Daily Archives: April 29, 2026

Authors: Rajat Srivastava, Mr. Ankit Singh, Sneha Mehrotra, Shaifali Singh, Shreyansh Srivastav

Abstract: There’s a lot more to student performance than just marks. Some kids barely pass written exams but shine in group projects or sports. The problem is, most colleges still judge students almost entirely by their test scores. That’s like judging a fish by its ability to climb a tree. By the time a teacher realizes someone’s struggling, that student might already be failing or even thinking of dropping out. So what if we could spot trouble earlier — way before the report card says it all? That’s what this paper is about. We used machine learning to sift through student data — attendance, past grades, even family background — and predict who might fall behind. Not just for the sake of prediction, but to actually give teachers a heads-up so they can step in and help. The results were pretty solid. Our model caught most at-risk students with over 90% accuracy. Not perfect, but a lot better than waiting till the end of the semester.

Published by: vikaspatanker

Authors: Avinash, Anshika, Shivam

Abstract: We know digital payment systems are growing faster all over the world, and as they grow, they have some consequences. One big problem among them is digital payment fraud, which rapidly increases as payment systems grow. Fraudsters can surpass rule-based detection systems as they adapt; they have new patterns for doing fraud. They find loopholes in the main architecture from where they manipulate data and do fraud. We study both problems and reach a very solid solution to track down all fraud patterns. We added artificial intelligence and the Hyperledger Fabric blockchain, which is used to detect the pattern of fraud, and a blockchain, which is used to make the payment system tamper-proof. All data related to the payment system are stored in a single system, which is very secure and not able to be encrypted. The detection system runs on four methods. For unstable workflow it uses LSTM networks. For rule-based classification, we used a random forest classifier. For fraud detection, we used a GraphSAGE network, and last, for any suspicious activity, we used an autoencoder. All the things are watched by a meta-learner, which analyzes and combines their output and provides data to trigger a smart contract response, which works automatically. Different financial institutions are used to train their systems without using shared row transaction data to make privacy learn their module detection. We concluded our study, but two public benchmarks are set by PaySim (6.35M transactions) and IEEE-CIS (590K transactions). In PaySim we succeed with up to 98.3% accuracy and an AUCROC of 0.991. Adversarial robustness testing shows the team requires 3.2 times larger to prevent any mistake for success for a single model. These results show much need of AI and blockchain. Using AI and blockchain is very efficient; they are better than anything else to detect fraud.

Published by: vikaspatanker

Authors: Vansh Sharma

Abstract: Banking and financial institutions are increasingly encountering the challenges of credit card fraud. Statistics suggest that each year financial institutions incur losses close to billions of dollars globally due to such frauds .Hence it is evident for financial institutions to continue to invest in advanced fraud detection systems to minimize the impact of credit card fraud on their bottom line and protect their customers from financial losses.Before deep diving into the solutions which can be proposed to solve the problem of credit card fraud , it is important to know the ways in which these frauds are taking place and what loopholes are being misused to catalyze these frauds .Hence in our research paper we first look at ways in which these frauds are taking place. Moreover, one of the other challenges to proposing a solution to this problem is the presence of highly imbalanced datasets to train the model , which motivates us to apply various techniques such as Synthetic Minority Oversampling Technique (SMOTE) to make the datasets balanced which will allow us to train the model better .We implement Artificial neural network + Recurrent neural network with auto-encoder architecture to make a model for one-class classification . The model uses these relationships to make predictions about the likelihood of fraud in new transactions. ANNs can be used to process large amounts of data and are particularly effective in detecting non-linear relationships between variables.

DOI: https://doi.org/10.5281/zenodo.19884020

Published by: vikaspatanker

Authors: Dr. Meghna Utmal, Sakshi Singh, Kunti Uikey, Vaishali Gupta, Sajal Pandey

Abstract: — Heart disease remains a major health concern worldwide, affecting a large proportion of the global population. According to reports by the World Health Organization (WHO), approximately 17.9 million deaths occur annually due to cardiovascular diseases. In the context of the COVID-19 pandemic and its post-infection complications, cardiac failure has emerged as a commonly observed condition, highlighting the critical need for early diagnosis and prediction of heart disease to enable effective prevention. Timely detection can significantly reduce mortality rates. Recent advancements in machine learning techniques have greatly contributed to the healthcare sector, particularly in the prediction of heart diseases, thereby saving numerous lives. This paper presents an efficient ensemble-based machine learning approach for predicting heart-related disorders, achieving an accuracy of 88.52%.

DOI: https://doi.org/10.5281/zenodo.19882012

Published by: vikaspatanker

Authors: Nv Subhasri, Madhunisha. A, Shruthi. T

Abstract: This dissertation examines the growing intersection of law, technology, and regulation in the context of autonomous vehicles (AVs) in India, with particular emphasis on issues of privacy, data protection, and cybersecurity. It analyzes existing Indian legal frameworks, such as the Information Technology Act and the proposed Personal Data Protection Bill, to assess whether they are equipped to handle the unique challenges posed by AV technology. The study also compares India’s approach with international standards, including the GDPR and regulatory models followed in countries like the United States and China. Through this comparative perspective, the research highlights existing gaps and suggests areas where India can strengthen its legal and regulatory response.

Published by: vikaspatanker

Authors: Rajat Takkar, Gunjan Lathwal, Devanshi Dadwal, Bhumika Aggarwal, Gaurang Batra

Abstract: AI chatbots and large language models show up almost everywhere these days – customer support, healthcare, schools, even hiring. While they’re good at handling language, there’s still a big question about bias. These systems often pick up biases from their training data and then reflect them back in their answers. That includes biases related to gender, jobs, places, or wealth. This study explores whether chatbots respond to demographic-based questions with built-in bias. We used a set of structured prompts and gathered answers from several AI chatbots, recording all responses for analysis. Every answer was examined using sentiment analysis and a neutrality scoring method. This measured how fair or unbiased each system was. We performed all our analysis using Python tools like Pandas, TextBlob, and Matplotlib. Our expectation was that chatbot responses would usually be objective, but some subtle biases could sneak in depending on how you ask the question or what the topic is. Some questions just lead to more bias than others. By scoring fairness, we can actually quantify differences and see which systems are more neutral. This approach helps assess how these AI tools deal with real-world issues and fairness.

Published by: vikaspatanker

Authors: Acharjee Partho Protim, Wei Zhang

Abstract: Lightweight design is essential in modern automotive systems to improve energy efficiency, reduce emissions, and enhance performance. This study presents a simulation-driven framework for the lightweight design of an automotive reducer housing using finite element analysis (FEA) and topology optimization (TO). A baseline reducer housing is analyzed under multiple load conditions, including maximum torque, emergency braking, and cornering. Stress distribution and deformation behavior are evaluated to identify structurally redundant regions. A Solid Isotropic Material with Penalization (SIMP)-based topology optimization method is applied with a volume reduction constraint to minimize compliance while maintaining stiffness. The optimized topology is reconstructed into a manufacturable design considering casting constraints. Comparative FEA validation shows significant mass reduction while preserving structural integrity, safety factor, and stiffness. The proposed methodology provides an effective and practical framework for lightweight automotive component design.

DOI: https://doi.org/10.5281/zenodo.19880845

Published by: vikaspatanker

Authors: Khadija Elkattany, Md Mutasim Billa

Abstract: This paper presents a hybrid machine learning framework that addresses scalability and accuracy challenges in retail inventory management by integrating real-time demand forecasting with anomaly detection, evaluated using Walmart’s historical sales data. Traditional approaches face a trade-off: maintaining individual models for each product category is computationally prohibitive, while generalized models often underperform for dissimilar items, resulting in stock outs or overstocking. To address this, we propose a department-level aggregation strategy that balances specificity and generalization, combined with a hybrid methodology: ARIMA for linear trend and seasonality modeling, cubic spline interpolation to capture nonlinear residual patterns, and neural networks for complex interactions. The framework dynamically adjusts predictions using real-time sales streams and applies residual-based anomaly detection with threshold triggers to identify sudden demand spikes or supply disruptions. Experiments on a filtered Walmart dataset (12 months, 15 departments) indicate an 18% reduction in mean absolute error (MAE) compared to exponential smoothing baselines, while spline-enhanced neural networks achieve a 24% improvement over standalone ARIMA. The anomaly detection module identifies 92% of simulated irregularities with a 7% false-positive rate. The proposed framework provides three principal advantages: (1) scalable department-level modeling without per-product customization, (2) real-time adaptability to fluctuating demand, and (3) cost-efficient inventory optimization through integrated anomaly alerts. This work offers a practical blueprint for retailers to enhance forecasting precision, mitigate supply chain risks, and reduce operational costs in volatile markets.

DOI: https://doi.org/10.5281/zenodo.19880805

Published by: vikaspatanker

Authors: K. Sagar, K.Ashok, G. Vijay Kumar

Abstract: This investigation explains the effect of addition of polypropylene fibers and Nano Silica into concrete. This investigation is divided into two phases. First Phase deals with calculations of Compressive and Split Tensile strength. Here we have done compressive strength tests for calculating the optimum percentage of Nano Silica with variation from0% to 3% of cement which is replaced with cement in concrete. Now polypropylene fiber is added to concrete from 0% to 1.4% of cement and those specimens were tested for compressive and Split Tensile strength and obtained the maximum percentage of fiber at which strengths maximum. In second phase, a modal equation is developed using Multiple Regression Analysis (MRA) for compressive and Split Tensile strength based on experimental results which are found in phase one. By using obtained modal equations we will calculate Predicted strength and their residuals and their graphical representation is shown.

DOI: https://doi.org/10.5281/zenodo.19879606

Published by: vikaspatanker

Authors: Rathod Neha, Mojidra kirtika, khandhediya Isha, Harkishan sir

Abstract: Federated Learning (FL), was created by McMahan et al (14), has become of interest because it offers a decentralized machine learning framework for developing large scale ML models. This allows many users (or clients) to collaborate on training a shared model while retaining control of their own data. FL is ultimately designed to provide a solution to the conflict between the data demands of machine learning systems and the desire of individuals/companies to keep their personal and commercial data private. This paper is a review of the privacy and confidentiality aspects of Federated Learning. A critical review of the fundamental algorithms used in FL, possible attacks against FL systems, and the four primary techniques for enhancing privacy in FL; Differential Privacy (DP), Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and hardware based Trusted Execution Environments (TEE), is provided. We will review aggregation protocols, determine the strength of FL systems against poisoning and inference attacks, and compare various FL systems implemented in three industries; healthcare, mobile communication and finance. A detailed review of FL reveals research issues related to; statistical heterogeneity, communication overhead, system heterogeneity and fairness. Finally, this review presents a prioritized set of research objectives for the next ten years, with an emphasis on situating FL within the larger context of privacy-preserving ML and potential regulatory developments.

DOI: https://doi.org/10.5281/zenodo.19878825

Published by: vikaspatanker