DEVELOPMENT OF AN INTELLIGENT SYSTEM FOR MODELING

To have insight into several model challenges, it is important to utilize artificial networks in models. Various research papers have been published about these artificial intelligence techniques. None of these publications addresses problems of the computation of estimates and forecasts for solving real-world data and models from estimated data. 

Aims/Objectives: The objectives of this research are (1)To develop an artificial neural method for solving problems (2)The development of techniques to solve complex problems (3)The computation of estimates and forecasts for real-world data (4)The development of models from estimated data. The techniques investigated in this research are important and necessary for solving vague complex, and bogus problems in artificial intelligence. Consequently, a thorough study comprising techniques applied in this study is used for comparisons and utilized to identify a reliable method for modeling and forecasting problems. This research investigates different procedures utilized in artificial intelligence for modeling an efficient decision-making process.

Methodology/approach:  Past studies of methods were utilized for this study. The methods applied in this paper include collecting data from REDcap, an online data collection tool. This was determined on the training dataset (70%) and evaluated on testing data (30%). The model is developed using the neural network, binary analysis, supervised learning classifier, and result determination.

Results/finding: The evaluation of results is done by comparing their performance using accuracy metrics. The model implementation was done using MATLAB programming language. The data was processed with an algorithm classifier.

Implication/impact: This work is advantageous in achieving efficiency in models. The artificial intelligence model is developed to improve the solution to issues in developing models.

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