Authors: Mr. Akula Sri Naga Sai Veera Pawan Anirudh, Mrs. G Prameela

Abstract: The rapid rise of e-commerce platforms has created a need for complex pricing systems that react to market conditions in real-time to improve market share and customer satisfaction. In this paper, we present a new Edge-AI powered situational pricing optimization framework based on a Deep Reinforcement Learning (DRL) model, leveraging the low latency pricing decision-making capability of a distributed edge computing network. In our model, we use federated learning processes with multi-agent deep reinforcement learning to create hybrid pricing intelligence based on the ongoing analysis of patterns of customer behaviour, competitors and market volatility signals. Our framework offers a solution to the fundamental limitations of cloud-based traditional pricing systems (and understandings) in shipping complex processes to ultra-sophisticated AI pricing engines that function on lightweight AI models located at edge nodes in the network, improving latency from seconds to milliseconds. Our experimental validation based on real e-commerce data shows a 23.4% im-provement in revenue optimizations, 18.7% improvements in reduction for de-cision latency of price adjustments and a remarkable 31.2% increase in customer satisfaction metrics relative to the previous centralized mode (cloud-based). This system offers a decentralized framework that can scale globally to support multi-market e-commerce operations, while also improving data privacy and confidential processing in compliance with regulatory demands.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.178

Published by: vikaspatanker

Authors: Amitesh Sarkar

Abstract: Peace education has become an important element in promoting peace, morality, and social unity in the modern societies. This paper examines how spiritual pedagogy, especially those applied by the Ramakrishna Mission can be used to enhance peace education. The study is descriptive and analytical and incorporates both philosophical and empirical information. This research points out the importance and impact of value based education based on spirituality on increasing emotional intelligence, ethical reasoning, and conflict management in learners. The main dimensions of peace education such as ethical awareness, emotional stability, social harmony, and conflict resolution are assessed with the help of a structured dataset. The results indicate that spiritual pedagogy plays a very important role in holistic growth and harmonious coexistence. It is concluded that the concept of incorporating spiritual values into the contemporary education systems can reinforce the peace-building processes at the international level.

Published by: vikaspatanker

Authors: Adi Gowri Tejaswini, DJ Rishika, Rumaan Tamheen, Vasa Sravya

Abstract: Monitoring crowd density is a crucial task for ensuring safety and preventing overcrowding-related issues. The traditional methods for monitoring crowds involve manual observation and camera surveillance, which are time-consuming and require continuous monitoring. This paper proposes a hybrid approach for crowd detection using Raspberry Pi, incorporating wireless device detection, Bluetooth scanning, infrared sensing, and computer vision. The system estimates the crowd density based on wireless device detection and verifies the presence of people through OpenCV-based human detection. The infrared sensor is used to improve the accuracy of the system by tracking entry and exit movements. The hybrid approach is an improvement over traditional methods, reducing the limitations associated with each method. The paper also discusses different approaches to crowd detection, highlighting the advantages and limitations of these methods, and the benefits of a hybrid approach for real-time applications.

DOI: https://doi.org/10.5281/zenodo.19510228

Published by: vikaspatanker

Authors: Dr. CH. Kishore Kumar, Vovaldas Tejaswini, Beeram Pranaya, Diya Shaik, Sana Shaik

Abstract: Fit Fuel is an integrated web-based fitness and nutrition platform designed to help users exercise correctly and maintain healthy eating habits in one place. Unlike fragmented solutions that separate workout guidance and diet planning across multiple platforms, Fit Fuel unifies both services within a single website for better convenience, consistency, and personalization. The system provides muscle- specific exercise guidance using clear posture images that help users understand correct workout techniques without relying on video streaming. In addition to exercise guidance, Fit Fuel generates personalized Indian meal plans based on the user’s daily calorie requirements, dietary preferences, and allergies. The platform also includes features such as streak tracking, daily journaling, and profile management to encourage regular engagement and long- term habit formation. By combining fitness instruction, nutrition planning, and motivational tools into a single web interface, Fit Fuel promotes a holistic and user-friendly approach to health management.

DOI: https://doi.org/10.5281/zenodo.19509880

 

Published by: vikaspatanker

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