertisements, FAQs, and general website content such as the About Us section. This enables real-time updates and efficient content delivery, which are crucial for maintaining the relevance and quality of educational offerings. The Client Side serves as the user-facing component where students and visitors can explore available courses, read tax-related blogs, submit enquiries, view ads, and browse frequently asked questions. Registered users can enroll in courses, monitor their academic progress, and download certificates upon successful course completion. This structure promotes independent learning and easy access to professional-level tax knowledge. The system is built using PHP and MySQL, with RESTful APIs acting as a bridge between the frontend and backend. The application is designed to be modular, scalable, secure, and responsive—ensuring a seamless user experience across different devices. Emphasizing user-friendliness and real-time interactivity, Tax Wizard stands as a digital solution to bridge the gap between tax educators and learners. This project addresses the lack of structured, accessible tax education online and brings a unified solution for both educational delivery and administrative efficiency in the digital age. Objectives: To provide a centralized digital platform for tax education and services Create a unified web- based system that delivers structured tax learning resources and related services to students and To design a user-friendly and responsive web application Ensure smooth navigation and accessibility across all devices, enhancing the overall user experience for both administrators and learners. To enable efficient management of educational content and user data Allow admins to easily create, update, and manage courses, blogs, FAQs, advertisements, and student records through the Admin Panel. To support self-paced learning and certification Empower students to enroll in courses, track their learning progress, and download certificates upon completion, promoting flexible, independent learning. To implement secure and scalable backend architecture Use PHP, MySQL, and RESTful APIs to maintain data security, ensure system stability, and allow for future expansion. Keywords: Tax Education, Web-based Application, Admin Panel, ClientPanel, Course Management, Student Dashboard,Online Learning, PHP and MySQL, RESTful APIs, Responsive Design, Certification System, Content Management System, Enquiry Management, Blog Integration, Digital Learning Platform

In the evolving landscape of financial transactions, ensuring the security and authenticity of signature verification on bank cheques is vital to prevent fraud. This paper presents a survey of techniques used in signature verification, covering traditional image processing methods such as feature extraction, edge detection, and shape analysis, alongside modern deep learning approaches like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models. The survey explores key challenges, including handwriting variations, image noise, and the need for reliable real-world systems. Solutions such as pre-processing techniques, data augmentation, and transfer learning are discussed to improve accuracy. Commonly used datasets for training and evaluating models are reviewed, highlighting their features and limitations. The paper also considers ethical concerns such as fairness, transparency, and the broader implications of deploying such systems in financial institutions. By integrating insights from both traditional and advanced methods, this survey provides a valuable reference for researchers and practitioners aiming to enhance the accuracy, robustness, and reliability of signature verification systems. Strengthening these systems contributes to the overall integrity and trust in digital financial transactions. Key words: Bank Cheques, CNN, OCR, Line Sweeping.

To reduce fraud in financial transactions, signature verification is important for security purposes. In this paper, an attempt has been made to analysis the performance of off-line handwritten signature verification using image-based features. Photocopies and scanned documents are considered as the best possible evidence in the situations when the original documents are either lost or damaged. Although the photocopies are the filtered images of original information and do not reproduce details as in the original documents. In this paper, combinations of four features, i.e., Average object area, mean, Euler number and area of signature image is used to verify the signature. Publically available database BHsig260 is used. In this database, two types of signature are available, i.e.,Bengali and Hindi. Proposed work shows that accuracy of Hindi off-line signature verification is 78.5% with sample size of 15 and accuracy of Bengali off-line signature verification is 69.1 with sample size of 20.

Abstract—The rise of cybercrime has made fraud detection a critical focus for financial services, where even minor improvements in detection rates can translate into significant savings. Traditional rule- based detection systems are increasingly unable to keep up with the complexity and evolving nature of fraudulent schemes. This work explores the role of artificial intelligence (AI), particularly deep learning, in enhancing fraud detection capabilities. Here we propose a novel AI-driven fraud detection system that incorporates Single Sign-On (SSO) identity and access management (IAM) frameworks, leveraging a dual-layered approach with batch and real-time processing. The batch layer establishes a user trust identity by analysing historical behavioural patterns, which informs an access-granting mechanism that evaluates real-time transaction data for fraud indicators. A deep iterative convoluted memory classifier then assesses transactions, for detecting frauds. This architecture automates fraud detection, allowing analysts to focus on high-priority cases and ensuring that only authenticated users can access sensitive information. Our experiments are Conducted in the MATLAB environment, From the analysis it was revealed that the model's effectiveness, showing promise for scalable, efficient fraud prevention in financial services. This work underscores the transformative potential of AI in securing financial transactions against sophisticated cyber threats. Index Terms— cybercrime, Fraud, Financial Transactions, artificial intelligence, identity and access management, fraud detection,

Handwritten signature identification is a process that determines an individual’s true identity by analyzing their signature. This is an important task in various applications such as financial transactions, legal document verification, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. However, verifying handwritten signatures in digital transactions and remote document authentication is still challenging. The inherent variety in people’s signatures, which may occur due to factors such as mood, exhaustion, or even the writing tool used, contributes to the problem. Furthermore, the proliferation of sophisticated forgery methods, such as freehand mimicking and sophisticated picture manipulation, necessitates the development of reliable and precise tools for identifying authentic signatures from fake ones.The present paper suggests a method for identifying signatures based on integrating static (off-line) handwritten signature data. This is done by fusing three types of signature features: Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency-features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, these fused features are inputted into four types of machine learning algorithms: Naive Bayes, K-Nearest Neighbor, Decision Tree, and AdaBoost classifiers, to identify each person and to find the most robust algorithm in terms of accuracy and precision and recall. For experiments, we have used two famous datasets: SigComp2011 and CEDAR. After training datasets, the highest accuracy achieved was 100% on the CEDAR dataset and 94.43% on the SigComp2011 dataset using a Naive Bayes classifier. Index Terms— Fast Fourier Transform, Gray-Level Co-occurrence Matrix, Handwritten Signature, Linear Discriminant Analysis, Machine Learning.

As the term, smart campus attracts professionals and academics from multiple disciplines, and the technology keeps intervening in every aspect of life; it becomes inevitable for the smart campus to take place and deploy the future vision of smart cities. As a first step to achieving this vision, it is very important to develop a clear understanding of a smart campus. The student e-card management system (SEMS) is a barcode-based cutting-edge technology and innovative software deployed in the AWS cloud environment. It uses the core infrastructure of the AWS cloud, which includes AWS DynamoDB, AWS EC2 cluster, AWS S3, and the REST API. There are many manual activities in the existing college management system and lead to problems as they are time-consuming and expensive. The proposed idea aims to design a student smart card that configures a bar code with each user’s ID card, and that bar code can be read through the web, mobile app, and card reader. With that said, the user’s ID card will be multi-functioned now and will serve many purposes. The users of this application can be anyone who is associated with the college. The ID card is used for financial transactions within the college’s boundary, which ultimately serves the biggest commitment of having cashless transactions across the country. The same ID card can be used to have books from the library and many more activities. As this paper is a full-fledged cloud computing-based system, we are also proposing an OTP-based transaction to provide the end-to-end reliability of the financial transaction. This feature shows the perspective and influence of SEMS and its versatility, practicality, and usability. This research opens the doors for future studies to gain a deeper insight into the type of decisions needed to transform a traditional campus into a smart campus.KeywordsBar codeREST APICloud computingDynamoDBPaymentsSEMS

Multi-platform devices are becoming the standard technology in software development. However, the problem of multi-platform is that it must provide a single parent data source, so there is a good relationship between users of multi-platform devices and servers. REST APIs are the best choices that bridge the servers and multiple platforms. That way, the use of REST APIs will be very beneficial, both in terms of servers and platforms as consumption. This research planned to create a REST API architecture that can run in multi-platform with testing techniques using black boxes through postman software. As the basis for making REST APIs, it is better to understand the Architecture of REST APIs, especially REST APIs as a multi-platform device.

This proposal outlines the design and development of a Java-based software artifact implementing a custom API Gateway to manage security and access control, replacing the need for JWT token systems. The proposal encompasses the definition of functional requirements, software design and architecture, implementation, and system deployment, aiming to offer a secure and efficient solution for communication and control between clients and a REST API. The Design Science Research (DSR) methodology was applied, focusing on the design and implementation process of the software product, which enables secure integration between a client and a REST API. Security in web applications and REST API architectures is essential to protect system integrity, confidentiality, and availability in an increasingly threat-exposed digital environment. Additionally, the Scrum methodology facilitates agile and collaborative project management, ideal for iteratively and adaptively implementing security solutions. During the design and coding phases in Java, the custom API Gateway is defined to manage incoming requests and safely route traffic to REST APIs. The custom API Gateway incorporates features such as authentication, user role validation, and request logging. Simple endpoints are created on the REST API to ensure seamless communication with the gateway. This proposal provides a robust solution for managing security and access control in distributed applications. By using the API Gateway instead of JWT tokens, the system centralizes security and authentication efficiently, enhancing fault tolerance and maintaining stable performance.

The verification of a person’s signature plays a vital role in determining their identity, detecting fraudulent activity, and validating documents. Traditional methods of manually verifying a signature tend to have a large margin for error so automating this process is crucial. This research focuses on the use of optical character recognition (OCR) to extract text and structural components for authentication purposes. This work consists of several parts, from image pre-processing and feature extraction to processing and classification using machine learning. The system aims to increase accuracy, reliability, and security when authenticating signatures in some applications such as banking, legal documents, and other financial transactions that require precise signature verification.

Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naive Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used. For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naive Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naive Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naive Bayes Tree and 88.12% (with forgeries).

Health Information Exchange (HIE) provides a morecomplete health record of an individual that improves patientcare with relevant data gathered from multiple healthinformation technology (HIT) systems. In support of HIE, theHealth Level Seven (HL7) XML standard was developed tomanage, exchange, integrate, and retrieve electronic healthinformation. In 2011, HL7 began drafting a next-generationstandard, Fast Healthcare Interoperable Resources (FHIR), tofacilitate the development and interaction of mobile health(mHealth) apps, HIT data sharing, and common format forinformation modeling. FHIR is based on RESTful APIs andsupported by a FHIR server infrastructure that facilitates theexchange in a cloud computing setting. FHIR while possessing asecurity specification, has yet to define and identify actualsecurity mechanisms for secure data exchange via RESTful APIcalls. In this paper, we incorporate role-based access control(RBAC) into FHIR to support the ability to control access ofwho can call which services of FHIR RESTful APIs that managesensitive healthcare data. The work is demonstrated utilizing amHealth application that communicates with the OpenEMRelectronic health record via the HAPI FHIR server.

Data is one of the most important things in this information and information technology era that evolving now. Currently, the government still has not used the public data maximally for administrative purposes. Utilization of this big population data is the creation of a web service application system with REST API where this data will be open and accessible to those who have access. One of the institutions that use this service is the Manpower and Transmigration Service where this system can make the Dinas staff more efficient to create and register job search cards using available community data. This application is able to provide and facilitate many parties, such as data administrators to monitor data usage, registration employee in input data, and people able to register independently.
 Index Terms—Web service, API, Rest api, People data