Stock Market Trends Analysis using Extreme Gradient Boosting (XGBoost)

Sharma, Priyanka; Jain, Mayank Kumar

doi:10.1109/icccis60361.2023.10425722

2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) 2023

DOI: 10.1109/icccis60361.2023.10425722

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Stock Market Trends Analysis using Extreme Gradient Boosting (XGBoost)

Priyanka Sharma,

Mayank Kumar Jain

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2024

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Cited by 8 publications

References 28 publications

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Algorithmic Issues, Challenges, and Theoretical Concerns of ChatGPT

Patil,

Kulkarni,

Sharma

2024

Applications, Challenges, and the Future of ChatGPT

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Large language model (LLM) ChatGPT has made tremendous progress in natural language processing (NLP), especially in human-quality text production, multilingual translation, and content creation. However, because of its extensive use, algorithmic issues, challenges, and theoretical queries come up. Fairness, bias, explainability, generalization, originality, inventiveness, and safety are the main topics of this study's examination of ChatGPT's intricate theoretical and algorithmic components. It looks into the possibility of explainability, transferability, generalization, bias in the data, and the model's capacity to provide original and imaginative content. It also covers possible issues including harmful use, disseminating incorrect information, and offensive or misleading content. These limitations can be addressed so that ChatGPT can be improved to provide LLMs that are more dependable, accountable, and long-lasting while posing no needless risks.

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Algorithmic Issues, Challenges, and Theoretical Concerns of ChatGPT

Patil,

Kulkarni,

Sharma

2024

Applications, Challenges, and the Future of ChatGPT

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Applications Areas, Possibilities, and Limitations of ChatGPT

Qamar,

Zardari

2024

Applications, Challenges, and the Future of ChatGPT

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AI developments have led to the creation of complex language models like ChatGPT, which can produce text that appears human. Although ChatGPT has limitations, it can be helpful in elucidating concepts and offering basic guidance. It cannot access original medical databases or offer the most recent scientific knowledge. Public opinion in a number of areas, including healthcare, education, manufacturing, artificial intelligence (AI), machine revolution, science, industry, and cyber security, has been scrutinizing ChatGPT. However, there hasn't been as much research done on the analysis of ChatGPT studies in these settings. The present chapter looks at the various potential applications, difficulties, and upcoming projects for ChatGPT. A synopsis of studies on ChatGPT in the literature on applications is given by this review. The authors also offer a thorough and original assessment of ChatGPT's future concerns in Diff applications, including their promise and limitations.

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Insights and Future Prospects for ChatGPT

Das,

Patro,

Panda

et al. 2024

Applications, Challenges, and the Future of ChatGPT

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Using regenerative artificial intelligence (AI) models, ChatGPT and its variations have quickly gained attention in scientific and public debate about the possible advantages and disadvantages they may have in economics, a republic, the community, and the environment. It is unclear if these advancements will create new jobs or eliminate existing ones, or if they redistribute human labour by producing additional knowledge and choices that may be insignificant or functionally unimportant. In light of the swift progress in productive neural networks (AI) as well as their arising consequences for job procedures worldwide and HR management in especially, this HRMJ argument writing generates jointly a variety of opinions concerning how we may improve HRM academic discourse. Giving a synopsis of the most recent advances in the discipline and creating a collection of possibilities for study are the main goals of this approach. By assuming tangible proof, we hope to advance the comprehension of artificial intelligence and push beyond the borders of what is currently known as science.

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Stock Market Trends Analysis using Extreme Gradient Boosting (XGBoost)

Cited by 8 publications

References 28 publications

Algorithmic Issues, Challenges, and Theoretical Concerns of ChatGPT

Algorithmic Issues, Challenges, and Theoretical Concerns of ChatGPT

Applications Areas, Possibilities, and Limitations of ChatGPT

Insights and Future Prospects for ChatGPT

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