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Research Paper

Nano-Enhanced Hydrophobic Coating with ZnO Nanoparticles for Preserving Cultural Heritage Building

Preserving outdoor heritage assets is an ongoing challenge in heritage conservation. The purpose of applying preservative coatings is to improve the hydrophobicity of exposed surfaces of building materials and protect against pollutants, microbiological growths, and especially the effects of rainwater. A good protective coating significantly lowers water absorption, maintains high water-vapor permeability, penetrates deeply, is UV light resistant, offers sufficient breathability, and is environmentally friendly. Polysiloxanes and their precursors have been widely used to protect stone surfaces. To avoid degradation or other modifications to the treated surfaces, the treatment's safety and effectiveness must be evaluated before being directly applied to historical materials. In the past, several nanoparticles were developed and tested to improve the functionality of these coatings. The initial protective layer is made of a solvent-based substance called silane-siloxane, which has poor adherence. As a result, water can partially wash off dirt, pollutants, and microbial colonies. The hydrophobicity of protective coating materials made of ZnO nanoparticles is substantially superior. Photocatalytic coatings can oxidize organic materials on surfaces. Pollutant particles and any other dry deposition were quickly and thoroughly removed by rainwater from protective coatings made of nanoparticles.

Published by: Dr. Vimal Kumar Jaiswal, Dr. S. Vinodh Kumar, Dr. M. K. Bhatnagar

Author: Dr. Vimal Kumar Jaiswal

Paper ID: V11I1-1337

Paper Status: published

Published: February 9, 2025

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Research Paper

Social Determinants Of Saving

This comprehensive research paper explores the complex landscape of household saving behaviors, moving beyond traditional economic models by integrating psychological, sociological, and economic perspectives. By examining the complex interplay of social determinants, this study employs a mixed-methods approach, incorporating quantitative data from national surveys and qualitative insights through interviews and case studies to provide a nuanced understanding of financial decision-making processes that shape individual and collective saving strategies.

Published by: Ayera Jain

Author: Ayera Jain

Paper ID: V11I1-1334

Paper Status: published

Published: February 8, 2025

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Review Paper

Trada – An AI-Driven Trading Assistant and Stock Analysis Service

The stock market poses significant challenges for traders and investors due to fragmented access to essential data and insights. Navigating multiple platforms to gather real-time data, historical trends, and financial information leads to inefficiencies and missed opportunities. Trada is an AI-driven trading assistant that centralizes stock analysis, providing users with real-time data and personalized buy/sell recommendations in one platform. By leveraging advanced machine learning algorithms such as ARIMA, LSTM, EMA, and XGBoost, Trada delivers predictive stock analysis and actionable insights. LLaMA enhances the platform's ability to summarize financial reports, helping users make informed trading decisions without the need to rely on multiple resources.

Published by: Rutuja Jamale, Aatish Jawalkar, Samruddhi Duse, Omkar Chole, Prachi Sorte

Author: Rutuja Jamale

Paper ID: V11I1-1332

Paper Status: published

Published: February 6, 2025

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Research Paper

Refined Detection of Knee Osteoarthritis Using Center Net with a Pixel-Wise Voting Approach

This paper introduces an advanced approach for detecting Knee Osteoarthritis (OA) using an optimized CenterNet framework integrated with a pixel-wise voting strategy. Early and accurate detection of knee OA is vital for timely intervention and efficient disease management. The proposed method enhances the CenterNet architecture—a leading object detection framework—by incorporating a pixel-based voting mechanism, which leverages local image data to improve detection accuracy. Each pixel contributes to determining whether it belongs to an object or the background, and this aggregated information enables precise identification of objects and their locations. Experiments conducted on a publicly available knee OA dataset demonstrate that the proposed method outperforms existing techniques, achieving state-of-the-art results. The integration of CenterNet with the pixel-wise voting strategy holds significant promise in aiding clinicians with early diagnosis and treatment planning for knee OA patients.

Published by: Usha Kumari V, Abhishek S, Ajay R, Karnan K, Asim Ulla Khan

Author: Usha Kumari V

Paper ID: V11I1-1318

Paper Status: published

Published: February 4, 2025

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Research Paper

Assessing Organization-Specific Vulnerability Patterns- (Identifying Unique Weaknesses within the Organization’s Systems, Processes, and Culture to Proactively Address and Mitigate Risks)

Assessing an organization's unique vulnerability patterns is crucial for identifying and addressing potential security risks specific to that organization. This process involves looking at internal systems, workflows, and external interactions to spot vulnerabilities that are particular to the organization’s structure and operations. By understanding these patterns, businesses can implement customized security measures, prioritize resources more effectively, and take proactive steps to defend against targeted threats. Effective vulnerability assessment requires ongoing monitoring, employee training, and collaboration across different departments to ensure a well-rounded understanding of the organization’s security situation. This approach not only strengthens the overall cybersecurity posture but also helps organizations align their defenses with their specific operational needs.

Published by: Poongodi R K, Ponnarasu M, Prithika R, Mohamed Aadhil A, Sheethal J

Author: Poongodi R K

Paper ID: V11I1-1246

Paper Status: published

Published: February 4, 2025

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Research Paper

Adopting Secure Software Development Practices to Improve Financial Transactions in the Banking Sector

Secure software development practices are believed to be the banking sector's backbone of financial transaction security. This research examines the issues of adoption, effectiveness, and challenges of secure software practices, focusing on their implications for transaction security, customer trust, and regulatory compliance. The data from structured surveys and machine learning analysis using the Random Forest algorithm provided actionable insights. The results reflected that secure coding standards and threat modeling were at the top and had brought the vulnerabilities down a lot, raising the security of financial transactions a notch. Security testing and continuous integration had an important role but were less influential. Organizations adopting these practices extensively reported increased operational efficiency, reduced data breaches, and higher levels of customer trust. However, high costs of implementation, lack of skilled personnel, and integration complexities with legacy systems remain a challenge. Indeed, the performance of the machine learning model was very strong, with 90.7% accuracy, 91.6% precision, and 90.7% recall; thus affirming its strength in prediction. The importance of features further emphasized secure coding and threat modeling. This research has identified that strategic investments in employee training, modern security tools, and infrastructure upgrades are needed to address the implementation challenges. The agenda of future research integrates blockchain and AI with secure practices for enhanced security. The general contribution of the study is that secure software development practices have the potential to transform the security of financial transactions, build customer trust, and facilitate regulatory compliance in the banking industry.

Published by: Rianat Abbas, Rasheed Afolabi, Ifeoma Eleweke, Adetomiwa Adesokan, Ahmed Akinsola, Laticbe Elijah

Author: Rianat Abbas

Paper ID: V11I1-1304

Paper Status: published

Published: February 4, 2025

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