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Recent Papers

Thesis

Efficient Calorie Counter

This paper presents a novel formula for calculating total calories burned, incorporating both physical activity and basal metabolic rate (BMR). The model integrates mass, time, distance covered, and heart rate intensity into a comprehensive equation. By adjusting for heart rate zones and converting energy units from joules to calories, the formula provides a more personalized estimate of energy expenditure. This approach improves accuracy by accounting for individual metabolic differences, aiming to enhance current methods for fitness tracking and calorie estimation.

Published by: Makwana Krishna

Author: Makwana Krishna

Paper ID: V10I5-1264

Paper Status: published

Published: October 3, 2024

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Research Paper

Data Mining

Data mining is the process of discovering patterns, correlations, and anomalies within large datasets to predict outcomes. By applying a variety of techniques from statistics, machine learning, and database systems, data mining transforms raw data into valuable insights. This paper explores the methodologies and applications of data mining, highlighting its significance in fields such as finance, healthcare, and marketing. Key techniques discussed include classification, clustering, regression, and association rule learning. The study also addresses the challenges and future directions in data mining, emphasizing the need for scalable and efficient algorithms to handle the ever-increasing volume of data.

Published by: V.Jyothika, A.MEENA

Author: V.Jyothika

Paper ID: V10I5-1252

Paper Status: published

Published: October 2, 2024

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Review Paper

Adoption and Impact of Cloud Computing in Enterprise and Business Management: A Literature Survey

Cloud computing has emerged as a transformative force in enterprise and business management, offering scalable, flexible, and cost-effective solutions. This literature review examines the adoption patterns and impact of cloud computing across various business environments, including large enterprises, small and medium-sized enterprises (SMEs), human resource (HR) management, and Enterprise Resource Planning (ERP) systems. The review reveals that cloud computing enables organizations to streamline processes, reduce overhead costs, and better manage resources. Cloud ERP systems improve operational workflows and boost productivity, while cloud-based HRMS enhance the flexibility and scalability of HR functions. Successful cloud adoption requires strong top management support and robust security frameworks. As businesses increasingly turn to cloud technologies for competitive advantages, developing advanced frameworks and solutions that address the unique challenges of SMEs and dynamic HR environments will be crucial. Cloud computing is poised to continue playing a transformative role in shaping the future of business management, offering unprecedented opportunities for efficiency and growth in a rapidly evolving technological landscape.

Published by: Spoorthy Reddy Maguluri

Author: Spoorthy Reddy Maguluri

Paper ID: V10I5-1256

Paper Status: published

Published: October 2, 2024

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Research Paper

The Rise and Impact of Deepfakes: A Comprehensive Analysis of Detection Criteria and Societal Implications

Some believe that the new era of deepfake technology has improved digital media, but others believe it has brought up major risks as well as creative opportunities. This study offers an investigation of deepfakes, concentrating on the detection criteria found by analyzing more than a thousand movies that were selected from Kaggle datasets. The study is based on formulae for inconsistent lighting and shadows, visual transitions, and auditory synchronization.

Published by: Samayra Chawla

Author: Samayra Chawla

Paper ID: V10I5-1236

Paper Status: published

Published: September 28, 2024

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Research Paper

Where Luxury Meets Art and Art Meets Profit: Artist Collaborations in High-End Branding

Artist collaborations have become a pivotal strategy in luxury brand marketing, blending creativity and exclusivity to captivate high-end consumers. These partnerships, whether through fashion, beauty, or technology, allow brands to rejuvenate their image, appeal to new audiences, and enhance their cultural relevance. By leveraging the artistic vision of designers like Sabyasachi Mukherjee, Masaba Gupta, and Manish Malhotra, luxury brands craft unique collections that resonate with consumers' desire for originality and status. However, these collaborations also raise important questions about brand dilution, balancing accessibility with exclusivity, and ethical concerns around cultural representation. This paper examines how artist collaborations enhance brand value while navigating the complexities of maintaining a luxury identity

Published by: Tara Wadhwani, Ria Jethi

Author: Tara Wadhwani

Paper ID: V10I5-1194

Paper Status: published

Published: September 27, 2024

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Research Paper

Self-Corrective Retrieval-Augmented Generation

Though they are quite good at producing text, large language models (LLMs) frequently make mistakes or give incorrect information. This occurs as a result of LLMs heavy reliance on training material, which may eventually become outmoded or lacking. Retrieval-Augmented Generation (RAG) was developed as a solution to this problem. In RAG, pertinent data is retrieved and integrated from outside sources by the model. RAG does have several drawbacks, though, like the ability to retrieve superfluous or irrelevant data, which might confuse the model and produce inaccurate or ineffective results. Self-Corrective Retrieval-Augmented Generation (SCRAG), a novel method, attempts to address these issues by merging the internal knowledge of the model with the world data systemThough they are quite good at producing text, large language models (LLMs) frequently make mistakes or give incorrect information. This occurs as a result of LLMs heavy reliance on training material, which may eventually become outmoded or lacking. Retrieval-Augmented Generation (RAG) was developed as a solution to this problem. In RAG, pertinent data is retrieved and integrated from outside sources by the model. RAG does have several drawbacks, though, like the ability to retrieve superfluous or irrelevant data, which might confuse the model and produce inaccurate or ineffective results. Self-Corrective Retrieval-Augmented Generation (SCRAG), a novel method, attempts to address these issues by merging the internal knowledge of the model with the world data systems. In SCRAG, the model uses a technique called reflection tokens to assess the value of the information it retrieves in addition to retrieving it. This enables the model to modify its behavior according on the task and the caliber of the data it has acquired. In order to address this, SCRAG includes a simple method for evaluating the accuracy of the data that is retrieved. The model conducts a more thorough search—it even retrieves information from the internet to identify more reliable sources if the data is erroneous or insufficient. SCRAG also employs a decompose-then-recompose procedure that aids in the model's ability to dissect the recovered data, concentrate on the most pertinent portions, and eliminate unimportant information. This guarantees that the model produces accurate and trustworthy replies by using only high quality data.

Published by: Priya Jadam, Syeeda Mujeebunnisa

Author: Priya Jadam

Paper ID: V10I5-1242

Paper Status: published

Published: September 26, 2024

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