AHFE Open AccessProposal for the Generation of Profiles using a Synthetic Database
Abstract
The lack of data to perform various models that feed an artificial intelligence with which you can get or discover various patterns of behavior in a set of data. Therefore, due to this lack of data, the systems are not well nourished with data large enough to fulfill its learning function, presenting a synthetic database which is parameterized with restrictions on the characteristics of graphomotor and language elements, which develops a set of combinations that will be the model for the AI. As effect to all this gave a commensurable amount of 777,600 combinations at the moment of applying the first filter with the respective restrictions, when taking the valid combinations that are 77304 a second filter is applied with the remaining restrictions that gave 57,672 valid combinations for the generation of the synthetic database that will feed the AI. It is concluded that the generation of synthetic data helps to create, according to its importance, more or less similar to real data and in this way ensures a quantity and no dependence on real or original data.
Keywords: data, models, artificial intelligence, learning, filter, constraints
DOI: 10.54941/ahfe1001462
Cite this paper
More from this volume
- Won’t you see my neighbor? User predictions, mental models, and similarity-based explanations of AI classifiers
- Using Artificial Intelligence to Improve Human Performance: A Predictive Management Strategy
- Robust AI for Accident Diagnosis of Nuclear Power Plants Using Meta-Learning
- Detection of inappropriate images on smartphones based on computer vision techniques
- Econometric Modeling for the Management and Decomposition of Financial Risk
- Artificial vision system to detect the mood of an Alzheimer's patient
- Analysis of citizen's sentiment towards Philippine administration's intervention against COVID-19
- The Effect of Varying Levels of Automation during Initial Triage of Intrusion Detection
- Generating a Multimodal Dataset Using a Feature Extraction Toolkit for Wearable and Machine Learning: A pilot study
- Hepatitis predictive analysis model through deep learning using neural networks based on patient history
- An analysis model for Machine Learning using Support Vector Machine for the prediction of Diabetic Retinopathy
- Supradyadic Trust in Artificial Intelligence