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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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Research Paper

Tangent Bundles Related to Various Properties of Differential Geometry

The objective of the present article is to investigate the lifts of a Fλ(2ν + 3, 2) -structure and determine its integrability requirements and partial integrability on the tangent bundle. Finally, the third tangent bundle T3M is examined in order to study the extension of a Fλ(2ν+3, 2) -structure.

Published by: Anowar Hussain Sadiyal

Author: Anowar Hussain Sadiyal

Paper ID: V11I1-1210

Paper Status: published

Published: February 3, 2025

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Research Paper

Do Early Medications in Cases of ADHD Help Children to Cope with Behavioural Issues?

Diagnoses of Attention Deficit Hyperactivity disease (ADHD), a common neurodevelopmental disease, have increased globally, especially since the 1980s. Until the late 20th century, ADHD was not as often diagnosed in other parts of the world as it is in Western nations like the United States. This study examines how the prevalence of ADHD diagnoses and medication use is rising across a range of demographics, with a particularly noticeable increase seen between 2005 and 2012. The study lists the main causes of ADHD, which include environmental variables, brain damage, prenatal substance exposure, and genetic factors. Although environmental variables including low birth weight and exposure to pollutants certainly play a part, genetics is the primary determinant, with heritability estimates ranging from 70 to 90%. Individuals of all ages are impacted by the wide-ranging effects of ADHD. Among these are increased chances of comorbid ailments like anxiety, depression, sleep disorders, and behavioral problems. Stimulants and other medications are frequently used to treat ADHD, but they can have negative side effects including appetite suppression, sleeplessness, and more severe mental health issues. The fact that people with ADHD are more likely to commit crimes further emphasizes the disorder's wider social and economic effects. ADHD is managed differently over the world; for example, Australia and India have established protocols for diagnosis and therapy. Although pharmacological therapies are frequently employed, non-pharmacological methods including behavioral therapy and cognitive training are becoming more popular due to worries about their long-term effectiveness and potential negative consequences. This essay promotes a comprehensive, tailored strategy for treating ADHD, stressing the significance of addressing the disorder's influencing hereditary and environmental components. This study adds to the continuing discussion about the diagnosis and treatment of ADHD by emphasizing the value of all-encompassing care plans that weigh the advantages and disadvantages of pharmaceutical treatments against non-pharmacological alternatives.

Published by: Falakh Jahid

Author: Falakh Jahid

Paper ID: V11I1-1278

Paper Status: published

Published: February 2, 2025

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Research Paper

The Current State of Research into the Efficiency of Distributed Machine Learning Algorithms for Cloud-Based Big Data Analysis

Today, data has become a driving force in nearly every business sector, and cloud computing, alongside artificial intelligence (AI), serves as a critical enabler that enhances business operations and performance. This research focuses on optimizing distributed machine learning (DML) algorithms within cloud environments to efficiently handle and process large datasets. The paper introduces a methodology for improving the performance of DML algorithms by utilizing the computational power and storage capacity of cloud platforms, coupled with parallel processing techniques. The experimental results demonstrate that the proposed approach reduces processing time by 40% and improves model accuracy by 15%, making it highly suitable for big data environments.

Published by: Shubham Malhotra

Author: Shubham Malhotra

Paper ID: V11I1-1269

Paper Status: published

Published: February 2, 2025

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