Robert Mills Gagne's five categories of learning have a profound influence on the many aspects of educational field.This essay attempts to differentiate and analyze the five categories of learning:motor skills,ver...Robert Mills Gagne's five categories of learning have a profound influence on the many aspects of educational field.This essay attempts to differentiate and analyze the five categories of learning:motor skills,verbal information,intellectual skills,cognitive strategies,and attitudes.And then applies Gagne's five categories of learning to design English teaching objectives.展开更多
Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradien...Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost’s algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.展开更多
Machine learning implementations are being done in a long way in science and technology and especially in medical stream. In this article, we are focusing on machine learning implementation on mall customers and based...Machine learning implementations are being done in a long way in science and technology and especially in medical stream. In this article, we are focusing on machine learning implementation on mall customers and based on their income and how they can invest in the purchase in a mall. This explains the features like Customer ID, gender, age, income, and spending score. There, we mentioned a score in purchasing the goods in the mall. In this scenario, we are implementing clustering mechanisms, and here we apply the dataset of mall customers which is a public dataset and create clusters related to the customer purchase. We implement machine learning models for the prediction of whether the visited customer will purchase any product or not. For this kind of works, we require many of the inputs like the features mentioned in the paper. To maintain the features, we require a model with machine learning capability. We are performing K-Means clustering and Hierarchical clustering mechanisms, and finally, we implement a confusion matrix to achieve and identify the highest accuracy in those two algorithms. Here, we consider machine learning mechanisms to predict the category of the customer about whether they can buy a product or not based on the independent variables. This work presents you a simple machine learning prediction model based on which we can predict the category of the customer based on clustering. Before clustering, we don’t know to what group they belong to. But after clustering, we can identify the category that data node belongs to. In this article, we are mentioning the process of determining the employee based information using machine learning clustering mechanisms.展开更多
为了精准定位窃电行为,减小电力窃取给电力系统带来的经济损失,提出了一种基于熵权法Stacking(stacking based entropy,E_Stacking)集成学习的多分类窃电检测模型。首先基于用电量信息共线性的特点,使用方差膨胀因子(variance inflation...为了精准定位窃电行为,减小电力窃取给电力系统带来的经济损失,提出了一种基于熵权法Stacking(stacking based entropy,E_Stacking)集成学习的多分类窃电检测模型。首先基于用电量信息共线性的特点,使用方差膨胀因子(variance inflation factor,VIF)作为标准对数据降维,以降低数据复杂度。然后在模型训练时嵌入k折交叉验证,有效防止模型过拟合。该模型包含初级学习器和元学习器两层学习器,可以充分结合两层学习器的优点,将学习的互补特征和判别特征相结合,进一步提高检测性能。最后,使用爱尔兰数据集和部分加州大学欧文分校(University of California Irvine,UCI)数据集验证模型,结果优于目前几种常见的方法,证明该模型的有效性和稳定性。展开更多
近年来,深度学习在自然语言处理(NLP)领域获得了很大成功,尤其是语义识别方面优势突出。但是,深度学习在分析句法构成和识别句法成分方面的效果较差。其中序列标注是自然语言处理领域中历史最悠久的研究课题之一,包括词性标签(Part of s...近年来,深度学习在自然语言处理(NLP)领域获得了很大成功,尤其是语义识别方面优势突出。但是,深度学习在分析句法构成和识别句法成分方面的效果较差。其中序列标注是自然语言处理领域中历史最悠久的研究课题之一,包括词性标签(Part of speech tagging)。对范畴语法标签这一任务进行研究,提出了一些技术,可以让赋予每个输入词的词法类别数目减少。研究目标是开发一个简单而准确的系统模型来解决范畴标签的挑战,同时利用神经网络后向传播算法必要的间接表示以避免复杂的人工特征选择。基于深度学习算法的研究,用Haskell语言设计并实现范畴语法系统,对词嵌入过程的监测,能更好地反映范畴的变化。展开更多
文摘Robert Mills Gagne's five categories of learning have a profound influence on the many aspects of educational field.This essay attempts to differentiate and analyze the five categories of learning:motor skills,verbal information,intellectual skills,cognitive strategies,and attitudes.And then applies Gagne's five categories of learning to design English teaching objectives.
文摘Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost’s algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.
文摘Machine learning implementations are being done in a long way in science and technology and especially in medical stream. In this article, we are focusing on machine learning implementation on mall customers and based on their income and how they can invest in the purchase in a mall. This explains the features like Customer ID, gender, age, income, and spending score. There, we mentioned a score in purchasing the goods in the mall. In this scenario, we are implementing clustering mechanisms, and here we apply the dataset of mall customers which is a public dataset and create clusters related to the customer purchase. We implement machine learning models for the prediction of whether the visited customer will purchase any product or not. For this kind of works, we require many of the inputs like the features mentioned in the paper. To maintain the features, we require a model with machine learning capability. We are performing K-Means clustering and Hierarchical clustering mechanisms, and finally, we implement a confusion matrix to achieve and identify the highest accuracy in those two algorithms. Here, we consider machine learning mechanisms to predict the category of the customer about whether they can buy a product or not based on the independent variables. This work presents you a simple machine learning prediction model based on which we can predict the category of the customer based on clustering. Before clustering, we don’t know to what group they belong to. But after clustering, we can identify the category that data node belongs to. In this article, we are mentioning the process of determining the employee based information using machine learning clustering mechanisms.
文摘为了精准定位窃电行为,减小电力窃取给电力系统带来的经济损失,提出了一种基于熵权法Stacking(stacking based entropy,E_Stacking)集成学习的多分类窃电检测模型。首先基于用电量信息共线性的特点,使用方差膨胀因子(variance inflation factor,VIF)作为标准对数据降维,以降低数据复杂度。然后在模型训练时嵌入k折交叉验证,有效防止模型过拟合。该模型包含初级学习器和元学习器两层学习器,可以充分结合两层学习器的优点,将学习的互补特征和判别特征相结合,进一步提高检测性能。最后,使用爱尔兰数据集和部分加州大学欧文分校(University of California Irvine,UCI)数据集验证模型,结果优于目前几种常见的方法,证明该模型的有效性和稳定性。
文摘近年来,深度学习在自然语言处理(NLP)领域获得了很大成功,尤其是语义识别方面优势突出。但是,深度学习在分析句法构成和识别句法成分方面的效果较差。其中序列标注是自然语言处理领域中历史最悠久的研究课题之一,包括词性标签(Part of speech tagging)。对范畴语法标签这一任务进行研究,提出了一些技术,可以让赋予每个输入词的词法类别数目减少。研究目标是开发一个简单而准确的系统模型来解决范畴标签的挑战,同时利用神经网络后向传播算法必要的间接表示以避免复杂的人工特征选择。基于深度学习算法的研究,用Haskell语言设计并实现范畴语法系统,对词嵌入过程的监测,能更好地反映范畴的变化。
