GovAI – Smart Government Scheme & Exam Finder Using Intelligent Eligibility Filtering
The increasing number of government welfare schemes and competitive examinations in India has made it difficult for citizens to identify opportunities suitable for their eligibility. Most users face challenges due to scattered information sources, a lack of awareness, and complex eligibility conditions. To solve this problem, the proposed project “GovAI – Smart Government Scheme & Exam Finder” provides a web-based recommendation platform that suggests suitable government schemes and competitive examinations based on user details. The system collects information such as age, income, gender, occupation, educational qualification, and state from users. Using eligibility-based filtering logic, the application recommends relevant schemes and examinations along with application links. The system is developed using Python, Flask, HTML, and CSS, and deployed online using GitHub and Render. The proposed platform reduces manual searching effort, improves accessibility, and provides a centralised solution for personalised recommendations. The project also demonstrates the practical use of intelligent filtering systems and modern web technologies in improving public service accessibility.
Published by: Mohammed Zakhwan, Dr. G. Sharmila Sujatha
Author: Mohammed Zakhwan
Paper ID: V12I3-1167
Paper Status: published
Published: May 18, 2026
Scalable Quality-Aware Depth Map Generation Using Edge-Conditioned Deep Learning Priors
While monocular depth estimation remains a primary hurdle in computer vision, this research presents a sophisticated hybrid framework designed to extract high-fidelity depth information from static 2D images. The core of this methodology lies in its dual-stream architecture: it synchronizes a global depth hypothesis generated via Deep Learning with a localized, edge-sensitive segmentation strategy. To ensure the system remains versatile across a spectrum of hardware from high-performance servers to resource-constrained mobile devices, this work implements a quality-scalable block partitioning scheme. By discretizing the image into adjustable blocks, the system can dynamically balance computational overhead against spatial precision. This process is deeply informed by the luminance channel's edge probability, which acts as a structural guide to ensure that depth transitions are mathematically anchored to the actual physical boundaries of objects. To bridge the gap between discrete block processing and a continuous, natural depth field, a guided bilateral filter is employed in the final stage. This specific refinement serves two purposes: it effectively dissolves 'staircase' or blocky artifacts resulting from the segmentation, while simultaneously acting as a 'boundary-lock' to preserve the crispness of foreground silhouettes. The resulting depth maps exhibit a granular level of detail, particularly at high-resolution block settings—providing the necessary structural accuracy for seamless 3D conversion, cinematic depth-of-field effects, and high-immersion Augmented Reality (AR) environments. GENERATION USING EDGE-CONDITIONED DEEP LEARNING PRIORS
Published by: Ramola Joy P, Remya Madhavan U
Author: Ramola Joy P
Paper ID: V12I3-1168
Paper Status: published
Published: May 18, 2026
End to End Retail Demand Forecasting for Inventory Optimization using Machine Learning and MLOps
Accurate demand forecasting is critical for modern retail supply chains to ensure optimal inventory management and reduce operational inefficiencies such as stockouts and overstocking. This paper presents an end-to-end cloud-native machine learning architecture for daily store-level retail demand forecasting. The proposed system integrates Amazon Web Services (AWS) components including Amazon S3 for scalable data storage, Amazon Athena for serverless analytics, SageMaker Feature Store for consistent feature management, XGBoost for predictive modeling, and SageMaker Model Monitor for production monitoring. The pipeline performs data ingestion, feature engineering, model training, batch prediction, real-time deployment, and automated monitoring. Experimental evaluation demonstrates the effectiveness of gradient boosting models combined with engineered time-series features for forecasting retail demand. The architecture highlights how cloud-based MLOps practices enable scalable and reliable forecasting systems in production environments.
Published by: Pratibha Kambi
Author: Pratibha Kambi
Paper ID: V12I2-1164
Paper Status: published
Published: May 15, 2026
Online Banking Services and Customer Retention in India
The rapid advancement of digital technology has transformed the banking sector across the globe. In India, online banking services have become an integral part of financial transactions, enabling customers to access banking facilities conveniently and efficiently. The growth of internet penetration, smartphone usage, and digital payment systems has significantly contributed to the expansion of online banking. This study examines the relationship between online banking services and customer retention in India. It highlights the role of service quality, customer satisfaction, security, trust, and technological innovation in retaining banking customers. The paper also discusses the challenges faced by banks in maintaining customer loyalty in an increasingly competitive digital environment. The study concludes that effective online banking services enhance customer retention by improving convenience, reliability, and overall customer experience.
Published by: Dr. V. Velvizhi
Author: Dr. V. Velvizhi
Paper ID: V12I3-1158
Paper Status: published
Published: May 13, 2026
Digitalization of Payments and GDP- A Global Perspective
This paper focuses on the growing importance of digital payment systems in facilitating the evolution of contemporary economies through increased efficiency and transparency in transactions as well as greater financial inclusiveness. Moreover, the comparison of the adoption and consequences of digital payments in advanced economies and EMDEs is analyzed. The development of technologies such as artificial intelligence and the Internet of Things contributes to increased efficiency, reliability, and safety of payments while posing threats that must be addressed. The paper considers the economic implications of digital payment systems, paying particular attention to their development in EMDEs, where the use of digital payments has been growing rapidly since 2014. In EMDEs, the percentage of adults using digital payments grew dramatically from 2014 to 2021. This paper considers the link between digital payment adoption and economic development by analyzing GDP per capita, total factor productivity, and employment in the informal economy. The paper also considers the role of central banks in fostering digital financial systems by providing efficient payment infrastructure and inclusive monetary policies. Overall, the study investigates whether digital payments have supported financial inclusion, economic modernization, and sustainable economic growth.
Published by: Rishaan Lulla
Author: Rishaan Lulla
Paper ID: V12I3-1161
Paper Status: published
Published: May 13, 2026
An IoT–AR Framework for Enhancing Interoperability for People with Disabilities
Advanced technologies continue to accelerate, particularly in the fields of the Internet of Things (IoT) and Augmented Reality (AR), which have demonstrated significant potential in assistive technologies. Globally, approximately 15% of the population lives with some form of disability, highlighting the urgent need for intelligent and accessible solutions. In Saudi Arabia, recent statistical reports indicate that 5.9% of Saudi citizens experience at least one mild physical difficulty, while 1.8% of the total population lives with at least one disability, with mobility and visual impairments among the most prevalent categories. To better understand real-world challenges, in 2024, an interview was conducted with a PhD holder with visual impairment, focusing on daily mobility challenges, current assistive technologies, and desired improvements. The findings revealed critical limitations in accuracy, response time, usability, and system integration. This paper analyzes existing AR–IoT assistive solutions and identifies key challenges, particularly latency, accuracy, and interoperability between heterogeneous devices and platforms. Current systems often require multiple software applications and hardware components, increasing complexity and cost. Therefore, this research proposes enhancing interoperability between AR and IoT technologies through a unified compatibility framework aimed at reducing system complexity, improving efficiency, and increasing accessibility for individuals with disabilities.
Published by: Abdulmalek ALdossery
Author: Abdulmalek ALdossery
Paper ID: V12I3-1155
Paper Status: published
Published: May 11, 2026
