Authors: Assistant Professor S.N.Sudhal, Mr.Vishal Patil, Mr.Aniruddha Jagadale, Mr.Harshvardhan Jadhav

Abstract: The growing global demand for sustainable energy solutions has accelerated the development and integration of biofuels in modern engine technologies. Biofuels—renewable fuels derived from biological sources such as crops, algae, and waste—offer a cleaner and more environmentally friendly alternative to fossil fuels. This paper explores the types of biofuels, including first, second, and third-generation fuels, and examines their physical and chemical properties relevant to combustion performance. Emphasis is placed on the compatibility of various biofuels with current internal combustion engine (ICE) systems, including spark-ignition and compression-ignition engines. Advances in engine modifications, fuel injection systems, and emission control technologies are discussed in the context of optimizing engine performance while minimizing environmental impact. The paper also addresses the technical and economic challenges in large-scale biofuel adoption and outlines future directions for research and development. Ultimately, the synergy between biofuels and evolving engine technology presents a promising pathway toward a more sustainable and energy-secure future.

Published by: vikaspatanker

Authors: Madhura.M.Raste, Aniruddha. R. Sawant,, Prathmesh.S.Patil,, Sourabh. S. Kurne, Sandip.S.Sawant,

Abstract: – In today’s digital age, farmers and livestock owners still face challenges when it comes to buying and selling animals or cattle feed. Traditional methods are often time-consuming, limited by geography, and involve middlemen who may increase costs. This project aims to develop a user-friendly website that serves as an online marketplace where farmers, feed suppliers, and livestock traders can connect directly. The platform allows users to list livestock for sale, browse available cattle feed, compare prices, and make purchases or inquiries all from their mobile or computer. It includes features like secure user accounts, search filters (by location, type of animal or feed, price range), and contact options for buyers and sellers. By bringing these transactions online, the platform helps reduce market inefficiencies, increase transparency, and give rural communities better access to trade opportunities. This website is designed to be simple, multilingual, and accessible even in low-connectivity areas. Overall, the goal is to modernize livestock and cattle feed trading, empowering farmers with the tools they need to grow their businesses more efficiently.

 

 

Published by: vikaspatanker

Authors: Assistant Professor H.S.Bhore, Mr.Shreyas Shivankar, Ms.Payal Kamble, Ms.Aishwarya Bansode

Abstract: This paper explores the applications of Artificial Intelligence (AI) in smart city management, focusing on traffic, waste, and resource management. We discuss the benefits and challenges of implementing AI-powered solutions in urban settings and propose a framework for integrating AI into smart city infrastructure.

Published by: vikaspatanker

Authors: Associate Professor Mrs. Archana Dongardive, Aakash Gophane, Vishwajit Godbole, Vivek Khairnar

Abstract: CivicCompass is a centralized, web-based platform developed to streamline public access to state and district- level welfare schemes in India. Government portals often contain fragmented and unstructured information, which makes it difficult for citizens—particularly from rural and underprivileged areas—to discover and understand available benefits. This project addresses that gap by implementing automated web scraping to collect data from various official sources. The extracted content is cleaned, categorized, and stored using structured CSV files via pandas, then dynamically displayed using Django's Model-View-Template (MVT) architecture. The portal allows users to filter schemes by state, district, and department. It also incorporates a feedback mechanism where users can submit comments or inquiries, which are reviewed through an admin panel before publication. The system was designed for scalability and maintainability, with future improvements possible through API integrations and multilingual support. Overall, the portal bridges the gap between digital governance and grassroots-level access, enabling inclusive participation in government programs.

DOI: https://doi.org/10.5281/zenodo.15727253

Published by: vikaspatanker

Authors: Abirama SundariAbstract: ETHICAL CONSIDERATION IN AI DRIVEN TRADING SYSTEM Artificial Intelligence (AI) has revolutionized financial markets, introducing unprecedented efficiency and capabilities in trading systems. However, this technological advancement brings with it a host of ethical challenges that demand careful consideration. This white paper explores the ethical dimensions of AI-driven trading systems, analyzing key issues such as fairness, transparency, accountability, market integrity, privacy, and human oversight. As a global leader in both artificial intelligence and financial technology, China stands at the forefront of AI-driven trading systems. With its rapidly growing economy, innovative tech sector, and forward-thinking regulatory approach, China offers unique insights into the ethical considerations surrounding AI in finance. This white paper examines the global landscape of AI-driven trading systems with a particular focus on China's contributions, challenges, and regulatory framework. The paper concludes with actionable recommendations for various stakeholders and a forward- looking perspective on the future of AI in financial markets.

 

 

Published by: vikaspatanker

Authors: Navyashree CM, Mr. Banibrata Paul

Abstract: Effective and timely disease prediction plays a crucial role in improving healthcare outcomes. This system leverages machine learning techniques to analyze patient symptoms and accurately predict possible diseases. By utilizing a Support Vector Classifier (SVC) model trained on comprehensive symptom data, the system achieves high prediction accuracy, enabling early diagnosis and timely intervention. In addition to disease prediction, the system provides personalized recommendations, including detailed disease descriptions, precautionary measures, suitable medications, recommended workouts, and dietary guidelines. These recommendations are generated based on the predicted disease, enhancing patient awareness and supporting self-care management, thus bridging the gap between diagnosis and treatment. The integration of user-friendly symptom input and an intelligent recommendation engine makes the system a valuable tool for both patients and healthcare providers. This approach promotes informed decision-making and contributes to efficient healthcare delivery, especially in scenarios with limited immediate access to medical professionals.

 

 

Published by: vikaspatanker

Authors: Abhishek Soni

Abstract: In the era of cloud computing and continuous delivery, achieving high availability and scalability is a critical objective for modern applications. This research paper explores the integration of DevOps practices with cloud-native features such as load balancing and auto- scaling. It delves into how DevOps tools and methodologies enhance the reliability, performance, and efficiency of cloud-based services, ensuring seamless user experiences and optimized resource utilization

 

 

Published by: vikaspatanker

Authors: Aarthi, Jeevatharani shree, Kanishka, Sasikanth

Abstract: Grain quality preservation alongside loss reduction function as essential benefits of paddy drying after harvest. The traditional drying procedures are inefficient and slow while strongly depending on weather conditions thus causing substantial post-harvest losses. The research develops an IoT- based Precision Dehumidifying System that implements automatic real-time sensing along with control methods to enhance paddy drying processes. The system contains DHT sensors for temperature and humidity measurements in addition to moisture sensors that check paddy water content. The system activates the automated drying process when it detects excessive moisture through the operation of a DC fan for application of controlled airflow while utilizing a Peltier crystal for heat generation. The system operates with precise parameters to guarantee drying quality because it avoids drying the paddy too much and not enough at the same time. The IoT-based control system enables time-based observation and limited human interaction to maintain power-efficient drying processes. Optimized energy usage and minimized waste loss through implementation of this solution leads to improved overall processing efficiency together with sustainability benefits. The introduction of smart dryers delivers two benefits which are better grain quality performance alongside economical operation versus traditional drying standards. The study helps precision agriculture progress through smart sensor implementation along with automatic systems and IoT-based decision systems. This solution demonstrates adjustable characteristics which enable scalability across different climate zones to become a legitimate method for advanced grain processing units. Further development demands sensor calibration enhancement parallel to power optimization alongside machine learning model implementation for predictive moisture control.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Shailesh Bisht, Sunny Nahar

Abstract: Agriculture is a vital sector of the Indian economy, ensuring national food security and supplying essential raw materials to various industries. As agricultural productivity becomes increasingly important in the face of climate variability and resource constraints, accurate crop yield forecasting has emerged as a critical need. This paper presents a machine learning-driven framework that leverages environmental factors such as weather conditions, soil characteristics, and the Normalized Difference Vegetation Index (NDVI) for yield prediction. The proposed system is structured into three stages: (i) forecasting weather parameters, (ii) estimating NDVI using predicted weather data, and (iii) predicting crop yield by integrating both outputs. Experiments using historical agricultural datasets demonstrate that ensemble learning techniques, particularly XGBoost and Random Forest, deliver robust performance, with XGBoost achieving the highest prediction accuracy of up to 97%.

Published by: vikaspatanker

Authors: M.G Vasundhara, C M Chaithra, Chandhini K.S, G.K Kalavathi

Abstract: This review consolidates recent advancements in study of porous journal bearings under the influence of magnetic fields, surface roughness and heterogeneous slip/no-slip surfaces. Using stochastic models primarily based on Christensen's theory and incorporating magnetohydrodynamic (MHD) considerations, researchers have explored the performance variations in short, long, and finite bearings. The studies indicate that appropriate combinations of surface roughness, permeability, Hartmann number, and engineered slip conditions can enhance bearing performance, load carrying capacity, and reduce frictional losses. This paper summarizes mathematical models, numerical methodologies, and key findings, highlighting opportunities for future developments in smart bearing design.

 

 

Published by: vikaspatanker