This paper proposes a quantum computational intelligence (QCI) model integrated with generative artificial intelligence (GAI) for Taiwanese/English language co-learning applications within human-machine interactions, ...
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This paper proposes a quantum computational intelligence (QCI) model integrated with generative artificial intelligence (GAI) for Taiwanese/English language co-learning applications within human-machine interactions, focusing on Trustworthy AI Dialogue Engine (TAIDE)-based knowledge graph construction and multimodal data transformation. The QCI model comprises two main phases: human-machine interaction and data processing for quantum circuit generation and real-world applications. During the human-machine interaction phase, a synergy between human intelligence (HI) and machine intelligence (MI) enables young students to gain familiarity with CI that converges with QCI. The second phase involves data processing, which encompasses stages of data preprocessing, analysis, and evaluation. The methodology is applied to two distinct applications: Application 1 focuses on constructing a knowledge graph using the Ollama platform and the TAIDE model developed by the Taiwanese government based on the LLaMa 2 model. Application 2 addresses the GAI images to text/voice and text/voice to GAI images, depending on the type of Taiwanese/English data collected. Subsequently, the QCI model is refined through particle swarm optimization (PSO) and genetic algorithm neural networks (GANN). Moreover, a quantum fuzzy inference mechanism (QFIM) is integrated into the developed QCI&AI-FML learning platform to generate quantum circuits for the QCI model, which helps teach young students and facilitate their learning of QCI. The experimental results indicate that the QCI model significantly enhances human-machine collaboration. Looking forward, we plan to extend the QCI model to reach more young learners.
In the light of the twenty first century, payment cards like credit and debit cards are absolute basics in the global economic transactions. As these payment methods are popularising, the information associated with t...
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ISBN:
(纸本)9781538677094
In the light of the twenty first century, payment cards like credit and debit cards are absolute basics in the global economic transactions. As these payment methods are popularising, the information associated with these are being accessed by a lot more people than required increasing the risk of a possible deceit. This paper presents a comprehensive and systematic review of artificial intelligence and machine learning algorithms and techniques for payment card fraud detection. This paper reviews that can be incorporated in the system according to specific needs.
Accurate simulations of river stages during typhoon events are critically important for flood control and are necessary for disaster prevention and water resources management in Taiwan. This study applies two artifici...
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Accurate simulations of river stages during typhoon events are critically important for flood control and are necessary for disaster prevention and water resources management in Taiwan. This study applies two artificial neuralnetwork (ANN) models, including the back propagation neuralnetwork (BPNN) and genetic algorithm neural network (GANN) techniques, to improve predictions from a one-dimensional flood routing hydrodynamic model regarding the water stages during typhoon events in the Danshuei River system in northern Taiwan. The hydrodynamic model is driven by freshwater discharges at the upstream boundary conditions and by the water levels at the downstream boundary condition. The model provides a sound physical basis for simulating water stages along the river. The simulated results of the hydrodynamic model show that the model cannot reproduce the water stages at different stations during typhoon events for the model calibration and verification phases. The BPNN and GANN models can improve the simulated water stages compared with the performance of the hydrodynamic model. The GANN model satisfactorily predicts water stages during the training and verification phases and exhibits the lowest values of mean absolute error, root-mean-square error and peak error compared with the simulated results at different stations using the hydrodynamic model and the BPNN model. Comparison of the simulated results shows that the GANN model can be successfully applied to predict the water stages of the Danshuei River system during typhoon events.
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