In this paper, we proposed a multi-task system that can identify dish types, food ingredients, and cooking methods from food images with deep convolutional neural networks. We built up a dataset of 360 classes of diff...In this paper, we proposed a multi-task system that can identify dish types, food ingredients, and cooking methods from food images with deep convolutional neural networks. We built up a dataset of 360 classes of different foods with at least 500 images for each class. To reduce the noises of the data, which was collected from the Internet, outlier images were detected and eliminated through a one-class SVM trained with deep convolutional features. We simultaneously trained a dish identifier, a cooking method recognizer, and a multi-label ingredient detector. They share a few low-level layers in the deep network architecture. The proposed framework shows higher accuracy than traditional method with handcrafted features, and the cooking method recognizer and ingredient detector can be applied to dishes which are not included in the training dataset to provide reference information for users.展开更多
Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected o...Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.展开更多
随着食品安全法规的不断发展,紧跟时事,及时了解条款修订情况对企业确保遵守并降低潜在的风险至关重要。利用计算机技术有助于自动识别监管变更,精简监察流程并能够及时地响应。该研究旨在探讨机器学习算法在食品安全风险管理中的应用...随着食品安全法规的不断发展,紧跟时事,及时了解条款修订情况对企业确保遵守并降低潜在的风险至关重要。利用计算机技术有助于自动识别监管变更,精简监察流程并能够及时地响应。该研究旨在探讨机器学习算法在食品安全风险管理中的应用。研究提出了一个提高合规风险管理效率和有效性的框架,基于Transformer的双向编码器表示(bidirectional encoder representations from transformers,BERT)——一个预训练的自然语言处理模型,以典型的监督学习模型为基线,被用来识别与特定食品类别潜在食品安全风险相关的新闻信息变化情况。新闻报道中的组织、食物、风险、法规等关键实体被BERT模型自动提取。以酒精饮料为例,结合领域专家提供的标注数据,研究得到了一个微调的(有提高的)BERT模型,该模型可以自动检测与酒精饮料和与之相关的关键实体相关的潜在监管变化。结果表明,相关性预测的F1分值为0.88,实体识别的F1分值为0.60。所提出的方法有可能显著减少手工工作,提高检测监管变化的准确性,最终强化食品企业的合规策略。展开更多
The study of parental food provisioning is essential for understanding the breeding ecology of birds.We conducted the first study using accelerometry to detect food provisioning in birds,using Support Vector Machine(S...The study of parental food provisioning is essential for understanding the breeding ecology of birds.We conducted the first study using accelerometry to detect food provisioning in birds,using Support Vector Machine(SVM)models to identify when adults feed chicks of three different age classes.Accelerometers were attached to the head of adult female Imperial Shags(Leucocarbo atriceps),and various attributes derived from the acceleration signals were used to train SVM models for each chick age class.Model performance improved with chick age class,with SVM models achieving high overall accuracy(>88%)and highest sensitivity in older chick categories(>91%).However,precision values,especially for younger chicks,remained relatively low(between 26%and 45%).The application of a time filter based on the minimum duration of the observed food provisioning behaviours for each chick age category,improved model performance by reducing false provisioning behaviours,particularly in the model for older chicks,which showed the highest precision(72.4%).This study highlights the effectiveness of accelerometry and machine learning in studying parental food provisioning in birds,providing a rapid and accurate data collection method to complement traditional techniques.The described methodology can be applied to any bird species that exhibits distinctive movements while feeding its offspring and has suitable characteristics for attaching an accelerometer to the body part that best captures this movement.Finally,it is hoped that the results of this study will contribute to future research on key questions in parental investment theory and reproductive strategies in birds.展开更多
Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known ...Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known as water guzzlers by consuming anywhere between 70%and 90%of all human water use globally.Given these facts and the increase in global population to nearly 10 billion by the year 2050,the need for routine,rapid,and automated cropland mapping year-after-year and/or season-after-season is of great importance.The overarching goal of this study was to generate standard and routine cropland products,year-after-year,over very large areas through the use of two novel methods:(a)quantitative spectral matching techniques(QSMTs)applied at continental level and(b)rule-based Automated Cropland Classification Algorithm(ACCA)with the ability to hind-cast,now-cast,and future-cast.Australia was chosen for the study given its extensive croplands,rich history of agriculture,and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing.This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer(MODIS)250-m normalized difference vegetation index 16-day composite time-series data for 16 years:2000 through 2015.The products consisted of:(1)cropland extent/areas versus cropland fallow areas,(2)irrigated versus rainfed croplands,and(3)cropping intensities:single,double,and continuous cropping.An accurate reference cropland product(RCP)for the year 2014(RCP2014)produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015.A comparison between the ACCA-derived cropland products(ACPs)for the year 2014(ACP2014)versus RCP2014 provided an overall agreement of 89.4%(kappa=0.814)with six classes:(a)producer’s accuracies varying between 72%and 90%and(b)user’s accuracies varying between 79%and 90%.ACPs for the individual years 2000–2013 and 展开更多
基金This work was supported by the National High Technology Research and Development 863 Program of China under Grant No. 2013AA013903, the National Natural Science Foundation of China under Grant No. 61373069, the Research Grant of Beijing Higher Institution Engineering Research Center, and the Tsinghua University Initiative Scientific Research Program.
文摘In this paper, we proposed a multi-task system that can identify dish types, food ingredients, and cooking methods from food images with deep convolutional neural networks. We built up a dataset of 360 classes of different foods with at least 500 images for each class. To reduce the noises of the data, which was collected from the Internet, outlier images were detected and eliminated through a one-class SVM trained with deep convolutional features. We simultaneously trained a dish identifier, a cooking method recognizer, and a multi-label ingredient detector. They share a few low-level layers in the deep network architecture. The proposed framework shows higher accuracy than traditional method with handcrafted features, and the cooking method recognizer and ingredient detector can be applied to dishes which are not included in the training dataset to provide reference information for users.
文摘Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.
文摘随着食品安全法规的不断发展,紧跟时事,及时了解条款修订情况对企业确保遵守并降低潜在的风险至关重要。利用计算机技术有助于自动识别监管变更,精简监察流程并能够及时地响应。该研究旨在探讨机器学习算法在食品安全风险管理中的应用。研究提出了一个提高合规风险管理效率和有效性的框架,基于Transformer的双向编码器表示(bidirectional encoder representations from transformers,BERT)——一个预训练的自然语言处理模型,以典型的监督学习模型为基线,被用来识别与特定食品类别潜在食品安全风险相关的新闻信息变化情况。新闻报道中的组织、食物、风险、法规等关键实体被BERT模型自动提取。以酒精饮料为例,结合领域专家提供的标注数据,研究得到了一个微调的(有提高的)BERT模型,该模型可以自动检测与酒精饮料和与之相关的关键实体相关的潜在监管变化。结果表明,相关性预测的F1分值为0.88,实体识别的F1分值为0.60。所提出的方法有可能显著减少手工工作,提高检测监管变化的准确性,最终强化食品企业的合规策略。
基金supported by a grant from the National Agency for the Promotion of Science and Technology of Argentina(grant PICT,2017-1996 to AGL)by two awards,one from the Association of Field Ornithologists and the other from Aves Argentinas to MDC。
文摘The study of parental food provisioning is essential for understanding the breeding ecology of birds.We conducted the first study using accelerometry to detect food provisioning in birds,using Support Vector Machine(SVM)models to identify when adults feed chicks of three different age classes.Accelerometers were attached to the head of adult female Imperial Shags(Leucocarbo atriceps),and various attributes derived from the acceleration signals were used to train SVM models for each chick age class.Model performance improved with chick age class,with SVM models achieving high overall accuracy(>88%)and highest sensitivity in older chick categories(>91%).However,precision values,especially for younger chicks,remained relatively low(between 26%and 45%).The application of a time filter based on the minimum duration of the observed food provisioning behaviours for each chick age category,improved model performance by reducing false provisioning behaviours,particularly in the model for older chicks,which showed the highest precision(72.4%).This study highlights the effectiveness of accelerometry and machine learning in studying parental food provisioning in birds,providing a rapid and accurate data collection method to complement traditional techniques.The described methodology can be applied to any bird species that exhibits distinctive movements while feeding its offspring and has suitable characteristics for attaching an accelerometer to the body part that best captures this movement.Finally,it is hoped that the results of this study will contribute to future research on key questions in parental investment theory and reproductive strategies in birds.
基金This work was supported by NASA MEaSUREs(grant number NNH13AV82I)U.S.Geological Survey provided sup-plemental funding from other direct and indirect means through its Land Change Science(LCS)Land Remote Sensing(LRS)programs as well as its Climate and Land Use Change Mission Area.
文摘Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known as water guzzlers by consuming anywhere between 70%and 90%of all human water use globally.Given these facts and the increase in global population to nearly 10 billion by the year 2050,the need for routine,rapid,and automated cropland mapping year-after-year and/or season-after-season is of great importance.The overarching goal of this study was to generate standard and routine cropland products,year-after-year,over very large areas through the use of two novel methods:(a)quantitative spectral matching techniques(QSMTs)applied at continental level and(b)rule-based Automated Cropland Classification Algorithm(ACCA)with the ability to hind-cast,now-cast,and future-cast.Australia was chosen for the study given its extensive croplands,rich history of agriculture,and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing.This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer(MODIS)250-m normalized difference vegetation index 16-day composite time-series data for 16 years:2000 through 2015.The products consisted of:(1)cropland extent/areas versus cropland fallow areas,(2)irrigated versus rainfed croplands,and(3)cropping intensities:single,double,and continuous cropping.An accurate reference cropland product(RCP)for the year 2014(RCP2014)produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015.A comparison between the ACCA-derived cropland products(ACPs)for the year 2014(ACP2014)versus RCP2014 provided an overall agreement of 89.4%(kappa=0.814)with six classes:(a)producer’s accuracies varying between 72%and 90%and(b)user’s accuracies varying between 79%and 90%.ACPs for the individual years 2000–2013 and
