Category Archives: Uncategorized

Authors: Mr. Pratik Kalukhe, Mr. Shriyash Korade, Mr. Ankit Kapure

Abstract: The sustainability and profitability of modern agriculture hinge critically on selecting the optimal crop for specific geographical and environmental conditions. Traditional crop selection methods often rely on generalized historical data or farmer intuition, failing to account for the maximum achievable yield potential. This limitation frequently leads to suboptimal land use and reduced profitability. he optimization of agricultural output requires selecting not just a suitable crop, but the highest-yielding crop for specific environmental conditions. Traditional methods of crop selection often lack the scientific depth to accurately forecast crop productivity, leading to suboptimal yields and resource mismanagement. This research proposes a Yield-Aware Crop Selection System Leveraging Machine Learning (ML) to address this gap. The system utilizes a robust classification model to perform the initial recommendation based on key soil parameters (N, P, K, pH) and climatic factors (temperature, humidity, rainfall). Comparative evaluation showed that the Random Forest algorithm delivered the highest accuracy for crop suitability, achieving 98.8%. This system is architecturally designed to integrate a subsequent yield prediction model (using regression analysis) to provide the expected output, thus enabling farmers to make a final, yield-optimized decision. The highly accurate selection phase lays a reliable foundation for maximizing profitability, promoting sustainable farming, and modernizing agricultural practices through data-driven insights. By integrating robust classification with precise yield regression, this system transforms crop selection from a suitability problem into an optimization problem. This approach offers farmers an effective tool for boosting agricultural output, improving resource efficiency, and enhancing economic viability.

DOI: https://doi.org/10.5281/zenodo.19183397

Published by: vikaspatanker

Authors: Mukesh Brijanand Yadav, Prof. Ankush Dhamal

Abstract: Maintaining an optimal body weight is a fundamental aspect of personal healthcare management, as it significantly influences overall well-being, disease prevention, and quality of life. However, many individuals face confusion due to contradictory information available online, lack of personalized guidance, and the limitations of generic weight charts and traditional formulas that fail to account for individual variations and complex interactions between demographic factors. This research proposes an AI-Powered Ideal Weight Prediction System Using Multivariate Regression Analysis designed to assist individuals in identifying their ideal body weight based on key anthropometric parameters including height, age, and gender. The proposed system utilizes machine learning algorithms to analyze user data collected through interactive input interfaces. Features such as height measurements (in centimeters), age demographics (18-100 years), and gender classifications (Male/Female) are used as input parameters for multivariate regression analysis. Multiple regression algorithms including Random Forest Regressor, Decision Tree Regressor, Support Vector Regression, and Linear Regression were implemented and compared to identify the optimal model for weight prediction. The system is trained and evaluated using a comprehensive synthetically generated dataset (n=2000 samples) incorporating realistic biological variations and age-based metabolic adjustments, with ideal weight values calculated using modified Devine formulas enhanced through multivariate analysis techniques. The performance of the models is assessed using standard evaluation metrics including R-squared (R²), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) . Experimental results demonstrate that the Random Forest Regressor with 100 estimators achieves superior prediction accuracy compared to other algorithms, effectively capturing complex non-linear relationships between demographic features and ideal weight that conventional univariate methods cannot represent. The multivariate regression approach enables the model to simultaneously analyze interactions between all three input parameters, resulting in more nuanced and personalized predictions.

 

 

Published by: vikaspatanker

Authors: Mr. Sanket P. Datir, Mr. Swaraj A. Kale, Mr. Sumit M. Bahakar, Mr. Vipin V. Thorat, Prof. Ravindra R. Solanke

Abstract: Road accidents caused by over-speeding are a major problem, especially in areas like school zones, hospitals, and residential areas. To improve road safety, an automatic vehicle speed control system using Radio Frequency (RF) technology is proposed. In this system, an RF transmitter is installed in restricted zones and an RF receiver is placed in the vehicle. When the vehicle enters the restricted area, the transmitter sends a signal that is received by the vehicle’s receiver. The microcontroller processes this signal and automatically limits the vehicle speed. When the vehicle exits the restricted zone, the system restores the normal speed. This system helps reduce accidents and improves safety in sensitive areas.

Published by: vikaspatanker

Authors: Ayush Chettri, Aakansh Rai, Ashish Sunar, Asish Shakya

Abstract: This paper presents the design and implementation of a web-oriented Learning Management System (LMS) that aims to improve academic management in an institute. The system integrates course management, role-based access control, and real-time attendance tracking using modern web technologies including React.js, Node.js, and PostgreSQL. A modular three-tier architecture is adopted to ensure scalability and maintainability. The system is evaluated through functional testing and user feedback, demonstrating improved efficiency, accuracy, and usability compared to traditional manual methods. The proposed LMS reduces administrative workload, enhances communication, and provides a structured digital learning environment, making it suitable for deployment in academic institutions.

DOI: https://doi.org/10.5281/zenodo.19177834

 

Published by: vikaspatanker

Authors: Bommisetty Srihari, K Balasubrahmanyam, Mareddy Sai Kotireddy, Dr. U. Saravanakumar, Mr. E. Vinoth Kumar

 

Abstract: Recent advances in smartphones and affordable open-source hardware platforms have enabled the development of low-cost architectures for Internet-of-Things (IoT)-enabled home automation and security systems. These systems usually consist of sensing and actuating layer that is made up of sensors such as passive infrared sensors, also known as motion sensors; temperature sensors; smoke sensors, and web cameras for security surveillance. These sensors, smart electrical appliances, and other IoT devices connect to the Internet through a home gateway. This paper lays out an architecture for a cost-effective smart door sensor that will inform a user through an Android application, of door open events in a house or office environment. The proposed architecture uses an Arduino-UNO board along with the API. Several programming languages are used in the implementation and further applications of the door sensor are discussed as well as some of its shortcomings such as possible interference from other radio frequency devices.

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Published by: vikaspatanker

Authors: Pratika Yadav

Abstract: Artificial Intelligence (AI) has become a revolutionary force in credit evaluation and SME (small and medium enterprises) financing in the quickly changing financial ecosystem. The underlying creditworthiness of SMEs is frequently overlooked by conventional credit evaluation techniques, which mostly rely on financial statements and collateral. This study contrasts traditional credit evaluation methods with AI-driven financial tools to see how they affect SME credit choices. For the study, a descriptive and quantitative research design was chosen. A structured questionnaire disseminated via Google Forms was used to gather primary data from 56 respondents. Awareness of AI tools, perceived effectiveness in evaluating credit risk, decision accuracy, transparency, processing speed, and confidence in AI-based lending systems were all evaluated by the questionnaire. Reliability testing, graphical depiction, mean score interpretation, and percentage analysis were used to assess the gathered data. The results show that AI-driven financial tools greatly improve decision consistency, shorten loan processing times, and increase the accuracy of credit risk assessments. However, due to worries about algorithm transparency, data privacy, and technology infrastructure, adoption rates are still moderate. Though it presently serves as a decision-support tool rather than a whole substitute for conventional techniques, AI-based credit evaluation is generally having a favorable impact on SME funding.

 

 

Published by: vikaspatanker

Authors: Rakesh saini

Abstract: This paper presents an analytical and computational investigation of the Compact Finite Difference Method (CFDM) and its applications to solving linear and nonlinear partial differential equations (PDEs). The CFDM, characterized by high-order spatial accuracy and minimal stencil width, provides superior resolution compared to traditional explicit schemes. The study includes derivation of compact finite difference schemes for first, second, and fourth spatial derivatives, followed by stability and convergence analysis using von Neumann analysis and eigenvalue methods. Numerical validation is demonstrated on model PDEs such as the 1D Heat Equation, Advection Equation, and Korteweg-de Vries (KdV) equation. Results demonstrate that CFDM achieves sixth-order accuracy in space with enhanced spectral fidelity and computational efficiency.

 

 

Published by: vikaspatanker

Authors: Khan Muawwaz, Ansari Adeen Sufyan, Shaikh Yaseen, Shaikh Faiz Mustafa

Abstract: DevDock is a comprehensive, cloud-based collaborative development platform designed to provide developers and students with a unified environment for repository management, real-time code collaboration, AI-assisted coding, and social networking. Inspired by the architecture of GitHub and similar platforms, DevDock introduces a DevDock- first approach where all repositories are created, stored, and managed natively within the platform, with GitHub serving as an optional publishing or backup mirror. The platform integrates Google OAuth 2.0 authentication for seamless login, a graphical repository creation and management system supporting multiple file types including .html, .js, .css, .env, .txt, .mp4, .png and more, a live multi-user collaborative code editor with PIN-secured rooms, an AI-powered coding assistant powered by the GROQ API leveraging open-source large language models, a real-time Instagram-inspired messenger module with friend management, a Git-like version history and rollback system, Markdown README rendering, role- based access control for public and private repositories, and basic repository analytics. This paper presents the complete system architecture, feature design rationale, database schema, security model, testing methodology, and results achieved during the development of DevDock as a final year engineering capstone project.

 

 

Published by: vikaspatanker

Authors: Nagender Yamsani

Abstract: Enterprise organizations increasingly rely on Master Data Management (MDM) systems to maintain consistent, accurate, and authoritative representations of core business entities such as customers, products, suppliers, and locations, forming the backbone of operational, analytical, and regulatory processes. While traditional MDM platforms excel at data governance, entity resolution, stewardship workflows, and lifecycle management, they are largely optimized for structured access patterns and predefined matching rules, which limits their ability to support flexible semantic search, exploratory querying, and cross-domain knowledge discovery over heterogeneous enterprise data landscapes that include structured records, metadata, documents, and contextual signals. Recent advances in Large Language Models (LLMs), particularly when combined with retrieval-augmented architectures, offer a promising pathway to address these limitations by enabling natural-language interaction, semantic reasoning, and context-aware synthesis grounded in authoritative enterprise data. By integrating dense retrieval techniques for semantic matching, generative reasoning for synthesis and explanation, and non-parametric enterprise corpora such as governed master data repositories and knowledge graphs, LLM-augmented enterprise search systems can transform MDM from a primarily administrative capability into an intelligent knowledge access layer. Drawing on foundational research in information retrieval, retrieval-augmented generation (RAG), and enterprise knowledge graphs, this article proposes a reference architecture for LLM-enabled MDM search, examines critical design considerations such as grounding, access control, and auditability, and discusses the broader implications for data quality, governance, trust, and explainability in enterprise environments.

DOI: https://doi.org/10.5281/zenodo.19130581

Published by: vikaspatanker

Authors: Ujwal Bhalgat, Swaraj Wetal, Ayush Shah, Prof. Pramod Jagdale

Abstract: This paper presents a comprehensive review of quantum communication using photons, based primarily on the foundational work of Krenn, Malik, Scheidl, Ursin, and Zeilinger. The review covers core quantum mechanical principles such as qubits, superposition, entanglement, and the no-cloning theorem, and explains how these principles underpin secure quantum communication. Key protocols including Quantum Key Distribution (QKD) and quantum teleportation are discussed. The paper further explores long-distance ground-based and space-based experiments, and examines the emerging role of high-dimensional quantum states using Orbital Angular Momentum (OAM) of photons. The aim is to provide a structured understanding of the current state and future potential of quantum communication technologies.

DOI: https://doi.org/10.5281/zenodo.19129375

Published by: vikaspatanker