A Collaborative Framework With Artificial Intelligence for Long-Term Care

Chou, Hsien-Ming

doi:10.1109/access.2020.2977043

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…Each architecture offers unique advantages and is applicable in different scenarios. The choice of model architecture should be based on the specific characteristics of the text data and the objectives of the task at hand [20]. Once the selected model has been established, the next step is to train it using the training data.…”

Section: Literature Reviewmentioning

confidence: 99%

Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI

Chou¹,

Cho²

2023

Preprint

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For highly time-constrained very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations in authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull-bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull-bear words from a futures corpus detection method that extracts valuable information from financial news, allowing investors to understand market trends quickly. Generative AI models are trained to provide real-time bull-bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with Random Forest and SVMs achieving an impressive 80% accuracy rate. MLP and Deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision-making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.

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Section: Literature Reviewmentioning

confidence: 99%

Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI

Chou¹,

Cho²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Each architecture offers unique advantages and is applicable in different scenarios. The choice of model architecture should be based on the specific characteristics of the text data and the objectives of the task at hand [22]. Once the selected model has been established, the next step is to train it using the training data.…”

Section: Literature Reviewmentioning

confidence: 99%

Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI

Chou,

Cho

2023

Applied Sciences

Self Cite

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For highly time-constrained, very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations between authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull–bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors, while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull–bear words using a futures corpus detection method that extracts valuable information from financial news, allowing investors to quickly understand market trends. Generative AI models are trained to provide real-time bull–bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with random forest and SVMs achieving an impressive 80% accuracy rate. MLP and deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.

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“…Some classification algorithms (e.g., sequential minimal optimization, also known as SMO) can handle multi-dimensional time-series data with a high level of noise and make coordinated multi-resolution predictions [16,17]. Therefore, the SMO algorithm can be used instead of numerical quadratic programming for analytic quadratic programming to solve optimization problems, making it suitable for use in stock forecasting [1,[18][19][20][21][22][23][24]. If we reframe an optimization problem as a binary classification problem of a dataset in the form of (𝑥 1 , 𝑦 1 ), ...., (𝑥 𝑛 , 𝑦 𝑛 ), where 𝑥 𝑖 are input vectors and 𝑦 𝑖 ∈ {−1, +1} is a binary dataset ranging from minus one to one.…”

Section: Figure 2: Multiple Turning Points For Rsi Index Numbersmentioning

confidence: 99%

Using Bull and Bear Index of Deep Learning to Improve the Indicator Model on Extremely Short-term Futures Trading

Chou

2023

Advances in Management and Applied Economics

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Day traders, who trade over time horizons of one day or less, account for around 30% of the total transaction volume at present. Most day traders’ trading strategies are based on their own experiences or news headlines. They may also rely on technical indicators, such as the Relative Strength Index (RSI), to predict short-term trading opportunities and stock index turning points for making selling and buying decisions with respect to stock index futures. This study determined exact RSI indicators, which enhanced the accuracy of short-term stock index prediction. We then tested the proposed model’s performance during an unprecedented crisis such as COVID-19. We used artificial intelligence techniques, such as the SMO algorithm, to evaluate the performance of the proposed model and apply empirical methods on short-term stock index futures datasets to explore the impact of different RSI indicators on the turning points of the stock index futures. The results show that RSI 20 based on regular and COVID-19 periods can enable day traders to achieve higher profits compared to the RSI 30 index. Keywords: Day traders, Relative strength index, Short-term investment, Artificial intelligence, COVID-19.

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A Collaborative Framework With Artificial Intelligence for Long-Term Care

Cited by 10 publications

References 29 publications

Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI

Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI

Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI

Using Bull and Bear Index of Deep Learning to Improve the Indicator Model on Extremely Short-term Futures Trading

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