Category Archives: Uncategorized

Authors: Ansari Zain, Khan Fahad, Rajput Burhan, Khan Shifa, Chandramohan Konduri

Abstract: A Learning Management System (LMS) is a comprehensive web-based application developed to streamline the process of teaching, learning, and academic administration. The main objective of the LMS is to provide a unified digital platform where educators can create, organize, and manage learning content, while learners can easily access courses, participate in discussions, submit assignments, and track their academic progress. The system eliminates geographical and time limitations, enabling flexible and self-paced learning for students across different devices. The proposed LMS includes essential modules such as user authentication, course management, content uploading, online assessments, grading, progress tracking, and communication tools like notifications and discussion forums. It leverages database management systems to securely store and retrieve user data, ensuring reliability and scalability.

DOI: https://doi.org/10.5281/zenodo.19642420

 

Published by: vikaspatanker

Authors: Smit Sudani

Abstract: With all the amount of data that is now available about the students in a school environment, there is no way one could analyze such data manually. The Student Performance Predictor is a web application I designed to help determine the final score that a particular student will get from mathematics class, basing on his demographics and background. The whole machine learning pipeline was implemented by me using the Python language. After experimenting with various models in Jupyter Notebooks and having my kernel crash quite a few times, I managed to find the most accurate one – Random Forest Regressor with an 80% accuracy rate. Next, I embedded this algorithm in my application, which uses the Flask server. User only needs to input some values in three fields to get the prediction instantly.

Published by: vikaspatanker

Authors: D.Hari Priya, Ch.Charmi Sri, A.Rohit, K.Harika Sri, Ms. M. Soumya

Abstract: This paper presents PDFChatBot, a comprehensive AI-driven system for automated PDF summarization and intelligent query answering. Our hybrid approach integrates Rhetorical Structure Theory (RST), transformerbased models (BERT, GPT-4, Gemini-1.5-Pro), and FAISS vector databases, achieving state-of-the-art ROUGE-L scores of 0.51 and F1-scores of 0.87 across 50 diverse documents spanning research papers, legal contracts, medical reports, financial statements, and technical manuals. The system processes 100-page documents in under 120 seconds, reducing document review time by 80% while maintaining semantic coherence. We demonstrate superior performance over TextRank (ROUGE-L: 0.37), BART-large (0.44), and T53B (0.47) baselines through rigorous evaluation across five distinct domains. Production-ready deployment via FastAPI, Streamlit, Docker, and Redis caching ensures scalability for enterprise applications with 99.9% uptime and sub-second query latency.

DOI: https://doi.org/10.5281/zenodo.19642249

Published by: vikaspatanker

Santhosh Kumar Maddineni
Affiliation	HCM and Integration Consultant
Email-Id:	santhoshkumarmaddineni2@gmail.com
Publication: Patents: Smart Power Bank for electrical Vehicle Application number: 6458271. Publications: Maddineni, S. K. (2024). Custom tax documentation in Workday using BIRT: Challenges and solutions in W-2 and 1095-C report design. International Journal for Novel Research in Economics, Finance and . Maddineni, S. K. (2023). Building cross-functional dashboards in Workday: from time off analytics to compensation reviews. International Journal of Scientific Research & Engineering Trends, 9(6). Maddineni, S. K. (2021). Configuring and managing core HCM with Workday: From supervisory organizations to cost center hierarchies. International Journal of Science, Engineering and Technology, 9(6). Maddineni, S. K. (2019). Toward AI-enhanced HR management: Predictive compensation reviews using Workday custom reports and calculated fields. International Journal of Trend in Research and Development, 6(4). Maddineni, S. K. (2018). A practical guide to document transformation techniques in Workday for non-standard vendor layouts. International Journal of Trend in Research and Development, 5(5).

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Authors: Zannatul Rumman Zinia, Abdullah Al Ruhul

Abstract: This study examines the economic impact of Foreign Direct Investment (FDI) on Bangladesh's automobile sector, with particular emphasis on sectoral output, employment generation, and macroeconomic determinants of investment inflows. Using annual time-series data and sector-specific indicators, the analysis integrates descriptive statistics, correlation assessment, multiple regression modeling, and iterative epoch-based robustness evaluation to investigate both the contribution and sustainability of FDI-led industrial growth. The empirical results indicate that manufacturing-oriented FDI exerts a positive and statistically significant influence on automobile sector gross value added (GVA), supporting the hypothesis that foreign capital contributes to capital deepening, technology diffusion, and production expansion. Real GDP, serving as a proxy for market size, emerges as a strong determinant of FDI inflows, while human capital development and trade openness demonstrate complementary roles in enhancing investment attractiveness. However, the employment elasticity of FDI remains moderate, suggesting that capital-intensive investment patterns dominate labor absorption effects. Productivity growth, measured as output per worker, exhibits gradual improvement but reflects structural constraints related to limited local value-chain integration. The findings suggest that while FDI plays a constructive role in supporting sectoral expansion, its long-term developmental impact depends on institutional quality, skill upgrading, and domestic supplier ecosystem strengthening. Policy recommendations emphasize targeted human capital development, enhanced local content integration, regulatory efficiency, and export-oriented industrial clustering to maximize the transformative potential of manufacturing FDI within Bangladesh's automobile industry.

DOI: https://doi.org/10.5281/zenodo.19640120

Published by: vikaspatanker

Authors: Abu Aasim

Abstract: The Iris Flower Classification problem is one of the most fundamental and widely studied benchmarks in supervised machine learning. It involves classifying iris flowers into three species (Setosa, Versicolor, and Virginica) based on four morphological features: sepal length, sepal width, petal length, and petal width. This review paper clearly defines the category of basic supervised machine learning tasks and explores the existing algorithms for classification. A novel comparative framework is proposed using Python and scikit-learn to evaluate five basic supervised algorithms—Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Naive Bayes—on the UCI Iris dataset. Performance is measured using accuracy, precision, recall, and F1-score. The study demonstrates that while all algorithms achieve high accuracy (>95%), KNN and SVM consistently outperform others in terms of perfect classification on the test set, highlighting their suitability for simple, linearly separable datasets. General Terms: Supervised Machine Learning, Classification, Comparative Analysis, Iris Dataset, Performance Metrics.

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Authors: Sai Rithwik Nooguri

Abstract: The emergence of Large Language Models (LLMs) represents one of the most consequential shifts in the history of artificial intelligence (AI) and natural language processing (NLP). Built on the Transformer architecture with self-attention mechanisms, LLMs such as BERT, GPT-3, T5, LLaMA, and GPT-4 have achieved state-of-the-art performance across a broad spectrum of linguistic tasks, fundamentally reshaping how machines comprehend and generate human language. This survey presents a systematic and comprehensive review of the evolution of NLP—from rule-based and statistical methods to the current era of foundation models—examining key architectural innovations, pre-training objectives, fine-tuning strategies including parameter-efficient methods such as Low-Rank Adaptation (LoRA), and alignment techniques including Reinforcement Learning from Human Feedback (RLHF). We critically assess performance across standard benchmarks including GLUE, SuperGLUE, and MMLU, and analyze persistent challenges such as hallucination, bias, computational cost, and explainability. Furthermore, we explore the expanding landscape of LLM applications in healthcare, education, legal reasoning, and code generation, and outline promising future directions including multimodal models, efficient inference, and AI alignment. This work aims to serve as both an accessible introduction and a scholarly reference for researchers and practitioners engaged with the rapidly evolving frontier of AI-powered language understanding.

DOI: https://doi.org/10.5281/zenodo.19630886

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Authors: Shruti Chouhan, Prof. Pankaj Raghuwanshi

Abstract: Skin cancer is one of the most prevalent and rapidly increasing forms of cancer worldwide, making early detection essential for improving patient survival and treatment outcomes. Traditional diagnostic methods rely heavily on visual examination and dermoscopic analysis by dermatologists, which may sometimes be subjective and dependent on clinical expertise. In recent years, machine learning (ML) and deep learning (DL) techniques have emerged as powerful tools for automated skin cancer detection and classification. These techniques utilize medical image datasets, particularly dermoscopic images, to identify patterns and features associated with malignant and benign skin lesions. This review presents a comprehensive analysis of recent research on ML and DL-based approaches for automated skin cancer detection. Various algorithms such as Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN), and transfer learning models are examined in terms of their methodologies, datasets, and performance metrics. Additionally, this study highlights the advantages, limitations, and challenges associated with these techniques. The review also discusses future research directions, including the development of more diverse datasets, interpretable models, and integration of AI-based systems into clinical practice to enhance diagnostic accuracy and healthcare efficiency.

Published by: vikaspatanker

Authors: Anushka Rastogi, Priya Gupta

Abstract: Urban areas are rapidly expanding and this brings with a set of complicated problems. Things like traffic jams, rising energy bills, overflowing landfills, and public safety concerns are becoming everyday issues for city residents. This paper looks at how artificial intelligence can play a practical role in fixing these problems. AI is making everything rapid and faster, from managing road signals to making everything work wisely and securely. At the same time, the paper does not ignore the hurdles, like data security, the cost of setting up these systems, and making sure that benefits reach every part of society, not just wealthy neighbourhoods. This study also examines how artificial intelligence can help cities better prepare for long-term challenges like climate change, population growth, and natural disasters. By looking at existing AI projects around the world, this research aims to give a realistic view of the current state and potential of AI in city infrastructure.

DOI: https://doi.org/10.5281/zenodo.19630016

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Authors: Suraj Yadav, Sagar Gupta, Tanishq Raj Mahaur, Mr. Anurag Anand Duvey

Abstract: The conventional electronic voting machines often have problems like, centralized vulnerabilities, lack of transparency, prone to single-point-of-failure attacks, as well as high administrative overhead for voter verification. This paper presents a solution: A Decentralized e-voting framework created on the Ethereum Virtual Machine (EVM) that tackles these issues through the integration of blockchain immutability and mobile-native biometric authentication. With the medium this project, we propose a system that implements Solidity smart contracts to manage election lifecycles and a React Native frontend for User friendly UI and cross-platform accessibility. The key innovations such as a cycle-based state management mechanism for optimizing the contract reusability and a cryptographic credential-hashing protocol that safeguards voter identity without the need for high-cost third-party verification services.

Published by: vikaspatanker