Daily Archives: April 11, 2026

Authors: sasiram anupoju, Lakshmi Narasimham gorthi, sai kalyan nallamadhi, suhas rallabandi

Abstract: This project presents the design and development of a Smart Campus Energy Usage Analysis and Prediction system that monitors and forecasts energy consumption across campus facilities. The system collects energy usage data from different buildings such as hostels, academic blocks, and libraries, and processes it using data analytics and machine learning techniques. A predictive model based on linear regression is used to estimate future energy consumption patterns. The system also provides interactive dashboards for real-time visualization, including consumption trends, building-wise distribution, and forecast insights. The proposed system aims to improve energy efficiency, reduce wastage, and support sustainable energy management in smart campus environments.

DOI: https://doi.org/10.5281/zenodo.19509795

 

Published by: vikaspatanker

Authors: V. Mounica, Peteti Anuneha, Shaik Abdul Karimulla, Sadhu B.S.V.V.N.S.R. Prasanth, Rangineni Sai Swarup, Badviti Sai Deepak

Abstract: Student engagement monitoring in modern classroom and online learning environments presents a significant challenge, as traditional attendance-based systems measure physical presence but fail to quantify cognitive attention. This paper presents NeuroFocusAI, an AI-based student concentration monitoring system that evaluates real-time attention levels using a multi-modal analysis pipeline comprising facial landmark tracking, eye gaze estimation, blink detection, emotion recognition, and environmental noise analysis. The system processes live webcam input using the MediaPipe FaceMesh model, which detects 468 facial landmark points to enable precise iris-based gaze tracking and Eye Aspect Ratio (EAR) blink detection. Emotional state classification is performed using the DeepFace library across six emotion categories. Environmental noise levels are concurrently measured using Root Mean Square (RMS) audio signal processing via the SoundDevice library. A weighted scoring algorithm combines gaze direction (60%), emotion state (20%), and environmental noise (20%) to compute a concentration score between 0 and 100, which is stored periodically for session analytics. The backend is implemented using FastAPI, with SQLite as the persistent data store, and a React.js-based dashboard provides real-time analytics for both students and teachers. Experimental results demonstrate that the system accurately classifies student attention into three levels — High Focus (80–100), Moderate Focus (60–79), and Low Focus (0–59) — with significant improvements over traditional attendance-based engagement measurement.

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Published by: vikaspatanker

Authors: Shashank Tiwari, Amrutha Uppala, Manasa Aerragunta, Rishikar Ummadi

Abstract: — Interview preparation is an important process for students and job seekers, but traditional preparation methods often lack personalized feedback and real interview experience. In this paper, an Agentic AI–based Interview Preparation System is presented that simulates interview scenarios and evaluates candidate responses. The system generates role-based interview questions using a job role and skills dataset and evaluates answers using Natural Language Processing techniques. It also provides feedback and improvement suggestions to help candidates enhance their performance. By automating interview practice and evaluation, the system provides a structured and interactive way to prepare for interviews. Overall, this approach improves interview readiness, confidence, an d skill assessment in a cost-effective and accessible way.

DOI: https://doi.org/10.5281/zenodo.19508550

 

Published by: vikaspatanker

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