Researchers from the Lebanese American University and the United Arab Emirates University have collaborated to successfully employ artificial intelligence (ai) through Scaled Conjugate Gradient Neural Network (SCJGNN), providing numerical solutions for the learning model based on language. This deep learning study classifies the differential language model into three classes: unfamiliar, familiar, and mastered, representing varying degrees of language proficiency. Using the Adam scheme for data set generation and the SCJGNN optimization method, the ai procedure achieves accurate results with a small calculated absolute error ranging between 10–06 to 10–08 for each model class.
The study highlights the application of ai in language models, emphasizing the importance of language models in enabling machines to understand and generate human language. Language models, a type of machine learning, recognize patterns in language usage, allowing them to perform various language processing tasks, such as text categorization, sentiment analysis, chatbot generation, translation, and summarization. The ai computational framework, along with SCJGNN, is detailed in two steps, addressing aspects such as problem description, data preprocessing, architecture design, weight initialization, activation functions, loss functions , optimization, training and evaluation. The robustness of the SCJGNN scheme is emphasized, particularly in handling large-scale problems and various forms of input data.
The ai-based SCJGNN procedure incorporates a log-sigmoid activation function, twelve neurons, SCJG optimization, and hidden and output layers. The convergence of results and the minimum absolute error confirm the accuracy of the model, emphasizing its accuracy in solving the language learning model. The numerical results of the language model demonstrate the effectiveness of the ai-based SCJGNN in solving the differential language learning model. Evaluation metrics validate model accuracy and convergence, including mean square error (MSE), state transition (SoT), error histograms, and correlation plots. Absolute error measures for each class of the language model confirm the accuracy of the ai procedure together with SCJGNN.
In conclusion, the research successfully applies ai through the SCJGNN solver to numerically solve the language-based learning model. The precision, convergence and minimum absolute error achieved underline the effectiveness of the proposed methodology. The study contributes to a broader understanding of the use of ai in linguistic models and its applications to solve differential models in the progression of language learning.
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Pragati Jhunjhunwala is a Consulting Intern at MarktechPost. She is currently pursuing B.tech from the Indian Institute of technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in the scope of data science software and applications. She is always reading about the advancements in different fields of ai and ML.
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