Daily Archives: April 2, 2026

Authors: Ishita Shinde, Tanushri Jadhav, Mrs. Patil P.M

Abstract: Managing the shared financial transactions has become a very difficult process with the rapid growth of group-oriented activities like shared accommodation, travel, events, and projects. Calculations and transparency issues, financial management problems, and interpersonal relationship issues result from the calculations performed for the splitting of bills and expense management in the traditional manner. Because of these constraints, a methodical and technologically simple way of managing the shared expense is necessary. Bill Splitting System – This is a simple application used to divide the bill or expense among a group of people. By providing an automated, methodical, and user-centered system that aims to simplify and enhance the management of group expenses, the proposed FairShare System: Bill Splitting and Expense Managing Assistant aims to resolve the problem. In current financial environments, FairShare System is designed to make group expense management simpler and quicker. It splits the bill among all the peoples or with friends. It is a very useful apporoch to avoid misunderstandings amoung group of peoples. It addresses common issues like inconsistent data, wrong settlements, duplicates, and the difficulty in tracking balances. It ensures fairness and transparency in the process using optimized algorithms and minimizes the total number of transactions required for settling debts.This system provides a reliable,scalable,and user friendly way of managing groupexpense with a well-oragnized backend that ensures the security and accuracy of the data.

DOI: https://doi.org/10.5281/zenodo.19386279

Published by: vikaspatanker

Authors: Aditya Kolpe, Swanandi Kamthe, Samiksha Jagtap, Sakshi Borude, Arti Patil

Abstract: DiploNxtPath AI is an advanced AI-powered academic support system developed to enhance the learning experience of diploma students by integrating intelligent automation with modern web technologies. The primary objective of the system is to assist students in generating structured study plans, accessing relevant learning resources, and receiving personalized academic guidance. Traditional learning methods often lack proper direction, resulting in inefficient study patterns, confusion in subject selection, and poor time management. DiploNxtPath AI addresses these challenges by providing a centralized platform that utilizes artificial intelligence to analyze student inputs and generate customized learning pathways. The system enables students to create personalized roadmaps, manage study schedules, and access curated educational content. Additionally, it includes a chatbot-based assistant that provides real-time support for academic queries and career-related guidance. The integration of AI allows dynamic content generation and adaptive recommendations based on user behavior.

Published by: vikaspatanker

Authors: R. Divya Shree, T. Sri Vidya, Sk. Jaheer Uddin, P. Hefayath Khan, Mr. K. P. Babu

Abstract: Employee attrition is a critical challenge for modern organizations, leading to increased recruitment costs, loss of skilled talent, and reduced productivity. This paper presents TalentGuard, a machine learning-based HR analytics system designed to predict employee attrition and provide actionable insights for workforce management. The proposed system leverages historical employee data, including job role, salary, department, tenure, performance metrics, and work conditions, to train and evaluate multiple machine learning models such as Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting algorithms of leaving, enabling organizations to take proactive measures. By combining predictive The system incorporates data preprocessing, feature engineering, and model optimization techniques to enhance prediction accuracy. Performance evaluation is conducted using metrics such as accuracy, precision, recall, and ROC-AUC score. In addition, TalentGuard integrates interactive dashboards and an AI- powered chatbot to assist HR professionals in analyzing attrition trends and generating retention strategies. The results demonstrate that machine learning models can effectively identify employees at risk primarily rely on reactive approaches, where analytics with intelligent user interaction, TalentGuard contributes to data-driven decision- making and improved employee retention strategies.

Published by: vikaspatanker

Authors: Kalpna Vats, Anjali Kaushik, Vaishali Munjal, Anisha

Abstract: The current paper seeks to present an in-depth analysis pertaining to the integration of CCNA security concepts with contemporary cloud computing networks. Over the years, enterprise networks have undergone a transition from traditional on-premises networks to cloud computing networks. As such, the traditional security concepts studied in the Cisco Certified Network Associate (CCNA) program need to be updated to incorporate the complexities inherent in cloud computing networks. Based on an in-depth analysis of recent developments in the field from 2021 to 2026, the paper seeks to examine the possibility of integrating traditional CCNA security concepts such as Access Control Lists (ACLs), Virtual Private Networks (VPNs), Port Security, and Hardening with contemporary cloud computing networks. A Hybrid Security Integration Framework (HSIF) is proposed as a means of integrating traditional CCNA security concepts with cloud computing networks. From the analysis, it is evident that more than 55% of enterprises currently use multiple cloud providers, while 24% are planning to adopt cloud firewalls as their main solution in the next two years. The adoption of Security Service Edge (SSE) and Secure Access Service Edge (SASE) solutions can be considered a development of CCNA security concepts into cloud technology. By comparing them through four analytical dimensions, it is evident that integration involves both technical and organizational adaptation through Dev Sec Ops practices.

DOI: https://doi.org/10.5281/zenodo.19383422

 

Published by: vikaspatanker

Authors: Mr.P.Tamilnesan, G.Devaprakash, J.Mouleeshwaran

Abstract: This project presents comprehensive power system studies of a 13.8 kV substation network to improve power quality and operational reliability. Load flow analysis is carried out to evaluate voltage profiles, power losses and reactive power flow while maintaining a minimum lagging power factor of 0.95 at the Point of Common Coupling. Short circuit analysis is performed to determine fault current levels and verify equipment and protection adequacy. Motor acceleration studies assess starting currents, voltage dips and dynamic performance of large motors. Power factor improvement is achieved through optimal selection and placement of capacitor banks. The impact of capacitor switching, including transients and self excitation effects is evaluated. Harmonic analysis is conducted to ensure acceptable distortion levels. Transient stability of the system during disturbances is analyzed using ETAP. The study demonstrates improved voltage stability, reduced losses and enhanced system reliability.

DOI: https://doi.org/10.5281/zenodo.19383047

 

Published by: vikaspatanker

Authors: Sowmya Yattapu

Abstract: Artificial Intelligence is fundamentally transforming the discipline of data engineering. This paper examines how AI is reshaping core data engineering functions including relational and cloud database management, data warehousing, enterprise analytics, digital analytics platforms such as Adobe Analytics, and cloud-native platforms such as Snowflake. Drawing on current industry practices and emerging platform capabilities, this paper analyzes the impact of AI on pipeline development, data quality management, automated metadata governance, and real-time analytics. This paper further discusses how the role of the data engineer is evolving from manual code writing to strategic architecture and AI-assisted orchestration. The paper also addresses key challenges including data privacy in regulated financial environments, skill evolution requirements, and the governance of AI-generated outputs. Paper findings indicate that organizations which invest in AI-ready data infrastructure, establish strong governance frameworks, and upskill their engineering teams will gain significant competitive advantages in the next decade.

DOI: https://doi.org/10.5281/zenodo.19382867

Published by: vikaspatanker

Authors: Mrs.M. Lavanya, U. Dhivakar, A. Mohamed Arsath, N. Mohamed Rasheen

Abstract: In many colleges, students face difficulties during placement preparation due to limited training resources, lack of real interview exposure, and insufficient guidance on resume building and job selection. Traditional placement training is often theoretical, time-restricted, and does not accurately reflect real company recruitment processes. As a result, students struggle with aptitude tests, technical interviews, and HR rounds, which affects their confidence and employability. Smart Placement is an AI-powered web-based application developed to overcome these challenges by providing a complete and structured placement preparation platform for all students. The system simulates real recruitment processes through aptitude, reasoning, technical, and HR interview modules. Aptitude and reasoning tests enhance logical thinking and problem-solving skills, while the technical round strengthens core subject knowledge. A key feature of the platform is the real-time AI based HR interview module, which evaluates communication skills, confidence level, and behavioral responses. The system also includes an automated resume builder that generates professional, industry-standard resumes for freshers and experienced candidates. Additionally, Smart Placement offers job search, job notifications, and company offer listings, enabling students to explore real-time job opportunities. By integrating AI-driven interviews, resume automation, and job availability tracking into a single platform, Smart Placement improves placement readiness, boosts confidence, and bridges the gap between college training and industry recruitment requirements.

DOI: https://doi.org/10.5281/zenodo.19382255

Published by: vikaspatanker

Authors: Nitesh Baranawal, Herambh Sakpal, Pranay Manoj, Kaustabh Kadam, Prof.Mohan Kumar

Abstract: The Steganography Hider System is a secure information-hiding solution designed to protect sensitive data by embedding it within digital images, making it imperceptible to unauthorized users. Unlike traditional encryption, which only disguises data, steganography conceals the very existence of the information, providing an additional layer of security. This system employs techniques such as Least Significant Bit (LSB) substitution, transform domain methods (e.g., DCT), or advanced neural network approaches to embed secret messages while maintaining the visual quality of the cover image. The proposed system allows users to securely hide and retrieve confidential information, ensuring data confidentiality, integrity, and robustness against common image processing operations such as compression, noise addition, and format conversion. This project serves as a practical demonstration of the importance of information security in today’s digital communication era, providing a user-friendly interface that can be applied in various fields such as secure communications, copyright protection, and digital forensics.

DOI: https://doi.org/10.5281/zenodo.19381939

 

Published by: vikaspatanker