Category Archives: Uncategorized

Authors: Jag Pratap Singh Yadav

Abstract: This thesis builds an interdisciplinary paradigm of how global warming needs to be considered as a crisis for the very existence of humankind and not just as a problem related to environmental studies and policies. A crucial limitation of the research is found between empirical climatology and normative ethics, and both are inadequate to analyze the issues associated with the risk of climate change. To fill in this gap, the thesis employs mathematical philosophy, applying the instruments of Bayesian epistemology, decision, game, and moral philosophies. Epistemically, what is shown is that while climate uncertainty may feature non-linearity, feedback mechanisms, and tipping points, it should not act as an excuse for not taking action. Rather, when viewed using the tools of Bayesian logic, fat tail risks, and Pascal’s wager approach, uncertainty acts as a strong rationale for preemptive action to be taken. From a strategic point of view, the research paper considers climate change as a game of asymmetric players with multiple agents and multiple generations. The analysis demonstrates that the traditional models of collective action cannot capture the differences in the degree of responsibility, vulnerability, and institutional capability among the parties. In applying the concept of game theory to the long-term decision-making process, the paper reveals the asymmetry between the two generations – present and future – from the ethical perspective, which states that any choice made by one generation will irrevocably alter the opportunity set for the other generation. Moreover, the paper examines flaws in traditional economic approaches to climate change valuation, such as the discounting of future well-being using positive pure time preference. It is shown that such an approach undermines the value of future generations and is inherently biased towards procrastination. On the contrary, the combination of almost zero interest rates and the priority-based welfare principle can provide a more logically consistent approach to environmental management, maintaining temporal impartiality and giving priority to disadvantaged groups in the current and future generations. The main conclusion of this research is that sustainability in terms of environmental protection must be considered as a basic axiom of rational and ethical choice when making decisions in the context of existential threats. Taking into account the uncertainty factor, catastrophic risks, dependency and justice, the paper provides a new definition of environmental sustainability, which is an important prerequisite for ensuring the survival of humanity. The conclusions made have practical implications related to the need for effective global climate.

DOI: https://doi.org/10.5281/zenodo.19834826

Published by: vikaspatanker

Authors: Dr. Jasjit Singh Samagh, Urvita and Chandan

Abstract: Accurate valuation of used automobiles remains a critical challenge in the automotive resale market, where traditional manual estimation methods suffer from inconsistency, subjectivity, and limited scalability. This paper presents a comprehensive comparative analysis of two fundamental machine learning regression techniques—Linear Regression and Random Forest—for automated car price prediction. We developed and evaluated two complete prediction systems: a web-based application using Linear Regression integrated with Streamlit, and a desktop GUI application employing Random Forest with Tkinter interface. Both systems were trained and tested on comprehensive used car datasets comprising over 6,700 vehicle records with features including brand, manufacturing year, kilometers driven, fuel type, transmission type, ownership history, engine specifications, and market pricing. The Linear Regression model achieved an R² score of 0.87, Mean Absolute Error (MAE) of 0.34 lakhs, and Mean Squared Error (MSE) of 0.18, while the Random Forest approach demonstrated superior performance with R² score of 0.94, MAE of 0.28 lakhs, and MSE of 0.60. Our comparative analysis reveals that Random Forest's ensemble learning approach captures non-linear relationships more effectively, achieving 7% higher variance explanation than Linear Regression, though at increased computational complexity. Statistical significance testing confirms that Random Forest's performance improvement is statistically significant (p < 0.01). Both systems provide real-time predictions through user-friendly interfaces—web-based for broader accessibility and desktop-based for offline usage. This research contributes practical insights into algorithm selection for automotive price prediction, demonstrating trade-offs between model simplicity, interpretability, and accuracy while providing deployment-ready solutions for diverse stakeholder requirements.

DOI: https://doi.org/10.5281/zenodo.19830626

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Authors: Prachi Durge, Mahek Shribas, Mohanish Lanjewar, Parth Gadwal, Pranay Wadibhasme, Pranjali Nakhate

Abstract: This paper presents a systematic comparative study of three tabular reinforcement learning (RL) algorithms—Q-Learning,State-Action-Reward-State- Action (SARSA), and Double Q-Learning—deployed within a configurable stochastic GridWorld environment. The environment incorporates slip-based stochastic transitions, trap cells, potential-based reward shaping grounded in the theoretical guarantees of Ng et al. [1], and partial observability modes. The central research hypothesis investigates whether Double Q-Learning’s decoupled selection-evaluation mechanism demonstrably reduces maximization bias compared to vanilla Q-Learning, particularly under elevated stochastic transition probabilities. An interactive web-based research platform is developed using Flask and Chart.js, enabling real-time policy visualization, value-function heatmaps, Q-table analysis, and multi-seed benchmark comparisons with confidence intervals. Experimental results across three canonical grid configurations demonstrate that Double Q- Learning achieves superior convergence stability and reduced overestimation in high-slip environments, while SARSA exhibits inherently conservative on-policy behavior that trades off peak performance for robustness near traps.

DOI: https://doi.org/10.5281/zenodo.19830361

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Authors: Yashvi Rajiv Vyas, Mohammad Armaan, Rida Sadiqa, Sarayu Anand Gongada, Mohammed Sufyan, Dr. Chandrasekhar V (Project Giude)

Abstract: ExplorAR Glasses is an intelligent augmented reality (AR)-based travel assistance system designed to enhance real-world exploration through contextual digital augmentation. The system integrates geolocation, artificial intelligence, computer vision, and real-time API services to deliver immersive, hands-free assistance to users. By combining GPS-based location tracking, AI-generated contextual insights, OCR-based translation, weather forecasting, and voice interaction, ExplorAR enables users to interact with their surroundings in a seamless and intuitive manner. The system is built using a modular architecture consisting of a lightweight mobile client and a cloud-based backend. The backend leverages large language models (LLMs) for contextual information generation, while external APIs provide navigation, translation, and environmental data. The frontend prototype, developed using a cross-platform framework, serves as an intermediary between device sensors and backend services. The solution is designed to be scalable and adaptable for integration with wearable AR devices such as smart glasses. This project demonstrates how emerging technologies can be combined to create a real-time, context-aware digital assistant that improves accessibility, travel experience, and user interaction with physical environments.

DOI: https://doi.org/10.5281/zenodo.19828864

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Authors: Arayan Gandre, Swaraj Sakpal, Unmesh Nhavelkar, Vedant Gaikwad, Prof. Smita Pawar

Abstract: In today’s highly competitive and data- driven marketplace, pricing strategy has become a decisive factor in determining a company’s profitability, customer satisfaction, and long-term sustainability. Traditional static pricing models, which rely on fixed markups or manually updated price lists, are often inadequate in responding to the dynamic nature of modern markets. These methods struggle to adapt to frequent fluctuations in consumer demand, competitor actions, supply chain disruptions, and seasonal influences. This research presents the design and development of an Artificial Intelligence (AI)-based Dynamic Pricing Engine that autonomously predicts and optimizes product prices in real time. The proposed framework integrates a variety of heterogeneous data sources — including historical sales transactions, customer purchasing behavior, inventory levels, market demand elasticity, and competitor pricing trends — to generate context-aware pricing recommendations. The system employs a hybrid machine learning approach: regression-based models are used for short- term price prediction, while reinforcement learning techniques enable continuous self-improvement through feedback-driven optimization. A prototype implementation was tested using real-world re- tail and e-commerce datasets to evaluate its effectiveness. The experimental results demonstrate that the AI-driven dynamic pricing model significantly enhances revenue optimization, profit margins, and inventory turnover compared to traditional rule- based or static pricing systems. Moreover, the model exhibits rapid adaptability to demand shifts and improved decision- making accuracy under volatile market conditions. The findings highlight the transformative potential of AI in automating strategic business decisions and emphasize the scalability and robustness of intelligent pricing systems. This study contributes to the broader field of intelligent commerce by providing a data-centric, adaptive, and scalable solution for modern enterprises seeking to maintain competitiveness in the evolving digital economy.

Published by: vikaspatanker

Authors: Sareddy Prasanna Reddy, Dr. P. Kowstubha, Parameshwari Rathod, Barla Ananda Sagar

Abstract: This paper presents the design and implementation of DC-DC buck converter using a digital PI control technique. The system converts a 24V DC input into a regulated 12V DC output. An microcontroller is used to implement closed-loop control and generate PWM signals. The controller continuously monitors the output voltage and adjusts the duty cycle to maintain stable output under different load conditions. The converter achieves an efficiency of around 90% with good voltage regulation. The results show that the proposed system is suitable for low-power embedded applications.

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Authors: Ms.C. Harivarshini, Ms.M. Shubhashree, Dr.R. Karthik

Abstract: The global workforce is undergoing rapid evolution. Current data-driven research into worldwide employment trends thus has become a pressing need. The objective of the present study was to conduct a comprehensive analysis of job advertisements and salary trends by reviewing 999 job records collected from 213 countries; these included a total of 13 data points. As part of its analytic process, the present study utilized a data preprocessing pipeline that involved the passing of data through multiple stages – data cleansing, data type conversion, aggregation, data partitioning, normalization, etc., prior to submission to various data visualisation techniques; these included bar charts, histograms, box plots, scatter plots, correlation heat maps, skills frequency heat maps, pie charts, violin plots, and choropleth maps. Among the most significant findings of the present study were the following: job advertisements show evidence of consistent salary levels based on both level of education and type of job; however, geographic region and industry sector appear to play an important role in determining salary levels. Additionally, the study concluded that the most common skills necessary for attaining such salaries are as follows: management skills; analytical skills; design skills; communication skills; and technical/data oriented skills. Consequently, the authors propose a framework that can be used to better understand the trends of employment and provide actionable insights into the employment market.

DOI: https://doi.org/10.5281/zenodo.19816389

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Authors: P Sudhakar Reddy, Pothireddy Nithisha, Sakamuru Hari priya, Saggam Ranjith Kumar, Panditi Prem Kumar, Yagnam setty chaithanya kumar

Abstract: Increasing water pollution due to floating solid waste in rivers, lakes, and drainage canals has become a major environmental concern, and manual waste collection in water bodies is inefficient, unsafe, and time-consuming. This project presents Roboclean, an ESP32-based automated garbage collection system designed specifically for collecting floating waste from water surfaces using a conveyor belt mechanism. The system employs dual conveyor belts driven by DC motors through motor driver modules to lift and transfer waste from water to a collection bin. An ESP32 microcontroller acts as the central control unit, coordinating motor operations and system monitoring. IoT connectivity using the Blynk platform enables real-time remote control and monitoring of the system through a mobile application. A 16×2 LCD display provides on-site status information, while a regulated power supply ensures reliable operation. By automating floating waste collection and enabling remote supervision, the proposed system reduces manual labor, improves safety, and enhances cleanliness of water bodies. Roboclean offers a cost-effective and scalable solution suitable for rivers, lakes, sewage canals, and smart city environmental management applications.

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Published by: vikaspatanker

Authors: Kura Sairam, Kurva Saisharath, Dr. P. Kowstubha, A. Sai Aditya

Abstract: Renewable energy sources such as solar power are highly dependent on environmental conditions, which often leads to fluctuations in output voltage and current. These variations create challenges for conventional inverter systems like Voltage Source Inverters (VSI), Current Source Inverters (CSI), and even traditional Z-Source Inverters (ZSI), affecting their efficiency and reliability. To address these issues, this paper focuses on the design and simulation of a Quasi Z-Source Inverter (QZSI) for photovoltaic (PV) energy conversion. The QZSI is an improved version of the ZSI, achieved by modifying the impedance network. This topology offers several advantages, including the ability to perform both buck and boost operations in a single stage, reduced component stress, and a continuous input current, which is particularly beneficial for PV systems. Additionally, the QZSI allows the use of shoot- through states without damaging the inverter, enabling effective voltage boosting under varying input conditions. In this work, the operating principle, voltage boost capability, and control strategy of the QZSI are studied. A simulation model is developed using MATLAB/Simulink to evaluate system performance under different operating scenarios. The results demonstrate that the QZSI provides improved voltage stability and overall efficiency, making it a suitable choice for renewable energy applications.

DOI: https://doi.org/10.5281/zenodo.19766037

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Authors: Dr. Jonathan Miller, Dr. Emily Carter, Michael Anderson, Dr. Sophia Reynolds, Daniel Thompson, Chaitanya Srinivas

Abstract: The rapid evolution of cloud computing has significantly transformed the role of database leadership, necessitating a shift from traditional management approaches to dynamic, automation-driven, and resilience-oriented strategies. This paper explores the redefinition of database leadership within cloud-native environments, where scalability, distributed architectures, and continuous integration and deployment pipelines are essential. It highlights the importance of leveraging automation, intelligent monitoring, and self-healing systems to ensure high availability and operational resilience. The study addresses key challenges such as maintaining data consistency across distributed systems, ensuring security in multi-tenant cloud environments, and optimizing performance under variable workloads. Furthermore, it examines how modern leadership practices incorporate cloud-native principles, including microservices architecture, containerization, and Infrastructure as Code (IaC), to enhance efficiency and system reliability. Based on conceptual analysis and practical insights, the paper proposes a strategic framework that emphasizes proactive decision-making, automation adoption, and resilience engineering to achieve scalable, fault-tolerant, and robust database systems while minimizing operational risks and downtime, ultimately underscoring the critical role of adaptive leadership in meeting the demands of modern cloud-native ecosystems.

DOI: https://doi.org/10.5281/zenodo.19765416

Published by: vikaspatanker