Evaluating the Role of Artificial Intelligence in Tax Administration in India
This study explores how Artificial Intelligence (AI) is transforming tax administration in India. AI is helping the government improve efficiency, reduce tax evasion, and make tax processes easier for both officials and taxpayers. With tools like data analytics and machine learning, AI can quickly detect fraud, analyze large sets of financial data, and ensure better compliance. It also supports faster processing of returns and smarter decision-making. As India moves toward a digital economy, AI plays a crucial role in modernizing tax systems, making them more transparent, accurate, and user-friendly for everyone involved. AI is reshaping the landscape of tax administration in India by automating routine tasks, minimizing human errors, and enhancing transparency. It enables tax departments to identify irregularities through predictive analysis and real-time monitoring of transactions. Chatbots and AI-driven platforms are improving taxpayer services by offering instant support and guidance. Additionally, AI helps in risk assessment, audit selection, and fraud detection, ensuring a fair and efficient tax system. As technology continues to evolve, AI has the potential to bridge gaps in tax compliance, reduce administrative costs, and build trust between taxpayers and authorities, paving the way for a more robust tax framework.
Published by: Delip Kumar S S, Dhanush Kumar A, Anshu kumar C, K S Monikkanth
Author: Delip Kumar S S
Paper ID: V11I2-1225
Paper Status: published
Published: April 15, 2025
The Impact of GST on Accounting Practices in Chartered Accountant Firms
This study seeks to examine the effects of the implementation of Goods and Services Tax (GST) on the accounting practices of Chartered Accountant (CA) firms in India. Since its introduction in 2017, the GST has significantly transformed the landscape of indirect taxation, influencing how businesses and professionals approach financial reporting and compliance. The study delves into changes in accounting methodologies, the advisory roles of clients, the workload associated with compliance, and the adoption of technology by CA firms. Utilizing both primary and secondary data, the research assesses how these firms have adjusted to the GST framework, the challenges encountered, and the opportunities that have arisen. The findings reveal that while GST has simplified tax reporting processes, it has simultaneously heightened the complexity and frequency of compliance requirements, leading CA firms to invest in automation and digital solutions.
Published by: Akshaya M, Arun Kumar S K, K M Rakshith
Author: Akshaya M
Paper ID: V11I2-1219
Paper Status: published
Published: April 15, 2025
Offline Multimodal Medical Assistant
Healthcare accessibility in regions with limited internet connectivity remains a critical challenge, as traditional telemedicine solutions are heavily reliant on online infrastructure. This paper presents Viksith, an innovative offline multimodal medical assistant designed to provide medical guidance through speech, text, and image inputs without requiring internet access. Viksith employs resource-efficient algorithms, including locally optimized machine learning models and rule-based decisionmaking, to deliver accurate and timely medical support on low-power devices. The architecture ensures compatibility with resource-constrained environments, making it ideal for rural and underserved areas. Comprehensive testing and pilot deployments demonstrate Viksith’s high performance in recognizing symptoms, analyzing visual inputs, and generating actionable medical insights. This paper provides a detailed exploration of Viksith’s design, implementation, and evaluation, positioning it as a scalable solution for bridging healthcare gaps in offline scenarios.
Published by: Priyanka Waghmare, Prathamesh Kulkarni, Aditya Pranekar
Author: Priyanka Waghmare
Paper ID: V11I2-1156
Paper Status: retracted
Submitted: April 15, 2025
Marine Pollution & Implementation of Regulations
The Global Environment Report on the Rule of Law, published by UNEP, emphasizes that inadequate enforcement of existing environmental laws and regulations is a significant barrier to preventing environmental degradation. Despite the existence of international legal frameworks to address and manage marine plastic pollution, they are frequently weakened by inconsistent enforcement and a lack of accountability. Consequently, these laws are not applied uniformly across nations, undermining their effectiveness in mitigating marine pollution. The lack of specific regional guidelines leaves countries to establish their standards, creating a disjointed and varied response to the issue. Many neighboring nations focus on enhancing their solid waste collection and management systems to combat marine plastic pollution. However, improving waste management infrastructure requires substantial financial investment, a considerable challenge for many low and middle-income countries. In this context, regional collaboration provides more benefits than multilateral agreements or bilateral pacts. Although global initiatives engage a broader range of stakeholders, countries' varying levels of commitment and capacity frequently hinder prompt collective action. Conversely, while bilateral agreements are simpler to negotiate, they have a limited scope and are less effective in addressing the cross-border nature of marine plastic pollution. As a result, regional cooperation emerges as a more practical solution because it reflects shared interests, considers geographical and political contexts, and enables customized strategies that meet the unique needs and priorities of the region.
Published by: Laranya Sharma, Prof. Arun D Raj
Author: Laranya Sharma
Paper ID: V11I2-1165
Paper Status: published
Published: April 14, 2025
Rehabilitation and Retrofitting of the Rajabai Clock Tower
The Rajabai Clock Tower, an iconic Gothic Revival structure in Mumbai, has stood as a cultural and architectural landmark since its completion in 1878. Over the years, factors such as weathering, pollution, material aging, and structural stress have led to deterioration, necessitating a comprehensive rehabilitation and retrofitting strategy to preserve its historical and structural integrity. This study focuses on assessing the structural condition of the tower using non-destructive testing (NDT) techniques, such as ultrasonic pulse velocity, rebound hammer tests, and thermal imaging. A detailed damage assessment was conducted to identify material degradation, cracks, moisture infiltration, and foundation settlement. Computational finite element analysis (FEA) was also used to evaluate load distribution and stress concentrations. The rehabilitation approach involves restoration of damaged stonework, stained glass panels, and intricate carvings using compatible materials. Retrofitting techniques include micro-jacketing, crack injection, fiber-reinforced polymer (FRP) reinforcement, and waterproofing to enhance the tower’s structural resilience while maintaining its original aesthetics. Advanced conservation methodologies ensure that modifications blend seamlessly with the existing heritage fabric. By implementing a sustainable and minimally invasive retrofitting approach, this project ensures that the Rajabai Clock Tower remains a historically preserved and structurally sound landmark for future generations. This research serves as a case study for heritage conservation, demonstrating a balance between architectural restoration and modern engineering solutions.
Published by: Muktesh Hemanta Patil, Aakash Kedar Chauhan, Sujal Arwel, Suraj Surve
Author: Muktesh Hemanta Patil
Paper ID: V11I2-1183
Paper Status: published
Published: April 14, 2025
Real-Time Fire Detection and Automated Vendor Alert System
Advancements in artificial intelligence (AI) have significantly improved environmental monitoring and safety measures, particularly in fire detection systems. Early fire detection is critical for minimizing response time and mitigating potential damage. Traditional sensor-based fire detection methods face limitations related to range, environmental conditions, and false alarms. In contrast, deep learning-based object detection models, such as the You Only Look Once (YOLO) architecture, enable accurate and efficient fire detection using video and image data. This study explores the capabilities of the latest YOLO version, YOLOv8, for real-time fire detection. YOLOv8’s lightweight architecture, coupled with its ability to process high-resolution images with minimal latency, makes it a suitable choice for real-world applications. The system detects flame and smoke patterns in video streams or images and provides timely alerts to facilitate faster responses. Key improvements in YOLOv8 include enhanced detection speed, increased accuracy, and reduced computational complexity, making it particularly effective in remote or hard-to-monitor areas such as forests, industrial zones, and residential spaces. The system is trained on a comprehensive dataset encompassing diverse fire scenarios to reduce false positives and improve its ability to differentiate fire from non-fire elements. Expected outcomes include reliable real-time fire detection, scalability across various environments, and robust performance under varying conditions.
Published by: Jesu Vimal Austin R
Author: Jesu Vimal Austin R
Paper ID: V10I6-1526
Paper Status: published
Published: April 14, 2025
