Category Archives: Uncategorized

Authors: Dr.M.Suganthi(Ap/Cse), K.Niranjana, T.Nisha, S.Prarthana

Abstract: College food courts often struggle with long waiting queues, overcrowding during peak hours, inefficient order management, and the absence of real-time order tracking; these challenges result in increased waiting time for students and difficulty for administrators in managing multiple food orders effectively, especially during busy lunch and break hours. This paper presents a Smart Food Court Ordering and Management System, a web-based platform designed to simplify food ordering and improve food court management within a college environment. The proposed system allows students to view the food menu, which includes food name, image, price, availability status, waiting time, and quality information, and place orders through an online or offline mode. The system also displays the current food court crowd level as high, medium, or low to help students decide the best time to place their orders. An admin management module enables administrators to monitor student orders, update order status such as waiting, preparing, or ready, manage food availability, and update crowd levels through an interactive dashboard. All system data, including student login details, food menu information, order records, order status updates, and food availability, are stored in a MySQL database using phpMyAdmin within the XAMPP control panel. The system operates as a web application without requiring additional hardware and aims to improve efficiency in food ordering, reduce waiting time, and enhance the overall food court experience for both students and administrators within the campus environment.

DOI: https://doi.org/10.5281/zenodo.19235190

 

Published by: vikaspatanker

Publishing a research paper is more than just completing your writing; it’s about sharing your ideas with the right audience. Many researchers, especially beginners, feel confused when it comes to choosing where and how to publish. With so many platforms available online, the real challenge is not finding a website, but finding the right one.

Submit Your Paper  / Check Publication Charges 

Today, research publishing has become much easier and faster. Digital platforms allow authors to submit their work, track progress, and reach a global audience without complicated processes. But at the same time, this convenience has also increased the risk of low-quality or fake platforms. That’s why understanding the basics of research publishing is very important.

Understanding How Publishing Works

When you submit your research paper to a publishing platform, it usually goes through a process called peer review. In this process, experts in your field check your work for quality, accuracy, and originality. This step is important because it ensures that only valuable and genuine research gets published.

Some platforms also offer open access publishing, which means your research becomes freely available to everyone. This increases visibility and helps your work reach more readers, including students, researchers, and professional,

Key Features to Look For

Before submitting your paper, always check a few important things. A good publishing platform will clearly mention its review process, publication timeline, and author guidelines. Transparency is a strong sign of credibility.

Indexing is another important factor. If a platform is indexed in well-known academic databases, it means your research will be easier to find and more widely recognized. This adds value to your work and improves your academic profile.

You should also check whether the platform provides proper certificates or publication proof. This is especially useful for students and professionals who need documentation for academic or career purposes.

Common Mistakes to Avoid

Many beginners make the mistake of choosing platforms that promise “instant publication” without proper review. While fast publishing may sound attractive, it often comes at the cost of quality and credibility.

Another common issue is paying high fees without verifying the authenticity of the platform. Not all paid platforms are bad, but you should always ensure that you are getting proper value in return, such as genuine review, indexing, and visibility.

Always prepare your paper according to the given format and guidelines. A well-structured paper has a higher chance of acceptance. Make sure your content is original and free from plagiarism, as most platforms strictly check for it.

Take time to read and understand submission requirements before applying. A small mistake in formatting or documentation can delay the process.

Research publishing is an important step in building your academic or professional journey. Instead of rushing, focus on choosing a reliable platform that values quality and transparency. A well-published paper not only shares your knowledge but also builds your credibility and confidence. it’s not just about getting published—it’s about getting published the right way.

Published by: Neeraj Mishra

Authors: Kishore A, Nawfees MI, Dr. S. Thilagavathi

Abstract: Early and accurate disease prediction is a major challenge in modern healthcare systems. Delayed diagnosis often leads to higher treatment costs and lower patient survival rates. Artificial Intelligence (AI) and Machine Learning (ML) techniques are widely used to help with medical decision-making by analyzing complex healthcare datasets. However, traditional machine learning models often face issues with inefficient feature selection, poor hyperparameter tuning, and slow convergence during optimization. This is especially true when working with high-dimensional medical data. To tackle these challenges, this paper presents an AI-based disease prediction framework that uses quantum-inspired optimization techniques. This approach combines classical machine learning classifiers with optimization strategies based on quantum computing principles, such as probabilistic state representation and superposition-based search. These quantum-inspired methods allow for efficient exploration of the solution space, which leads to better feature selection and optimized model parameters. We evaluate the proposed framework using a publicly available healthcare dataset from Kaggle. We compare the performance of traditional machine learning models and quantum-inspired optimized models using accuracy, precision, recall, and F1-score metrics. The experimental results show that the quantum-inspired optimized model consistently performs better than conventional approaches. This study demonstrates that quantum-inspired optimization provides a practical and scalable solution for improving AI-driven disease prediction systems without the need for actual quantum computing hardware.

DOI: https://doi.org/10.5281/zenodo.19232740

Published by: vikaspatanker

Authors: Muneeswaran B, Shanmuga Eswari M

Abstract: This research presents an innovative hybrid machine learning framework that amalgamates density-based clustering with ensemble regression and logistic classification to improve the precision of student performance prediction. We use DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering on the StudentPerformanceFactors dataset to find hidden student behavioural phenotypes. These phenotypes are then used as engineered features for supervised learning models. An automated hyperparameter tuning system uses silhouette score maximisation to systematically test different DBSCAN settings and find the best density parameters (eps=1.0, min_samples=5) without any human input. The final cluster assignments are used in both a RandomForestRegressor to predict test scores and a Logistic Regression model to classify performance into categories. This creates a hybrid framework that captures both clear academic metrics and more subtle behavioural patterns. Experimental validation shows performance gains that are statistically significant. The hybrid RandomForest gets an MSE of 4.45 on test data that wasn't used to train it, and the hybrid Logistic Regression gets an accuracy of 82.3%. Feature importance analysis shows that Attendance (33.4%), Hours_Studied (23.9%), and Previous_Scores (9.8%) are the most important predictors. DBSCAN_Cluster also adds useful discriminative power. Five-fold cross-validation verifies model robustness (CV-MSE=4.88±0.12). This study enhances educational data mining by implementing unsupervised learning for supervised improvement, providing interpretable student groupings that uncover density-based behavioural phenotypes affecting academic performance. The proposed framework shows that it can be used in real life for early intervention systems by giving teachers useful student types based on regular academic data.

Published by: vikaspatanker

Authors: Omkar Ganesh arnikar, Siddhi Rupesh Datar, Ishwari Sanjay Karad, Kiran bapu karhe

Abstract: The IoT-based full-range audio system with gesture control is a smart audio system that allows users to control music using hand gestures without physical touch. An ESP32 microcontroller works as the main controller, while an APDS9960 gesture sensor detects hand movements such as up, down, left, right, and near to perform functions like play/pause, next track, previous track, and volume control. The audio signal is processed using a 3-way active crossover and amplified by TPA3116D2 class-D amplifiers to drive a subwoofer, midrange speaker, and tweeter, producing clear full-range sound. The system is powered using a 12-0-12 transformer and voltage regulation circuits. This project combines IoT technology, gesture-based control, and high-quality audio output to create a modern and user-friendly sound system.

DOI: https://doi.org/10.5281/zenodo.19229877

Published by: vikaspatanker

Authors: Ms.K.Madhunitha, Bharath K, Deva Senathipathi M, Mukilan R

Abstract: Address Resolution Protocol (ARP) spoofing is a critical security threat in wireless networks where an attacker sends forged ARP messages to link their device with the IP address of a legitimate user. This attack allows malicious users to intercept, modify, or block data traffic between communicating devices, leading to serious issues such as data theft, session hijacking, and denial-of-service attacks. Traditional detection mechanisms mainly rely on software-based identifiers such as IP addresses and MAC addresses. However, these identifiers can be easily manipulated by attackers, making conventional solutions less effective in detecting sophisticated attacks. To overcome this limitation, this study proposes a defense mechanism against ARP spoofing in Wi-Fi networks using Radio Frequency (RF) fingerprinting. RF fingerprinting identifies wireless devices based on unique hardware-level characteristics of their transmitted signals. Features such as frequency offset, phase noise, and signal transient patterns are analyzed to generate distinct RF signatures for each device. The proposed system continuously monitors wireless transmissions and compares them with stored RF fingerprints to identify anomalies and detect unauthorized devices. By leveraging physical layer characteristics, the approach provides a reliable and difficult-to-forge method of authentication. Experimental results indicate that RF fingerprinting significantly improves the accuracy of ARP spoofing detection and strengthens overall wireless network security without requiring major modifications to existing infrastructure.

DOI: https://doi.org/10.5281/zenodo.19221425

 

Published by: vikaspatanker

Authors: Ashish Binay Pandey, Dr Sangeeta Gupta

Abstract: The decision of Indian youth to choose a career in science is one of the spheres of academic study because of its effects on the national development, innovation, and staff support. This paper will discuss the variables that affect science, technology, engineering, and mathematics (STEM) as a career option among Indian students with both theoretical approaches to career choice, including Social Cognitive Career Theory (SCCT), and practical results in both international and local settings. The study being examined is a quantitative descriptive study based on the data obtained in a survey to investigate how personal, social, and institutional factors influence career choices. Results indicate that parental effect, self-efficacy, academic success, socioeconomic status, and exposure to STEM education have a substantial influence on career aspirations. Perceived utility of science careers and social persuasion are mentioned as the leading predictors. Stereotypes and cultural norms of gender difference also shape the mode of decision making, which in most cases restricts the involvement of females in STEM. The researchers declare that the policy interventions, career guidance, and enhanced educational infrastructure should be put in place to boost STEM among young people in India. The results are valuable to the large discussions on the development of career among youths and offer practical implications on educators and policymakers.

Published by: vikaspatanker

Authors: Shekar Vollem

Abstract: Modern digital applications demand the ability to process massive numbers of transactions while maintaining reliability, scalability, and responsiveness across geographically distributed infrastructures. Traditional monolithic architectures often struggle to support the throughput requirements of large-scale distributed systems due to tight coupling between components, limited horizontal scalability, and the difficulty of isolating failures within a single codebase. As workloads grow and user bases expand globally, these limitations become increasingly evident in areas such as transaction latency, system availability, and deployment agility. Distributed microservices architectures offer a viable alternative by decomposing applications into smaller, independently deployable services that communicate through lightweight APIs or event-driven messaging systems. This architectural paradigm enables organizations to scale services horizontally, optimize resource utilization, and process transactions concurrently across distributed environments. In such systems, each microservice typically manages its own data store and business logic, allowing for flexible scaling and improved resilience. This paper examines the architectural principles, distributed transaction models, and performance optimization strategies that enable high-throughput transaction processing in microservices environments. The study reviews existing research on distributed transaction processing systems, including distributed OLTP platforms and main-memory databases that reduce I/O bottlenecks and improve transaction latency. It also analyzes microservice orchestration patterns and coordination mechanisms that enable reliable transaction management across multiple services. Particular attention is given to techniques such as data partitioning, asynchronous messaging, event-driven communication, and Saga-based transaction coordination, which collectively help maintain data consistency without sacrificing system performance. Through the analysis of existing systems, architectural patterns, and prior research studies, the paper highlights approaches that significantly improve transaction throughput while preserving fault tolerance, service autonomy, and data consistency in complex distributed computing environments.

DOI: https://doi.org/10.5281/zenodo.19219630

Published by: vikaspatanker

Authors: Shekar Vollem

Abstract: Modern digital platforms require infrastructure that can scale dynamically, recover quickly from failures, and operate with minimal operational overhead while supporting rapidly changing workloads. Traditional infrastructure models often require significant manual configuration and capacity planning, which can limit scalability and increase operational complexity. Serverless computing has emerged as a promising cloud computing paradigm that abstracts infrastructure management from developers, allowing applications to run in environments where the cloud provider automatically handles resource provisioning, scaling, monitoring, and fault tolerance. In serverless architectures, developers deploy small, stateless functions or services that are executed in response to events such as API requests, database updates, or messaging events. This event-driven execution model enables systems to scale automatically according to workload demand, ensuring that resources are allocated efficiently without manual intervention. Cloud platforms such as AWS Lambda, Azure Functions, and Google Cloud Functions provide built-in mechanisms for automatic scaling, load balancing, and fault recovery, which contribute to high system availability. This article examines deployment strategies for building high-availability platforms using serverless architectures, focusing on how distributed cloud services can support reliable and scalable application infrastructures. The study analyzes architectural models that combine event-driven processing patterns, stateless computing components, and distributed service orchestration to achieve resilient system designs. It also explores how serverless frameworks integrate capabilities such as auto-scaling, multi-region redundancy, and managed infrastructure services to ensure continuous system availability even under fluctuating workloads or infrastructure failures.

DOI: https://doi.org/10.5281/zenodo.19219568

Published by: vikaspatanker

Authors: Chaitanya Khandbahale, Mohammad Junaid Shaikh, Arnav Raut, Darshan Sonar, Professor Kalyani Pawar

Abstract: Smart agriculture has emerged as a key solution to address critical challenges in traditional farming, including inefficient irrigation, excessive resource usage, delayed disease detection, and limited accessibility to modern technologies, especially in rural areas. The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) has enabled data- driven decision-making, real-time monitoring, and automation in agricultural practices. This survey presents a comprehensive review of IoT- and AI-based smart agriculture systems reported in recent literature. Various system architectures, sensing technologies, communication methods, and AI techniques used for irrigation control, crop health monitoring, disease detection, and yield prediction are analyzed and compared. The survey also examines connectivity models, including internet- dependent and offline solutions, power management approaches such as solar-based systems, and user-access mechanisms like mobile applications, SMS alerts, and voice interfaces. Key challenges related to cost, scalability, data reliability, and rural deployment are discussed. Finally, the paper identifies existing research gaps and outlines future directions for developing affordable, scalable, and intelligent smart farming solutions, providing design insights for next- generation agricultural monitoring systems.

Published by: vikaspatanker