Journal of Advanced Research in Computer Technology & Software Applications

Financial Fraud Detection Using Cyber Security

Ujjwal Kaul — Thu, 25 Jun 2026 00:00:00 +0000

Financial technology (fintech) and the rapid growth in mobile payment systems have transformed the financial sector with faster, more convenient and ubiquitous digital transactions. With the greater use of digital financial services, however, comes an increased risk of cyberattacks and financial fraud. Phishing, SIM-swap attacks, identity theft, faux Know Your Customer (KYC) methods and transaction manipulation are among the methods cybercriminals use to compromise mobile apps, digital wallets, APIs, and fintech platforms. While there are advanced cybersecurity solutions, such as AI-driven fraud detection, blockchain technology, biometric authentication, and multi-factor authentication, that have been developed to address these threats, there are several challenges to the effectiveness of these solutions. Challenges encompass data privacy, data model opacity, new fraud methods and regulatory intricacies. The research aims to explore the key issues related to detecting and preventing financial fraud in mobile payment and fintech settings. The study summarises the latest trends in cybersecurity, discusses the shortcomings of current fraud detection systems, and suggests a combination of explainable artificial intelligence, real-time analytics, behavioural authentication, and secure mobile architecture to address fraud more effectively. The proposed approach seeks to boost the accuracy of fraud detection, increase the transparency of the system, bolster user authentication and create a more flexible defence mechanism for new cyber threats in the digital financial system.

DOI: https://doi.org/10.24321/3051.4304.202606

How to cite this article:
Kaul U. Financial Fraud Detection Using Cyber Security. J Adv Res Comp Tech Soft Appl 2026; 10(2): 10-19.

Examining the Transition from Automation to Augmentation: Assessing the Impact of Artificial Intelligence on Employee Productivity and Business Efficiency in Pune’s IT Industry

Niki ved, Amol Pande — Tue, 29 Sep 2026 00:00:00 +0000

Artificial Intelligence (AI) is increasingly transforming knowledge-intensive work by shifting organisations from isolated task automation toward collaborative human–AI augmentation. This paper examines this transition in the context of the Information Technology (IT) industry in Pune, India, with particular emphasis on employee productivity, work quality, and overall business efficiency. The study synthesises recent empirical evidence concerning generative AI, software development, knowledge work, and enterprise-level AI adoption and develops a Pune-focused research framework incorporating AI adoption, automation, augmentation, employee productivity, work quality, business efficiency, AI literacy, employee training, and organisational governance. Findings reported in controlled experiments, field studies, and enterprise surveys suggest that AI-enabled assistance can reduce task completion time, improve selected dimensions of output quality, enhance software-development productivity, and reduce the effort associated with repetitive knowledge-intensive activities. However, the benefits of AI adoption are not uniform and may depend on task complexity, employee experience, quality of organisational implementation, availability and quality of data, technological infrastructure, and the level of human oversight. The paper therefore distinguishes between automation, in which AI performs or substitutes selected routine activities, and augmentation, in which AI capabilities are integrated with human judgement, creativity, domain expertise, and decision-making. To empirically examine these relationships, a quantitative cross-sectional research methodology is proposed, targeting approximately 250–400 IT professionals and managers working in Pune’s technology ecosystem. Primary data may be collected through a structured questionnaire measuring AI adoption, perceived automation and augmentation, productivity, work quality, business efficiency, AI literacy, training, and governance practices. The proposed research model and hypotheses can be evaluated using reliability and validity analysis, correlation, multiple regression, and moderation or Structural Equation Modelling (SEM), depending on the characteristics of the collected data. The study argues that the strategic value of AI in Pune’s IT industry is likely to be maximised not through automation alone, but through a balanced approach that automates repetitive and standardised activities while deliberately augmenting employees in coding, data analysis, problem solving, communication, innovation, and managerial decision-making. The proposed framework provides a basis for understanding how organisations can pursue productivity improvements while retaining meaningful human involvement and developing AI-ready capabilities in the workforce.

Quiz Management System

Tue, 29 Sep 2026 00:00:00 +0000

The Quiz Management System (QMS) is a web-based application designed to automate the process of creating, conducting, and evaluating quizzes and assessments in an academic environment. Traditional paper-based examinations are time-consuming, prone to human error during evaluation, and difficult to scale for large groups of students. The proposed system enables instructors to design question banks, configure timed quizzes, and publish them to students, while students can attempt quizzes online and receive instant, automatically generated results. This paper presents the architecture, methodology, and implementation approach adopted for the development of the Quiz Management System. The system was built using a three-tier architecture comprising a front-end interface, an application layer for business logic, and a back-end relational database for persistent storage. The results indicate that the system significantly reduces evaluation time, improves accuracy of scoring, and provides administrators with analytical insights into student performance. The paper concludes with a discussion of the system's advantages, limitations, and directions for future enhancement.

Blockchain for Cybersecurity: Ensuring Data Integrity and Secure Transactions in the Agricultural Industry and Supply Chain Management

Sat, 13 Jun 2026 00:00:00 +0000

The rapid digitalization of agriculture has significantly improved operational efficiency, precision farming, and supply chain transparency. Traditional centralized information systems can face problems in offering enough security, traceability, and trust in complicated multi-stakeholder agricultural supply chains. This paper explores the possibility of blockchain as a cybersecurity architecture to provide data integrity and secure transactions in agricultural applications. A qualitative research method and structured analysis of 47 scholarly papers and four real-world blockchain deployments (IBM Food Trust, AgriDigital, TE-FOOD, and Ambrosus) are used in the study. The results show that blockchain technology can substantially improve the security and transparency of agricultural value chains through various mechanisms such as distributed ledger, consensus validation, smart contracts, decentralized identity management, and role-based access control. By leveraging case studies, it is evident that traceability has improved significantly, fraud prevention has increased, auditability has been enhanced, and transaction security has been bolstered; in some deployments, traceability time is in the order of seconds rather than days. Various mitigation measures such as IoT data attestation, HSMs, consortium governance models and harmonising policies are explored. The study concludes that blockchain technology offers a strong and durable cybersecurity infrastructure for the modern agricultural ecosystem by providing a platform for transparent and trusted data sharing, tamper-resistant data recording, and safe digital transactions throughout the supply chain.Keywords—Blockchain, Cybersecurity, Agricultural Supply Chain, Data Integrity, Smart Contracts, Distributed Ledger Technology, Food Traceability, IoT Security, Secure Transactions, Consortium Blockchain.
DOI: https://www.doi.org/10.24321/3051.4304.202605

How to cite this article:
Afroz M, Vishnu D, Alam I, Lamkuche H S, Patheja P S, Blockchain for Cybersecurity: Ensuring Data Integrity and Secure Transactions in the Agricultural Industry and Supply Chain Management. J Adv Res Comp Tech Soft Appl 2026; 10(2): 26-32.

DOI: https://www.doi.org/10.24321/3051.4304.202605

Movie Recommendation and Ticket Booking System: A Unified Python-Based Approach

Tue, 29 Sep 2026 00:00:00 +0000

Online movie ticket booking has become an integral part of the entertainment industry, yet most commercial platforms such as BookMyShow, Paytm Movies, PVR Cinemas and INOX focus primarily on transactional booking with limited personalised recommendation capability. This paper presents the design and implementation of a Movie Recommendation and Ticket Booking System, a desktop application developed in Python that integrates a content-based recommendation engine with a complete online ticket booking workflow. The system allows users to register, search movies by title, genre and language, receive personalized recommendations based on preferences and booking history, select a city, theatre, screen and seat, simulate payment, and maintain a persistent booking history using an SQLite database. An administrative module enables management of movies, theatres, screens and users. The proposed system was implemented using Tkinter for the graphical interface, SQLite for data storage, and Pillow for image handling. Results demonstrate that combining recommendation and booking functionality in a single lightweight application improves user convenience, reduces manual effort, and provides a scalable foundation for future enhancements such as AI-based recommendations, live payment gateway integration and mobile deployment.

Sorting Algorithm Visualizer & Performance Analyzer

Tue, 29 Sep 2026 00:00:00 +0000

The Sorting Algorithm Visualizer & Performance Analyzer is an interactive web-based application developed to simplify the understanding of sorting algorithms through visual representation and performance analysis. Sorting algorithms are an essential part of Data Structures and Algorithms (DSA), but understanding their internal operations can be difficult through theoretical explanations alone.

The proposed system visually demonstrates algorithms such as Bubble Sort, Insertion Sort, and Merge Sort using animated bars. It allows users to generate or enter data and observe operations such as comparisons, swaps, and movements in real time. Along with visualization, the system analyzes algorithm performance using parameters such as execution time, number of comparisons, and number of swaps.

The application helps users compare different sorting techniques and understand their time complexity and efficiency for different datasets. It provides a simple and interactive learning environment for students, making DSA concepts more practical, engaging, and easier to understand. The project can be further enhanced by adding advanced sorting algorithms, performance graphs, AI-based recommendations, and additional learning features.

A Review on Hybrid Machine Learning and Deep Learning Approaches for Fraud Detection in Online Marketplace Transactions

Mayank Mishra, Arun Kumar Rai — Fri, 19 Jun 2026 00:00:00 +0000

E-commerce platforms have expanded rapidly over past decades, resulting in significantly increased online transactions and increased fraudulent activities. Traditional techniques in fraud detection, which include rule-based systems and conventional machine learning models, struggle to accommodate high-dimensional data, swift evolving fraud patterns, or severe class imbalance. The study offers an extensive review of the existing approaches in fraud detection, focusing on the hybrid approaches of machine learning and deep learning. Particular emphasis is given to the use of advanced pre-processing methods, such as data normalization, categorical encoding, and Synthetic Minority Over-sampling Technique (SMOTE), to deal with data imbalance problems. The study also observes and evaluates different algorithms like Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and CatBoost, plus deep learning techniques-such as Autoencoders-in detecting anomalies. Another point to note is the importance of hybrid approaches utilizing both supervised and unsupervised learning to improve both performance accuracy and model robustness. The study also indicates that CatBoost is good at handling categorical features, while also restricting overfitting; Autoencoders, due to their reconstruction errors, tend to effectively capture stealth anomalies. The research also elaborates on key evaluation metrics, sufficient accuracy, precision, recall, and F1-score, to assess the performance of models for imbalanced datasets. It argues that hybrid methods do much better than traditional technique performances by enhancing the sensitivity of fraud detection while reducing false negatives. Finally, paper ends with future research trends Danstellerity defines the future trends in this discussion by integrating explainable AI and superior deep learning architectures, which would create significant fraud detection systems on a larger scale.

How to cite this article:
Mishra M, Rai A K. A Review on Hybrid Machine Learning and Deep Learning Approaches for Fraud Detection in Online Marketplace Transactions. J Adv Res Comp Tech Soft Appl 2026; 10(2): 1-9.

Preserving Indian Knowledge Systems through Blockchain-Based Cybersecurity: Interdisciplinary Perspectives for Bharat 2047

Seema Bajaj — Mon, 03 Aug 2026 00:00:00 +0000

Indian Knowledge Systems (IKS) constitute a vast repository of indigenous wisdom encompassing Ayurveda, Yoga, Sanskrit literature, Vedic sciences, traditional agriculture, philosophy, architecture, and ecological practices developed over centuries. In the contemporary digital era, preserving and securing this knowledge has become increasingly challenging due to cyber threats, unauthorised access, data manipulation, intellectual property infringement, and unethical commercialisation. Simultaneously, the national vision of Bharat 2047 emphasises sustainable development, digital transformation, cultural preservation, and technological self-reliance. In this context, blockchain technology combined with cybersecurity mechanisms offers a promising solution for ensuring secure, transparent, and decentralised preservation of India’s intellectual and cultural heritage.

This paper examines the interdisciplinary role of blockchain-based cybersecurity systems in safeguarding Indian knowledge systems through a qualitative research methodology based on systematic literature review and secondary data analysis. Research papers from IEEE Xplore, SpringerLink, ScienceDirect, MDPI Sustainability, and Google Scholar were critically analysed to identify key technological trends, cybersecurity concerns, and digital preservation strategies. The study proposes a conceptual blockchain-based cybersecurity framework integrating encryption, decentralised ledgers, smart contracts, authentication systems, and governance mechanisms for secure preservation of indigenous knowledge repositories.

The findings suggest that blockchain-enabled preservation ecosystems can significantly improve authenticity verification, transparency, cyber resilience, intellectual property protection, and ethical knowledge-sharing practices. The paper further highlights implementation challenges, sustainability implications, and future opportunities associated with AI-integrated blockchain ecosystems in alignment with the vision of Sustainable Bharat 2047.

AI Resume Builder: An Intelligent Framework for ATS Optimization and SkillBased Job Matching

Aditi Chavan, Nikita Kolhe, Prachi Mahajan — Wed, 30 Sep 2026 00:00:00 +0000

In the contemporary job market, the increasing volume of applications has led to the widespread adoption of Applicant Tracking Systems (ATS) by recruiters. However, job seekers often face challenges in optimising their resumes to meet these algorithmic criteria. This paper presents the design and implementation of an AI-powered Resume Builder, a comprehensive web-based platform that leverages Natural Language Processing (NLP) and Large Language Models (LLMs) to automate resume generation and refinement. The proposed system integrates advanced techniques such as Retrieval Augmented Generation (RAG), semantic similarity analysis, and transformer-based parsing to provide personalised content suggestions, ATS scoring, and skill-gap analysis. Empirical evidence suggests that AI-driven optimisation can reduce processing time by up to 95% and significantly improve candidate-job alignment. This study further explores the ethical implications of algorithmic hiring, addressing biases in LLM-based screening and proposing mitigation strategies for equitable recruitment.

Index Terms—AI resume builder, natural language processing, large language models, applicant tracking systems, semantic matching, resume optimisation, algorithmic fairness.

Design and Implementation of AI-Enhanced Online Internship and Job Portal Systems

Tue, 29 Sep 2026 00:00:00 +0000

The paradigm of recruitment has shifted significantly from traditional manual processes to sophisticated digital ecosystems. Online internship and job portals have become central to bridging the gap between talent and opportunities. This paper explores the architectural design, core functional modules, and technological stacks of modern job portals. Emphasis is placed on the integration of Artificial Intelligence (AI) and Machine Learning (ML) for resume parsing, intelligent recommendation engines, and candidate-job matching. By analyzing various implementations, including specialized systems for internships, blue-collar workers, and specially-abled individuals, this report provides a comprehensive overview of the current state and future directions of e-recruitment technology.

Integrating Artificial Intelligence with Microservices Architecture: Enabling Scalable and Self-Optimizing Software Systems

Tue, 29 Sep 2026 00:00:00 +0000

Microservices architecture is a modern way of building software applications where the system is divided into small, independent parts called microservices, instead of building one large single program. Each microservice performs a specific function, such as handling user login, processing orders, or managing payments, and works independently from the others. These services communicate with each other using APIs, which act like messengers passing information between them. This approach makes applications easier to build, update, and manage because developers can work on one service without affecting the rest of the system. Microservices architecture also allows applications to scale easily, meaning that only the parts of the system that need more resources can be expanded, saving time and cost. It improves fault tolerance, since if one service fails, the other services can continue to work normally. Different microservices can even be built using different technologies, giving developers more flexibility. However, there are some challenges too, such as increased complexity in managing multiple services, the need for proper coordination between them, and higher effort in monitoring and deployment. Despite these challenges, microservices architecture is widely used in modern software development because it makes systems more flexible, reliable, and easier to maintain. As applications continue to grow in size and complexity, microservices architecture is expected to play an even more important role in building efficient and scalable software systems.