This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed...This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.展开更多
A system that allows computer interaction by disabled people with very low mobility and who cannot use the standard procedure based on keyboard and mouse is presented. The development device uses the patient’s volunt...A system that allows computer interaction by disabled people with very low mobility and who cannot use the standard procedure based on keyboard and mouse is presented. The development device uses the patient’s voluntary biomechanical signals, specifically, winks—which constitute an ability that generally remains in this kind of patients—, as interface to control the computer. A prototype based on robust and low-cost elements has been built and its performance has been validated through real trials by 16 users without previous training. The system can be optimized after a learning period in order to be adapted to every user. Also, good results were obtained in a subjective satisfaction survey that was completed by the users after carrying out the test trials.展开更多
文摘This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.
文摘A system that allows computer interaction by disabled people with very low mobility and who cannot use the standard procedure based on keyboard and mouse is presented. The development device uses the patient’s voluntary biomechanical signals, specifically, winks—which constitute an ability that generally remains in this kind of patients—, as interface to control the computer. A prototype based on robust and low-cost elements has been built and its performance has been validated through real trials by 16 users without previous training. The system can be optimized after a learning period in order to be adapted to every user. Also, good results were obtained in a subjective satisfaction survey that was completed by the users after carrying out the test trials.
