Artificial Intelligence : JobBot
The rapid advancement of artificial intelligence has paved the way for innovative solutions in the recruitment process. This abstract introduces the AI JobBot, a cutting-edge system designed to enhance the interview experience for candidates through personalized and domain-specific interactions. Upon candidate selection of their domain, the JobBot employs natural language processing to engage in a human-like conversation, tailoring questions to the specific requirements of the chosen field. The dynamic interview process adapts to candidate responses, ensuring a comprehensive evaluation of their skills and knowledge. The AI JobBot leverages machine learning algorithms to continually refine its questioning techniques, mimicking the adaptability of human interviewers. This not only provides candidates with a realistic and engaging interview experience but also ensures that the evaluation is aligned with industry standards. Furthermore, the JobBot goes beyond the conventional interview experience by offering constructive feedback to candidates. Through real-time analysis of their responses, the AI system provides personalized insights into strengths and areas for improvement. This feedback is invaluable for candidates seeking to enhance their interview skills and refine their expertise.
Published by: Sushil Kumar B, Shiv Shobhith M
Author: Sushil Kumar B
Paper ID: V11I6-1300
Paper Status: published
Published: December 19, 2025
HPTLC-Based Qualitative Identification of Gallic Acid in Rauvolfia serpentina Roots Collected from Diverse Agro-Climatic Zones of India
Gallic acid is a bioactive phenolic compound widely recognized for its antioxidant and pharmacological properties. The present study qualitatively investigates the presence of gallic acid in root samples of Rauvolfia serpentina (Sarpagandha) collected from four distinct agro-climatic zones of India using High Performance Thin Layer Chromatography (HPTLC). Methanolic extracts of root samples were analyzed alongside a gallic acid standard employing a standardized solvent system. The chromatographic profiles of all samples exhibited peaks corresponding to the retention factor (Rf) range of the reference standard, confirming the presence of gallic acid across all evaluated samples. This study supports the phytochemical consistency of Rauvolfia serpentina roots and contributes to quality control and standardization efforts of this important medicinal plant.
Published by: Poornima Shrivastava, Aparna Alia, Bharty Kumar
Author: Poornima Shrivastava
Paper ID: V11I6-1306
Paper Status: published
Published: December 19, 2025
Expenzo – The Smart Finance Tracker
The rapid growth of digital transactions and cashless payment systems has increased the complexity of personal financial management. Individuals often lack effective tools to systematically record expenses, analyze spending behavior, and maintain financial discipline. EXPENZO – The Smart Finance Tracker is a secure, web-based financial management system developed to address these challenges by providing an automated and structured approach to tracking income and expenses. The system allows users to log financial transactions in real time, classify them into predefined categories, and generate analytical summaries that reflect spending trends and saving patterns. Advanced dashboard visualizations and monthly financial reports support data-driven budgeting decisions and promote improved financial awareness. The application integrates essential security mechanisms such as user authentication and controlled data access to ensure the confidentiality and accuracy of sensitive financial information. EXPENZO is implemented using Python with the Flask framework to manage backend operations and application logic. SQLite is employed as a lightweight relational database for efficient data storage and retrieval, while HTML, CSS, and Bootstrap are used to develop a responsive and user-centric interface. The modular architecture of the system ensures scalability, maintainability, and cross-device accessibility. The proposed solution demonstrates the effectiveness of a lightweight web-based platform in simplifying personal finance management. By combining automation, visualization, and secure data handling, EXPENZO enhances users’ ability to monitor financial activities, optimize budgeting strategies, and plan for long-term financial stability. Overall, EXPENZO serves as a practical digital tool for tracking finances, improving saving habits, and developing financial discipline. The application demonstrates how a simple and user-friendly solution can help individuals gain better control over their personal finances and plan more effectively for future financial needs.
Published by: Sahil C. Madankar, Sankalp S. Pawar, Tanushree S. Patle, Vidhi P. Harode, Shivang R. Nagpure, Sakshi S. Zade, Shrawani H. Bijwar, Prof. P. A. Kuchewar, Prof. M. R. Balbudhe
Author: Sahil C. Madankar
Paper ID: V11I6-1305
Paper Status: published
Published: December 19, 2025
Hydrogen Yield Efficiency Based on Current Density in AWE
Hydrogen production through alkaline water electrolysis (AWE) remains one of the most reliable and economically feasible pathways for generating clean hydrogen. However, the efficiency of AWE is strongly influenced by the operating current density, particularly at higher loads, where bubble accumulation, increased overpotential, and mass-transport limitations reduce the practical hydrogen yield. This study examines the effect of varying current density on hydrogen yield efficiency by comparing the experimentally collected hydrogen volume with theoretical values derived from Faraday’s law. Electrolysis was performed using stainless steel electrodes in a 0.50 M NaOH electrolyte over current inputs ranging from 0.10 A to 0.50 A. For each current setting, the corresponding hydrogen volume was measured via the water displacement method, converted to moles using the ideal gas law, and evaluated against the predicted stoichiometric output. The results show a near-linear increase in hydrogen production at lower current densities but a noticeable deviation from ideal Faradaic behaviour at higher currents. Faradaic efficiency decreased from approximately 94% at 0.10 A to around 85% at 0.50 A, confirming that bubble blockage, resistive heating, and kinetic limitations become more pronounced as current density increases. The study provides a clear, empirical relationship between current density and hydrogen yield efficiency in a simple AWE system, offering useful insights for small-scale electrolysis applications and highlighting the practical limitations encountered when transitioning to higher operational loads. Beyond quantifying efficiency trends, this study also demonstrates the importance of understanding electrochemical behaviour when scaling up hydrogen production systems. Since many educational and laboratory AWE setups operate without advanced engineering features—such as forced electrolyte circulation, porous electrodes, or catalytic coatings—the findings provide a realistic baseline for performance expectations in simple electrolyzers. The observations reinforce that while increasing current density boosts hydrogen output, it simultaneously introduces non-idealities that lower conversion efficiency. These insights can support future optimisations in electrode design, electrolyte composition, and cell configuration for improved hydrogen yield in low-cost AWE systems. Overall, the study highlights the value of Faradaic efficiency as a diagnostic tool for evaluating real-world electrolyzer performance. By directly comparing theoretical and experimental hydrogen yields, the method used here provides a simple yet powerful way to identify operational losses without requiring advanced instrumentation. This approach can be applied in future work to assess the influence of factors such as electrode spacing, electrode surface treatment, electrolyte concentration, and temperature on hydrogen output. The findings, therefore, not only document the behaviour of AWE under varying current densities but also establish a practical framework for improving system efficiency in academic, laboratory, and introductory research settings.
Published by: Harmaya Thukral
Author: Harmaya Thukral
Paper ID: V11I6-1278
Paper Status: published
Published: December 16, 2025
Geospatial Threat Assessment: Safety Analytics using RNN, XGBoost and Isolation Forest
Personal safety applications today mostly respond after an incident occurs, which limits their ability to prevent harm. In this work, we develop a proactive safety-risk prediction system that estimates how dangerous a location may become in the near future. The system combines sequential deep-learning models with boosted decision-tree techniques to understand how local crime risk evolves over time and space. Historical crime records, temporal patterns, nearby points of interest, and environmental context are merged into structured spatio-temporal data grids. The proposed approach uses an LSTM network to learn short-term temporal changes in risk at the grid-cell level, while an XGBoost model evaluates spatial and contextual factors to produce interpretable risk scores. An Isolation Forest module is used alongside these models to detect sudden, unusual conditions that may indicate unsafe situations. The outputs of all three models are merged into a unified risk score that updates continuously and highlights emerging danger zones. When the score crosses certain thresholds, the system can issue early warnings, suggest safer travel routes, or escalate alerts if needed. The system is evaluated on real crime datasets using spatio-temporal cross-validation, and performance is measured using metrics suited for imbalanced data such as AUC, Precision@K, and F1-score. Results demonstrate that the system can provide meaningful early-risk signals while maintaining transparency and privacy-aware processing.
Published by: Mukul Malviya, Mohak Pandagre, Kushagra Singh Chouhan, Mayank Dhakar
Author: Mukul Malviya
Paper ID: V11I6-1228
Paper Status: published
Published: December 16, 2025
Greenscan: An AI-Powered, Cross-Platform System for Instant Plant Identification and Care Guidance
This paper presents GreenScan, an intelligent and interactive web platform developed to enable fast, accurate, and user-friendly plant species recognition through uploaded images. Addressing the persistent challenges of manual plant identification, such as inefficiency, limited accessibility, and a lack of centralized information, GreenScan leverages the power of Artificial Intelligence (AI) and Deep Learning to deliver real-time classification of more than 100 distinct plant species. The system employs a Convolutional Neural Network (CNN) model trained on a large and diverse dataset of plant images to ensure high recognition accuracy, even under varying lighting and background conditions. The platform integrates a responsive and intuitive web interface, allowing users to seamlessly upload images, view classification results, and explore detailed plant profiles. Each identified species is linked to a comprehensive backend database containing essential details such as taxonomy, physical characteristics, ideal growing conditions, and care guidelines. Furthermore, GreenScan provides external purchase links and educational resources, making it an invaluable tool for students, researchers, horticulturists, and nature enthusiasts. A key feature of GreenScan is its feedback-driven learning mechanism, which enables continuous model retraining based on user input to progressively enhance prediction precision over time. The platform’s implementation achieved high confidence scores, including a 91% accuracy rate for identifying species such as the Snake Plant. Beyond its technical merits, GreenScan contributes significantly to promoting environmental education, sustainable living, and ecological awareness by bridging the gap between modern technology and biodiversity knowledge. This work demonstrates the potential of AI-powered solutions to transform traditional plant identification into a more engaging, efficient, and educational digital experience.
Published by: Vaishnavi Duratkar, Twinkal Sapate, Snehal Ninawe, Sanket Barapatre, Ashwary Dhakate, Sharwari Mohadikar, Prajakta Singham, Mamta Balbudhe
Author: Vaishnavi Duratkar
Paper ID: V11I6-1296
Paper Status: published
Published: December 13, 2025
