Daily Archives: September 2, 2026

Authors: Vikrant Diwakar

Abstract: User-generated content (UGC) has emerged as a key element in the digital economy, posing intricate issues about copyright ownership, management, and monetization. This paper investigates the changing legal framework for UGC, highlighting the dynamic interplay among creators, online platforms, and new technologies. Users are generally the initial creators and may be the principal copyright owners of their content, subject to statutory exceptions; however, ownership can be materially affected in practice by platform licensing terms, which may confer extensive permissions on service providers. The study examines moral and economic rights, contractual licensing, rights-management tools such as metadata and Creative Commons licensing, blockchain-based provenance, content removal and archiving, revenue-sharing, artificial intelligence, privacy, publicity and cross-border enforcement. The revised version preserves the original discussion while correcting legal overstatements, especially the description of copyright as a 'Fourteenth Amendment System' and the treatment of non-copyrightable material as automatically public domain. It also adds a stronger Indian legal framework, verified authorities, comparative analysis and a focused research gap. Particular attention is given to AI-assisted and AI-generated UGC, including human authorship, originality, training-data questions and the 2026 Indian framework concerning synthetically generated information. The paper advocates a balanced and adaptive approach to copyright governance that protects creator interests, promotes transparent platform licensing and revenue sharing, supports responsible moderation, and permits technological innovation.

DOI: https://doi.org/10.5281/zenodo.22253782

Published by: vikaspatanker

Authors: Balvir Singh Thakur, Devanshi

Abstract: Regression testing is a vital part of software testing. Executing all test cases from large test suites without considering their historical failure behaviour may lead to time-consuming and inefficient execution. Running the test cases that are more prone to failure first can improve this process. In this paper, an AI-based test case prioritisation approach for the automated testing framework based on TestNG using machine learning and historical test execution data is proposed. The proposed approach extracts execution-based features like total test runs, number of failures, failure rate, average execution time and recent failure information and uses a Random Forest classifier to identify high-risk test cases. The predicted risk information is then used to produce an ordered list of the test suite, ordered by priority. The approach was tested on 70 TestNG test cases with 20 previous executions compared to a normal execution order and an alphabetically ordered static baseline using the Average Percentage of Faults Detected (APFD) metric. The experimental results show that the AI-prioritized execution order results in an APFD of 0.7100, compared to 0.5643 for the normal order and 0.5729 for the static priority order, improvements of 25.82% and 23.94%, respectively. The results show that taking into account historical failure behaviour in machine learning-based test prioritisation can improve the early detection of failing test cases and provide a more risk-aware execution strategy for regression testing.

DOI: https://doi.org/10.5281/zenodo.22252547

Published by: vikaspatanker

Authors: Professor Priyanka Mahajan, Rohit Balaji Pingale, Vaishnavi Laxman Shinde

Abstract: A India's electronic waste (e-waste) generation has increased rapidly, while formal collection and recycling infrastructure remains concentrated in major metropolitan areas. Tier-2 and Tier-3 cities continue to face challenges in planning efficient collection networks due to limited data-driven decision support. This paper presents a machine learning-based framework for estimating ward-level e-waste generation using publicly available demographic, economic, and administrative indicators. A Random Forest regressor is proposed as the primary prediction model and is benchmarked against Gradient Boosting and Ordinary Least Squares regression to evaluate predictive performance. The framework incorporates a reproducible data-engineering pipeline integrating data from the Central Pollution Control Board (CPCB), State Pollution Control Boards (SPCBs), Census records, and Urban Local Bodies (ULBs). To validate the proposed workflow before large-scale field deployment, a clearly labelled synthetic dataset is developed for model training and evaluation. Feature importance analysis is further employed to identify the key factors influencing e-waste generation, enabling practical insights for municipal authorities and Extended Producer Responsibility (EPR) stakeholders. The proposed approach offers a scalable, low-cost, and replicable methodology for supporting sustainable e-waste management, optimizing collection planning, and reducing the infrastructure gap between metropolitan and emerging urban regions in India.

DOI: https://doi.org/10.5281/zenodo.22252069

Published by: vikaspatanker

Authors: Komal, Balvir Singh Thakur

Abstract: A major challenge for the telecommunication industry is the frequent customer switching, resulting in substantial revenue loss and increased customer acquisition costs. Most of the existing studies on churn prediction focus on the predictive accuracy and pay less attention to the interpretability of the model and how to convert the model output to business-facing visual tools. In this study, an integrated framework is proposed with XGBoost for churn classification, SHAP (Shapley Additive explanations) for global and local interpretability and Tableau for the interactive dashboard visualization. The Telco Customer Churn dataset with 7,043 customer records and 21 features is publicly available and is used for experimentation. The accuracy of the XGBoost model on a held-out test set of 1,409 customers is 80.41%, with better results on the majority (retained) class than on the minority (churned) class, which is consistent with the moderate imbalance between classes in the dataset. This outcome will be contextualized through baseline comparison against Logistic Regression and Random Forest. SHAP analysis reveals that contract type, tenure, monthly charges, total charges and internet service type are the dominant churn drivers globally, and local SHAP explanations illustrate how these factors combine for individual customers. In addition to the SHAP-ranked churn drivers, an interactive Tableau dashboard was created to visualize churn distribution by contract type, payment method, and internet service type for business-facing exploration of these insights. The study concludes that the combination of predictive accuracy with explainability and visualization leads to a more actionable churn-management framework than accuracy-oriented approaches alone.

DOI: https://doi.org/10.5281/zenodo.22245020

Published by: vikaspatanker