Daily Archives: May 26, 2026

Authors: Mr.B.Ajantha Reddy, Mr.K.Ch.Malla Reddy, Immadi Venkata Naga Sai Pujitha, Byragani Manasa, Bathula Varshini, Shaik Fathima

Abstract: The purpose of this project is to make home or office or any area secure. When someone presses the doorbell, then the doorbell makes a video call to the registered number. If someone roams in front of the door it notifies you by sending message. Then he can see the person who is roaming in front of our door. So, if the person is known we can open the door otherwise we can be alert. And also, we can talk to the person through mobile only and the person can reply there itself, because it contains the audio speaker so that we can hear the outside people talks trough the mobile once we pick up the video call. If someone tries to steal it then the steal alarm will be activated.

DOI: http://doi.org/10.5281/zenodo.20394540

Published by: vikaspatanker

Authors: Burhan Sheikh, Vighnesh Muppawar, Jiya Khan Pathan, Suyog Madavi, Prof. Sachin Dhawas

Abstract: CompThe Pradhan Mantri Awas Yojana (PMAY) is a flagship initiative by the Government of India aimed at providing affordable housing to the rural and urban poor. However, at the local administrative level (Zila Parishad), the management of beneficiary data involves processing massive, decentralized Excel datasets. This manual approach leads to data redundancy, lack of real-time monitoring, and significant difficulty in identifying stalled projects. This paper proposes "Data Driven Housing Intelligence," a web-based analytical application designed to automate and centralize PMAY data management. Developed using the Python Flask framework, the system integrates bulk data ingestion, automated data cleaning using the Pandas library, and persistent storage in a structured SQLite database. It features a multi-level drill-down dashboard with interactive visualizations (Bar and Pie charts) for block-wise analysis. Furthermore, it introduces a "Visual Audit" mechanism allowing stage-wise photo uploads for transparency and an automated algorithm to flag delayed projects. The implementation results demonstrate a significant reduction in administrative overhead and improved data accessibility for government officials.

Published by: vikaspatanker

Authors: Dr. Isha Patnaik

Abstract: The rapid advancement of digital technologies has transformed traditional marketing practices and significantly influenced consumer behavior. Digital marketing has emerged as a crucial strategy for organizations to enhance brand visibility, improve customer engagement, and influence purchasing decisions. The present study examines the influence of digital marketing on brand awareness and its impact on consumer purchase decisions. The primary objective of the study is to analyze the relationship between digital marketing activities, brand awareness, and consumer purchasing behavior. The research adopts a descriptive research design, utilizing both primary and secondary data sources. Primary data were collected through structured questionnaires distributed among 300 respondents who actively use digital platforms. The study employed statistical techniques such as descriptive analysis, correlation analysis, and regression analysis to examine relationships among variables. The findings reveal that digital marketing has a significant positive influence on brand awareness, indicating that increased exposure to digital marketing activities improves brand recognition and familiarity among consumers. Furthermore, the results demonstrate that higher brand awareness positively affects consumer purchase decisions by increasing trust and purchase intention. Regression analysis confirms that digital marketing and brand awareness together significantly contribute to predicting consumer purchasing behavior. The study concludes that digital marketing serves as an important strategic tool for strengthening brand awareness and influencing consumer buying decisions in the digital environment. The findings suggest that businesses should invest in effective digital marketing strategies to enhance brand positioning, improve customer engagement, and achieve sustainable competitive advantage. Future research may explore additional variables and advanced analytical methods to provide deeper insights into consumer behavior in evolving digital marketplaces.

DOI: http://doi.org/10.5281/zenodo.20393866

Published by: vikaspatanker

Authors: B. M. Promod Kumar, Namith Kumar Y, Pruthvi M C, Poorvik K V, Jagadeesh M

Abstract: This concept is based on patient’s classification in an Emergency Department in a hospital according to their critical conditions. Machine learning can be applied based on the patient’s condition to quickly determine if the patient requires urgent medical intervention from the clinicians or not [1]. Basic vital signs like Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Respiratory Rate (RR), Oxygen saturation (SPO2), Random Blood Sugar (RBS), Temperature, and Pulse Rate (PR) are used as the input for the patients’ risk level identification [2]. High-risk or non-risk categories are the outcome for patient classification. ML algorithms such as Gaussian NB, KNN or DT are applied for the data analysis and for the classification. We'll use a many of supervised learning methods before deciding which one is best for the model. Existing systems rely on classical learning models, which are inefficient and imprecise. They aren't as accurate as the proposed model and take a little longer to process. Many existing topics on patient’s classification where they have built models and shown results generated using R language, Python language and data science tools. All existing works are just models, cannot be applied as application useful in real time. In our project work we build an application with ML models that can classify high risk patients and non-risks patients in an emergency department and provides doctors with the information of how to handle patients and treat better [5]. Our proposed work is a real-world medical system useful for hospitals and doctors and built using trending tools such as Visual Studio code, PYTHON and MYSQL Server.

Published by: vikaspatanker

Authors: Ms. K. Sabitha, B. Monish, M. Nithishkumar, S. Mohammed Al Ameen, S. Samvarthini

Abstract: The process of selecting an appropriate engineering course and college has become increasingly challenging due to the large volume of information and the complexity of admission procedures such as TNEA. Students often face difficulty in understanding cutoff trends, identifying suitable colleges, and making informed decisions because the available information is scattered and sometimes unreliable. To address this issue, this project proposes an intelligent academic assistance system that combines Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs). The system is designed to provide accurate and user-friendly guidance by retrieving verified academic data and presenting it through an interactive conversational interface. The retrieval component ensures that information such as cutoff marks and college details is obtained from structured datasets, while the language model supports explanation-based queries related to courses and career paths. The system is implemented as a web-based application using modern technologies, enabling real-time interaction between the user and the system. By combining data retrieval techniques with intelligent response generation, the proposed solution improves accuracy, reduces misinformation, and enhances user experience. This approach simplifies the decision-making process and helps students choose suitable academic paths with confidence.

Published by: vikaspatanker

Authors: Prof. P. Prasanna, Arjun C, Chinmay J, Deepak B B, Dhanush S Yadav

Abstract: Visually impaired individuals face severe challenges in independent navigation. Traditional white canes provide only contact-based obstacle detection and cannot warn users of overhead hazards, wet surfaces, or distant obstacles—significantly limiting their mobility, safety, and self-reliance. Existing com-mercial smart canes, while technologically superior, remain prohibitively expensive and require specialised training. This paper presents the design and implementation of a Smart Blind Assistive Stick: a low-cost, IoT-enabled, real-time navigation aid built around an ATmega328 microcontroller. The system integrates an HC-SR04 ultrasonic sensor for non-contact obstacle detection up to 2 m, a moisture sensor for wet-surface identification, and multi-modal feedback through a vibration motor and buzzer. An optional GSM/GPS module enables real-time location sharing with caregivers via SMS. The firmware was developed in Embedded C on the Arduino IDE, simulated in Proteus, and physically prototyped. Testing confirmed obstacle detection accuracy within a 2 m range, sub-100 ms system response time, reliable wet-surface detection, and successful emergency SMS transmission with live GPS coordinates. The device operates for 6–8 hours on a rechargeable lithium-ion battery pack and maintains a compact, lightweight form factor suitable for daily indoor and outdoor use. Results demonstrate that a well-integrated multi-sensor embedded system can effectively bridge the technological gap in assistive mobility devices.

DOI: http://doi.org/10.5281/zenodo.20391169

Published by: vikaspatanker

Authors: Aryan Baban Repe, Atharv Narayan Rane, Harshvardhan Vijay Desai, Keshiraj Mahesh Lad, Sumit Vasant Bhatane, Mrs. Anuradha S. Solanki

Abstract: Augmented Reality (AR) has gained considerable traction in educational settings, offering interactive three-dimensional experiences that go beyond what conventional two-dimensional instructional materials can provide. This paper describes the design, development, and evaluation of an AR-Based Interactive Solar System Visualization system built using Unity 2022 and the Vuforia Engine. The system employs markerless ground-plane detection to overlay a fully interactive three-dimensional Solar System model onto the user’s physical surroundings, supporting planetary orbital revolution, axial rotation, touch-based planet selection, and dynamic educational information panels. The primary contribution of the proposed system is the integration of stable ground-plane AR tracking, structured educational interfaces, and a modular software architecture within a lightweight mobile deployment requiring only a standard Android smartphone. Prototype evaluation on Android devices yielded an average frame rate of 45–60 FPS, AR tracking accuracy of approximately 92%, an interaction response time below 100 ms, and a user satisfaction score of 88%, indicating measurable gains in learner engagement and conceptual retention relative to conventional instructional methods.

 

Published by: vikaspatanker

Authors: Ritik Rana

Abstract: Sustainable agricultural production largely depends on the proper development, conservation, and efficient utilization of irrigation resources at the micro level. In Himachal Pradesh, diverse geographical conditions have led to the adoption of traditional irrigation systems known as Kuhls, which play a vital role in supporting agriculture and rural livelihoods. Kuhls are narrow, manually constructed surface channels that divert water from natural streams and ravines through gravity flow to irrigate terraced fields. These systems, built mainly with local materials such as river boulders and soil, represent an eco-friendly and community-managed method of irrigation. Despite their historical and agricultural significance, Kuhls today face several challenges including structural deterioration, water losses, changing climatic conditions, and inadequate maintenance. The present study focuses on the Palampur region of Himachal Pradesh to examine the problems associated with traditional Kuhl irrigation and explore modern solutions for improving irrigation efficiency and agricultural sustainability. The study highlights the need for technological improvements, conservation measures, and integrated water management practices to preserve this traditional irrigation heritage while meeting present-day agricultural demands.

DOI: https://doi.org/10.5281/zenodo.20390582

Published by: vikaspatanker

Authors: Darshana N V, Nithyalakshmi.B

Abstract: The fast pace of urbanization and worsening climate-driven stressors have disrupted the natural cycles of urban hydrological processes, making existing linear infrastructures increasingly susceptible to extreme pluvial flooding events. The Sponge City concept can be seen as an essential paradigm shift towards a decentralized nature-based method for urban areas to manage rainwater in terms of its absorption, storage, infiltration, and purification.In this review paper, we synthesize empirical data, policies, and hydrological models of ten key studies to examine the effectiveness of the Sponge City paradigm at various scales. This paper will analyze the development trends of LID-based structural controls, quantitative limitations for peak flows, life-cycle maintenance challenges, and multiple ecological benefits. The synthesized literature reveals that although green infrastructure produces impressive hydrological and economic benefits when dealing with conventional rainfall, its performance suffers considerably when confronted with an extreme cloudburst. Therefore, this paper sets up a robust research agenda for future urban planners, namely that a mandatory paradigm must be embraced in the form of a "green-gray hybrid infrastructure" system with institutional and technological arrangements for real-time monitoring.

Published by: vikaspatanker

Authors: Sukanya H N, Assistant Professor, Pavan Kumar T S, Prajwal S Shetty, Pranay Ekunde, Sanjay M

Abstract: Improper waste disposal remains one of the most pressing environmental challenges in both urban and rural settings, contributing to pollution, health hazards, and reduced recycling efficiency. Traditional manual waste segregation is error-prone, labour-intensive, and cannot scale to the volumes of waste generated daily. This paper presents EcoSort, an AI-powered full-stack web application that automates waste classification using a fine-tuned MobileNetV3 Large deep learning model trained via transfer learning. The system classifies waste images into three categories—Recyclable, Non-Recyclable, and Hazardous—achieving approximately 94 % overall accuracy with precision values of 0.95, 0.94, and 0.94 respectively. EcoSort integrates real-time webcam-based detection, a microservices architecture (React/Vite front-end, Node.js/Express back-end, Flask AI service, MongoDB Atlas), Role-Based Access Control (RBAC), JWT authentication, and perceptual hashing (pHash) for duplicate-image detection. A gamification layer comprising reward tiers (Bronze to Platinum), a coupon marketplace, and a community leaderboard motivates responsible waste disposal. Load testing confirmed stable operation under 100 concurrent users with average response times below 3.5 seconds. The platform aligns with UN Sustainable Development Goals SDG 3, SDG 11, SDG 12, and SDG 13, offering a scalable, intelligent pathway toward smarter waste management.

Published by: vikaspatanker