Category Archives: Uncategorized

Authors: Tanu Yadav, Neelam Sahu, Deepak Sahu

Abstract: The rapid expansion of the pre-owned automobile industry has increased the demand for reliable and intelligent digital platforms for vehicle trading. Traditional used-car marketplaces often face challenges such as lack of transparency, inefficient search mechanisms, inconsistent pricing, and fraudulent listings, which reduce user trust and overall customer satisfaction. This research proposes an AI- powered car marketplace designed to improve the process of buying, selling, and exchanging second- hand vehicles through intelligent automation and secure digital infrastructure. The proposed system integrates advanced technologies including intelligent search optimization, personalized recommendation systems, automated listing moderation, and secure authentication mechanisms to enhance platform reliability and usability. The platform provides users with detailed vehicle listings, filtering and comparison features, responsive communication channels, and mobile-friendly accessibility to simplify customer interaction and decision-making. The backend architecture is developed to support scalable data management and efficient transaction handling using modern web technologies. Artificial Intelligence modules are incorporated to improve recommendation accuracy, optimize search relevance, and identify suspicious or duplicate listings. Experimental evaluation indicates that the proposed system improves search efficiency, recommendation precision, and operational transparency compared to conventional online used-car trading systems. The research demonstrates how AI-driven digital marketplaces can enhance trust, user engagement, and efficiency within the pre-owned vehicle industry while providing a scalable solution suitable for modern automotive e-commerce applications.

Published by: vikaspatanker

Authors: M.R.Manas, Dr. Umakanta Choudhury

Abstract: This study provides an in-depth analysis of the electromagnetic comparative assessment of the unskewed and skewed rotors for a 1 kW, three-phase, inner-rotor permanent magnet synchronous generator intended for small-scale direct-drive wind power applications. The generator has 36 stator slots and 12 rotor poles, with a 220 mm outer diameter of the stator and a stack length of 60 mm. The unskewed generator uses a ring magnet rotor design and features a gap size of 2.0 mm, while the skewed rotor design uses a block magnet rotor with a linear step of 10 degrees in three stages, with the air gap size of 1.5 mm. Performance criteria used for the finite-element-based simulations using Altair FluxMotor include the following: cogging torque, back-EMF waveform quality, losses, torque ripple, voltage, and efficiency, combined with thermal analysis. The reduction of the peak-to-peak cogging torque of the skewed rotor reaches 84.5phase back-EMF decreases by 67requirements of IEEE 519 regarding harmonics. Both the unskewed and skewed rotors show comparable efficiency at the same operating point (19 N·m and 500 rpm): 95.34full-load efficiency of the unskewed rotor (92.87the corresponding efficiency of the skewed rotor (91.96.

DOI: https://doi.org/10.5281/zenodo.20120373

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Authors: Abdullahi Idris, Aminu A. Abdullahi, Jamilu Awwalu, Abdullahi Uwaisu Muhammad

Abstract: Clinical time-series data are inherently complex, characterized by temporal dependences, irregular sampling and missing observations making accurate longitudinal risk prediction a challenging task. The study presents a novel hybrid Transformer framework for temporal representation learning and longitudinal risk prediction in clinical time-series that integrates the strengths of self-attention mechanism of Transformers to capture long-range interactions across time steps with the LSTM networks in modeling short-term temporal dependencies. A fusion module is introduced to adaptively combine representations from both components, enabling robust learning from irregular and partially observed clinical data. The experimental results demonstrate that the hybrid transformer framework effectively categorized patients into high-risk and low-risk categories based on their attributes. The training results indicate that the model performed well, with an accuracy of 98.6%, a sensitivity of 96.2% and a specificity of 97.8%. The model correctly identified 11 out of 18 high-risk patients and 16 out of 22 low-risk patients, with apparent errors of 38.9% and 27.3% respectively. These findings indicate that the hybrid Transformer framework can successfully learn patterns associated with cardiovascular risk from training data. Similarly, the test results confirm the model’s ability to predict previously unseen data. The model correctly categorized 9 out of 12 high-risk cases and 6 out of 8 low-risk cases, resulting an overall accuracy of 91.2%, sensitivity of 89.3% and specificity of 92.0% with a 25% apparent error in both cases.

DOI: https://doi.org/10.5281/zenodo.20118703

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Authors: Priyanka N Godiyal, Rutuja Amrale, Revati Ma’am, Archana Ma’am.

Abstract: Road traffic accidents are a leading cause of mortality worldwide, with India recording over 1.5 lakh fatalities annually. Identifying 'black spots' — specific road segments with disproportionately high accident frequency — is critical for targeted infrastructure intervention. Traditional methods of black spot identification rely on statistical thresholds applied to historical data, which are often reactive and location-agnostic. This paper proposes an integrated framework combining Machine Learning (ML) and Geographic Information Systems (GIS) for predictive black spot detection. We review and compare ML algorithms including Random Forest, XGBoost, Support Vector Machines (SVM), and Deep Neural Networks applied to multi-source data comprising accident records, road geometry, traffic volume, and environmental factors. Spatial analysis techniques such as Kernel Density Estimation (KDE) and spatial autocorrelation are used for feature engineering. Results show that ensemble methods achieve accuracy above 90%, with XGBoost yielding the highest AUC-ROC of 0.94. GIS-integrated output maps provide actionable, zone- specific risk rankings to support road safety planning.

DOI: https://doi.org/10.5281/zenodo.20118287

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Authors: Assistant Professor Biju George.G

Abstract: Earthquakes often create hazardous environments that delay rescue operations and increase risks to emergency responders. This paper presents a low-cost Internet of Things (IoT)-enabled autonomous rescue robot designed to assist in earthquake search and rescue missions through real-time monitoring and multi-sensor integration. The proposed system employs an Arduino Uno as the central controller and integrates a Passive Infrared (PIR) sensor for human detection, an ultrasonic sensor for obstacle avoidance, an MQ-series gas sensor for hazardous gas detection, a GPS module for victim localization, and an ESP32-CAM module for live video surveillance. Sensor data are transmitted through an ESP8266 NodeMCU to the Blynk IoT platform, enabling remote monitoring of environmental conditions, survivor alerts, and robot location. The developed prototype was experimentally evaluated under laboratory conditions to validate its sensing, navigation, communication, and monitoring capabilities. Experimental results demonstrate reliable human detection, obstacle avoidance, hazardous gas monitoring, GPS-based localization, and real-time data transmission with low implementation cost and reduced computational complexity. The proposed system offers a practical and scalable solution for improving rescue efficiency while minimizing the exposure of rescue personnel to hazardous environments. The modular architecture also provides flexibility for future enhancements using artificial intelligence and advanced sensing technologies.

DOI: https://doi.org/10.5281/zenodo.21884018

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Authors: Harini . K, Assistant Professor S.Janani

Abstract: The rapid growth of Over-The-Top (OTT) platforms has fundamentally changed the way users consume multimedia content on the internet. Streaming services like Netflix have set a highstandard for user experience, interface design and content discovery. This project, titled “Netflix Clone – Web Application Using HTML, CSS, JavaScript, React and Vite” . The backend is built using Node.js, providing RESTful APIs to handle user interactions and data management. PostgreSQL is used as the relational database for storing user data, movie details, and other application information., aims to design and implement a responsive and interactive web interface that emulates the core look and feel of the Netflix platform using modern front-end technologies. The primary objective of this project is to build a single-page application (SPA) that allows users to browse a catalogue of movies and series, view categorized lists such as “Popular”, “Trending” and “Top Rated”, and navigate to detailed information pages for each title. The user interface isimplemented using HTML5 for structure, CSS3 for styling, and JavaScript for dynamic behavior. React is used as the front-end library to efficiently manage UI components and state, whileserves as a fast and optimized build tool and development server. The system implements CRUD (Create, Read, Update, Delete) operations for managing users, movies, and preferences. The application is developed using Visual Studio Code and follows modern web development practices. Together, these tools provide a modern, modular and scalable environment for building the Netflix clone. The project follows a structured approach including requirement analysis, interface design, component design, implementation, and basic testing. Emphasis is placed on responsive design to ensure that the application works across desktops, laptops and mobile devices. Features such as reusable React components, props and state, routing (if used), and API-like data retrieval from static JSON or mock data are incorporated to simulate real-world application behaviours

DOI: http://doi.org/

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Authors: Nimisa Bhagchandani

Abstract: The rapid growth of digital commerce and the increasing use of artificial intelligence have significantly changed the way consumers make purchase decisions online. One of the most common applications of AI in this space is the use of personalization algorithms, which provide users with tailored product recommendations based on their preferences and past behaviour. While these systems are designed to improve convenience and enhance user experience, they may also create unintended challenges for consumers. This study examines the impact of personalization algorithms on consumer decision fatigue and purchase decision-making in digital commerce contexts. The research focuses on understanding whether personalized recommendations simplify the decision-making process or contribute to cognitive overload. Decision fatigue is considered as a key factor that may influence how consumers respond to multiple product options and recommendations. The findings of the study are expected to provide insights into how personalization influences consumer behaviour beyond its intended benefits. It highlights the need for digital platforms to balance personalization with user comfort and cognitive ease. The study contributes to a better understanding of the psychological effects of personalization and its role in shaping online consumer decision-making.

DOI: https://zenodo.org/records/20099925

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Authors: Sonal Saral, Anurag Gangwar

Abstract: Agricultural residue burning is a major contributor to seasonal air pollution in northwestern India, significantly affecting air quality across the Indo-Gangetic Plain. This study presents a comprehensive Spatio-temporal and seasonal analysis of crop residue burning in Punjab and Haryana during 2021–2025 using satellite-derived fire count data (MODIS and VIIRS), with a focus on pre-monsoon (Rabi: April–May) and post-monsoon (Kharif: October–November) periods. The results indicate that post-monsoon burning dominates total fire activity, accounting for approximately 70–75% of annual fire counts, with Punjab alone contributing more than 80% of regional fire events. Peak Kharif fire activity exceeded 170,000 events in Punjab, while Haryana recorded comparatively lower counts (~21,000 events). In contrast, Rabi burning remained relatively stable, averaging ~85,000–90,000 fires in Punjab and ~25,000–26,000 fires in Haryana. Temporal trends reveal a substantial decline in Kharif fire counts, with reductions of nearly 90–94% between 2021 and 2025, indicating the effectiveness of policy interventions and residue management technologies. However, Rabi burning exhibited limited reduction, highlighting a critical gap in mitigation strategies. Spatial analysis shows dense clustering of fires in central and northwestern Punjab, whereas Haryana exhibits more dispersed burning patterns. The strong seasonal concentration and magnitude of fire activity confirm that biomass burning remains a dominant driver of particulate pollution. These findings emphasize the need for crop-specific, season-targeted mitigation strategies to achieve sustained improvements in regional air quality.

DOI: https://zenodo.org/records/20096389

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Authors: Ankita Tripathi, Anurag Gangwar

Abstract: This study investigates the spatial and temporal variability of aerosols over Western and Central India using satellite-derived Aerosol Optical Depth (AOD) and Ångström Exponent (AE) for the period 2016–2025. AOD provides information on aerosol loading, while AE is used to infer particle size distribution. The analysis was carried out at monthly, seasonal, and annual scales using a zonal approach to distinguish regional characteristics. The results reveal significant seasonal variation in aerosol properties. AOD shows maximum values during the pre-monsoon season, particularly over Western India (~0.47), attributed to enhanced dust activity and dry atmospheric conditions. In contrast, AOD decreases during the monsoon season due to wet scavenging processes. AE exhibits an opposite trend, with higher values during monsoon and post-monsoon seasons (up to ~1.63 in Central India), indicating the dominance of fine-mode aerosols from anthropogenic emissions and biomass burning. Monthly analysis further confirms this inverse relationship between AOD and AE, reflecting the transition from coarse to fine particles across seasons. Interannual analysis indicates relatively stable aerosol patterns with noticeable fluctuations, including a decline in AOD during 2020, likely associated with reduced anthropogenic activities. A clear regional contrast is observed, where Western India is dominated by coarse-mode dust aerosols (high AOD, low AE), while Central India shows a higher influence of fine-mode anthropogenic aerosols (moderate AOD, high AE). Overall, the combined assessment of AOD and AE provides critical insights into aerosol behavior, sources, and seasonal dynamics. The findings are relevant for improving air quality management, understanding aerosol–climate interactions, and supporting environmental policy development in India

DOI: https://zenodo.org/records/20096175

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Authors: Harsh Vardhan, Aanurag Gangwar

Abstract: Air pollution continues to be one of the most important environmental problems in fast-growing parts of the Indo- Gangetic Plains (IGP), such as Agra and Noida, characterized by declining air quality levels. The current paper offers a comparative analysis of the Air Quality Index (AQI) temporal and seasonal trends in the cities of Agra and Noida based on the daily data from the Central Pollution Control Board (CPCB) for the period of 2021–2025. The descriptive statistical analysis demonstrates that AQI is higher and more variable in Noida than Agra, which implies that Noida is under higher levels of pollution. The time-series analysis reveals considerable AQI dynamics in the two cities, including peaks of the parameter under discussion during winter months. The results of the monthly and seasonal analysis also suggest strong seasonality, according to which AQI scores peak during winter and post-monsoon months, while monsoon months are associated with improved air quality. In terms of AQI categories, Noida witnesses more days classified as “Poor,” “Very Poor,” and “Severe,” while Agra shows a larger number of “Moderate” and “Satisfactory” days. Finally, the autocorrelation analysis demonstrates a high level of AQI dependence on the time dimension in both cities. These differences have been observed due to variations in the sources of emission, population density, pollution transport dynamics, and weather conditions. In conclusion, the analysis shows that Noida is more heavily and variably polluted than Agra. This study offers valuable guidance for designing air quality management plans specific to regions and helps frame effective measures for pollution-prone urban areas.

DOI: https://zenodo.org/records/20095989

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