Daily Archives: April 18, 2026

Authors: Soumya M Achari, Pakhi Singha, Nithin Ramakrishnan

Abstract: On demand delivery began as a competitive edge in the consumer market. Quick commerce sites provided customers access to products within the shortest possible time to stand out from rivaling brands. However, this fast growth of 10 minute and 1 day delivery services leave traditional delivery services irrelevant. Due to the customer’s opting for convience and speed, retailers selling stock struggle to meet these expectations and lose profitability. Access to real time data updates and optimisation has hence become significant in ensuring delivery to correct locations, punctually and efficiently. Current local systems struggle to respond to dynamic data, leading to missed delivery time slots, manual intervention requirement, excessive fuel and time wastes, poor customer feedback and so on. In order to remain competitive in such consumer markets, business require real time updates on demand and supply chains, delivery agent availability, client shopping patterns and traffic volume information. To counter these challenges artificial intelligence can be used to understand real time data and set parcel delivery time slots automatically while routing delivery agents through optimal pathways and monitoring the system of the agents and customer to align with their available schedules. The AI will utilise previous ETA, traffic congestion, pattern recognition in relation to prior on time articles that were received and user presence to define schedules for delivery and update the consumers, drivers and supervisors accordingly. This proposed intelligent system would solve the common E-Commerce problems faced by traditional delivery systems by ensuring routes are mapped to avoid redundancy, increase time efficiency, deliver as per consumer availability, especially for cash on delivery where the client is required at the home for payment, provide real time transportation status of the products to supervisors and customers, therefore increasing the trust of the user in the brand and providing an avenue for the manager to handle mismanaged deliveries. Such a system would bolster customer satisfaction and also reduce fuel and time consumption for the drivers, enhancing their work life balance. Deliveries that are more likely to be missed or routes that could result in accidents would be information sent to the supervisor, customer and delivery agents respectively, hence, preventing missed deliveries, injuries and delays. Such systems have been applied experimentally at a smaller scale and proven successful in reducing time, fuel, costs and injury risk, while improving customer satisfaction, making them a worthwhile subject of research.

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Published by: vikaspatanker

Authors: Manasi Rathod, Jayesh Mahajan, Rutwij Landge

Abstract: Storytelling represents one of the most effective techniques for communication, education, and knowledge transfer across diverse domains. However, traditional text-based storytelling methods often fail to maintain engagement among modern learners who increasingly prefer visually rich multimedia experiences. Creating animated storytelling videos manually requires expertise in scripting, illustration, animation design, narration recording, and editing tools. This paper presents STORYTALES – AI Tells the Story, an automated Generative Artificial Intelligence framework that converts textual narratives into animated storytelling videos with synchronized narration and scene-wise visualization. The system integrates Large Language Models for semantic scene segmentation, Stable Diffusion XL for visual synthesis, Stable Video Diffusion for animation generation, Coqui XTTS for narration synthesis, and FFmpeg for automated multimedia composition. Experimental evaluation confirms that the proposed architecture significantly reduces multimedia production complexity while improving accessibility for educators and content creators.

DOI: https://doi.org/10.5281/zenodo.19644537

Published by: vikaspatanker

Authors: Prof. Biju Balakrishnan, Deep Patel, Pankitkumar Patel, Virajkumar Suthar, Dharmik Kanojia

Abstract: Healthcare delivery in its conventional form continues to face persistent operational hurdles — prolonged patient waiting periods, excessive administrative burden, and geographic constraints. This paper introduces MEDICO, a security-focused and patient-centred web portal engineered to establish a fluid digital healthcare environment. The platform was built using the Python Django framework, adopting a Waterfall development methodology and implementing a Model-View-Template (MVT) architecture to support dual-role access control and an intelligent appointment scheduling engine. System capabilities — including practitioner discovery, profile browsing, and automated notification dispatch — were evaluated through Unit, Integration, and System-level testing. Quantitative stress testing demonstrated complete transactional integrity while concurrently processing 50 simultaneous appointment requests, recording zero system failures or scheduling conflicts. This lays a dependable technical foundation directly combating the inefficiencies of conventional booking methods. Subsequent development phases will focus on Artificial Intelligence (AI) for personalised doctor recommendations and a fully integrated Electronic Health Record (EHR) management module.

DOI: https://doi.org/10.5281/zenodo.19642551

Published by: vikaspatanker

Authors: Rathod Alfaz, Ravi Ranjan Kumar Pandey

Abstract: Artificial Intelligence (AI) has changed many industries, and its influence on cybersecurity is very significant. This research paper studies the progress of AI and its role in handling the changing challenges of cybersecurity. It examines the possible benefits of AI in threat detection, vulnerability assessment, incident response, and predictive analytics. In addition, the paper discusses the ethical concerns and possible risks connected with AI in cybersecurity. Through the study of current research, case studies, and industry practices, this paper aims to provide clear insights into the opportunities and challenges created by the use of AI in the field of cybersecurity.

Published by: vikaspatanker

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).

Published by: vikaspatanker

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.

Published by: vikaspatanker