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Research Paper

IoT-Enabled Smart Parking and Blind Spot Detection

Accelerating urbanisation, combined with a sharp rise in privately owned automobiles, has placed considerable pressure on city infrastructures, particularly with respect to parking management and vehicular safety. Locating an available parking slot consumes significant driver time, indirectly amplifying fuel usage, traffic density, and urban air pollution. Simultaneously, limited driver visibility around a vehicle—especially in blind-spot regions—continues to be a leading contributor to low-speed accidents during parking manoeuvres. This paper offers a thorough review of prevailing technological frameworks, with particular emphasis on deploying ESP32 microcontrollers alongside arrays of heterogeneous sensors to achieve real-time proximity monitoring and obstacle awareness. The study traces the evolution from conventional camera-only solutions toward affordable, omnidirectional IoT architectures designed to strengthen situational awareness comprehensively. Key findings underscore the value of ultrasonic sensing paired with cloud-connected dashboards and smartphone interfaces in building a safer, fully networked vehicular ecosystem.

Published by: Rahul G P, Prithviraj R, KS Maneesh Kumar, Manvith DO, Raghuveer C M

Author: Rahul G P

Paper ID: V12I3-1182

Paper Status: published

Published: May 20, 2026

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Review Paper

Review of Heart-Risk Monitoring System

Cardiovascular diseases continue to be among the major causes of death globally, hence making early diagnosis and monitoring crucial to enhance the quality of care. Systems that utilise electrocardiogram (ECG) signals together with artificial intelligence,e such as machine learning, deep learning, and the Internet of Things (IoT), together with wearable health devices, have revolutionised cardiac diagnostics in the contemporary age. In this literature review, there will be an extensive evaluation of new developments in ECG signal processing and arrhythmia detection techniques, wearable ECG monitors, and intelligent health applications. This work assesses different machine learning algorithms that include SVM, CNN, LSTM, MLP and hybrid deep learning algorithms that can be applied to classify ECG signals. Other areas that are covered include remote IoT healthcare systems, cloud computing based on ECG monitoring, explainable artificial intelligence models, FHIR interoperability standards and others. The strengths, limitations, data sets, pre-processing techniques, and results achieved by recent studies are reviewed.

Published by: D R Vishal, Harshith Kumar K M, Jashwanth S R, Bipin Babu R, Harshada J Patil

Author: D R Vishal

Paper ID: V12I3-1177

Paper Status: published

Published: May 18, 2026

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Research Paper

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

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Research Paper

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

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Research Paper

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

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Research Paper

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

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