Daily Archives: January 16, 2026

Authors: Arona Mumtaz

Abstract: Cybercrime has emerged as one of the most significant threats in the digital era, particularly impacting vulnerable groups such as women and children. With the growing use of the internet, cybercriminals exploit anonymity to engage in illegal activities that range from harassment to defamation and pornography. This paper examines various forms of cybercrime, with a particular focus on crimes targeting women, such as cyber harassment, cyber stalking, and cyber pornography. It discusses notable cases and the legislative framework in India aimed at combating these crimes. Despite existing laws, the paper highlights gaps in enforcement and the challenges posed by anonymity on the internet. Additionally, empirical evidence is presented to highlight the prevalence of cybercrime, its impact on victims, and the challenges in enforcement. The article concludes by offering suggestions for improving legal enforcement, public awareness, and privacy protection to combat the rising tide of cybercrime.

DOI: https://doi.org/10.5281/zenodo.18266294

 

Published by: vikaspatanker

Authors: Oksana Anatolyevna Malysheva

Abstract: They've opened up the door to instant fund transfers and around-the-clock availability. But at the same time made us more exposed to scammers and money laundering schemes, when real-time payments became a norm. Coming racing up against the tight timeframes and limited space to go back and correct anything that's gone wrong, the old way of doing things just isn't working anymore. This paper takes a hard look at the analytical and infrastructure-related issues surrounding the detection of fraud and money laundering in real-time payment systems, where speed, accuracy and meeting regulations all need to be juggled at the same time. Well-known techniques won’t cut it anymore in the world of real-time, so the researchers here take a more applied approach, merging real-time analytics systems, cutting-edge fraud detection and money laundering models. They lay out a comprehensive blueprint for real-time transaction analysis, fine-tuning features for ultra-fast decision-making, hybrid rule-based and AI-driven systems and risk-scoring that’s tailored to the flow of instant payments. When evaluating the performance of fraud and money laundering systems in real-time, this paper looks beyond the traditional measure of accuracy and zeroes in on things like response times, scalability and false positives, which are pretty critical in the real-time world. The real contributions of this work are threefold: a crystal-clear picture of the threats facing real-time payments, a logical analytical framework that gets the balance between detection models, real-world timeframes and regulatory expectations, and some down-to-earth advice on how to run your fraud and money laundering systems in a way that is not only effective but also explainable and scalable.

Published by: vikaspatanker

Authors: James Anderson

Abstract: The use of artificial intelligence technologies in tax administration is becoming increasingly widespread worldwide to increase efficiency and detect fraud. Tools such as chatbots, risk ratings and predictive analytics optimise workflows, but their wider use in administrative decision-making raises legal and structural challenges. There is a critical difference between decision-supporting and autonomous artificial intelligence. Over-reliance on automated systems risks eroding legal expertise and obscuring decision-making, making it difficult for taxpayers to seek redress. Taxpayer profiling carries the risk of discriminatory treatment, so rigorous testing and minimisation of bias are necessary. In terms of methodological foundations, the study used dogmatic and transdisciplinary analysis to examine the opportunities and risks from an international perspective. The advantages of artificial intelligence include the real-time analysis of large amounts of data, which helps to filter out tax avoidance schemes and reduce the administrative burden on taxpayers (e.g. pre-filled tax returns). At the same time, the "black box" phenomenon violates the principle of transparency. The US and the OECD aim to improve efficiency and develop taxpayer services using artificial intelligence tools. The EU takes a risk-based approach, imposing strict requirements on high-risk artificial intelligence systems and emphasising the need for human oversight and legal remedies. Australian examples (Robodebt, Pintarich cases) highlight the legal and human rights risks of faulty algorithms, underlining the need for accountability. Success lies in striking a balance: while exploiting technological efficiency, it is necessary to guarantee human oversight, the accountability of algorithms and the protection of taxpayers' fundamental rights. Artificial intelligence should support fair law enforcement, not replace it.

DOI: http://doi.org/10.5281/zenodo.18265078

Published by: vikaspatanker

Authors: Pavan M, Pavan V S, Pranav H Nayak, Sanath G, Dr. R Manjunatha

Abstract: Glaucoma is a serious eye disease that leads to irreversible vision loss if not detected at an early stage. It is primarily caused by increased intraocular pressure, which damages the optic nerve. Early diagnosis plays a crucial role in preventing permanent blindness; however, traditional diagnostic methods are time-consuming and require expert ophthalmologists. This project presents an automated glaucoma detection system using digital image processing techniques applied to retinal fundus images. The proposed system focuses on extracting key features such as the optic disc, optic cup, and calculating the cup-to-disc ratio (CDR), which is a significant indicator of glaucoma. Image preprocessing techniques including noise removal, contrast enhancement, and segmentation are employed to improve accuracy. The extracted features are then analyzed to classify the eye as normal or glaucomatous. The system aims to provide a cost-effective, efficient, and reliable method for early glaucoma screening, thereby assisting ophthalmologists in diagnosis and reducing the risk of vision loss.

Published by: vikaspatanker