Image Generation Using GPT-2

Pupale, Rushikesh; Shrivastava, Adarsh; Singh, Pradeep

doi:10.1007/978-3-031-13150-9_11

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…A linear support vector classifier (SVC) has been selected for the study as it seeks to identify the decision border with the greatest margin of separation between classes. The support vector refers to the data points close to the boundary, while the margin is the separation between the dividing boundary and its nearest data points (Pupale, 2018). A linear support vector classifier can discover the "best fit" hyperplane to classify given some linearly separable input data.…”

Section: Development Of a Machine Learning Model To Predict The Satis...mentioning

confidence: 99%

Understanding College Students’ Satisfaction With ChatGPT: An Exploratory And Predictive Machine Learning Approach Using Feature Engineering

Pabreja,

Pabreja

2024

MJESTP

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Artificial Intelligence (AI) technologies are continually improving and becoming more pervasive in many facets of our lives. ChatGPT is one such cutting-edge artificial intelligence application, and it has received a lot of worldwide media attention, specifically from educationists, technologists, and learners. It is imperative to understand and evaluate the impact of ChatGPT on computer science students as it directly and holistically influences them. A quantitative instrumental case study explores ChatGPT’s impact on early adopters in education. A survey of undergraduate computer science students at a state university of Delhi was conducted to get insight into their opinion on adopting this revolutionising technology for their education, career, and overall satisfaction. An end-to-end data science approach is applied to encompass exploratory and predictive modelling with feature engineering solutions. Results reveal the most influential features contributing to students’ satisfaction in adopting ChatGPT for their day-to-day chores concerning their social life, education, and career. The Linear Support Vector classifier, a machine learning algorithm for predicting the satisfaction or dissatisfaction in students’ shows an accuracy score of 72.73% and 97.72%, respectively. The AUC for this multiclass prediction model is convincing and is 0.74, 0.71, and 0.96 for satisfied, neutral, and dissatisfied classes, respectively.

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Section: Development Of a Machine Learning Model To Predict The Satis...mentioning

confidence: 99%

Understanding College Students’ Satisfaction With ChatGPT: An Exploratory And Predictive Machine Learning Approach Using Feature Engineering

Pabreja,

Pabreja

2024

MJESTP

View full text Add to dashboard Cite

show abstract

Enhancing Image Generation by fusing Auto Encoder & Transformative Generation Approach

et al. 2024

2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)

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Image Generation Using GPT-2

Cited by 2 publications

References 11 publications

Understanding College Students’ Satisfaction With ChatGPT: An Exploratory And Predictive Machine Learning Approach Using Feature Engineering

Understanding College Students’ Satisfaction With ChatGPT: An Exploratory And Predictive Machine Learning Approach Using Feature Engineering

Enhancing Image Generation by fusing Auto Encoder & Transformative Generation Approach

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