Transforming Business with Generative AI Models Applications Trends

Generative artificial intelligence (AI) has unexpectedly emerged as a transformative generation, with the functionality to revolutionize numerous business sectors by way of developing new and realistic content material from present records. Unlike traditional AI fashions that cognizance in the main on class and reputation obligations, generative AI excels at producing pics, text, tune, and films, which drives innovation and operational performance across a couple of industries. This examine explores the impact of generative AI, evaluating its packages, overall performance metrics, and emerging traits using a complete dataset from Kaggle that includes numerous statistics kinds such as pics, textual content, track, and video. The research technique includes a radical manner of information collection, rigorous preprocessing, and distinct function extraction and engineering. Data preprocessing is important, related to the removal of replica records, addressing missing values through imputation techniques, and standardizing records codecs to make certain consistency and reliability. Feature extraction focuses on figuring out key attributes associated with generative AI applications and their effect on commercial enterprise performance. Feature engineering extends this through growing and selecting new metrics to seize the nuanced elements of AI improvements. Classification is completed the usage of both supervised and unsupervised learning techniques to successfully categorize and examine the effect of generative AI on exclusive commercial enterprise procedures. The proposed method achieves an outstanding accuracy of 98%, demonstrating its effectiveness and reliability. Implemented in Python, this approach indicates tremendous ability for boosting innovation and operational efficiency.

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Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/icaiqsa64000.2024.10882293
Citations
1
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
0.53
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article

Transforming Business with Generative AI Models Applications Trends

Shital Kakad, L. Chandra Sekhar Reddy, A. Balakumar, Franciskus Antonius Alijoyo et al.
1 citations
Business Process Modeling and Analysis
0.53
article

Transforming Business with Generative AI Models Applications Trends

Shital Kakad, L. Chandra Sekhar Reddy, A. Balakumar, Franciskus Antonius Alijoyo, Rekha Murugan, V. Selvi
article en
1 citations

Abstract

Generative artificial intelligence (AI) has unexpectedly emerged as a transformative generation, with the functionality to revolutionize numerous business sectors by way of developing new and realistic content material from present records. Unlike traditional AI fashions that cognizance in the main on class and reputation obligations, generative AI excels at producing pics, text, tune, and films, which drives innovation and operational performance across a couple of industries. This examine explores the impact of generative AI, evaluating its packages, overall performance metrics, and emerging traits using a complete dataset from Kaggle that includes numerous statistics kinds such as pics, textual content, track, and video. The research technique includes a radical manner of information collection, rigorous preprocessing, and distinct function extraction and engineering. Data preprocessing is important, related to the removal of replica records, addressing missing values through imputation techniques, and standardizing records codecs to make certain consistency and reliability. Feature extraction focuses on figuring out key attributes associated with generative AI applications and their effect on commercial enterprise performance. Feature engineering extends this through growing and selecting new metrics to seize the nuanced elements of AI improvements. Classification is completed the usage of both supervised and unsupervised learning techniques to successfully categorize and examine the effect of generative AI on exclusive commercial enterprise procedures. The proposed method achieves an outstanding accuracy of 98%, demonstrating its effectiveness and reliability. Implemented in Python, this approach indicates tremendous ability for boosting innovation and operational efficiency.

Jain University (IN), Mother Teresa Women's University (IN), CMR University (IN)
Openalex Percentile: Top 22%
Business Process Modeling and Analysis
0.53
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