Category Archives: Uncategorized

Authors: Jayashree Pasalkar, Vedant Mahanavar, Pranav Patil, Om Mahajan

Abstract: Counterfeit academic certificates have increased sig- nificantly enough so they now create problems for many organiza- tions (i.e., schools, employers, government agencies) because they reduce faith in the ability of organizations to verify credentials. Most current methods used to manage academic certificates are primarily manual and/or based on centralized database storage; therefore, most are subject to various forms of manipulation (e.g., unauthorized access/modification), delayed processing, and additional risks associated with verification processes. Blockchain technology has recently emerged as a possible solution for authenticating certificates securely; however, many of the current blockchain implementations are built upon platforms such as Ethereum, which experience both high transaction costs, and limited scalability. To overcome these constraints, this research will present a blockchain-based certificate management and verification system that utilizes the high-performance and low cost attributes of the Solana blockchain platform with a Django- based backend system. With this system, academic institutions can issue certificates (while maintaining the original formatting), or register external certifications issued to students/alumni. All generated certificates are hashed using the SHA-256 hashing algorithm, and each unique hash is stored on the Solana blockchain via a Rust-based Anchor smart contract. Upon receipt of a certificate to be verified, the proposed system hashes the submitted certificate, and then compares its hash value with the unalterable blockchain record to authenticate/verify the legitimacy of the submitted certificate, or identify if the submitted certificate was altered/tampered. In combination with the security provided by blockchain, the scalability of the Solana blockchain, and an efficient backend architecture, this proposed system provides a highly effective method of verifying the authenticity of academic certificates, while reducing the risk of fraudulent activity.

DOI: https://doi.org/10.5281/zenodo.19508445

 

Published by: vikaspatanker

Authors: Adlin Jebakumari, Uzefa Begum, Kathi Harshitha Reddy, Mohammed Rameez

Abstract: The growth of digital media in recent years has created a major public issue. This is evident in the increase of false information, often called fake news. Fake news refers to any news item that contains false information for the audience. This research project combines traditional machine learning methods with modern deep learning techniques to detect fake news using a hybrid detection system. The news articles will undergo several preprocessing steps: text cleaning, tokenization, stop word removal, and text data normalization for analysis. The team will preprocess the textual data, which will then be converted into numeric data for machine learning and deep learning models. This will use feature extraction methods like tokenization and word embeddings. The project will apply traditional machine learning models to create training data that captures the unique features of fake news and real news articles. The study will also use various deep learning models, including LSTM Networks and BERT. These models will help identify sequential and contextual relationships in articles by understanding complex language patterns and the connections among different types of text data.

DOI: https://doi.org/10.5281/zenodo.19508418

Published by: vikaspatanker

Authors: M.S.R.naidu, Barri Kuvalaya, Bandaru Jyothika, Barle hemanth kumar, Amjuru bhanuprakash

Abstract: This paper introduces Bidirectional Encoder Representations from Transformers (BERT), a transformer-based natural language processing framework for sentiment analysis of Twitter data. Large amounts of opinion-rich textual data are produced by social media platforms, reflecting the public's feelings about societal issues, events, and products. Conventional sentiment analysis methods have trouble deciphering the informal language, contextual meaning, and semantic ambiguity seen in tweets. A pretrained BERT model is optimized for multi-class sentiment classification in order to get over these restrictions. An end-to-end pipeline comprising data preprocessing, tokenization, model training, evaluation, and result display is demonstrated in the built notebook. Experimental data reveal that contextual embeddings and attention mechanisms greatly boost sentiment classification accuracy compared to conventional approaches, validating the usefulness of transformer-based models for social media opinion mining.

 

 

Published by: vikaspatanker

Authors: Nishikant Kshirsagar, Manas Lonkar, Pratik Ingle, Suraj Kushwaha, Prof. Madhavi Patil

Abstract: The Unified Health System (UHS) integrates multi- ple healthcare stakeholders into a single digital platform to im- prove patient care, records management, and treatment decision- making. This paper examines the design and implementation of a web-based UHS using Spring Boot for backend microservices, MongoDB as a NoSQL cloud database, and React JS as a dy- namic frontend framework. The system enables real-time access to patient medical history, digital prescriptions, lab reports, and appointment scheduling with hospitals. The project demonstrates reduced administrative delays, secure role-based data access, and a modern patient-centric healthcare experience. Existing research confirms that fragmented healthcare data and the absence of interoperable systems remain critical barriers to efficient clinical outcomes [1], [3]. By adopting a microservice-based approach and leveraging NoSQL document storage, this system overcomes the scalability limitations of monolithic architectures. The find- ings align with recent studies demonstrating that cloud-based digital platforms can significantly enhance healthcare workflow efficiency and reduce manual intervention [5], [7].

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Published by: vikaspatanker

Authors: Saurav Patankar, Abhijeet Waghmare, Swaraj khadhe, Durvesh Kavire, Prof. Pradnya Satpute

Abstract: Communication across different languages has become a major challenge in today’s globalized world. The need for a system that enables seamless interaction between users speaking different languages has led to the development of multilingual communication platforms. This paper presents Chat Verse, a multilingual chat application that allows users to communicate in real time without language barriers. The application provides automatic language detection and real-time message translation, enabling users to send messages in their native language while the system translates them into the receiver’s preferred language. The system is developed using Android Studio, with Java for application logic and XML for user interface design, ensuring a responsive and user-friendly experience. Chat Verse includes essential features such as user authentication, private and group chat functionality, language preference settings, notification system, and feedback module. The application focuses on delivering a smooth communication experience by integrating translation capabilities within the chat interface. The motivation behind developing Chat Verse is to create an efficient, accessible, and intelligent communication platform that removes language barriers and enhances global connectivity. Traditional messaging applications often lack seamless multilingual support, making communication difficult for users from different linguistic backgrounds. This system aims to address that limitation by providing an intuitive and automated translation-based chat environment. The proposed system emphasizes usability, efficiency, and scalability, and demonstrates how multilingual chat applications can play a significant role in improving communication in fields such as business, education, and social networking. The study also highlights the future potential of integrating advanced AI-based translation techniques for even more accurate and context-aware communication.”

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Published by: vikaspatanker

Authors: Jag Pratap Singh Yadav

Abstract: Classical diffusion models based on Fick’s law assume Brownian motion and local transport, leading to a mean squared displacement that grows linearly with time. However, many physical, biological, and engineering systems exhibit anomalous diffusion, where the mean squared displacement follows a power law in time rather than a linear relationship. Such behavior commonly arises in heterogeneous porous materials, crowded biological environments, polymeric systems, and disordered media, where long trapping times and memory effects invalidate standard integer-order diffusion equations. Despite significant progress in fractional modeling, there remains a need for mathematically consistent and computationally efficient formulations that clearly link the physical origin of anomalous transport to robust numerical implementation. In this paper, we develop a time-fractional diffusion model using the Caputo fractional derivative to represent memory-dependent transport induced by heavy-tailed waiting times. Starting from the conservation of mass and a constitutive relation with temporal memory, we derive a physically meaningful fractional diffusion equation. An analytical solution for a benchmark initial-boundary value problem is presented using Laplace and Fourier transforms, and a numerical approximation based on the L1 finite difference scheme is constructed. The stability and convergence properties of the numerical method are discussed. Numerical experiments demonstrate that the fractional order controls the transition from normal to subdiffusive transport and accurately reproduces power-law mean squared displacement behavior. The model captures anomalous transport with significantly fewer parameters than multi-scale classical alternatives. These results show that fractional differential equations provide an effective and parsimonious framework for describing memory-driven diffusion processes, with direct relevance to transport in porous media, biological tissues, and complex soft matter systems.

DOI: http://doi.org/10.5281/zenodo.70

 

 

Published by: vikaspatanker

Authors: Dr. Harsha R. Vyawahare, Sukhada Shripad Tare, Ashwini Nitin Shingane, Shreya Sunil Shinde, Bhavika Suraj Jain

Abstract: This paper presents a practical and lightweight bidirectional communication system that translates between speech/text and Indian Sign Language (ISL) using machine learning and computer vision techniques. The system supports two modes: Speech-to-ISL and ISL-to-Text/Speech. In Speech Mode, spoken input is converted into text using speech recognition, then mapped to corresponding ISL alphabet images. In Camera Mode, hand gestures are captured using a webcam and classified using a Convolutional Neural Network (CNN) model to generate text and voice output. The system is implemented using Streamlit for the user interface, OpenCV for image processing, TensorFlow/Keras for gesture recognition, and pyttsx3 for speech synthesis. The proposed system provides a simple, real-time, and cost-effective solution to improve communication accessibility for the Deaf and Hard-of-Hearing (DHH) community

DOI: https://doi.org/10.5281/zenodo.19484295

Published by: vikaspatanker

Authors: Assistant Professor Anshul Khandelwal, Abhishek Pakhariya, Associate Professor Brajesh Tripathi

Abstract: A comprehensive comparative computational fluid dynamics (CFD) investigation is presented, analyzing three representative wing configurations: the transonic ONERA M6 benchmark wing, a finite wing based on the symmetric NACA 0012 airfoil section, and a tapered finite wing evaluated at low subsonic speeds. The primary objective is to examine benchmark-oriented transonic flow prediction capabilities and evaluate low-speed finite-wing performance parameters within a unified aerodynamic framework. For the ONERA M6 configuration, the flow field is simulated under the standard validation conditions of a Mach number of 0.8395, an angle of attack of 3.06°, and a Reynolds number of $11.72 \times 10^6$ based on the mean aerodynamic chord. For the low-speed wings, integrated aerodynamic loads at a free-stream velocity of 50 m/s are utilized to determine the aerodynamic coefficients and efficiency trends across various angles of attack. The CFD solver successfully reproduces the expected transonic pressure redistribution, including the characteristic shock-dominated flow structure over the ONERA M6 wing. In the low-speed analysis, the rectangular NACA 0012 wing achieves its maximum aerodynamic efficiency near an 8° angle of attack, whereas the tapered wing exhibits superior aerodynamic efficiency at low angles but suffers a more rapid degradation at higher incidences due to accelerated drag growth. This study effectively consolidates benchmark computational validation, finite-wing aerodynamic theory, and comparative performance analysis.

Published by: vikaspatanker

Authors: Muthulakshmi M, Saravanan P, Srihari M, Shunmugapandian P

Abstract: This paper presents Q-Track, a Smart QR Code and Geo-Fenced Attendance System designed to provide a secure, efficient, and automated solution for attendance management in educational institutions. Traditional attendance systems, including manual registers and biometric methods, suffer from limitations such as time consumption, proxy attendance, and lack of real-time monitoring. To overcome these challenges, the proposed system integrates dynamic QR code generation with geo-location verification.In this system, a unique and time-bound QR code is generated for each class session by the faculty. Students scan the QR code using their mobile devices to mark attendance. To ensure authenticity, the system incorporates geo-fencing technology, which validates the real-time location of the student. Attendance is recorded only when both QR authentication and location verification are successful, thereby eliminating proxy attendance and ensuring reliability.The system is implemented as a web-based application with a user-friendly interface accessible on both mobile and desktop devices. It includes modules for user authentication, QR code generation, attendance tracking, and report generation. Real-time data processing enables faculty to monitor attendance instantly and generate detailed reports for analysis.The proposed solution enhances accuracy, reduces manual workload, and improves transparency in attendance management. By combining QR technology with geo-location services, the system provides a scalable and cost- effective approach suitable for modern academic environments.

Published by: vikaspatanker

Authors: Mr. R.Rajesh, Bura Keerthi, K.Keerthan Reddy, A.Siddartha

Abstract: Smart Loan is an intelligent system designed to predict loan eligibility and assess risk using machine learning techniques. Traditional loan approval processes are time-consuming and prone to human bias. This system automates the evaluation process by analyzing applicant data such as income, credit history, employment status, and financial behavior. The model predicts whether a loan should be approved and categorizes applicants based on risk level (low, medium, high). The system ensures faster decision-making, reduces default risks, and improves efficiency for financial institutions.

Published by: vikaspatanker